ELF>@KE@8@C$C$0(0(0(,,Xd6Xd6Xd6(T(,Xu6Xu6Xu6Ptd333PPQtdRtdXd6Xd6Xd6++E@EvxbJ2;Qv75S_.H/E$RAI @.:nlWbM}~pEY8Z.RuzU6*mt&(3Bc1r)~`i/j^?s^(W(HL"%$4 K|Cxk->HY_Bk6L:il^LLBIA>| Oo]8z%}G-!]q Z2suopfijc q*|)'\y[$58nA2[ mGhZmofSt 7~k!9'd@q9v4, Q3z'g 9qVj#9x TtO<}gD1a{>ht*PQV*?h=%+(~<U"b1OQIa,5M?vFP}]F{|us,Y[0=TDV ONeXP;w4/N.{o)0VK+`{6d= 0UyC1R-X+"f7m  i ngrJl_I ej^p#P6kM,W;c rX3!WgBl&zca/F#x \GD0AwJ<K eh)UY RSMJ8@ :S X&H\`s;#]Gu=:>4wn\<dp%Z "F5y!b[CT&2Nef-ND_+`?$Kywa d'TCr73 $&"G`AÐ0`  @Q`@ E"X@XADa@H @J$(IE@D@ AB`0dD$  `P,0 @h D%$0`K!"I"4A- A# L @PD@    !#$%&()*+,./0123456789:;=>?@ABCDEFGHIJKLNPSTUVXYZ[\]^_abcefghiklmnpqrstuwxyz{}~!޲Dof|ș b͐\AoSoy+W8,'$'=w M)nΓ<1A1dVϡ}N 1],je)?qNkYq4օ =4멾1aU̹~dny ݙ?cCsVgec|FU=E ;&R{Yk wn2>aiF0'Į9H*   3uIR"<*a yQn#L*A e)r #tsr?L@O)SĠzM(DL}5#ַ*aMjv.+ uXC=$lnF" ]U`p! ?l3w<UPDb+([&O >^`,Ldx7X6MJ 4NW2% 5>?yy ixP<*V53BArTY?8K4q -?Eynlc8T'"U;%t*g8RG)=S4B @b5=xug@](~m Gg(D$7_n !Z*G&+%K `e 5X-[tO7 ;' >$y ?!f$7s6[,`9,0_z/=c/"oAz:+ !l2)$ h~$\%Xc; M.(M9eopd3'F|lDL 8|?, aK?*fr)Y4 \jP,<k_K5Ha{?vQESQO+2+I"3X)"Z $W(ymvC\),6vQW>'X-d_@fZJ Yc(`Y#m,&IujF!^J/b1,ClHq= q##M[{iR%E.7HF Ŷak6;1xi| 3 [g5p8J$$a N =7@R@RPWN+  R$ 796 VM: C$Gj5[?Bc*:=YHXz" *a*7wb JOM+OeKi!+& 8nh=o& !WFeS/GW0\RizH·=qS7Fb-[e 4O&,C U;rH \2+HJ,<h)"uk|<*F/6&#4!xj6| `k #(! 3$" -%]V!o68%s" pr#!3!m6 j" `"Yi!ph67!hn6 p! u68888$!i6 k!g6" 'C}|" p$! 3C j" "Y!f688! 3:! 3O@,!0g6!pg68!Hj6!j6,! 3g$! 32N!Pp68& `۵!k6! 3" L% 88!@h6 % C!h6̕" `'D֫!j6 @#p!n6  P~- _q ! 3]! p3" P%!`j6H k.!t68!f6! `3S" P&!h6 88%  PE88&!j6V  Ќ$5@9ۉ" P& 489i!0j6ُ" &! 33]!n68Ί" k&3! 3A|/!t68ۙ!f6R!i6 ڰ! 3Z '!l6e_!8o68!k6  !l6(! 3Lt!`s68  |J!t68! 3." @' Ɲ!Xg6i! 3Ф! 3P!f6S!p68n!h68h!r68#|!p68iK!@t68Θ! @3/88" @&! `3Y  ! `3"!g6d @U!n6 ! P3V! 33P9n! 3X<! 3O&! 3VD! 3Q! P3p 3  `! i6 xG!Xl6  884H!po68!i6+ u,!Xh6K! 3! 3! 3V/!s68  c!i6! 3'ʯ! k6!h64`9 a! 3(!8k6q!g68֧! 3LŞ! 03c88g!r68`88!hk6O88Q!p68Hk!s68x!g6! 3.+`88! 3D<!k6T!r68v! 3Q}" @$! @3cʥ!i6!@i6|q!o68(0!q68V! 3Z! 3Bf" F"Y  p2!8l6 __gmon_start___init_fini_ITM_deregisterTMCloneTable_ITM_registerTMCloneTable__cxa_finalize_Py_NoneStructPyType_Type__stack_chk_failPyBaseObject_TypePyExc_TypeErrorPyErr_FormatPyExc_ValueError_ZdlPvm_ZN5arrow3ipc14DictionaryMemoD1EvPyGILState_EnsurePyErr_OccurredPyGILState_Release_PyObject_GetDictPtrPyObject_SetAttrPyObject_GetAttr_Py_DeallocPyUnicode_FromFormatPyDict_NextPyExc_SystemErrorPyErr_SetStringPyDict_SizePy_EnterRecursiveCallPy_LeaveRecursiveCallPyObject_CallPyDict_GetItemWithErrorPyDict_NewPyList_AppendPyEval_RestoreThread__cxa_rethrow__cxa_begin_catchPyExc_MemoryError__cxa_end_catchPyExc_IOErrorPyExc_IndexErrorPyExc_OverflowErrorPyExc_ArithmeticErrorPyExc_RuntimeError_Unwind_Resume__gxx_personality_v0PyCapsule_NewPyDict_SetItemPyType_IsSubtypePyObject_InitPyObject_GC_Track_PyObject_GC_NewPyObject_GetAttrStringPyDict_SetItemStringPyExc_AttributeErrorPyErr_ExceptionMatchesPyErr_ClearPyUnicode_TypePyUnicode_Resize_PyUnicode_FastCopyCharactersPyUnicode_Concat_PyUnicode_ReadyPyThreadState_GetPyFrame_NewPyObject_ClearWeakRefsPyObject_CallFinalizerFromDeallocPyObject_GC_IsFinalized_ZN5arrow3gdb11TestSessionEvPyImport_AddModulePyMethod_NewPyUnicode_InternFromString_ZTVSt18bad_variant_access_ZNSt9exceptionD2EvPyModule_AddObject_Z19pyarrow_wrap_bufferRKSt10shared_ptrIN5arrow6BufferEE_Z29pyarrow_wrap_resizable_bufferRKSt10shared_ptrIN5arrow15ResizableBufferEE_Z22pyarrow_wrap_data_typeRKSt10shared_ptrIN5arrow8DataTypeEE_Z18pyarrow_wrap_fieldRKSt10shared_ptrIN5arrow5FieldEE_Z19pyarrow_wrap_schemaRKSt10shared_ptrIN5arrow6SchemaEE_Z19pyarrow_wrap_scalarRKSt10shared_ptrIN5arrow6ScalarEE_Z18pyarrow_wrap_arrayRKSt10shared_ptrIN5arrow5ArrayEE_Z26pyarrow_wrap_chunked_arrayRKSt10shared_ptrIN5arrow12ChunkedArrayEE_Z30pyarrow_wrap_sparse_coo_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCOOIndexEEEE_Z30pyarrow_wrap_sparse_csc_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSCIndexEEEE_Z30pyarrow_wrap_sparse_csf_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSFIndexEEEE_Z30pyarrow_wrap_sparse_csr_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSRIndexEEEE_Z19pyarrow_wrap_tensorRKSt10shared_ptrIN5arrow6TensorEE_Z18pyarrow_wrap_batchRKSt10shared_ptrIN5arrow11RecordBatchEE_Z18pyarrow_wrap_tableRKSt10shared_ptrIN5arrow5TableEE_Z21pyarrow_unwrap_bufferP7_object_Z24pyarrow_unwrap_data_typeP7_object_Z20pyarrow_unwrap_fieldP7_object_Z21pyarrow_unwrap_schemaP7_object_Z21pyarrow_unwrap_scalarP7_object_Z20pyarrow_unwrap_arrayP7_object_Z28pyarrow_unwrap_chunked_arrayP7_object_Z32pyarrow_unwrap_sparse_coo_tensorP7_object_Z32pyarrow_unwrap_sparse_csc_matrixP7_object_Z32pyarrow_unwrap_sparse_csf_tensorP7_object_Z32pyarrow_unwrap_sparse_csr_matrixP7_object_Z21pyarrow_unwrap_tensorP7_object_Z20pyarrow_unwrap_batchP7_object_Z20pyarrow_unwrap_tableP7_object_ZN5arrow4util5Codec26UseDefaultCompressionLevelEv_Py_TrueStruct_Py_FalseStructPyObject_IsTruePyGC_DisablePyType_ReadyPyGC_EnablePyInterpreterState_GetIDPyExc_ImportErrorPyModule_NewObjectPyModule_GetDictPyStaticMethod_NewPyDict_Type_PyDict_SetItem_KnownHashPyObject_SetItemPyExc_KeyErrorPyErr_SetObjectPyTuple_PackPyMethodDescr_TypePyDescr_NewClassMethodPyMethod_TypePyClassMethod_NewPyUnicode_FromStringPyRun_StringFlagsPyErr_WriteUnraisablePyExc_RuntimeWarningPyErr_WarnExPyImport_ImportModulePyOS_snprintfPyLong_TypePyLong_FromLongPyFloat_TypePyNumber_AddPyFloat_FromDoublePyBytes_FromStringAndSizePyCode_NewWithPosOnlyArgsPySlice_New_ZTIPFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZTIPFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_Znwmmemcpymemmove_ZSt17__throw_bad_allocvPyUnicode_DecodePyUnicode_FromStringAndSizePyObject_HashPySlice_TypePyObject_GetItemPyCapsule_GetPointermallocfreestrrchrPyObject_SetAttrStringPyException_GetTraceback_ZZNSt19_Sp_make_shared_tag5_S_tiEvE5__tag_ZTSSt19_Sp_make_shared_tagstrcmp_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_createERmm_ZN5arrow19default_memory_poolEv_ZN5arrow3ipc7MessageD1Ev_ZSt28__throw_bad_array_new_lengthv_ZTSSt14default_deleteIN5arrow4util5CodecEE_ZTSSt14default_deleteIN5arrow15ResizableBufferEE_ZTSSt14default_deleteIN5arrow6BufferEEPyCFunction_Type_ZN5arrow9utf8_viewEv_ZN5arrow4utf8EvPyLong_FromSsize_tPyTuple_SizePyTuple_GetSlicePyObject_RichComparePyImport_ImportModuleLevelObject_ZN5arrow12ArrayBuilder13UnsafeSetNullEl_ZN5arrow12ArrayBuilder16UnsafeSetNotNullElPyObject_GC_UnTrackPyUnicode_NewPyModule_GetNamePyImport_GetModulePyErr_GivenExceptionMatchesPyObject_GC_DelPyCMethod_New_PyThreadState_UncheckedGetPyThreadState_GetFramePyExc_StopIterationPyErr_SetNonePyTuple_NewPyObject_FreePyExc_DeprecationWarningPyErr_WarnFormatPyErr_PrintExPyErr_FetchPyErr_Restore_ZN5arrow21ResetSignalStopSourceEvPyNumber_IndexPyLong_AsSsize_tPyLong_AsLongPyMem_MallocPyTuple_GetItemPyMem_FreePyErr_NoMemoryPyObject_GenericGetAttr_PyObject_GenericGetAttrWithDictPyObject_RichCompareBool_PyType_LookupPyDict_DelItemPyType_ModifiedPyWrapperDescr_Type__pyx_wrapperbase_7pyarrow_3lib_10StructType_6__len____pyx_wrapperbase_7pyarrow_3lib_10StructType_8__iter____pyx_wrapperbase_7pyarrow_3lib_10StructType_11__getitem____pyx_wrapperbase_7pyarrow_3lib_13ExtensionType_2__init____pyx_wrapperbase_7pyarrow_3lib_5Array_53__getitem____pyx_wrapperbase_7pyarrow_3lib_12ChunkedArray_27__getitem____pyx_wrapperbase_7pyarrow_3lib_8_Tabular_8__getitem____pyx_wrapperbase_7pyarrow_3lib_9UnionType___len____pyx_wrapperbase_7pyarrow_3lib_9UnionType_2__iter____pyx_wrapperbase_7pyarrow_3lib_9UnionType_7__getitem____pyx_wrapperbase_7pyarrow_3lib_15TimestampScalar_2__repr____pyx_wrapperbase_7pyarrow_3lib_10ListScalar___len____pyx_wrapperbase_7pyarrow_3lib_10ListScalar_2__getitem____pyx_wrapperbase_7pyarrow_3lib_10ListScalar_4__iter____pyx_wrapperbase_7pyarrow_3lib_9MapScalar___getitem____pyx_wrapperbase_7pyarrow_3lib_9MapScalar_2__iter__PyExc_NameError_PyDict_GetItem_KnownHashmemcmpPyList_NewPyErr_NormalizeExceptionPyException_SetTracebackPyList_TypePyTuple_TypePyUnicode_ComparePyIter_NextPyObject_GetIterPyTraceBack_TypePyObject_IsSubclassPyException_SetCausePyObject_CallObjectPyCapsule_IsValidPy_OptimizeFlagPyTraceBack_HerePyCode_NewEmptyPyMem_Realloc_ZN5arrow24GetCpuThreadPoolCapacityEv_ZN5arrow2py15get_memory_poolEv_ZN5arrow18system_memory_poolEv_ZN5arrow2io23GetIOThreadPoolCapacityEv_ZNK5arrow6Tensor6EqualsERKS0_RKNS_12EqualOptionsE_ZNK5arrow12SparseTensor6EqualsERKS0_RKNS_12EqualOptionsEPyEval_SaveThread_ZNK5arrow3ipc7Message6EqualsERKS1__ZN5arrow3ipc14IpcReadOptions8DefaultsEvPyByteArray_TypePyBytes_AsStringAndSize_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_assignERKS4__ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE10_M_disposeEv_ZTIN5arrow2py15PyExtensionTypeE_ZTIN5arrow13ExtensionTypeE__dynamic_cast_ZNK5arrow2py15PyExtensionType11GetInstanceEv_ZNKSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE7compareEPKcPyLong_FromSize_t_ZNK5arrow16KeyValueMetadata6EqualsERKS0__ZNK5arrow6Scalar4hashEv_ZNK5arrow6Schema10num_fieldsEvPyExc_NotImplementedErrorPyObject_Str_ZTIN5arrow14FixedWidthTypeE_ZTIN5arrow8DataTypeEPyObject_SizePySequence_Tuple_ZNK5arrow5Array10null_countEv_ZNK5arrow6Tensor13is_contiguousEv_ZNK5arrow6Tensor4sizeEv_ZNK5arrow12SparseTensor4sizeEv_ZNK5arrow12BooleanArray11false_countEv_ZNK5arrow12BooleanArray10true_countEv_ZNK5arrow11RecordBatch11num_columnsEvPyExc_BufferErrorPyObject_Format_ZN5arrow2io12CacheOptions12LazyDefaultsEv_ZNK5arrow9UnionType4modeEvPyNumber_InPlaceAddPyUnicode_Format_ZN5arrow2io21FixedSizeBufferWriter19set_memcopy_threadsEi_ZNK5arrow6Buffer6EqualsERKS0_PySequence_ListPyBool_TypePyObject_IsInstance_ZNK5arrow16KeyValueMetadata4sizeEv_ZNK5arrow5Field6EqualsERKS0_b_ZNK5arrow6Schema6EqualsERKS0_bPyLong_FromUnsignedLong_ZN5arrow2py15PyHalf_FromHalfEtPySequence_Contains_ZN5arrow4util15TotalBufferSizeERKNS_5ArrayEPyNumber_Or_ZNK5arrow18RunEndEncodedArray18FindPhysicalOffsetEv_ZNK5arrow18RunEndEncodedArray18FindPhysicalLengthEv_ZN5arrow4util15TotalBufferSizeERKNS_12ChunkedArrayE_ZNK5arrow12ChunkedArray6EqualsERKS0_RKNS_12EqualOptionsE_ZN5arrow4util15TotalBufferSizeERKNS_11RecordBatchE_ZN5arrow4util15TotalBufferSizeERKNS_5TableE_ZN5arrow6Buffer11ToHexStringB5cxx11Ev_ZNK5arrow16KeyValueMetadata8ToStringB5cxx11Ev_ZNK5arrow16KeyValueMetadata3keyB5cxx11El_ZNK5arrow16KeyValueMetadata5valueB5cxx11ElPyGen_TypePyIter_SendPyAsyncGen_Type_PyGen_SetStopIterationValuePyExc_StopAsyncIteration_ZNK5arrow6Schema5fieldEiPyDescr_IsData_PyDict_NewPresized_ZN5arrow17LoggingMemoryPoolC1EPNS_10MemoryPoolE_ZN5arrow15ProxyMemoryPoolC1EPNS_10MemoryPoolEmemsetPyUnicode_FromOrdinal_ZNK5arrow5Table6EqualsERKS0_bPyVectorcall_FunctionPyObject_VectorcallDictPyList_SetSlicePyList_AsTuplePyObject_Repr_ZNK5arrow2io16MemoryMappedFile15file_descriptorEvPyExc_UnboundLocalError_ZNK5arrow3ipc7Message16metadata_versionEvPyBytes_Type_ZN5arrow4util5Codec16GetCodecAsStringB5cxx11ENS_11Compression4typeE_Py_NotImplementedStruct_ZNK5arrow6Schema8metadataEv_ZN5arrow12GetBuildInfoEv_ZNK5arrow18FixedSizeListArray6valuesEv_ZNK5arrow15DictionaryArray10dictionaryEv_ZNK5arrow15DictionaryArray7indicesEv_ZNK5arrow10Decimal2568ToStringB5cxx11Ei_ZNK5arrow10Decimal1288ToStringB5cxx11Ei_ZNK5arrow5Field8ToStringB5cxx11Eb_ZNK5arrow6Schema13GetFieldIndexERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK5arrow16KeyValueMetadata8ContainsESt17basic_string_viewIcSt11char_traitsIcEE_ZNK5arrow10StructType13GetFieldIndexERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK5arrow3ipc7Message4typeEv_ZN5arrow3ipc17FormatMessageTypeB5cxx11ENS0_11MessageTypeEPyDict_ContainsPyExc_GeneratorExitPyArg_UnpackTuplePyCoro_TypePyDict_Copy_ZN5arrow2py9benchmark28Benchmark_PandasObjectIsNullEP7_object_ZTVN5arrow13StringBuilderE_ZN5arrow2py23set_default_memory_poolEPNS_10MemoryPoolE_ZTVN5arrow17StringViewBuilderEPyFloat_AsDouble_ZN5arrow2io12CacheOptions22MakeFromNetworkMetricsElldl_ZN5arrow2io21FixedSizeBufferWriter21set_memcopy_thresholdEl_ZN5arrow2io21FixedSizeBufferWriter21set_memcopy_blocksizeElPyUnicode_JoinPyExc_ModuleNotFoundErrorPySequence_GetSlicePy_VersionPyObject_SelfIter__pyx_module_is_main_pyarrow__libPyImport_GetModuleDictPyDict_GetItemStringarrow_init_numpy_ZN5arrow2py14import_pyarrowEv_ZN5arrow2py8internal24NewMonthDayNanoTupleTypeEvPyExc_ExceptionPySet_NewPySet_Add__pyx_wrapperbase_7pyarrow_3lib_12StructScalar_9__getitem__PyByteArray_FromStringAndSize_ZN5arrow2py8IsPyBoolEP7_object_ZN5arrow2py9IsPyFloatEP7_object_ZN5arrow2py7IsPyIntEP7_object_ZNK5arrow6Tensor8dim_nameB5cxx11Ei_ZNK5arrow12SparseTensor8dim_nameB5cxx11EiPyList_SortPyList_ReversePyMemoryView_TypePyLong_AsUnsignedLongPyCapsule_TypePyCapsule_GetNamePyBytes_FromStringstrlenPyNumber_Remainder_ZN5arrow4util5Codec11IsAvailableENS_11Compression4typeE_ZN5arrow4util5Codec24SupportsCompressionLevelENS_11Compression4typeE_PyStack_AsDict_ZNK5arrow8DataType6EqualsERKS0_b_ZNK5arrow5Array4DiffB5cxx11ERKS0_PyNumber_NegativePyNumber_Subtract_ZNK5arrow5Array6EqualsERKS0_RKNS_12EqualOptionsE_ZTISt18bad_variant_access_ZN5arrow4util6detail19StringStreamWrapperC1Ev_ZSt16__ostream_insertIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_PKS3_l_ZNSo9_M_insertIlEERSoT__ZN5arrow4util6detail19StringStreamWrapper3strB5cxx11Ev_ZN5arrow4util6detail19StringStreamWrapperD1Ev_ZN5arrow6StatusC1ENS_10StatusCodeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow14GetRuntimeInfoEvPyInit_libPyModuleDef_Init_ZN5arrow2py16GetPrimitiveTypeENS_4Type4typeE_ZN5arrow9MakeArrayERKSt10shared_ptrINS_9ArrayDataEE_ZN5arrow2io12BufferReaderC1ESt10shared_ptrINS_6BufferEE_ZTVSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py14PyReadableFileC1EP7_object_ZTVSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py14PyOutputStreamC1EP7_object_ZTVSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EEPyBytes_AsStringPyNumber_FloorDivide_ZN5arrow7MapTypeC1ESt10shared_ptrINS_5FieldEES3_b_ZTVSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow8durationENS_8TimeUnit4typeE_ZN5arrow6time64ENS_8TimeUnit4typeE_ZN5arrow6time32ENS_8TimeUnit4typeE_ZN5arrow9timestampENS_8TimeUnit4typeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow9timestampENS_8TimeUnit4typeE_ZN5arrow2py23MakeStreamTransformFuncENS0_26TransformInputStreamVTableEP7_object_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14ExtensionArrayC1ERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEE_ZN5arrow3ipc15IpcWriteOptions8DefaultsEv_ZNK5arrow6Scalar6EqualsERKS0_RKNS_12EqualOptionsE_ZTVN5arrow5ArrayE_ZTVN5arrow7compute11CastOptionsE_ZNK5arrow5Array5SliceEll_ZNK5arrow5Array5SliceEl_ZTVN5arrow6ScalarE_ZNK5arrow11StructArray5fieldEi_ZNK5arrow11StructArray14GetFieldByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow2py9IsPyErrorERKNS_6StatusE_ZGVZNK5arrow6Status7messageB5cxx11EvE10no_message_ZGVZNK5arrow6Status6detailEvE9no_detail_ZNK5arrow6Status8ToStringB5cxx11Ev_ZN5arrow2py14RestorePyErrorERKNS_6StatusE__cxa_guard_acquire_ZZNK5arrow6Status7messageB5cxx11EvE10no_message_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEED1Ev__cxa_atexit__cxa_guard_release_ZZNK5arrow6Status6detailEvE9no_detail_ZN5arrow8internal15ErrnoFromStatusERKNS_6StatusE_ZN5arrow8internal18WinErrorFromStatusERKNS_6StatusE_ZN5arrow8internal16SignalFromStatusERKNS_6StatusE_ZNK5arrow8DataType6EqualsERKSt10shared_ptrIS0_Eb_ZN5arrow13ExtensionType9WrapArrayERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEE_ZN5arrow13ExtensionType9WrapArrayERKSt10shared_ptrINS_8DataTypeEERKS1_INS_12ChunkedArrayEE_ZNK5arrow13ListViewArray5sizesEv_ZNK5arrow3ipc7Message8metadataEv_ZNK5arrow9ListArray7offsetsEv_ZNK5arrow14LargeListArray7offsetsEv_ZNK5arrow18LargeListViewArray7offsetsEv_ZNK5arrow18LargeListViewArray5sizesEv_ZNK5arrow13ListViewArray7offsetsEv_ZTVN5arrow15ExtensionScalarE_ZN5arrow23ExportRecordBatchReaderESt10shared_ptrINS_17RecordBatchReaderEEP16ArrowArrayStream_ZN5arrow2io16MemoryMappedFile6ResizeEl_ZN5arrow2py8internal12check_statusERKNS_6StatusE_ZN5arrow2py24SparseCSFTensorToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSFIndexEEEEP7_objectPS9_SA_SA__ZN5arrow2py24SparseCSCMatrixToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSCIndexEEEEP7_objectPS9_SA_SA__ZN5arrow2py24SparseCSRMatrixToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSRIndexEEEEP7_objectPS9_SA_SA__ZN5arrow2py24SparseCOOTensorToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCOOIndexEEEEP7_objectPS9_SA__ZN5arrow2py15TensorToNdarrayERKSt10shared_ptrINS_6TensorEEP7_objectPS7__ZN5arrow17ExportRecordBatchERKNS_11RecordBatchEP10ArrowArrayP11ArrowSchema_ZN5arrow18ExportChunkedArrayESt10shared_ptrINS_12ChunkedArrayEEP16ArrowArrayStream_ZNK5arrow12ChunkedArray12ValidateFullEv_ZNK5arrow12ChunkedArray8ValidateEv_ZN5arrow11ExportArrayERKNS_5ArrayEP10ArrowArrayP11ArrowSchema_ZNK5arrow5Array12ValidateFullEv_ZNK5arrow5Array8ValidateEv_ZN5arrow10DebugPrintERKNS_5ArrayEi_ZNK5arrow6Scalar12ValidateFullEv_ZNK5arrow6Scalar8ValidateEv_ZN5arrow12ExportSchemaERKNS_6SchemaEP11ArrowSchema_ZN5arrow11ExportFieldERKNS_5FieldEP11ArrowSchema_ZN5arrow10ExportTypeERKNS_8DataTypeEP11ArrowSchema_ZN5arrow31RegisterCancellingSignalHandlerERKSt6vectorIiSaIiEE_ZN5arrow11PrettyPrintERKNS_12ChunkedArrayERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow11PrettyPrintERKNS_6SchemaERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow2py15PyExtensionType9FromClassESt10shared_ptrINS_8DataTypeEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEP7_objectPS2_INS_13ExtensionTypeEE_ZN5arrow19SetSignalStopSourceEv_ZNK5arrow6Status4WarnEv_ZN5arrow10StopSource5tokenEv_ZN5arrow6Status8CopyFromERKS0__ZSt9terminatev_ZN5arrow4util20ReferencedBufferSizeERKNS_11RecordBatchE_ZN5arrow4util20ReferencedBufferSizeERKNS_5TableE_ZN5arrow4util20ReferencedBufferSizeERKNS_12ChunkedArrayE_ZN5arrow4util20ReferencedBufferSizeERKNS_5ArrayE_ZN5arrow2py8internal36MonthDayNanoIntervalScalarToPyObjectERKNS_26MonthDayNanoIntervalScalarE_ZN5arrow2py8internal33MonthDayNanoIntervalArrayToPyListERKNS_25MonthDayNanoIntervalArrayE_PyBytes_Resize_PyBytes_Join_ZN5arrow4util5Codec23MaximumCompressionLevelENS_11Compression4typeE_ZN5arrow4util5Codec23MinimumCompressionLevelENS_11Compression4typeE_ZN5arrow4util5Codec23DefaultCompressionLevelENS_11Compression4typeE_ZN5arrow6dlpack11ExportArrayERKSt10shared_ptrINS_5ArrayEE_ZN5arrow6dlpack12ExportDeviceERKSt10shared_ptrINS_5ArrayEE_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_appendEPKcm_ZN5arrow8internal14DieWithMessageERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE10_M_replaceEmmPKcm_ZSt20__throw_length_errorPKc_ZN5arrow3ipc18GetRecordBatchSizeERKNS_11RecordBatchEPl_ZN5arrow3ipc13GetTensorSizeERKNS_6TensorEPl_ZN5arrow2py15PyForeignBuffer4MakeEPKhlP7_objectPSt10shared_ptrINS_6BufferEE_ZN5arrow2io23SetIOThreadPoolCapacityEi_ZN5arrow2io11HaveLibHdfsEv_ZN5arrow11PrettyPrintERKNS_5ArrayERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow2py23RegisterPyExtensionTypeERKSt10shared_ptrINS_8DataTypeEE_ZN5arrow2py8internal14StringToTzinfoERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow2py25UnregisterPyExtensionTypeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow21jemalloc_set_decay_msEi_ZN5arrow20mimalloc_memory_poolEPPNS_10MemoryPoolE_ZN5arrow20jemalloc_memory_poolEPPNS_10MemoryPoolE_ZN5arrow10InitializeERKNS_13GlobalOptionsE_ZN5arrow8internal10SendSignalEiPyErr_SetInterruptPyErr_CheckSignals_ZN5arrow33UnregisterCancellingSignalHandlerEv_ZNK5arrow9StopToken4PollEv_ZN5arrow8internal18SendSignalToThreadEim_ZN5arrow24SetCpuThreadPoolCapacityEi_ZTVN5arrow15DictionaryArrayEPy_IsInitializedPyGILState_Check_ZTVN5arrow16DictionaryScalarE_ZN5arrow12ArrayBuilder6FinishEPSt10shared_ptrINS_5ArrayEE_ZN5arrow3ipc14DictionaryMemoC1Ev_ZTVSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow17ExportDeviceArrayERKNS_5ArrayESt10shared_ptrINS_6Device9SyncEventEEP16ArrowDeviceArrayP11ArrowSchema_ZN5arrow23ExportDeviceRecordBatchERKNS_11RecordBatchESt10shared_ptrINS_6Device9SyncEventEEP16ArrowDeviceArrayP11ArrowSchema_ZN5arrow12ArrayBuilder5ResetEv_ZTVN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEE_ZTVN5arrow12ArrayBuilderE_ZNK5arrow2py15PyExtensionType11SetInstanceEP7_object_ZTVN5arrow17BinaryViewBuilderE_ZTTN5arrow2io16MockOutputStreamE_ZTVN5arrow2io16MockOutputStreamE_ZTVSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow3ipc7Message4bodyEv_ZN5arrow21ExtensionTypeRegistry17GetGlobalRegistryEv_ZN5arrow2io21FixedSizeBufferWriterC1ERKSt10shared_ptrINS_6BufferEE_ZTVSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow15run_end_encodedESt10shared_ptrINS_8DataTypeEES2__ZNK5arrow6Schema14RemoveMetadataEv_ZN5arrow2py24MakeTransformInputStreamESt10shared_ptrINS_2io11InputStreamEENS0_26TransformInputStreamVTableEP7_object_ZNK5arrow5Field14RemoveMetadataEv_ZNK5arrow6Schema12WithMetadataERKSt10shared_ptrIKNS_16KeyValueMetadataEE_ZNK5arrow5Field12WithMetadataERKSt10shared_ptrIKNS_16KeyValueMetadataEE_ZN5arrow16TableBatchReaderC1ERKNS_5TableE_ZN5arrow16TableBatchReader13set_chunksizeEl_ZNK5arrow3ipc7Message11SerializeToEPNS_2io12OutputStreamERKNS0_15IpcWriteOptionsEPl_ZNK5arrow5Field8WithNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow14Decimal256TypeC1Eii_ZTVSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14Decimal128TypeC1Eii_ZTVSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py23TensorToSparseCOOTensorERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCOOIndexEEEE_ZN5arrow2py23TensorToSparseCSFTensorERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSFIndexEEEE_ZN5arrow2py23TensorToSparseCSRMatrixERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSRIndexEEEE_ZN5arrow2py23TensorToSparseCSCMatrixERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSCIndexEEEE_ZTVN5arrow17FixedSizeListTypeE_ZTVSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow8ListTypeE_ZTVSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow12BaseListTypeD2Ev_ZTVN5arrow13LargeListTypeE_ZTVSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow5Field8WithTypeERKSt10shared_ptrINS_8DataTypeEE_ZN5arrow3ipc11WriteTensorERKNS_6TensorEPNS_2io12OutputStreamEPiPl_ZNK5arrow5Field12WithNullableEb_ZTVN5arrow19FixedSizeBinaryTypeE_ZTVSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow9list_viewESt10shared_ptrINS_5FieldEE_ZN5arrow15large_list_viewESt10shared_ptrINS_5FieldEE_ZNK5arrow10UnionArray5fieldEi_ZNK5arrow12ChunkedArray5SliceEll_ZNK5arrow12ChunkedArray5SliceEl_ZNK5arrow11RecordBatch6EqualsERKS0_bRKNS_12EqualOptionsE_ZN5arrow14MakeNullScalarESt10shared_ptrINS_8DataTypeEE_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow4nullEv_ZTVN5arrow10NullScalarE_ZTVSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow6Schema18GetAllFieldsByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK5arrow5Field7FlattenEv_ZNK5arrow10StructType18GetAllFieldsByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow6SchemaC1ESt6vectorISt10shared_ptrINS_5FieldEESaIS4_EES2_IKNS_16KeyValueMetadataEE_ZTVSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py17NumPyDtypeToArrowEP7_object_ZN5arrow7compute11CastOptionsC1Eb_ZN5arrow2py14NdarrayToArrowEPNS_10MemoryPoolEP7_objectS4_bRKSt10shared_ptrINS_8DataTypeEERKNS_7compute11CastOptionsEPS5_INS_12ChunkedArrayEE_ZN5arrow10ImportTypeEP11ArrowSchema_ZN5arrow2py14InferArrowTypeEP7_objectS2_b_ZNK5arrow6Schema18GetAllFieldIndicesERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK5arrow10StructType18GetAllFieldIndicesERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow12ChunkedArrayC1ESt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EES2_INS_8DataTypeEE_ZNK5arrow11StructArray17GetFlattenedFieldEiPNS_10MemoryPoolE_ZNK5arrow18LargeListViewArray7FlattenEPNS_10MemoryPoolE_ZNK5arrow13ListViewArray7FlattenEPNS_10MemoryPoolE_ZN5arrow11ImportArrayEP10ArrowArrayP11ArrowSchema_ZNK5arrow5Array4ViewERKSt10shared_ptrINS_8DataTypeEE_ZN5arrow19MakeArrayFromScalarERKNS_6ScalarElPNS_10MemoryPoolE_ZN5arrow15MakeArrayOfNullERKSt10shared_ptrINS_8DataTypeEElPNS_10MemoryPoolE_ZN5arrow27SupportedMemoryBackendNamesB5cxx11Ev_ZN5arrow25DefaultDeviceMemoryMapperEil_ZN5arrow23ImportDeviceRecordBatchEP16ArrowDeviceArraySt10shared_ptrINS_6SchemaEERKSt8functionIFNS_6ResultIS2_INS_13MemoryManagerEEEEilEE_ZN5arrow23ImportDeviceRecordBatchEP16ArrowDeviceArrayP11ArrowSchemaRKSt8functionIFNS_6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEE_ZN5arrow17ImportRecordBatchEP10ArrowArrayP11ArrowSchema_ZN5arrow17ImportRecordBatchEP10ArrowArraySt10shared_ptrINS_6SchemaEE_ZN5arrow11RecordBatch15FromStructArrayERKSt10shared_ptrINS_5ArrayEEPNS_10MemoryPoolE_ZN5arrow3ipc15ReadRecordBatchERKNS0_7MessageERKSt10shared_ptrINS_6SchemaEEPKNS0_14DictionaryMemoERKNS0_14IpcReadOptionsE_ZN5arrow23AllocateResizableBufferEllPNS_10MemoryPoolE_ZTVSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow12ArrayBuilder6ResizeEl_ZN5arrow23AllocateResizableBufferElPNS_10MemoryPoolE_ZN5arrow2io18BufferOutputStreamC1ERKSt10shared_ptrINS_15ResizableBufferEE_ZTVSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14AllocateBufferEllPNS_10MemoryPoolE_ZTVSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EEll_ZN5arrow14AllocateBufferElPNS_10MemoryPoolE_ZN5arrow2py8PyBuffer12FromPyObjectEP7_object_ZN5arrow15SliceBufferSafeERKSt10shared_ptrINS_6BufferEEll_ZN5arrow15SliceBufferSafeERKSt10shared_ptrINS_6BufferEEl_ZN5arrow3ipc20SerializeRecordBatchERKNS_11RecordBatchERKNS0_15IpcWriteOptionsE_ZN5arrow3ipc15SerializeSchemaERKNS_6SchemaEPNS_10MemoryPoolE_ZNK5arrow12SparseTensor8ToTensorEPNS_10MemoryPoolE_ZNK5arrow11RecordBatch8ToTensorEbbPNS_10MemoryPoolE_ZN5arrow9extension20FixedShapeTensorType10MakeTensorERKSt10shared_ptrINS_15ExtensionScalarEE_ZNK5arrow9extension21FixedShapeTensorArray8ToTensorEv_ZN5arrow3ipc10ReadTensorEPNS_2io11InputStreamE_ZTVSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow16KeyValueMetadata3GetB5cxx11ESt17basic_string_viewIcSt11char_traitsIcEE_ZN5arrow2py8internal14TzinfoToStringB5cxx11EP7_object_ZN5arrow11ImportFieldEP11ArrowSchema_ZN5arrow12ImportSchemaEP11ArrowSchema_ZNK5arrow6Schema8SetFieldEiRKSt10shared_ptrINS_5FieldEE_ZNK5arrow6Schema11RemoveFieldEi_ZNK5arrow6Schema8AddFieldEiRKSt10shared_ptrINS_5FieldEE_ZN5arrow3ipc10ReadSchemaERKNS0_7MessageEPNS0_14DictionaryMemoE_ZN5arrow3ipc10ReadSchemaEPNS_2io11InputStreamEPNS0_14DictionaryMemoE_ZTVSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14DictionaryTypeC1ERKSt10shared_ptrINS_8DataTypeEES5_b_ZNK5arrow16DictionaryScalar15GetEncodedValueEv_ZNK5arrow12ChunkedArray9GetScalarEl_ZNK5arrow5Array9GetScalarEl_ZN5arrow18ImportChunkedArrayEP16ArrowArrayStream_ZN5arrow17DictionaryUnifier17UnifyChunkedArrayERKSt10shared_ptrINS_12ChunkedArrayEEPNS_10MemoryPoolE_ZN5arrow2py17ConvertPySequenceEP7_objectS2_NS0_19PyConversionOptionsEPNS_10MemoryPoolE_ZN5arrow2py14GetResultValueISt10shared_ptrINS_5ArrayEEEET_NS_6ResultIS5_EE_ZN5arrow17ImportDeviceArrayEP16ArrowDeviceArraySt10shared_ptrINS_8DataTypeEERKSt8functionIFNS_6ResultIS2_INS_13MemoryManagerEEEEilEE_ZN5arrow17ImportDeviceArrayEP16ArrowDeviceArrayP11ArrowSchemaRKSt8functionIFNS_6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEE_ZN5arrow11ImportArrayEP10ArrowArraySt10shared_ptrINS_8DataTypeEE_ZN5arrow15DictionaryArrayC1ERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES9__ZTVSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow15DictionaryArray10FromArraysERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES9__ZNK5arrow11StructArray7FlattenEPNS_10MemoryPoolE_ZNK5arrow11RecordBatch13ToStructArrayEv_ZNK5arrow12ChunkedArray7FlattenEPNS_10MemoryPoolE_ZN5arrow2py14GetResultValueISt10shared_ptrINS_11RecordBatchEEEET_NS_6ResultIS5_EE_ZN5arrow17RecordBatchReader7ToTableEv_ZN5arrow17DictionaryUnifier10UnifyTableERKNS_5TableEPNS_10MemoryPoolE_ZNK5arrow5Table13CombineChunksEPNS_10MemoryPoolE_ZN5arrow2py14GetResultValueISt10shared_ptrINS_5TableEEEET_NS_6ResultIS5_EE_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow16TableBatchReaderC1ESt10shared_ptrINS_5TableEE_ZTIN5arrow2io19BufferedInputStreamE_ZTIN5arrow2io11InputStreamE_ZN5arrow2io19BufferedInputStream6DetachEv_ZTIN5arrow2io16RandomAccessFileE_ZN5arrow2io16RandomAccessFile9GetStreamESt10shared_ptrIS1_Ell_ZN5arrow2io16MemoryMappedFile4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS0_8FileMode4typeE_ZN5arrow2io16MemoryMappedFile6CreateERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEl_ZN5arrow2io12ReadableFile4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPNS_10MemoryPoolE_ZTIN5arrow2io20BufferedOutputStreamE_ZTIN5arrow2io12OutputStreamE_ZN5arrow2io20BufferedOutputStream6DetachEv_ZN5arrow2io16FileOutputStream4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEb_ZN5arrow2io21CompressedInputStream4MakeEPNS_4util5CodecERKSt10shared_ptrINS0_11InputStreamEEPNS_10MemoryPoolE_ZN5arrow3ipc13MessageReader4OpenERKSt10shared_ptrINS_2io11InputStreamEE_ZN5arrow2io22CompressedOutputStream4MakeEPNS_4util5CodecERKSt10shared_ptrINS0_12OutputStreamEEPNS_10MemoryPoolE_ZN5arrow2io19BufferedInputStream6CreateElPNS_10MemoryPoolESt10shared_ptrINS0_11InputStreamEEl_ZN5arrow2io20BufferedOutputStream6CreateElPNS_10MemoryPoolESt10shared_ptrINS0_12OutputStreamEE_ZTVN5arrow4util12CodecOptionsE_ZN5arrow4util5Codec6CreateENS_11Compression4typeERKNS0_12CodecOptionsE_ZTVSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow4util5Codec6CreateENS_11Compression4typeEi_ZTVSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow3ipc11ReadMessageEPNS_2io11InputStreamEPNS_10MemoryPoolE_ZN5arrow3ipc14MakeFileWriterESt10shared_ptrINS_2io12OutputStreamEERKS1_INS_6SchemaEERKNS0_15IpcWriteOptionsERKS1_IKNS_16KeyValueMetadataEE_ZN5arrow3ipc16MakeStreamWriterESt10shared_ptrINS_2io12OutputStreamEERKS1_INS_6SchemaEERKNS0_15IpcWriteOptionsE_ZN5arrow2py14GetResultValueINS_23RecordBatchWithMetadataEEET_NS_6ResultIS3_EE_ZN5arrow2py19PyRecordBatchReader4MakeESt10shared_ptrINS_6SchemaEEP7_object_ZN5arrow23ImportRecordBatchReaderEP16ArrowArrayStream_ZN5arrow2py24CastingRecordBatchReader4MakeESt10shared_ptrINS_17RecordBatchReaderEES2_INS_6SchemaEE_ZN5arrow3ipc23RecordBatchStreamReader4OpenERKSt10shared_ptrINS_2io11InputStreamEERKNS0_14IpcReadOptionsE_ZN5arrow3ipc21RecordBatchFileReader4OpenEPNS_2io16RandomAccessFileElRKNS0_14IpcReadOptionsE_ZN5arrow3ipc21RecordBatchFileReader4OpenEPNS_2io16RandomAccessFileERKNS0_14IpcReadOptionsE_ZN5arrow8MapArray10FromArraysESt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES7_S7_PNS_10MemoryPoolE_ZN5arrow8MapArray10FromArraysERKSt10shared_ptrINS_5ArrayEES5_S5_PNS_10MemoryPoolE_ZN5arrow18FixedSizeListArray10FromArraysERKSt10shared_ptrINS_5ArrayEES1_INS_8DataTypeEES1_INS_6BufferEEl_ZN5arrow18FixedSizeListArray10FromArraysERKSt10shared_ptrINS_5ArrayEEiS1_INS_6BufferEEl_ZN5arrow9ListArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZN5arrow9ListArray10FromArraysERKNS_5ArrayES3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZN5arrow14LargeListArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZN5arrow14LargeListArray10FromArraysERKNS_5ArrayES3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZN5arrow13ListViewArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_S6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZN5arrow13ListViewArray10FromArraysERKNS_5ArrayES3_S3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZN5arrow18LargeListViewArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_S6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZN5arrow18LargeListViewArray10FromArraysERKNS_5ArrayES3_S3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZN5arrow18RunEndEncodedArray4MakeERKSt10shared_ptrINS_8DataTypeEElRKS1_INS_5ArrayEES9_l_ZN5arrow18RunEndEncodedArray4MakeElRKSt10shared_ptrINS_5ArrayEES5_l_ZN5arrow11RecordBatch4MakeESt10shared_ptrINS_6SchemaEElSt6vectorIS1_INS_5ArrayEESaIS6_EE_ZN5arrow5Table4MakeESt10shared_ptrINS_6SchemaEESt6vectorIS1_INS_12ChunkedArrayEESaIS6_EEl_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow5FieldE_ZTVSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow5FieldD0Ev_ZNSt6vectorISt10shared_ptrIN5arrow15ResizableBufferEESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE5clearEv_ZN5arrow2py20ConvertArrayToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_5ArrayEEP7_objectPS8__ZN5arrow2py27ConvertChunkedArrayToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_12ChunkedArrayEEP7_objectPS8__ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE17_M_realloc_insertIJRKS5_EEEvN9__gnu_cxx17__normal_iteratorIPS5_S7_EEDpOT__PyList_ExtendPySet_ContainsPySet_TypePyFrozenSet_TypePyFrozenSet_New_ZN5arrow16KeyValueMetadataC1ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EES9__ZNK5arrow11RecordBatch13RenameColumnsERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EE_ZNK5arrow5Table13RenameColumnsERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EE_ZN5arrow2py15NdarrayToTensorEPNS_10MemoryPoolEP7_objectRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EEPSt10shared_ptrINS_6TensorEE_ZNSt6vectorISt10shared_ptrIN5arrow6SchemaEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow12UnifySchemasERKSt6vectorISt10shared_ptrINS_6SchemaEESaIS3_EENS_5Field12MergeOptionsE_ZN5arrow5Field12MergeOptions10PermissiveEv_ZNSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow10StructTypeC1ERKSt6vectorISt10shared_ptrINS_5FieldEESaIS4_EE_ZTVSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNSt6vectorIaSaIaEE17_M_realloc_insertIJRKaEEEvN9__gnu_cxx17__normal_iteratorIPaS1_EEDpOT__ZN5arrow12sparse_unionESt6vectorISt10shared_ptrINS_5FieldEESaIS3_EES0_IaSaIaEE_ZN5arrow11dense_unionESt6vectorISt10shared_ptrINS_5FieldEESaIS3_EES0_IaSaIaEE_ZNSt6vectorIlSaIlEE17_M_realloc_insertIJRKlEEEvN9__gnu_cxx17__normal_iteratorIPlS1_EEDpOT__ZN5arrow2py25NdarraysToSparseCOOTensorEPNS_10MemoryPoolEP7_objectS4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCOOIndexEEEE_ZN5arrow2py25NdarraysToSparseCSCMatrixEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSCIndexEEEE_ZN5arrow2py25NdarraysToSparseCSRMatrixEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSRIndexEEEE_ZN5arrow9extension20FixedShapeTensorType4MakeERKSt10shared_ptrINS_8DataTypeEERKSt6vectorIlSaIlEESB_RKS7_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISH_EE_ZN5arrow2py25NdarraysToSparseCSFTensorEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEES9_RKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSFIndexEEEE_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow15DictionaryArrayC1ERKSt10shared_ptrINS_9ArrayDataEE_ZNSt6vectorISt10shared_ptrIN5arrow9ArrayDataEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EES4_IS1_IS0_ESaIS9_EEll_ZNSt6vectorISt10shared_ptrIN5arrow5ArrayEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow11StructArray4MakeERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EERKS1_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES2_INS_6BufferEEll_ZN5arrow11StructArray4MakeERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EERKS1_IS2_INS_5FieldEESaISA_EES2_INS_6BufferEEll_ZN5arrow11ConcatenateERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS3_EEPNS_10MemoryPoolE_ZN5arrow12ChunkedArray4MakeESt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EES2_INS_8DataTypeEE_ZNSt6vectorISt10shared_ptrIN5arrow12ChunkedArrayEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow5Table17FromRecordBatchesESt10shared_ptrINS_6SchemaEERKSt6vectorIS1_INS_11RecordBatchEESaIS6_EE_ZNSt6vectorISt10shared_ptrIN5arrow5TableEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow17ConcatenateTablesERKSt6vectorISt10shared_ptrINS_5TableEESaIS3_EENS_24ConcatenateTablesOptionsEPNS_10MemoryPoolE_ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EE17_M_default_appendEm_ZNSt6vectorIiSaIiEE17_M_realloc_insertIJiEEEvN9__gnu_cxx17__normal_iteratorIPiS1_EEDpOT__ZNK5arrow5Table13SelectColumnsERKSt6vectorIiSaIiEE_ZNK5arrow11RecordBatch13SelectColumnsERKSt6vectorIiSaIiEE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE9_M_assignIRKSI_NS7_17_ReuseOrAllocNodeISaINS7_10_Hash_nodeIS5_Lb1EEEEEEEEvOT_RKT0__ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE18_M_assign_elementsIRKSI_EEvOT__ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EES4_IS1_IS0_ESaIS9_EES9_ll_ZSt16__do_uninit_copyIN9__gnu_cxx17__normal_iteratorIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIS7_SaIS7_EEEEPS7_ET0_T_SG_SF__ZN5arrow15DenseUnionArray4MakeERKNS_5ArrayES3_St6vectorISt10shared_ptrIS1_ESaIS6_EES4_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES4_IaSaIaEE_ZN5arrow16SparseUnionArray4MakeERKNS_5ArrayESt6vectorISt10shared_ptrIS1_ESaIS6_EES4_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES4_IaSaIaEE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE9_M_rehashEmRKm_ZSt11_Hash_bytesPKvmm_ZNKSt8__detail20_Prime_rehash_policy14_M_need_rehashEmmm_ZN5arrow2py20ConvertTableToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_5TableEEPP7_object_ZSt16__do_uninit_copyIN9__gnu_cxx17__normal_iteratorIPKN5arrow8FieldRefESt6vectorIS3_SaIS3_EEEEPS3_ET0_T_SC_SB__ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEC1EOS4__ZNK5arrow12StructScalar5fieldENS_8FieldRefE_ZN5arrow6SchemaD0Ev_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTVN10__cxxabiv117__class_type_infoE_ZTSN5arrow8internal20ArrayBuilderExtraOpsINS_17BaseBinaryBuilderINS_10BinaryTypeEEESt17basic_string_viewIcSt11char_traitsIcEEEE_ZTIN5arrow8internal20ArrayBuilderExtraOpsINS_17BaseBinaryBuilderINS_10BinaryTypeEEESt17basic_string_viewIcSt11char_traitsIcEEEE_ZTSN5arrow4util18EqualityComparableINS_6ScalarEEE_ZTIN5arrow4util18EqualityComparableINS_6ScalarEEE_ZTSSt23enable_shared_from_thisIN5arrow6ScalarEE_ZTISt23enable_shared_from_thisIN5arrow6ScalarEE_ZTSFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZTIFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZTVN10__cxxabiv120__function_type_infoE_ZTSFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_ZTIFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_ZTSN5arrow4util18EqualityComparableINS_7compute15FunctionOptionsEEE_ZTIN5arrow4util18EqualityComparableINS_7compute15FunctionOptionsEEE_ZTSSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTISt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE_ZTVN10__cxxabiv120__si_class_type_infoE_ZTSSt18bad_variant_access_ZTISt9exception_ZTSN5arrow5ArrayE_ZTIN5arrow5ArrayE_ZTSN5arrow15DictionaryArrayE_ZTIN5arrow15DictionaryArrayE_ZTSN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEE_ZTIN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEE_ZTVN10__cxxabiv121__vmi_class_type_infoE_ZTIN5arrow12ArrayBuilderE_ZTSN5arrow13BinaryBuilderE_ZTIN5arrow13BinaryBuilderE_ZTSN5arrow13StringBuilderE_ZTIN5arrow13StringBuilderE_ZTSN5arrow17StringViewBuilderE_ZTIN5arrow17StringViewBuilderE_ZTIN5arrow17BinaryViewBuilderE_ZTSN5arrow6ScalarE_ZTIN5arrow6ScalarE_ZTSN5arrow10NullScalarE_ZTIN5arrow10NullScalarE_ZTSN5arrow8internal19PrimitiveScalarBaseE_ZTIN5arrow8internal19PrimitiveScalarBaseE_ZTSN5arrow16DictionaryScalarE_ZTIN5arrow16DictionaryScalarE_ZTSN5arrow15ExtensionScalarE_ZTIN5arrow15ExtensionScalarE_ZTSN5arrow2io12OutputStreamE_ZTIN5arrow2io13FileInterfaceE_ZTIN5arrow2io8WritableE_ZTSN5arrow4util12CodecOptionsE_ZTIN5arrow4util12CodecOptionsE_ZTSN5arrow7compute15FunctionOptionsE_ZTIN5arrow7compute15FunctionOptionsE_ZTSN5arrow7compute11CastOptionsE_ZTIN5arrow7compute11CastOptionsE_ZTSPFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_ZTVN10__cxxabiv119__pointer_type_infoE_ZTSPFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZTSSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow12ArrayBuilder12AppendScalarERKNS_6ScalarEl_ZN5arrow12ArrayBuilder13AppendScalarsERKSt6vectorISt10shared_ptrINS_6ScalarEESaIS4_EE__cxa_pure_virtual_ZN5arrow17BinaryViewBuilder5ResetEv_ZN5arrow17BinaryViewBuilder16AppendArraySliceERKNS_9ArraySpanEll_ZN5arrow17BinaryViewBuilder14FinishInternalEPSt10shared_ptrINS_9ArrayDataEE__pthread_key_create_ZTINSt8ios_base7failureB5cxx11E_ZTISt10bad_typeid_ZTISt11range_error_ZTISt12domain_error_ZTISt12out_of_range_ZTISt14overflow_error_ZTISt15underflow_error_ZTISt16invalid_argument_ZTISt8bad_cast_ZTISt9bad_alloclibarrow_python.solibarrow.so.1601libstdc++.so.6libgcc_s.so.1libc.so.6GCC_3.0GLIBC_2.4GLIBC_2.2.5GLIBCXX_3.4.18CXXABI_1.3.9GLIBCXX_3.4.9GLIBCXX_3.4.29CXXABI_1.3GLIBCXX_3.4CXXABI_1.3.5GLIBCXX_3.4.21$ORIGIN/../../..:$ORIGIN:/opt/conda/lib/python3.11/site-packages/pyarrowXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX     P&y 0ii ui Ƹh Ҹyѯ )yӯk t)uѯ "q/Xd6 `d6 hd63pd63xd63d6r3d63d6Pr3d6P3d63d63d63d63d63d6p3d6h3d6`3d63d63d63d6B3e63e6@3e6/3e6)3 e6#3(e6^30e6j38e63@e683He6Y3Pe63Xe63`e63he63pe63xe63e6T3e63e63e6pz3e63e63e603e6t3e63e63e6x3e63e63e63e63e63f603f63f63f6Z3 f6[3(f6q30f6J38f6N3@f6G3Hf6@3Pf63Xf693`f6X3hf63pf6S3xf63f63f63f6`3f63f6p3 l6(l60l6 Hl6Pl6hl6`pl6l6@ l6 l6Pl6Sl6El6Rl6Fl6 l6P m6m6pm6@ m6 (m6P0m6S8m6E@m6RHm6F`m6 hm6P pm6 *m60m6m6@ m6 m6Um6Fm6Tm6Gm6)n6n6(n6 0n6вHn6`Pn6Xn6P `n6` xn6n6 n6 n6n6`n6n6` n6 n6p n6` n6$o60 o6  o6@ (o6P 0o6p$Ho6 Po6 Xo6 `o6@ ho6$o6p o6 o6p o6 o6 o6` o6 o6Їo6 o6 o6P o6 p6 p6 p6 (p6@ 0p6` 8p6 @p6p Hp6 `p60 hp6@ pp6P xp6P p6p p6 p6 p6 p60 p6@ p6 p6 p6 p6 p6 q6 q6q6 q6(q6 @q6 Hq6Pq6 Xq6`q6 xq6 q6q6` q6q6 q6 q6q60 q6q6P q6 q6`q6 r6pr6 r6 (r6@0r6 8r6P@r6 Xr6 `r6 hr6 pr60xr6 r6 r6r6 r6 r6&r6` r6r6p r60 r6P's6 s6s60 s6 s6P 8s6 @s6Hs6 Ps6PXs6'ps6 xs6s6s6@s6's6 s6s6 s60s6s6 s6s6 s6t6 t6p t6p(t6 0t68t6 Pt6` Xt6P`t6P ht6`pt6p t6P t60t6 t6@t6@ t6@ t6t6 t6 t6 t60 u6u6 u6u6 0u6 8u6@u6 Hu6Pu6 666g(06`l60 66 66@6666p6g(66A6g(Ȓ6g(В6ؒ6P@67)6g(606g(@6g(h6g(68)6 h(6h( 6h((6P86@8)@6#J(H6X68)`6X(h6x69)6H9)Д6`6  6X6 `66@666666@@6=(H66h(6$h(6606-h(660065h(86`@6?X6g(`6`h6?6g(6@ 6@6?h(66DЗ6Ih(ؗ66D6Rh(6P 6_h((6P H6kh(P6p6xh(x66h(6` 6h(Ș6` 6h(6.6pA6h(6. 6pA86h(@60.H6B`6h(h6Pp6>6h(6p6h(6,(6PN(@68H66636pAؚ6h((6606`6x6 6 6@y666@sh6 6,6o<(66@(6X6x9)p6d6p x669)6Y6 6P8669)6P[86 @60I686 :)P6_آ60 X6أ6P:)6bx6 6@x6:)6`6P 6P6:)06]6 866:)Ш6p\X6p ة6X6;)p6^6 x6`6@;)6g6p 6 6p;)6R86P 686;)P6Piد6 X6Pذ6;)6tx6 6x6<)60Q6 6P-66(<)06`6 86`6X<)е6`PX6 ض6X6<)p6O6 x66<)6N6p 66<)6M86P 686=)P60cؼ6 X6ؽ60=)6 Mx6 6x6`=)6PL6 66=)06!6 6=6@666866i(6б6=)6F(66=)6g(6@@6f8(H6#X6*`6\a(h6`C x6*6 .)6s#6 *6c(6&6*6$6(66`*67(66*6o<(6P6* 6@((6@86*x6(>)6H6666f8(6p`#6* 6o<((686*@6@(H6X6*6P>)6h66x6 66 6F((6 86=)6f8(6#6*6o<(66*6@(6`6*6x>)066 666866"i(6`65i(6` 6f8((6`i#86`*@6o<(H6`X6 *`6@(h6x6*6Gi(6!P6>)x6 6 6@666@6d(H6#X6@*`6ai(h606d(6PS#6*6o<(66@*6@(6`6*6?)0668?)66(6 =866o<(6p6 *6@(6@ 6*6ji(06060@)6P 6p;66(6866o<(6`6*6@(66*6i(066686@6_(66p*6o<(66@*6@(66*86@B)P66`B)66X6 6\(6 6 *6o<(66*6@(6`6*X6XD)p66D)(66x6+6<(6 6 *6M(66* 6M((686*@6o<(H6X6*`6@(h6x6`*6i(6P6F)X6 `6<6@66@6A(H6}X6*`6o<(h6x6*6@(66*6i(6 p6hJ)x6P 6P<6`66`6Zd(h6"x6 *6f8(6"6*6JX(6@<6 *6A(6 6*6o<(66*6@(66*X6i(p6p6L)6 6;(66x6@'6B(66*6B(66* 6B((686*@6o<(H6X6`*`6@(h6x6 *6i(66`66@6P6XR)6 6666J@6 `6 6j(6 6AF((6$86`(-@6K(H6SX6@'-`6[(h6x6&-6o<(66@&-6@(66&-6j(6 H66P666 S)6`66661666j(6`6AF(h60 x6 ,-6K(6W6+-6[(66@*-6o<(6@6*-6@(66)-86S)P66T)6P 6I66X6{63D(6Ж6@7-6b(6.!64-6S(6@z61-X6V)p66V)6P 6I(6`6866x6p6*j(6060W)6 K(6 6W)`6e(h60;$x6E-6pg(66C-6P(6=6 >-6?(6V6<-6?(6W6`;-863j(P66(X)6P 6I66X6t6Pj(6=6X)86dj(P66Y)6P 6I66X60t6P(6#6Y-6j(066`Y)6P 6I86z6j(6P6Y)X6P `6I60{X6j(p66Y)6P 6Ix6{6j(66(Z)6P 6I6z6j(606pZ)86P @6I6y86j(P66Z)6P 6IX6Py6Z)6p6[)6`66@C`6X(h6x6&.6b(6)!6`%.6k(6p6`[)666`66`6N(h6x6[)6J(66).6V(66 (.862k(P66[)6@666X66N(66[)6Jk(660\)@6J(H6X6 *.6p\)607\)h77x7 77 7N((7И87[)7J(7p7*.7Tk(7p7\)777`77`7D(h7@x7])7N(7Н!7P])7rc(77NK(7x$7+. 7=((7>87@+.@7J(H7X7*.7qk(77@77 707])X7h777C(7X7,7J(7=7+.7k(787H77P77h77])7:7 77`7;7'7;7,7: 7f=( 7< 7@.. 7J(( 7P8 7,.@ 7)J(H 70X 7,.` 7G(h 7  7]) 7 7CX 7k(p 7x 7B 7k( 77C7^)77pC87k(P777777(^)7777@7X77@77@7,@7 K(H7@7J(707`..7k(77I7 l(77D87(l(P7X7H7Fl(7p7X^)7`77`D`7J(h7 x7/.7^)77BX7_l(p7x70D7}l(77^)777 7H(7 :7@0.7J(77/.7^)77 _)7 77 7 7 7N( 7p 7h_) 7J( 7 70.8!7l(P!7!7_)"7 #7"7"7X"7"7N("7 #7J((#7p8#7@1.x#7l(#7#7$7_)H$7`%7X$7%7$7@%7N(%7@`%7J(h%74x%72.%7G(%7%7 2.%7l(%7p&7_)&7'7&7`'7&7`'7N(h'7'7J('7'73.(7l(0(7(7 `)(7*7(7)78)7`)7N()7@*7J(*7"*7 4.X*7l(p*7*7P`)(+7@,78+7+7x+7+7N(+7@,7J(H,74X,74.,7m(,70-7`)h-7.7x-7 .7-7 .7N((.7@.7J(.7P/.7`5..71m(.7p/7`)/7`07/7@`07J(h07$x075.07Nm(07P17`)17@2717@27J(H27@*X27@6.27km(27037 a)h37 4737  47J((47.8476.x47m(4757Pa)H576757 67J(67-677.X67m(p6767Pa)(7777x7777J(77,77`7.887m(P8787a)9797X9797J(97,977.:7m(0:7:7a):7;78;7`;7J(;70+;7 8.;7m(<7<7a)<7=7=7=7J(=7`*=78.=7n(=7p>7b)>7`?7>7`?7J(h?7)x?78.?7n(?7P@7@b)@7@A7@7`@A7J(HA7(XA7@9.A72n(A70B7pb)hB7 C7B7 C7J((C7'8C79.xC7Kn(C7D7b)HD7E7D7@E7J(E7 'E7:.XE7bn(pE7E7b)(F7F7xF7F7J(F7P&F7`:.8G7zn(PG7G7c)H7H7XH7H7J(H7P%H7:.I7n(0I7I70c)I7J7(J7 8J7@J7J(J7J7 ;.J7hc)K7@K7L7L7$L7X(L7PL7B.L7o<(L7PL7A.L7@(L7L7A.M7c)0M7 M7c)M7N7(N78N7N7N6(N7N7/N7n(O7O7e)P7P7n(P70Q7hg)Q7p8R7n(PR7R7S7R7S7R7i)R7S7T7S7@T7XS7pS70S7S70S7p,@T7n(HT7XT7n)hT7g(pT7`T7 o)T7=(T7T7$/U7n(0U7U7@p)U7V7(V7@8V7`V7o<(V7V7@D/V7@(V7V7D/W7o(0W7`W77o(W7X7(X78X7X7o<(X7X7D/X7@(X7X7D/Y7Ko(0Y7Y7q)Y7 Y7Y7@_7Y7Z7(Z78Z7 Z7Q(Z7Z7fo(Z7 Z7k;(Z7[7D;( [7@[7?i(H[7ph[7;(p[7[7;([7P[7;([7[7C;([7`\7>i(\70\7j;(8\7 X\7n;(`\7\7y;(\7\7R(\7@\7[H(\7\7A(]7 ]7;((]7`H]7;(P]7p]7;(x]7 ]7;(]7]7;(]7]7;(]7@^7;(^78^7;(@^7`^7 <(h^7p^7<(^7^7<(^7P^7#<(^7@_7A(H_7X_7M/`_7R(h_7x_7M/_7<(_70|_7L/_7<(_7{_7L/_7$<(_7y_7L/_7XR(_7@^_7`I/`7o<(`7`7 I/ `7@((`7pW8`7H/x`7mo(`7a7 a7-Ha7`b7Xa7b7a7b7&;(b7`b7f(hb7&xb7@P/b7$6(b7 b7P/b77(b7b7O/b7o<(b7b7O/b7@(b7 b7@O/8c7o(Pc7Pd7d7Xd7d7o<(d7pd7S/d7@(d7`d7R/8e7o(Pe70xe7pe7q)f7@g7f7f7Hf7pL#Xf7 f7:h(f7f7o(f7Y(f7g7`y)@g7M(Hg7Xg7`*`g7Q(hg7pxg7*g7Q(g7g7*g7Z(g7P9g7@*g7Y(g76g7*g7Y(g73g7*h7GJ(h7 h7* h7O((h7 8h7*@h7G(Hh7`h7o<(hh7xh7*h7@(h7h7*h7o(h7 pi7y)i7i7@k7i7`j7i7i7`j7o(hj7pj7j7o(j7`j7j7 p(j7j70j7p(j7 j7@k7sM(Hk7pXk7*`k7NK(hk7xk7`*k7=(k7Xk70*k7p(k7pl7~)l7l7l7m7l7`m7l7`m7g(hm7'xm7h)m7ai(m7m7[(m7m7@*n7.)n7p#n7* n7غ((n7p8n7*@n7c(Hn7"Xn7@*`n7X(hn7/xn7*n7$6(n7Pn7*n77(n7an7@*n77C(n7#n7*n7Y(n7n7*o7`(o7#o7* o7B((o7@&8o7*@o7`(Ho7@#Xo7@*`o7A(ho7mxo7*o7e(o7#o7*o7o<(o7o7*o7@(o7o7*p7)0p7p7)p7r7p7q78q7q7F(q7Pq7)r7fb(r7r7@* r7U_((r7 68r7*@r7>X(Hr72Xr7`*`r7X(hr71xr7*r7$6(r70r7*r77(r7gr7`*r7o<(r7r7 *r7@(r7r7*8s7Ѓ)Ps7s7)t7t7Ht7`*#Xt7t7o<(t7@t7*t7@(t7t7*8u7)Pu7u7Ȇ)v7v7Hv7"#Xv7v7o<(v7v7@*v7@(v7 v7*8w7x)Pw7w7)x7x7Hx7<#Xx7 x7Hd(x7 #x7*x7o<(x7x7*y7@(y7 y7`*Xy7)py7y7ȍ)(z7z7hz7p2#xz7@z7Hd(z7"z7*{7o<({7{7@* {7@(({78{7*x{7=p({7{7|7)8|7@|70H|7}7X|7}7|7}7n(}7}7)(}7Tp(0}7 }7$6(}7}7 +}77(}7P\}7 +}7G(}7}7:(}7}7 +~7B(~7 ~7 + ~7fB((~78~7 +@~7B(H~7pX~7` +`~7A(h~7phx~7 +~7X(~7 I~7+~7_8(~7`#~7+~7B(~7~7+~7oB(~7Р~7`+7RB(77 + 7_((7`"87+@73H7"X7+`7_(h7^x7 +7X(7@?7+7^(77+7fb(7 7+7Y(7 (7+7Od(7`"7* 7_((7y87*@7LB(H7X7`*`7-B(h7x70*7Y(7P!7*7B(7d7*7B(Ȁ7`؀7*7ai(7@7b(7` 7 * 7B((7^87*@7"B(H7PX7*`7Y(h7px7*7qS(77*7o<(707`*7@(ȁ7`؁7 *7[p(077)77877JX(7P87*7wp(787PwH77P77777)7Ȅ7 7؄7 7777|7@{77@{؅7`, 7_((787)H7p(P7`7)p7p(x7p7`)7p(77)74(Ȇ7) 7G((7`w@7@(H7X7*`7W(h7 x7*7=(77*7N(77@*7<(ȇ7؇7*7p(077Д)77;7777877p(7x7H)ȉ7p(Љ7Py70)7g(7y7x)7p( 707)7q?(707`,7=(77@,7X(Ȋ7~؊7`,7r_(7H7 ,7V(7p7, 7>((7Є87{,@7P(H7PX7{,`7d(h7 ~ x7u,7c?(7w 7r,7c(7Pi 7n,7}e(ȋ7$؋7@k,7b(7 7e,7W(77 `, 7+P((7 87Y,@7=(H7psX7`T,`7f(h7P 'x7 P,77C(77`J,7@(7>7 J,74e(Ȍ70T$،7=,7pg(7p'74,7C(7c 7`1, 7M((7z"871,@7b(H7 !X7',`7Y(h7 zx7`%,7`(7[ 7#,7](7Щ7 ,7B(ȍ7P؍7@,7`(70U 7 ,7[(7`7, 7fa((7L 87,x7p(77@)H77X7777g(7x7)(7p(07<@7)P7p(X7pxh7)x7p(707)7q?(77,7X(7v7, 7=((7p87,@7W(H7pX7,`7+P(h7@x7,7f(7&7,7r_(7PB7+7-`(ȑ7`"ؑ7+7P(7`"7+7c(7`"7+ 7=((7\87@+@77C(H7X7+`74e(h7e$x7+7pg(70(7+7C(707+7M(Ȓ7Nؒ7@+7e(70&7+7](7w7`+ 7>d((7"87+@7@(H7X7+`7V(h7x7+7>(77+7P(7 7p+7d(ȓ70"ؓ7+7c?(7"7+7c(7"7`+ 7}e((7$87 +@7B(H7X7+`7@(h7x7@+7AO(7 7 +7R(7 B7`}+7B(Ȕ7ؔ7{+7p(07X7p<h77p777)ȕ7%7@77 7(7 \87 7@t7@7@tؖ7P, 7p((7/87P)H7e(P75`7X)p7p(x7@7p(77m:(ȗ7vؗ7)7g(7@7T(H70X7,`7C(h7`Bx7,7G(7<7V(7f7,7C(Ș79ؘ7`,7q?(7V70,7C(747, 7D((7187,@7[F(H7@-X7,`75Q(h7Pix7,7kQ(7Po7,7W?(7Pu7 ,7'O(ș7ؙ7`,7C(7P(7 ,7J(7C7, 7D((7a87`,@7Y(H7 X7,`7c?(h7vx7@,7O(7u7,7d(7<7,7gC(Ț7`#ؚ7 ,7o<(7P{7,7@(77,X7 q(p7707777ț77x)77>77 r(7787`7h7x77д77д7@,`7u(h779(747H)7j(7sȝ78)؝7p(7P7@)7%q(7ps7)(7P(07P@7)P79h(X7s7=(7@]7%-729(Ȟ7`؞7$-7G(7@7Y(7p7 - 73S((787@-@7X(H7ДX7`-`7>(h7`x7 -7P(77-7[E(7Њ7-7BE(ȟ7؟7-7'E(7P7-7 E(7`~7`- 7=((7a87 -@7@(H7E'X7 -`7X(h7$x7 -7T(7w7` -77C(7o7-7.6(Ƞ7 gؠ7-7-`(70"7@,7P(77, 7E((7 c87,@7D(H7 _X7`,`7W(h70kx7,7+P(7X7,7D(7PP7,7C(ȡ7`Kء7 ,7D(7G7 ,7O(7P7@, 7c((70!87,@72H(H7 X7,`7/?(h7dx7@,7D(7`;7,7B(77,7`(Ȣ70!آ7,7)077)7P 7I7787q7D(77@-70q(77 )7P 7Iȥ77إ777q7V(77V(7 j79-87Kq(P7PЧ7X)ا7`7pJ7@777X7P|7rc(Ȩ7P7a(7@7.6(H7X7X-`7DD(h7Px7@X-7P(7p>&7T-7pg(7pW"7O-7gq(77)7P 7I7s7q(707)87P @7I7r87q(P7Э7@)ح7P 7I77X7Po7Pj(Ȯ7`=خ7)87q(P7Я7)د7P 7I77X7s7P(Ȱ70ذ7[-7q(077h)7P 7I7 77787ps7g(77q(Ȳ7P(в77) 7 N((7@87c-@7[F(H7PcX7a-`7f(h7 m'x7_-7g(7'7]-س7 )7p7@)x7P 7I777`77pz`7 K(h7x7)7pg(ȵ7#ص7d-7r(077)7P 7I7 77787|7a=(77)ȷ7f=(з7`7) 7pg((7#87j-x78)77X)7P 7IH77X777Pr7 K(77)(7P(07 @7()P7r(X7P h7)7pg(7($7}-7-`(Ⱥ7 غ7x-7r(077)7P 7I7@77787pp7 K(77)ȼ7P(м77)7r(77)@7pg(H7$X7`-`7-`(h7 x7-75r(н7P7)X7P `7I777@7ؾ7pw@7 K(H7X7)h7P(p7 70)7pg(ȿ7`$ؿ7@-7Pr(077x)7P 7I7 77787w7 K(707)7P(7` 7) 7pg((7#87-x7fr(77P 7IH7770n7-`(77- 7dD((787@-@7VD(H7@X7 -7r(707*87P @7Ih7 77o 7[F((787N-@7a(H7 X7L-`7-`(h7p"x7 K-7pg(7m&7G-7O(77E-7r(77(*7P 7I7x7r(707p*87P @7I7x87*P77*7P 7IX7n7r(7p70*x7P 7I7px7r(77p*7P 7I7~7r(077*7P 7I87Ѐ7s(7P7*X7P `7I70~X72s(p770*7P 7Ix77Is(77p*7P 7I7P7as(707*87P @7I7}87xs(P77*7P 7IX7~7s(7p70*x7P 7I7}x7s(77p*7P 7I7p}7s(077*7P 7I877*7P7*X7P `7I7wX7s(p77H*7P 7Ix70x7s(77*7P 7I7n7t(707*87P @7Ix7 770q 7t((7@qH7+t(P7q76t(7P7*X7P `7I7pX7Lt(p7@778*770=7p(7 7877h7x7@7p(7p@7ht(7i07m:(87kX7_(`7pp7mt(7p7wt(7@:79(7p 7G((7@7B(H7`X7+`7e(h7%x7+7i^(7`7+7X(70L7`+7b(7 !7+7=(7P7+7O(7o7+ 7o<((7 87+@7@(H7X7`+7t(7p707*87@7>H70h77x7 77p7  7p((7?H7ht(P7gp7m:(x7`l7_(7@h7mt(77wt(797t(7h879(@70i7G(77B(7@76+7e(7`N%7 5+7&f(7 &73+ 7f((7%87`2+@7i^(H7X71+`7X(h7sx71+7X(7pi70+7X(7b70+7b(7!7/+7=(77.+7O(7d7-+ 7o<((787@-+@7@(H7X7-+7t(77p07*87@79H7h7`7x7 77P7 7p((7p?H7ht(P7fp7m:(x70m7_(7g7mt(77wt(7@979(7pg`7G(h77B(77 !+7e(7b%7+7&f(7 %7@+7i^(7 7`+7X(7V7+ 7X((7N87`+@7b(H7`!X7+`7=(h7`x7+7O(7@j7+7o<(7p7+7@(7@7`+7t(07X707*77 :77777(7877p(7>7ht(7Pe7m:(7n7_( 7e@7mt(H7h7wt(p7879(70f7G(7@7B(77`++ 7e((7z%87(+@7&f(H7%X7'+`7i^(h7x7&+7X(7_7 &+7X(7X7%+7b(7!7 %+7=(7p7 $+7O(7_7 #+ 7o<((7 87"+@7@(H7X7"+7t(77 7 707H*87@7@9H7ph77x7@777 7'@7mt(H70X7 *h7p(p7p>7 *7u(7b7@ *7ht(7Pc7 *7_(7c7*7m:(7o 7*07|:(87nH7H*X79(`70d7d:(7d7s:(7d7xA(7@N7A+ 7G((7@7e(H7 %X7`?+`7X(h7Pux7=+7=(77:+7O(7`Y78+7o<(7078+7@(7 7`8+87u(P7x7777777*7P 7I77`7@87@7H7@X77^77^70,@7j(H7ah7p(p7p7*7?(7<7*79(7b79h(7pb@84Y(H8X8-`8S(h8rx8-87C(808-82a(8! 8-8LS(88 -8E(808@-8.6(8p8- 8D((8p88-@84e(H8GX8-`8=(h80#x8P-8P(8R&8-8>(8Q8 -8P(8`8-8G(88Y(8`Y8- 83S((888-@8=(H8X8 -`8[E(h8px8-8BE(8p8 -8'E(88-8 E(88`-8W(88`-8C(8л8- 8D((888`-@8+P(H8/X8 -`8D(h8px8`-8O(8`8-8@(8PI'8`-8T(88 -8X(8@$8-8D(88 - 8D((888-@8X(H8X8-`8m](h87x8-8Y(8 8@-8`(8O"8`-8](808-8`(8 8`-8[(88- 8fa((8F"88-@83Z(H8 <X8-`8>Z(h8?x8 -8*88@88 @8@(H8 X8-`8o<(h8Bx8`-8@(8p8 -8#u(88@88;H8p8*8P8 88` 88#8` 89(h 804x 8* 8'E( 8 ^ 8H* 87C( 80+ 8@>. 8X( 8 8`<. 8G(( 8P@ 8=(H 8p|X 8;.` 8=(h 8@'x 8;. 8J( 8 8p;. 86u( 8 8( 8 80 8` 88 80ZH 80p 8* 80  8@] 8 8 8 8 8` 8` 8Yh 8 8Y 8 , 8Iu( 8 8* 8g( 8 8*0 8;(8 81H 8*X 83` 8`p 8P * 8=( 8 8. 8P( 8p 8.8aF(8F8. 8=((888.@84e(H8X8.`8[F(h8@=x8.8@(88@.8K(88.8RH(8 :8 .8`(8 8.8AF(898. 8Gc((8Py!88`.@8-c(H8r!X8`.`8c(h8i!x8.8!F(838.8](8j8.8b(8!8`.8](8@H8.8Y(8P8. 8Y((888.@8`(H8c!X8 .`8G(h88]Y(88`.8`(8^!8@.8Yu(888X80h8/8@"*88:8p8`8888889_(8X80%*8:h(88X&*83808'*89(8`Y`8=(h8x8@/8=(88/8G(88](8@r8`/8](8`N8 /8^(8`8. 81^((888.@84_(H8@,X8.`8-`(h8x8.8Y(88.8`(8`V!8.8]Y(88 .8`(8P!8.88ku(P8x8А88888Т8@(*8 8`e8@8H8$X88p 8@!8p 8,8`@@8=(H85X8@ /`8G(h8 8=(8Z8 /8H(88` /8N(8`8 /8a=(8 8 /8 K(8 8 / 8f=((8P88` /@8uF(H8QX8 /`8mF(h8Lx8 /8u(88 X8u(p88 )*(88h8x808=(88`/8N6(8f8 / 89=((8 c88/@8S=(H8X8/8**808**h88x8 88 8f((888@-*H8m:(P8:!`8x-*p8mt(x8e'8-*8u(8*08@1808@181(H18pVX18P?*18v(18P28@*28@3828@381(H38VX38 A*38v(38P48A*485848@5848@581(H58UX588C*h58R(p5858D*58=(5858$/68v(06868D*68@886878878p78v(78@U78E*78w(78278F*78f(78p288G*@88=(H88PX881/88 w(88е098H*h98 :898@ :8o<((:8 8:8`1/@:8@(H:8X:8 1/:8(w(:8:8@<8:8 <80;80I*X;8`Th;8<8;8P <8Q(<8PR@<8QX<8+<8RH(<8pD<8,/<8`(<8<8(/<8=(<8 <8p'/8=8?w(P=8=8HL*>8`?8>8>8X>80>8]w(>8P>8@M*>8f(>81?8N*?8iw(?8pQ(?8N*`?8=(h?8`x?80/?8sw(?8P@8O*@8@B8@8@A8@8@A8w(HA8XA8P*hA8w(pA8A8Q*A8w(A8@A8pR*A8w(A80A8PS*A8w(A8PA8 T*@B8=(HB8XB800/B8w(B80C8U*hC8D8xC8 D8C8 D8]w((D8P8D80V*HD8f(PD8 1`D8W*D8=(D8D8`0/D8w(E8E8W*E8G8E8F8F8F8]w(F8OF8X*F8f(F80F8Y*G8=(G8G80/XG8w(pG8G8PZ*(H8`I88H8H8xH8H8]w(H8OI8f(I8@0 I8h[*`I8=(hI8pxI80/I8 x(I8PJ80\*J8K8J8@K8J8@K8]w(HK8NXK8]*hK8f(pK8/K8]*K8=(K8`K80/L8!x(0L8XL8xL8KL8 L8^*L8`RL8N8L8M8(M8*8M8M8-E(M8LM86x(M8MM8x_*M8@x(M8N8P`*N8Kx( N8N0N8(a*@N8Vx(HN85XN8b*N8=(N8N88/N8G(N8N8=(N86N8`6/O8Q(O80mO8`5/ O8Y((O8@8O8@4/@O8`(HO8XO83/`O8]Y(hO8`xO82/O8`(O80O81/O8bx(O8P8pP8c*P8Q8P8`Q8P8&P80`Q8yx(hQ8`7xQ8d*Q8L(Q8%Q8@G/Q8<(Q8Q8F/R8<(R8R8E/ R8G((R8@R8o<(HR8XR8@E/`R8@(hR8@xR8E/R8x(R8 !R8OPS8e*S8 U8S8@T8S8S8`@T89(HT8OhT83pT8T86i(T8 T8x(T8 U8=((U8 8U8*@U8 ^(HU8 XU8*`U8b(hU8 xU8*U8G(U8OU8o<(U80U8*U8@(U8U8*V8x(0V8*V8`e*V8@X8V8W8(W8P8W8W8x(W8HW8@IW8x(W80JW8JW868(W8P X8'@X8o<(HX8XX8*`X8@(hX8xX8*X8x(X8 PY8g*Y8[8Y8@Z8Y8sY8 @Z8x(HZ8APZ8`BhZ8#i(pZ8PCxZ8CZ86i(Z8Z8ppZ81M(Z8 Z8`F#Z8x(Z8DZ8 E[8y([8F[8F0[8c(8[8pG@[8G[8o<([8[8@*[8@([8[8*[8y(\8]8 ]8=M(]8p]8 x*]8(]8]8x*]8S(]8Z]8x*]8)L(]8]8@y*^8a(^8 ^8y* ^8Zc((^8!8^8{*@^8d(H^8W#X^8|*`^8b(h^83!x^8}*^8"_(^8%^8`*^8PZ(^8F^8*^8rZ(^8I^8*^8@(^8@^8*_8o<(_8P_8* _87((_88_8*@_8$6(H_8X_8`*`_8c(h_8&x_8*_8 .)_8s#_8 *_8\a(_8`C _8*_8f8(_8#_8*_8@(_8_8*`8o<(`8`8* `8f8((`8p`#8`8*@`8@(H`8`X`8*``8o<(h`8x`8*`8f8(`8#`8*`8@(`8`8*`8o<(`8`8*`8e(`8#`8*a8A(a8ma8* a8`((a8@#8a8@*@a8B(Ha8@&Xa8*`a8`(ha8#xa8*a8Y(a8a8*a87C(a8#a8*a87(a8aa8@*a8$6(a8Pa8*b8X(b8/b8* b8c((b8"8b8@*@b8غ(Hb8pXb8*`b8.)hb8p#xb8*b8[(b8b8@*b8 M(b80Eb8@*b8@(b8b8*b8o<(b8`b8 *c8f8(c8`i#c8`* c8@((c88c8*@c8o<(Hc8Xc8 *`c87(hc8gxc8`*c8$6(c80c8*c8X(c81c8*c8>X(c82c8`*c8U_(c8 6c8*d8fb(d8d8@* d8@((d8`8d8*@d8o<(Hd8Xd8@*`d8d(hd8PS#xd8*d8d(d8#d8@*d8@(d8d8*d8o<(d80d8*d8b(d8 d8*e8 ^(e8 e8* e8=((e8 8e8*@e8@(He8Xe8*`e8o<(he8xe8@*e8@(e8e8*e8o<(e8e8*e8WP(e8e8*e8>P(e8e8*f8O(f8`f8* f8GJ((f8`8f8*@f8@(Hf8Xf8*`f8o<(hf8xf8*f8O(f8 f8*f8GJ(f8 f8*f8Y(f83f8*f8Y(f86f8*g8Z(g8P9g8@* g8Q((g88g8*@g8Q(Hg8pXg8*`g8M(hg8xg8`*g8=(g8Xg80*g8NK(g8g8`*g8sM(g8pg8*g8)M(g8ug8`*h8H(h8Rh8* h8Z((h8L8h8*@h8b(Hh8 !Xh8*`h8Q(hh8@)xh8*h8H(h8Vh8p*h87(h85h8*h8@(h8@ h8*h8o<(h8ph8 *i8@(i8 i8`* i8o<((i88i8*@i8Hd(Hi8 #Xi8*`i8@(hi8xi8*i8o<(i8i8@*i8Hd(i8"i8*i8@(i8i8*i8o<(i8@i8*j8@(j8 j8* j8o<((j88j8@*@j8@(Hj8Xj8*`j8o<(hj8`xj8*j8@(j8j8*j8o<(j8j8@*j8_(j8j8p*j8@(j8`j8*k8o<(k8k8* k8\((k8 8k8 *@k8a(Hk8 Xk8*`k8JX(hk8P8xk8*k8<(k8k8*k8N(k8k8@*k8=(k8k8*k8W(k8 k8*l8@(l8l8* l8@((l88l8`*@l8o<(Hl8Xl8*`l8M(hl8xl8*l8M(l8l8*l8<(l8 l8 *l8@(l8l8*l8o<(l8l8*m8A(m8}m8* m8N((m88m8@*@m8 L(Hm8Xm8*`m8@(hm8xm8*m8o<(m8m8*m8A(m8 m8*m8JX(m8@<m8 *m8f8(m8"m8*n8Zd(n8"n8 * n8@((n88n8 *@n8o<(Hn8Xn8`*`n8B(hn8xn8*n8B(n8n8*n8B(n8n8*n8@(n8`n8 *n8o<(n80n8`*o83y(o8@ o8qS((o88o8*@o83y(Ho8`o8wQ(ho8 o8wQ(o8`o8Y(o8po8*o8"B(o8Po8*o8B(o8^o8*p8b(p8` p8 * p8B((p8`8p8*@p8B(Hp8dXp8*`p8Y(hp8P!xp8*p8-B(p8p80*p8LB(p8p8`*p8_(p8yp8*p8Od(p8`"p8*q8Y(q8 (q8+ q8fb((q8 8q8+@q8^(Hq8Xq8+`q8X(hq8@?xq8+q8_(q8^q8 +q83q8"q8+q8_(q8`"q8+q8RB(q8q8 +r8oB(r8Рr8`+ r8B((r88r8+@r8_8(Hr8`#Xr8+`r8X(hr8 Ixr8+r8A(r8phr8 +r8B(r8pr8` +r8fB(r8r8 +r8B(r8 r8 +s8:(s8s8 + s87((s8P\8s8 +@s8$6(Hs8Xs8 +`s8Z(hs8Rxs8@ +s8s7(s80s8 +s8Z(s8Vs8+s8@(s8s8`+s8o<(s8 s8+t8O(t8ot8+ t8=((t8P8t8+@t8b(Ht8 !Xt8+`t8X(ht80Lxt8`+t8i^(t8`t8+t8e(t8%t8+t8B(t8`t8+t8@(t8@t8`+u8o<(u8pu8+ u8O((u8@j8u8+@u8=(Hu8`Xu8+`u8b(hu8`!xu8+u8X(u8Nu8`+u8X(u8Vu8+u8i^(u8 u8`+u8&f(u8 %u8@+v8e(v8b%v8+ v8B((v88v8 !+@v8@(Hv8Xv8"+`v8o<(hv8 xv8"+v8O(v8_v8 #+v8=(v8pv8 $+v8b(v8!v8 %+v8X(v8Xv8%+w8X(w8_w8 &+ w8i^((w88w8&+@w8&f(Hw8%Xw8'+`w8e(hw8z%xw8(+w8B(w8w8`++w8@(w8w8-+w8o<(w8w8@-+w8O(w8dw8-+x8=(x8x8.+ x8b((x8!8x8/+@x8X(Hx8bXx80+`x8X(hx8pixx80+x8X(x8sx81+x8i^(x8x81+x8f(x8%x8`2+x8&f(x8 &x83+y8e(y8`N%y8 5+ y8B((y8@8y86+@y8@(Hy8 Xy8`8+`y8o<(hy80xy88+y8O(y8`Yy88+y8=(y8y8:+y8X(y8Puy8=+y8e(y8 %y8`?+z8xA(z8@Nz8A+ z8PS((z88z8`B+@z87(Hz8Xz8L+`z8jQ(hz8@xz8 M+z84Q(z8|z8@O+z8qf(z8&z8`Q+z8S(z8z8Y+z85T(z8z8@i+{8eA({8 I{8z+ {8B(({88{8{+@{8R(H{8 BX{8`}+`{8AO(h{8 x{8 +{8@({8{8@+{8B({8{8+{8}e({8${8 +{8c({8"{8`+|8c?(|8"|8+ |8d((|80"8|8+@|8P(H|8 X|8p+`|8>(h|8x|8+|8V(|8|8+|8@(|8|8+|8>d(|8"|8+|8](|8w|8`+}8e(}80&}8+ }8M((}8N8}8@+@}8C(H}80X}8+`}8pg(h}80(x}8+}84e(}8e$}8+}87C(}8}8+}8=(}8\}8@+}8c(}8`"}8+~8P(~8`"~8+ ~8-`((~8`"8~8+@~8r_(H~8PBX~8+`~8f(h~8&x~8,~8+P(~8@~8,~8W(~8p~8,~8=(~8p~8,~8X(~8v~8,8q?(88, 81g((8P'88,@8KA(H8DX8 ,`8fa(h8L x8,8[(8`8,8`(80U 8 ,8B(8P8@,8](8Щ8 ,8`(8[ 8#, 8Y((8 z88`%,@8b(H8 !X8',`8M(h8z"x81,8C(8c 8`1,8pg(8p'84,84e(Ȁ80T$؀8=,8@(8>8 J,87C(88`J, 8f((8P '88 P,@8=(H8psX8`T,`8+P(h8 x8Y,8W(88 `,8b(8 8e,8}e(ȁ8$؁8@k,8c(8Pi 8n,8c?(8w 8r, 8d((8 ~ 88u,@8P(H8PX8{,`8>(h8Єx8{,8V(8p8,8r_(8H8 ,8X(Ȃ8~؂8`,8=(88@,8q?(808`, 8@((888,@8o<(H8P{X8,`8gC(h8`#x8 ,8d(8<8,8O(8u8,8c?(ȃ8v؃8@,8Y(8 8,8D(8a8`, 8J((8C88,@8C(H8P(X8 ,`8'O(h8x8`,8W?(8Pu8 ,8kQ(8Po8,85Q(Ȅ8Pi؄8,8[F(8@-8,8D(818, 8C((8488,@8q?(H8VX80,`8C(h89x8`,8V(8f8,8C(8`B8,8T(ȅ80؅8,8Wf(8&8 ,8`(80!8, 8B((888,@8D(H8`;X8,`8/?(h8dx8@,82H(8 8,8c(80!8,8O(Ȇ8P؆8@,8D(8G8 ,8C(8`K8 , 8D((8PP88,@8+P(H8XX8,`8W(h80kx8,8D(8 _8`,8E(8 c8,8P(ȇ8؇8,8-`(80"8@,8.6(8 g8- 87C((8o88-@8T(H8wX8` -`8X(h8$x8 -8@(8E'8 -8=(8a8 -8 E(Ȉ8`~؈8`-8'E(8P8-8BE(88- 8[E((8Њ88-@8P(H8X8-`8>(h8`x8 -8X(8Д8`-83S(88@-8Y(ȉ8p؉8 -829(8`8$-8=(8@]8%- 8@((888&-@8o<(H8X8@&-`8[(h8x8&-8K(8S8@'-8AF(8$8`(-8@(Ȋ8؊8)-8o<(8@8*-8[(88@*- 8K((8W88+-@8AF(H80 X8 ,-`8O(h85x8--8=f(8 &8--8S(8@z81-8b(ȋ8.!؋84-83D(8Ж8@7-8V(8 j89- 8?((8W88`;-@8?(H8VX8<-`8P(h8=x8 >-8pg(88C-8e(80;$8E-8O(Ȍ8،8E-8pg(8m&8G-8-`(8p"8 K- 8a((8 88L-@8[F(H8X8N-`8pg(h8pW"x8O-8P(8p>&8T-8DD(8P8@X-8.6(ȍ8؍8X-8P(8#8Y-8P(808[- 8g((8'88]-@8f(H8 m'X8_-`8[F(h8Pcx8a-8 N(8@8c-8pg(8#8d-8pg(Ȏ8#؎8j-8-`(8 8x-8pg(8($8}- 8-`((8 88-@8pg(H8$X8`-`8pg(h8`$x8@-8pg(8#8-8VD(8@8 -8dD(ȏ8؏8@-8-`(88-8D(88@- 8>Z((8?88 -@83Z(H8 <X8-`8fa(h8F"x8-8[(88-8`(8 8`-8](Ȑ80ؐ8-8`(8O"8`-8Y(8 8@- 8m]((8788-@8X(H8X8-`8D(h8x8-8D(88 -8X(8@$8-8T(ȑ8ؑ8 -8@(8PI'8`-8O(8`8- 8D((8p88`-@8+P(H8/X8 -`8D(h8x8`-8C(8л8-8W(88`-8 E(Ȓ8ؒ8`-8'E(88-8BE(8p8 - 8[E((8p88-@8=(H8X8 -`83S(h8x8-8Y(8`Y8-8P(8`8-8>(ȓ8Qؓ8 -8P(8R&8-8=(80#8P- 84e((8G88-@8D(H8pX8-`8.6(h8px8-8E(808@-8LS(88 -82a(Ȕ8! ؔ8-87C(808-8S(8r8- 84Y((888-@8@(H8pX8 -`8o<(h8Bx8`-8@(8 8-8f(8 _'8`.8H(ȕ8jؕ8.8`(8p8`.8Ca(8' 8. 8Va((81 88 .@8!L(H8PX8.`8c(h8!x8.8AQ(88 .8`(8`8 .8c(Ȗ809"ؖ8!.8b(8)!8`%.8X(88&. 8V((888 (.@8J(H8X8).`8J(h8x8 *.8J(8p8*.8J(88*.8=(ȗ8>ؗ8@+.8NK(8x$8+.8J(8=8+. 8)J((8088,.@8J(H8PX8,.`8f=(h8<x8@..8J(808`..8J(8 8/.8J(Ș8ؘ8/.8H(8 :8@0.8J(880. 8J((8p88@1.@8J(H84X82.`8J(h8x83.8J(8"8 4.8M(884.8J(ș84ؙ84.8J(8P/8`5.8J(8$85. 8J((8@*88@6.@8J(H8.X86.`8J(h8-x87.8J(8,8`7.8J(8,87.8J(Ț80+ؚ8 8.8J(8`*88.8J(8)88. 8J((8(88@9.@8J(H8'X89.`8J(h8 'x8:.8J(8P&8`:.8J(8P%8:.8J(ț8؛8 ;.8J(88p;.8=(8@'8;. 8=((8p|88;.@8X(H8X8`<.`87C(h80+x8@>.8n*8 y8 A.88.8}V(8C8`.8V(ȡ8Jء8.8V(8Q8.8Z(8px8. 8b((8H!88.@8YU(H8pX8.`8kU(h8x8`.8~U(88 .8U(8`8.8U(Ȣ8آ8.8U(88 .8U(8P8. 8U((888`.@8U(H8X8.`8U(h8@x8.8[F(8p$8.8e(8@%8.8`(ȣ8^!أ8@.8]Y(88`.8`(8c!8 . 8Y((888.@8Y(H8PX8.`8](h8@Hx8.8b(8!8`.8](8j8.8!F(Ȥ83ؤ8.8c(8i!8.8-c(8r!8`. 8Gc((8Py!88`.@8AF(H89X8.`8`(h8 x8.8RH(8 :8 .8K(88.8@(ȥ8إ8@.8[F(8@=8.84e(88. 8=((888.@8aF(H8FX8.`8P(h8px8.8=(88.8`(8P!8.8]Y(Ȧ8ئ8 .8`(8`V!8.8Y(88. 8-`((888.@84_(H8@,X8.`81^(h8x8.8^(8`8.8](8`N8 /8](ȧ8@rا8`/8=(88/8=(88@/ 8J((8X88 /@8mF(H8LX8 /`8uF(h8Qx8 /8f=(8P8` /8 K(8 8 /8a=(Ȩ8 ب8 /8N(8`8 /8H(88` / 8=((8Z88 /@8=(H85X8@ /`8[(h8~x8/8[(88@/8N6(88/8S=(ȩ8ة8/89=(8 c8/8N6(8f8 / 8=((888`/@8f6(H8X8/`8=(h8x8/86(8P8 /8f6(8P8`/86(Ȫ8ت8/8=(8i8p/89=(88/ 8N6((8P88 /@81(H8X8!/`8f6(h8x8"/86(8P8`#/8=(8P8#/8=(ȫ8ث8 $/8=(88@$/8=(88`$/ 8=((888$/@8=(H8X8$/`8[F(h8,x8@%/8=(8 8p'/8`(88(/8[F(Ȭ8.ج8*/8RH(8pD8,/8=(8`80/ 8=((88800/@8=(H8X8`0/`8=(h8x80/8=(8p80/8=(8`80/8=(ȭ8Pح81/8@(88 1/8o<(8 8`1/ 8`((80881/@8]Y(H8`X82/`8`(h8x83/8Y(8@8@4/8Q(80m8`5/8=(Ȯ86خ8`6/8=(888/8[F(8*88/ 8Q((888`9/@8,I(H8X89/`8LI(h8x89/8<(88 :/8py(8; 8@:/8[(ȯ8د8:/8y(88=/8]I(88=/ 8L((8@<88>/@8y(H8`X8@?/`8y(h8x8?/8y(88@/8@(898@/8A(Ȱ8=ذ8B/8y(88 C/8@(88D/ 8o<((888@D/@8@(H8X8D/`8o<(h8x8D/8@(8@8E/8o<(88@E/8<(ȱ8ر8E/8<(88F/8L(8%8@G/ 8@((8pW88H/@8o<(H8X8 I/`8XR(h8@^x8`I/8A(8k8I/8A(8 8J/8A(Ȳ8ز8J/8R(88 K/8R(88`K/ 8;((8q88K/@8R(H8X8K/`8R(h8px8 L/8R(8w8`L/8$<(8y8L/8<(ȳ8{س8L/8<(80|8L/8Q(808M/ 8R((8P88@M/@8R(H8X8M/`8A(h8x8M/83[(88N/8NT(88N/8@(ȴ8 ش8@O/8o<(88O/87(88O/ 8$6((8 88P/@8f(H8&X8@P/`84A(h8Ax8P/8@(8`8R/8o<(8p8S/87(ȵ8uص8@S/8G(88S/8I(8 8S/ 8I((888T/@8I(H8X8U/`8y(h8 x80U/8`N(88DU/8qN(88KU/8T[(ȶ8Pض8`U/86(8@8V/8UM(8 8G((8@8G(H8`8P(h8\8G(808G(88P(ȷ8P8@(88o<(8f6f6f6f6g6Hg6h6f6Yf6f6f6f6f6f6g6g6?g60g6Xg6g6g6g6(h6@h6Xh6ph6h6h6@i6Xi6pi6i6i6i6i6i6j6j60j6Hj6`j6xj6j6j6j6j6j6k6 k68k6Pk6hk6k6k6k6k6k6k6 g6(g68g6d@g6p8Pg6u`g6hg6@l6pg6]g6]h6]xg6Ng6{g6Ag6(g6l6g6g6_g6jg6g6)h6h6.0h68h6Ph6h6n6Hh6z`h6hh6xh6th6h66h6h6h6^h6h6Qh6!i6 i6i6i6S(i6<8i6Hi6Pi6 hi6 i6 i6 i6 i6 i6 i6 j6 (j6 @j6 Xj6 pj6 j6 j6 j6 j6 j6 k6 k6 0k6 Hk6 `k6 xk6 k6 k6 k6 k6 k6 l6 `i6xi6i6Ri6i6 i6i6j6 j68j6Pj6hj6j6j6Jj6j6j6j6\k6(k6f@k6hXk6Cpk6k6gk6vk6/k6ik6l6l6`l6Pl6Pm6m6l6Xm6m6l6m6m6m6m6Jm6U n6@n6spn6n61n6|n6o6@o6xo6o6o6y p6Xp6qp65p6q6:8q6pq6+q6q6#r6Pr6r6r6r60s6{hs6s6s6t6GHt6t6t6t6(u6w6w6w6w6*w6w6w6w6w6w6w6w6w6 w6w6 w6 x6 x6 x6x6 x6(x60x68x6@x6Hx6Px6Xx6`x6hx6lpx6xx6x6x6x6x6x6 x6x6x6x6x6 x6!x6"x6#x6$x6%x6&y6'y6(y6)y6* y6+(y6,0y6-8y6.@y6UHy6/Py60Xy6I`y6hy61py62xy63y6xy64y65y6y6y66y67y6y6y68y69y6:y6py6;y6<y6=z6>z6?z6@z6A z6B(z6C0z68z6D@z6EHz6 Pz6FXz6G`z6Hhz6Ipz6Jxz6Kz62z6Lz6Mz6z6Nz6Oz6Pz6Qz6Rz6z6Sz6Tz6Uz6Vz6wz6W{6K{6X{6Y{6Z {6[({6\0{6]8{6^@{6_H{6`P{6aX{6b`{6ch{6dp{6ex{6f{6g{6h{6i{6j{6k{6l{6m{6n{6n{6o{6X{6p{6q{6r{6s{6t|6u|6v|6w|6x |6y(|6z0|6|8|6}@|6~H|6P|6X|6`|6h|6p|6x|6 |6|6|6|6|6|6|6|6|6|6|6|6|6|6|6|6}6}6}6}6 }6(}60}68}6@}6H}6P}6X}6`}6h}6p}6x}6}6}6}6}6}6}6}6}6}6}6}6}6}6}6}6}6~6~6~6~6 ~6(~6L0~68~6@~6H~6P~6X~6`~6h~6p~6x~6~6~6~6~6~6~6~6~6~6~6~6~6~6~6~6~66666 6(60686@6MH6P6&X6`6h6p6x6666,6e6666666666666666 6(60686@6H6P6X6`6h6p6x666666$6666Ȁ6Ѐ6؀6966666666 6a(606 86 @6 H6DP6 X6 `6h6p6x6666666666ȁ6Ё6؁6666 6!6"6#66$ 6%(6&06V86'@6[H6(P6)X6*`6h6+p6,x6-6.6/666061626763Ȃ64Ђ65؂6667696-6r6;6<6=6> 6?(6@06A86B@6CH6DP6EX6F`6Gh6Hp6Ix6J6K6L6M6N6O6P6Q6R6Fȃ6cЃ6S؃6T66U6>6V6W6X6Y6T 6Z(6[06886\@6]H6^P6_X63`6Eh6`p6ax6b6c6d6e6f6g6h6i6j6kȄ6lЄ6m؄6n6o6p6q6%6r6s6t6u 6v(6w0686x@6yH6zP6{X6|`6}h6~p6x6666666666ȅ6Ѕ6؅666666666 6(606W86@6H6P6X6`6h6p6x6666666666Ȇ6І6~؆666o666666 6(60686@6H6P6X6`6h6p6x6666=6666}666ȇ6Ї6؇666666666 6(60686@6H6P6X6`6h6p6x6666666666Ȉ6Ј6؈6666466666 6(60686@6H6P6X6`6h6p6x666m6666666ȉ6Љ6؉6666666"66 6(6 06 86 @6 H6 P6X6`6h6p6x666666666 6Ȋ6Њ6؊6;666666 6!6" 6#(6$06%86&@6'H6(P6X6)`6*h6+p6,x6-6.6/606166062663ȋ64Ћ65؋6k67696:6;6<6=6>6? 6@(6@06A86B@6CH6EP6FX6G`6h6Hp6Ix6K66L6M6N6B6O66P6QȌ6RЌ6S،6T66V66W6X6Z6Y6Z 6H(6\06^86_@6`H6aP6bX6c`6dh6ep6fx6g6h6i6j6k6l6m6n6p6qȍ6rЍ6s؍66t6u6v6w6x6y6z6 6{(6|06}86~@6H6P6X6`6h6p6x6666666666Ȏ6Ў6؎666666666 6(60686@6H6P6X6`6h6p6bx66666O66666ȏ6]Џ6؏66666 8(8808D88@8[H8P8:X8o`8h8x88HO H5Z2%\2@%2fUHAVAUIATE1SLwHM9}'IEJHx8HtHHX8Ht+ I[1A\A]A^]UHATISa2HHuӪ2HuLHH[A\]UHSQ2H(e2HHH H &HcH_2HHPHH2H8ӣ2Z[]%242HHPHH2H822HHPHH2H822HHPHH2H8`2ș2HHPHHm2H8<2d2HHPHH2H82=z2HHPHHO2H82S2HHPHH2H8Ǣ2,2HHPHH2H822HHPHHj2H8y2ޘ2HHPHHC2H8R2z2H(2H5M$H802X Hި2H}2H̨2Hk2X[]U1HATIHS1 2Hu2I$HH5o5HS2H߅x G( 1>( [A\]UHAWAVAUIATE1SHQLvM9}SKDLxHt LH2u0HL2tLH2H5$1H8+2IHuHؙ2HZH[A\A]A^A_]UHAWEAVIAUIHATSAQm2Ht/H;2Iu1AtLLL2LS' H2H82t̞21Z[A\A]A^A_]UH2HtH]UHAWL=K$AVIAUIATILSQH=5ʕ2HHu3l22HHuhHH=g5HL2xDL1LA2IHt.HHL2Lyv& o& Hg& 1 H[& Z[A\A]A^A_]UHLK$H5H=r$HSQyHҖ$H53H=;r$xHܖ$H5? H=K$xH$H5 H=V$xHJ$H5 H=/m$xH$H5kH=H52H$H=}I$=H5^2H$H=oI$H52H$H=`I$H5*2Hۙ$H=$H5h2H$H=R$H5֏2HW$H=$rH52H$H=Κ$qPH5Қ2HӚ$H=H$O.H52H$H=H$- H52H$H=H$ H52H$H=H$H52H+$H=H$H5h2H9$H=H$H5Δ2HG$H=H$bH5̥2HU$H=wH$a@H5 2Hc$H=kH$?H52Hq$H=^H$H52H$H=$H5l2Hś$H=$H52H$H=4$H52HA$H=r$tH5~2H$H=G$sRH52H$H=G$Q0H5r2H$H=G$/H7E$H5H=G$ HƐ$H5H=$HG$H5F HH=G$HH5E H=G$HH5vJ H=~G$iHH5E H=tG$lKHH5G H=gG$N-HH5H H=[G$0HH5~I H=NG$HH5D H=IG$HH5H H==G$HH5J H=1G$HH5J H=0G$yHH5E H=/G$|[HH5JF H=.G$^=HH5,G H=-G$@HH5J H= G$"Z[]UHAUATSHQHPHL HwHyH9~gHDu#HPH2H5}$H812AlMu,H t"HHHj2HH5p$H812H؋2HHA2HAEt2ZD[A\A]]UHAUATISQb2Hx82H2HuH2Hu |H9tH2H5$H8Y2^H5HtHH5y$L2IHt2H2LH HtH2IHuH 1AH E$HLH}E$xAH nE$LLHjE$xAH [E$LLHZE$xE1H NE$LLHJE$gdZH[A\A]]UIHAVAUATIH53 5SHHz' t[LA2IHHB2H9CuIL$LLH2ALLH̕2ALy @H2H9CuIL$LHL[A\A]A^]%2LHL[A\A]A^]%2[DA\A]A^]ULOIH52LH& tIp(Ix]%2L; 2u Ix]%22L]%(2UHATIS2HH2HH5 D$H=2xH 5H5D$H"2xdLH5J$H 2xMH 5H5C$H2x2H=5$E1HHھI2HHt4 H, H5Ht Hk 1 H_ ^[A\]UHAWMAVMAUAATS1H}H=#/UT2IHtIPE@EuHME11}Pu8u8u0u(u uuAWAV2LHH`I HeL[A\A]A^A_]U1HAUATSVH k5H k5H5 k5e2H6k5HubH5*:51=2Hk5HtH5B512Hj5HtHF5H5[512Hj5HtHc>5H5Z51ς2Hj5HfH5g512Hj5HBHa5H,H5"51qv2H"a5HH5,51Mv2Ha5HH^5H5"51"v2H`5HH551u2H`5HH5G51u2H`5HqH5+51u2H`5HMH551u2Hs`5H)H5K+51nu2HW`5HH5/+51Ju2H;`5HH5 51&u2H`5HH75H5L51t2H_5HH5$!51t2H_5HnH=[5HH2H-_5HKH5 51t2H_5H'H5a 51lt2H_5HH5= 51Ht2Hi_5HHiS5H5"Q51t2HF_5HHHH2H^5HHHZ51s2H_5HnHZ5H5A^51s2H^5HCH5 51s2H^5HH59 51ds2H^5HH5 51@s2H^5HH5 51s2Hu^5HH5 51r2HY^5HH5 51r2H=^5HkH5 51r2H!^5HGH5 51r2H^5H#H5} 51hr2H]5HH5 51Dr2H]5HH55 51 r2H]5HH5 51q2H]5HH5 G51q2Hy]5HoH5F51q2H]]5HKH5F51q2HA]5H'L411L 41APh5:355I5AQAQAQAQ"H@Hj5HL411L 41APh5255I5AQAQAQAQH@Hj5HH 85H751H5Z/5p2H}\5H[L411L 4APh5.55-I5AQAQPAQTH@H.j5H L411L 4APh54.55H5AQAQ5[5AQH@Hi5HH5J51o2H[5HH5VV51o2H[5HpH5-51o2H[5HLH5f51o2Hj[5H(H5D51mo2HN[5HH5)51Io2H2[5HH5*51%o2H[5HH5vR51o2HZ5HH5z41n2HZ5HtH5v:51n2HZ5HPH551n2HZ5H,H5f51qn2HZ5HH5J51Mn2HnZ5HH551)n2HRZ5HH551n2H6Z5HH551m2HZ5HxH541m2HY5HTH541m2HY5H0H5*41um2HY5H H551Qm2HY5HH5r51-m2HY5HH5!51 m2HrY5HH551l2HVY5H|H551l2H:Y5HXH551l2HY5H4H5V51yl2HY5HH S5HS51H5S5Gl2HX5HL8Q5H 151HK5H55HzP5H5+-5k2HW5HL^4L O41APj5\>55&K5AQAQPAQH@Hd5HMH5n21j2HCW5H)H5G51nj2H'W5HL4L 41APj'5E55J5AQAQPAQH@Hd5HLx4L i41APj)5I55@J5AQAQ5V5AQH@Hc5HbL$4L 41APj15"=55I5AQAQ52V5AQVH@Hpc5HL4L 41APj>5D55I5AQAQ5U5AQH@H$c5HL|4L m41APj@5H55DI5AQAQ5U5AQH@Hb5HfL B5L B51H B5HA5H5 B5h2HPU5H&L4L 41APj5A55H5AQAQPAQH@HQb5HL411L 41APj-5+55@5AQAQAQAQH@Hb5HH5O+51g2HT5HiL+4L 41APj?5iE55S@5AQAQPAQbH@Ha5HH5\>51_g2H0T5HL411L 4APh5>55?5AQAQPAQH@H9a5HH5i=51f2HS5HLE411L -4APh5=55m?5AQAQPAQ|H@H`5H4L411L 41APh57155!?5AQAQAQAQ/H@H`5HH-5H5 =51%f2HS5HL~4L o41APj5,-55f>5AQAQPAQH@H`5HmH5:51e2HR5HIL 4L 41APj*5qH55=5AQAQPAQBH@H_5HHA5H5:518e2H)R5HL4L s4APj95/55v=5AQAQPAQH@H7_5H}L?4L 041APj-5}D55?=5AQAQ5]Q5AQqH@H^5H)H B5H651H5A5`d2HYQ5HL4L 41APjG5_155<5AQAQPAQH@Hr^5HLj4L [41APj5>552D5AQAQ5P5AQH@H&^5HTL4L 41APj5A55C5AQAQ5P5AQHH@H]5HH5r*51Ec2HFP5HL4L 41APh59*55;5AQAQPAQH@Hl]5HH $J5H J51H5I5b2HO5HXHI5H5I51b2HO5H-H I5HI51H5I5db2H}O5HH=e2H5fI5H5n2HL5HHnI5H5GI51b2H3O5HHC@5H5?51a2HO5H~L@4L 141APh5.55=:5AQAQPAQtH@H\5H,L4L 41APh5(5595AQAQ5 N5AQH@H[5HL (5L(51H (5H(5H5,>5`2H/N5HLW4L H41APh5(55T95AQAQPAQH@H=[5HCL4L 41APj5s;55@5AQAQ5#M5AQ7H@HZ5HL4L 41APj57>55y@5AQAQ5L5AQH@HZ5HH25H 651H5p15_2H M5HHfH5851_2HL5HB5e5 15655+55+556"55P"5HQE5L 2"5L *5H ,>5H5E5G_2H0HL5HHH5!41_2HmL5HH$5H5f=51^2HJL5HHH5 51^2H+L5HaH551^2HL5H=L411L 4APj>5:5565AQAQPAQ9H@HY5HH35H5551/^2HK5HL4L y41APjP5;5565AQAQPAQH@HX5HwH -5H51H5:5]2H'K5HEL4L 41APjr5%;55G65AQAQPAQ>H@HX5HL4L 41APjv56!5555AQAQ5J5AQH@HW5HH15H5:51\2HaJ5HwL94L *41APh5)55v55AQAQPAQmH@HWW5H%H'#5H5951c\2HI5HL4L 41APh525545AQAQPAQH@HV5HLj4L [41APh5*5545AQAQ5H5AQH@HV5HQL4L 41APh5)55P45AQAQ5.H5AQBH@HDV5HL4L 41APh5)5535AQAQ5G5AQH@HU5HLe4L V41APh5(5535AQAQ5HH5AQH@HU5HLL4L 41APh5(55K35AQAQ5G5AQ=H@HWU5HL4L 41APh5(5525AQAQ5G5AQH@HU5HL`4L Q41APh53(5525AQAQ5CG5AQH@HT5HGL 4L 41APh5t(55F25AQAQ5F5AQ8H@HjT5HL4L 41APh5u'5515AQAQ5F5AQH@HT5HL[4L L41APh5'5515AQAQ5>F5AQH@HS5HBL4L 41APh5&55A15AQAQ5E5AQ3H@H}S5HL4L 41APh5p#5505AQAQ5E5AQH@H.S5HH +5H(51H545W2H\E5HbL$4L 41APh5"55a05AQAQPAQXH@HR5HL4L 41APj5@25575AQAQ5C5AQH@HfR5HL~4L o41APj5555F75AQAQ5C5AQH@HR5HhH-5H5351V2H?D5H=L4L 41APj'5155//5AQAQPAQ6H@HQ5HL4L 41APj6555.5AQAQ5B5AQH@H\Q5HH15H5 351U2HyC5HoL14L "41APj=5'55a.5AQAQPAQhH@HP5H L4L 41APj5P05555AQAQ5B5AQH@HP5HL4L 41APj5355V55AQAQ5A5AQH@HRP5HxL:4L +41APj5/5555AQAQ5XA5AQlH@HP5H$L4L 41APj5l25545AQAQ5@5AQH@HO5HL4L 41APj5/55Z45AQAQ5@5AQH@HnO5H|L>4L /41APj515545AQAQ5D@5AQpH@H"O5H(H5*51mS2HA5HL411L 4APj|555+5AQAQPAQH@HN5HH(5H5)51R2H@5HLO4L @41APh5*55|+5AQAQPAQH@HEN5H;L4L 41APh5#55*+5AQAQ5@5AQ,H@HM5HL411L 4APh5255*5AQAQ5?5AQH@HM5HH")5H5+51Q2H?5HeL'411L 4APh5%!55W*5AQAQPAQ^H@H8M5HL411L 4APh5#55*5AQAQ5>5AQ H@HL5HL4L u41APh5.55)5AQAQ5>5AQH@HL5HkL-4L 41APh5!55Z)5AQAQ5=5AQ\H@HNL5HH5.P21YP2H>5HL411L 4APh5@355(5AQAQ5=5AQH@HK5HH551O2H=5HxL:4L +41APh5=55g(5AQAQPAQnH@HpK5H&H 5H51 51dO2H5=5HL411L 4APh 5/55'5AQAQPAQH@HJ5HH551N2H<5HH5z51N2H<5HdH5^51N2H<5H@H5*51N2Hv<5HH5^051aN2HZ<5HH5*051=N2H><5HH5051N2H"<5HH5/51M2H<5HH5^151M2H;5HhL*4L 41APh5u55&5AQAQPAQ^H@HpI5HL 25L$51H 15H5H5M 5?M2H`;5HL4L 41APh555 &5AQAQPAQH@HH5HH56]21L2H:5H`LR15H K51H5H5 5L2H:5H'L4L 41APh5t55^%5AQAQPAQH@H?H5HL5H 51Hr!5H5 5L2H>:5HL^4L O41APh5Q.55$5AQAQPAQH@HG5HJH5H5(51K2H95HL4L 41APh555V$5AQAQPAQH@HGG5HL4L 41APhF5%55$5AQAQ575AQH@HF5HvL 8 5Lq 51H 3 5H< 5H5'5J2H85H6L4L 4APhR555j#5AQAQPAQ)H@HkF5HL4L 41APhp5,55#5AQAQ565AQH@HF5HH5H5&51I2H85H_L!4L 41APh|555"5AQAQPAQUH@HE5H HO%5H551KI2H75HL4L 41APh555"5AQAQPAQH@H2E5HH " 5Hs 51H5%5H2H(75H^L 4L 41APh5C55!5AQAQPAQTH@HD5H H f 5H51H5%5CH2H65HL4L 41APh555!5AQAQPAQH@H:D5HLJ4L ;41APj5"55(5AQAQ5h45AQ|H@HC5H4L4L 41APj5|%55'5AQAQ535AQ(H@HC5HL4L 41APh5"55 5AQAQ535AQH@HSC5HLK4L <41APh5!555AQAQ5f35AQzH@HC5H2L4L 41APhE5W!55i5AQAQ535AQ#H@HB5HL4L 41APhk5!555AQAQ525AQH@HfB5HLF4L 741APh5 555AQAQ5a25AQuH@HB5H-L4L 41APh5R 55d5AQAQ5 25AQH@HA5HL4L 41APh"555 5AQAQ515AQH@HyA5HH5H5!51D2H.35HTL4L 41APh5555AQAQPAQJH@HA5HL4L 41APh5 5595AQAQ5g25AQH@H@5HLm4L ^41APh5555AQAQ5@25AQ蜿H@Hf@5HTL4L 41APh5y555AQAQ5105AQEH@H@5HL4L 41APhV5 5545AQAQ5b15AQH@H?5HLh4L Y41APhv5555AQAQ5/5AQ藾H@Hy?5HOL4L 41APh5t555AQAQ5,/5AQ@H@H*?5HL4L 41APhT555/5AQAQ5.5AQH@H>5HLc4L T41APhi5555AQAQ5~.5AQ蒽H@H>5HJL 4L 41APh5o555AQAQ5'.5AQ;H@H=>5HL4L 41APh555*5AQAQ5-5AQH@H=5HL^4L O41APh5455AQAQ5y-5AQ荼H@H=5HEL4L 41APh5Z45|5AQAQ5"-5AQ6H@HP=5HH 5H51H5S5%@2H.5HL~4L o41APh5A&555AQAQPAQ費H@H<5HjL,4L 41APh5455AQAQ5G,5AQ[H@H<5HH 5H&51H5x5J?2H-5HL4L 41APh5455AQAQPAQ׺H@H <5HLQ4L B41APh5555AQAQ5l+5AQ耺H@H;5H8L4L 41APh5=45o5AQAQ5+5AQ)H@Hk;5HL4L 41APh5455AQAQ5*5AQҹH@H;5HLL4L =41APh5455AQAQ5g*5AQ{H@H:5H3L4L 41APh5X55j5AQAQ5*5AQ$H@H~:5HܹL4L 41APh5455AQAQ5)5AQ͸H@H/:5HLG4L 841APh#5555AQAQ5b)5AQvH@H95H.L4L 41APh'5K45e5AQAQ5 )5AQH@H95H׸L5H 51H5H5-4<2H*5HL`4L Q41APh*5455AQAQPAQ蔷H@H95HLH!5H541;2H*5H!L4L Ծ41APhH555X5AQAQPAQH@H85HϷL4L 41APhk5455AQAQ5'5AQH@HJ85HxHZ5H551:2HO)5HML4L 41APhr5555AQAQPAQCH@H75HHe4H5519:2H(5HжL4L 41APh5555AQAQPAQƵH@H`75H~H`5H55192He(5HSL4L 41APh58555AQAQPAQIH@H65HLü4L 41APh35&5585AQAQ5%5AQH@H65HLl4L ]41APh65555AQAQ5'5AQ蛴H@HM65HSL4L 41APhB5555AQAQ5&5AQDH@H55HL4L 4#1APhN55535AQAQ5a&5AQH@Hg.5HLg4L X4#1APhR5555AQAQ5 &5AQ薳H@H.5HNL4L 4#1APhV5555AQAQ5%5AQ?H@H-5HL5H 51H5H5U5'72H%5HL4L q41APhZ5555AQAQPAQ贲H@Hv45HlL5H 51Hq5H5562HU%5H3L4L 41APhg555j5AQAQPAQ)H@H35HH+4H55162H$5HLx4L i41APhu5[455AQAQPAQ謱H@H~35HdH5F2152Hr$5H@H 4H 51H55w52HH$5HLи4L 41APh545E5AQAQPAQH@H25HL~4L o41APh555 5AQAQ5!5AQ議H@H25HeL5H `41H5H5542Hn#5H,L4L ߷41APh 555c 5AQAQPAQ"H@H 25HڰH5H5M5142H"5HLq4L b41APh) 5D55 5AQAQPAQ襯H@H15H]LG4H X41HJ5H5532Hv"5H$L4L ׶41APhC 555[ 5AQAQPAQH@H15HүH 4H=51H575 32H!5HLb4L S41APhf 555 5AQAQPAQ薮H@H05HNL P4L41H 4H5H55w22Hp!5HLе4L 41APh 5{55E 5AQAQPAQH@H05HH ^4HG41H5!512H 5HLL4L =41APh 545 5AQAQPAQ耭H@H/5H8L4L 41APh 5e45o 5AQAQ55AQ)H@HC/5HH # 5H41H5412H! 5HLq4L b41APh 5|45 5AQAQPAQ襬H@H.5H]L4L 41APh 5B45 5AQAQ55AQNH@Hx.5HH 4H41H5 5=02HN5HԬL4L 41APh 545 5AQAQPAQʫH@H-5HLD4L 541APhI 5 555AQAQ5_5AQsH@H-5H+L5H >41H 5H5 5[/2Ht5HL4L 41APhO 5 55)5AQAQPAQH@H*-5HH b4H{41H5 5.2H5HnL04L !41APh 5455AQAQPAQdH@H,5HLޱ4L ϱ41APh 545S5AQAQ55AQ H@H_,5HŪQ 1555*5545v555L 95L4H s5H4H54-2H0H5HbL$4L 41 APh 5455AQAQPAQXH@H+5HH 4H41H5u 5G-2Hx5HީL4L 41APh8 5{455AQAQPAQԨH@H6+5HH 4H41H5 5,2H5HZL4L 41APh` 5455AQAQPAQPH@H*5HH Z5Hs41H5m 5?,2H5H֨L4L 41APho 545 5AQAQPAQ̧H@H>*5HLF4L 741APh 5455AQAQ55AQuH@H)5H-L4L 41APh 5J45d5AQAQ55AQH@H)5H֧H4H5I51+2H]5HLm4L ^41APh 5455AQAQPAQ衦H@H+)5HYL [4LD41H 4H4H55*2H5HLۭ4L ̭41APh 545P5AQAQPAQH@H(5HǦH 4HZ41H5,5)2HW5HLW4L H41APh 5555AQAQPAQ若H@H%(5HCL4L 41APh< 5(55z5AQAQ55AQ4H@H'5HH4H5_51*)2H5HL4L t41APha 5455AQAQPAQ跤H@Ha'5HoL)4H R41H$4H55(2H5H6L4L 41APhn 555m5AQAQPAQ,H@H&5HLv4H 41Hy4H5B5(2H5HLm4L ^41APh 5p555AQAQPAQ衣H@H[&5HYHK4H551'2H5H.L4L 41APh 555e5AQAQPAQ$H@H%5Hܣ1AT54H 5L 5LI5H 25H5+5&2H~5A]ZHLS4L D41APh 5 554AQAQPAQ臢H@HQ%5H?H5Y&21HH~&2H5HLש4L ȩ41APh 5B45L4AQAQ55AQH@H$5HL4L q41APh 5454AQAQ55AQ诡H@H$5HgL)4L 41APh 5L454AQAQ55AQXH@H:$5HH R5H41H55G%2H5HޡL4L 41APh& 5454AQAQPAQԠH@H#5HL 4L41H 5Hz4H55$2HN5HLL4L 4APh@ 5554AQAQPAQ?H@H1#5HS15:4555&5L 4L4H q4H5H534 $2H H5HLb4L S41APh 5=454AQAQPAQ薟H@H"5HNL411L 41APh 5454AQAQAQAQIH@HK"5HLæ411L 41APh 545>4AQAQAQAQH@H"5HLv411L ^41APh5G554AQAQAQAQ诞H@H!5HgL)411L 41APh-5j454AQAQAQAQbH@H|!5HLܥ411L ĥ41APhH5e55W4AQAQAQAQH@H7!5H͞L411L w41APhc545 4AQAQAQAQȝH@H 5HLB411L *41APh~5554AQAQAQAQ{H@H 5H3L411L ݤ41APh545p4AQAQAQAQ.H@Hh 5HL411L 41APh5a55#4AQAQAQAQH@H# 5HL[411L C41APh5454AQAQAQAQ蔜H@H5HLH5v51 2H:5H(L4L ۣ41APh5E55_4AQAQPAQH@Hp5H֜H5H5A41 2H5HLm4L ^41APh5554AQAQPAQ衛H@H5HYL c4L41H F5Hg5H5(52H;5HLۢ4L ̢41APh35N55P4AQAQPAQH@Hq5HǛL4H 41H5H552H5HLP4L A41APhn5K554AQAQPAQ脚H@H5H412H 5HhL*4L 41APh5u454AQAQPAQ^H@H5HH5x51[2H4 5HL4L 41APh545)4AQAQ5 5AQH@H5HL]411L E41APh5454AQAQAQAQ薖H@HX5HNL411L 41APh5454AQAQAQAQIH@H5HH{4H541?2H 5H֖L4L 41APh545 4AQAQPAQ̕H@H5HLF411L .41APhE5454AQAQAQAQH@HY5H7L411L 41APha545t4AQAQAQAQ2H@H5HL411L 41APh545'4AQAQAQAQH@H5HL_411L G41APh5454AQAQAQAQ蘔H@H5HPL411L 41APh5454AQAQAQAQKH@HE5HLU4H 41H(4H5432H5HʔL4L }41APh5454AQAQPAQH@H5HxH5r412H5HTL4L 41APh5454AQAQPAQJH@HT5HLĚ4L 41APh35?4594AQAQ55AQH@H5HLm4L ^41APhY5454AQAQ55AQ蜒H@H5HTH 4H41H5q42H5H"L4L ՙ41APh545Y4AQAQPAQH@H:5HВL%&21L2H5HH k4H41H542H5HwL94L *41APh5|454AQAQPAQmH@H5H%L1n2H5HL 4L41H 4H+4H54.2HG5HőL4L x41APh5Z454AQAQPAQ軐H@H5HsL4H ~41HP4H5942H5H:L4L 41APhY545q4AQAQPAQ0H@Hj5HL4L 41APh{5}454AQAQ5-5AQُH@H5HH 4H41H5^42H5H_L!4L 41APh5454AQAQPAQUH@H5H L w4L41H 4H4H5462Hg5H͏L4L 41APh5J454AQAQPAQÎH@H5H{54 15454545O454L R4L4H 4H4H5.42H0H5HLՕ4L ƕ41 APh5X45J4AQAQPAQ H@Hc5HH21HH2HA5HH5i412H%5HsH5412H 5HOH5412H5H+H5u41p2H5HH)4H5r41E2H5H܍L4L 41APh5454AQAQPAQҌH@H45HH4H5E412H95H_L!4L 41APh5454AQAQPAQUH@H5H L1V2H5HAS15o45454L 4L4H 4H4H542H H5HLW4L H41APh5454AQAQPAQ苋H@H5HCH5E412H5HL4L Ғ41APh5l45V4AQAQPAQH@H5H͋H5412H4HLk4L \41APh5454AQAQPAQ蟊H@H!5HWL4L 41APh5454AQAQ5 4AQHH@H5HL‘4L 41APh 54574AQAQ54AQH@H5HLk4L \41APh45454AQAQ54AQ蚉H@H45HRL4L 41APj5454AQAQ524AQFH@H 5HL4L 41APj5F454AQAQ54AQH@H 5HH 4H41H54 2Hz4HxL:411L "4APh;5454AQAQPAQqH@H# 5H)H541n 2H4HLǏ411L 4APhJ545?4AQAQPAQH@H 5HL `4La41H 4H4H5 4 2H4HvL84L )41APjG5454AQAQPAQoH@H1 5H'IHHH޿1g 2H4HH4H5q41< 2H4HӇL4L w4APj_5`454AQAQPAQɆH@H 5HLC4L 441APj~5i454AQAQ5Q4AQuH@HG 5H-L4L 41APh5R45L4AQAQ5 4AQH@H 5HֆL4L 41APh5454AQAQ54AQDžH@H 5HLA4L 241APh5454AQAQ5\4AQpH@HZ 5H(H4H541f 2H'4HL4L 41APh5B454AQAQPAQH@H 5HLm4L ^41APh5454AQAQ54AQ蜄H@H 5HTL4L 41APh545s4AQAQ594AQEH@HG 5HL4L 41APh5B454AQAQ54AQH@H5HLh4L Y41APh5454AQAQ54AQ藃H@H5HOL4L 41APh545n4AQAQ544AQ@H@HZ5HL4L 41APh 5=454AQAQ54AQH@H 5HLc4L T41APh5454AQAQ54AQ蒂H@H5HJL 4L 41APh#545i4AQAQ5/4AQ;H@Hm5HL4L 41APh058454AQAQ54AQH@H5HL^4L O41APh=5454AQAQ54AQ荁H@H5HEL4L 41APhJ545d4AQAQ5*4AQ6H@H5HH 4HQ41H5S4%2H4HL~4L o41APhW5454AQAQPAQ貀H@H5HjL,4L 41APhk5454AQAQ5W4AQ[H@H5HLՇ4L Ƈ41APh5X4524AQAQ54AQH@Hf5HL~4L o41APh5454AQAQ54AQH@H5HeL _4L41H 4H;4H542H_4H%L4L ؆41APh545D4AQAQPAQH@H5HL4L 41APh5454AQAQ54AQ~H@H>5H|L>4L /41APh5454AQAQ5i4AQm~H@H5H%L_4H 41Hz4H54U2H.4H~L4L 41APh5145 4AQAQPAQ}H@Hl5H~L4H 41H4H542H4Ha~L#4L 41APh35454AQAQPAQW}H@H5H~H 4H41H5t4F2H/4H}L4L 41APhb5"454AQAQPAQ|H@Hm5H}LM4L >41APht5Ƚ454AQAQ5p4AQ||H@H5H4}H&4H541r2Hc4H }L˃4L 41APh{5N45(4AQAQPAQ{H@H5H|Ly4L j41APh5454AQAQ54AQ{H@HZ5H`|H4H5411H4H5|L4L 41APh5z45T4AQAQPAQ+{H@H5H{H 4H5V41H1H4H{Lw4L h41APh5:454AQAQPAQzH@Hm5Hc{Hu4H5411H4H8{L4L 41APh5}45W4AQAQPAQ.zH@H4HzH 4Hy41H5K41H.4HzLv4L g41APh!5454AQAQPAQyH@H|4HbzL$4L 41APhK5454AQAQ5G4AQSyH@H-4H z5F4 15A45454L 4L4H 4H94H541H H14HyLq4L b41 APhX5454AQAQPAQxH@H4H]yL4L 41APh545|4AQAQ5:4AQNxH@H84HyL4L 41APh5K45%4AQAQ54AQwH@H4HxLq4L b41APh5454AQAQ54AQwH@H4HXxH4H5411H4H-xL~4L ~41APh5r45L4AQAQPAQ#wH@H%4HwL~4L ~41APh5 454AQAQ54AQvH@H4Hw54154L 4L4H 4H4H541H4AYAZH4wL}4L }41APh545S4AQAQPAQ*vH@H<4HvL}4L }41APh5w454AQAQ54AQuH@H4HvL4H 41Hк4H541H4HRvL}4L }41APh)5o45q4AQAQPAQHuH@Hj4Hv54 15454545454L 4Lx4H Q4H4H5s41H0H:4Hu LK|4L <|4APhy5z454AQAQPAQtH@H4HCuH4H5F411H4HuL{4L {41APj:5454AQAQPAQtH@HC4HtL 4L41H 4H4H541H;4HtLK{4L <{41APjk5454AQAQPAQsH@H4H:tAP1545V454545l45V454545̳45.4545z45 4545p45454L U4L4H 4H4H541HĐHE4HsLMz4L >z41APjy5#454AQAQPAQrH@H4Hk41APhI5454AQAQPAQcH@H4H9dL{4H 41Hv4H54i1Hr4HdLj4L j41APho5454AQAQPAQbH@H(4HcHx1H5 411H4HcHݳ4H5411H4HXcLj4L j41APh5454AQAQPAQNbH@H4HcLi4L i41APh5Ӭ454AQAQ54AQaH@H94HbH A4Hڷ41H541H4H}bL0i4L !i4APh545 4AQAQPAQpaH@H4H(bL ҷ4LӬ41H 4H4H54Q1Hz4HaLh4L h4APh545t4AQAQPAQ`H@H-4HaL4H 41H84H541H4HZaLh4L h4 1APh5g454AQAQPAQP`H@H4HaH541M1H4H`L>4H 41H4H5B41HU4H`Lmg4L ^g4 1APh5P45:4AQAQPAQ_H@H4HY`Lc4H 41HF4H541H4H `Lf4L f41APh5454AQAQPAQ_H@H4H_54154H554L 64L4H `4H41H<4^_H_LBf4L 3f41APh5454AQAQPAQv^H@H4H._H1H5A11l1H4H_H4H5v41A1H4H^Le4L e41APh?545g4AQAQPAQ]H@HH4H^LHe4L 9e41APhI5454AQAQ5c4AQw]H@H4H/^Ld4L d4APhO5454AQAQ5 4AQ]H@H4H]H4H5H411H|4H]Lld4L ]d41APhq54594AQAQPAQ\H@H24HX]L J4L41H 4H~4H541H4H]Lc4L c41APh}5E454AQAQPAQ\H@H4H\P154H b4H4L $4L4H541Ha4ZYH}\L?c4L 0c41APh5J45 4AQAQPAQs[H@H4H+\P 1545P45b45454L 4L4H q4H4H5c451H0H4H[Lb4L {b41 APh545W4AQAQPAQZH@Hh4Hv[L 04L41H K4H$4H541H(4H6[La4L a41APh5 45Ŷ4AQAQPAQ,ZH@H4HZLa4L a41APh545s4AQAQ5y4AQYH@H4HZLOa4L @a41APh5j454AQAQ5*4AQ~YH@H@4H6ZH ȣ4Ha41H54m1H4HZL`4L `41APh85y454AQAQPAQXH@H4HYH4H5%411H4HYLI`4L :`41APhP5454AQAQPAQ}XH@HO4H5YL4H x41H4H54e1H4HXL_4L _41APh'5454AQAQPAQWH@H4HXLl_4L ]_41APh`54594AQAQ54AQWH@H}4HSXL_4L _41APhq5 454AQAQ504AQDWH@H.4HWL^4L ^41APh5454AQAQ54AQVH@H4HW54 15c45͚4545454L 4L4H >4Ho4H541H0HO4H=WL]4L ]41 APh5245̲4AQAQPAQ3VH@H-4HVL]4L ]41 APh[ 545z4AQAQ54AQUH@H4HV5׵4 15R45454545Z45̮454L 4L(4H Y4H*4H541H@H:4H VL\4L \41 APh 5454AQAQPAQUH@H 4HUL 4LѮ41H 4H\4H5%41H4HULP\4L A\41APh 5454AQAQPAQTH@H4HFH@H4HFLM4L M41APh 5#454AQAQ54AQEH@H4HFL4H 41H4H5M41H@4HfFL(M4L M41APh:5 454AQAQPAQ\EH@H64HFLL4L L41APh~5454AQAQ54AQEH@H4HEH 4H41H5"41Hm4HELML4L >L41APh5454AQAQPAQDH@Hk4H9EL C4L41H ~4H4H5H4b1H4HDLK4L K41APh5V454AQAQPAQCH@H4HD1V5Z4Ls4L $4H 4H.4H5_41HR4_AXH]DLK4L K41APhD5454AQAQPAQSCH@HM4H DH4H5֪41I1Hں4HCLJ4L J41APhx545o4AQAQPAQBH@H4HCLPJ4L AJ41APj!5ނ4504AQAQ5ַ4AQBH@H4H:CH 4H=41H54q1H 4HCLI4L I41APj45h454AQAQPAQBH@H4HBHÛ4H5,411H4HBLPI4L AI41APj@5Ύ4504AQAQPAQAH@H4H?BLI4L H41APj5o45ɥ4AQAQ54AQ3AH@HU4HALH4L H41APj5345u4AQAQ54AQ@H@H 4HALYH4L JH41APjc54594AQAQ5ߵ4AQ@H@H4HCALH4L G41APjv5454AQAQ54AQ7@H@Hq4H@LG4L G41APh5,454AQAQ54AQ?H@H"4H@LZG4L KG41APj5Ȟ45"4AQAQ5x4AQ?H@H4HD@LG4L F41APj545Σ4AQAQ5 4AQ8?H@H4H?LF4L F41APjG5454AQAQ5Я4AQ>H@H>4H?L^F4L OF41APj`5d45ƛ4AQAQ5|4AQ>H@H4HH?5415~4H 4H`4L 4L4H54e1H4ZYH>LE4L E4APjt545!4AQAQPAQ=H@HZ4H>LjE4L [E4 1APh545Ϛ4AQAQ54AQ=H@H 4HQ>LE4L D4APh5ۦ45u4AQAQ5+4AQ?=H@H4H=LD4L D41APh5t454AQAQ54AQ4L >41APh545N4AQAQ54AQ7H@H 4H7L>4L >41APh5u454AQAQ54AQ6H@H4Hy7L;>4L ,>41APhG5^454AQAQ54AQj6H@Hl4H"7L=4L =41APh5745I4AQAQ5g4AQ6H@H4H6H5411H4H6LZ=4L K=4APh545˒4AQAQ54AQ5H@H4HM6L=4L =41APh545t4AQAQ54AQ>5H@HX4H5L<4L <41APh5454AQAQ5ӥ4AQ4H@H 4H5La<4L R<4 1APhE545Ƒ4AQAQ54AQ4H@H4HH5Ly4H ӏ41H݊4H54x1HY4H5L;4L ;41APh\54564AQAQPAQ4H@H74H4L;4L p;41APh5y454AQAQ5"4AQ3H@H4Hf4L(;4L ;4#1APh5454AQAQ5˥4AQW3H@H4H4H 4HJ41H5t4F1H/4H3L:4L :41APh*5™454AQAQPAQ2H@H4H35Ɩ415}4L 4Lw4H lx4He4H5֓41H4A\A]H;3L94L 941APh:50s45b4AQAQPAQ12H@H{4H2Lc4H u41Hv4H541H4H2Lr94L c941APhd545׎4AQAQPAQ1H@H4H^2S 15q4545v45O45v4L v4Lt4H q4H4H5q4h1H0He4H1L84L 841 APh5v45"4AQAQPAQ0H@HK4H1AQ15;4L Lq4Luw4H >x4H{4H54ʴ1HӨ4AZA[H]1L84L 841APh%5p454AQAQPAQS0H@H4H 1LuY4H Fp41H4H5i4;1HL4H0L74L 741APh85x454AQAQPAQ/H@H24H0H1H5+111Hק4HU0L74L 741APhy5 v45|4AQAQ54AQF/H@H4H/H {4H41H5c451HV4H/L64L 641APh|5iz454AQAQPAQ.H@H<4Hz/L<64L -641APh57454AQAQ5W4AQk.H@H4H#/L54L 541APh5t45J4AQAQ54AQ.H@H4H.L54L 541APh5x454AQAQ54AQ-H@HO4Hu.L754L (541APh%5z454AQAQ5ڟ4AQf-H@H4H.L4H 941H#4H5ts4N1Hw4H-L44L 441APhB5b{45 4AQAQPAQ,H@H}4H-LU44L F441APh5 {454AQAQ54AQ,H@H.4H<-L34L 34#1APh545c4AQAQ54AQ-,H@H4H,L?}4H 841H"4H5C41HF4H,Ln34L _34 1APh545ӈ4AQAQPAQ+H@HT4HZ,L34L 341APh15454AQAQ54AQK+H@H4H,L q4Ln41H {4Hv4H5Z4,1He4H+L24L v241APhW5454AQAQPAQ*H@H{4Hq+1V5)Lo4H u41Hx4H54n1HǠ4H)L/4L /41APh5g45,4AQAQPAQ'H@H4H(H n4H~t41H541HK4H(LC/4L 4/41APh5g454AQAQPAQw'H@Hi4H/(L.4L .41APj5_454AQAQ54AQ#'H@H4H'L.4L .41APj5#45e4AQAQ54AQ&H@Hѱ4H'LI.4L :.41APh 5Dy454AQAQ5d4AQx&H@H4H0'L-4L -41APh 5U45W4AQAQ5 4AQ!&H@H34H&L-4L }-4APh 5c454AQAQ54AQ%H@H4H&Li4H i41H4|4H5݆41H4HF&L-4L ,41APh 5 45m4AQAQPAQ<%H@H^4H%L64H 'v41Hyu4H5R4$1H4H%L},4L n,41APh 5pk454AQAQPAQ$H@Hۯ4Hi%L+,4L ,41APhI 5s454AQAQ5n4AQZ$H@H4H%L+4L +41APhd 5dž4594AQAQ54AQ$H@H=4H$5.g415g4L :g4Lsj4H lp4H-t4H54ا1HQ4AZA[Hk$L-+4L +41APhg 5(c454AQAQPAQa#H@H4H$H f4Hs41H5~4P1Hћ4H#L*4L *41APh 5t454AQAQPAQ"H@H'4H#LW*4L H*41APh 5454AQAQ524AQ"H@Hح4H>#L y4Lf41H e4Hy4H54g1H4H"L)4L )41APh 545%4AQAQPAQ!H@HN4H"L^{4H d41HAx4H5 4ܥ1Hm4Hs"L5)4L &)41APh7 5845~4AQAQPAQi!H@Hˬ4H!"L(4L (41APhg 545H~4AQAQ5N4AQ!H@H|4H!L(4L }(41APh 5m45}4AQAQ54AQ H@H-4Hs!1W54L Wz4L4H )f4H2z4H5Á41H.4AXAYH(!L'4L '41APh 5 l45O}4AQAQPAQ H@H4H L p4Lc41H Sc4Hf4H5-41H4H LX'4L I'41APh 545|4AQAQPAQH@H4HD V15Gw45k45d4L e4Lx4H 4H4H54Z1H H4HL&4L &41APh: 5bd45|4AQAQPAQH@Hm4HH r4HFx41H54Ң1H4HiL+&4L &4 1APhy 545{4AQAQPAQ_H@H4HQ 15a45tu45^45pf45d4L Kv4Ly4H 4Hg4H5Gd4!1H0H֖4HLv%4L g%41 APh| 5l45z4AQAQPAQH@HD4Hb5}4 15d4545\v45`45(b4L ]4Ls4H K~4Ht4H5]4g1H0H$4HL$4L $41 APh 5j45!z4AQAQPAQH@H4HHja4H5k411H4H}L?$4L 0$41APh> 52k45y4AQAQPAQsH@H4H+H _4H`41H5}4b1H34HL#4L #41APh` 545 y4AQAQPAQH@H4H5:w415`4H.t4L O`4L0r4H |4H5|4ğ1H4A]ZHXL#4L #41APhn 5u45x4AQAQPAQNH@H4HH Т1H11H51=1H4HL"4L "41APh 5g45w4AQAQ5i4AQH@H4H}1AS5__4L ^4LY_4H _4H{{4H5\k41H4[A\H2L!4L !41APh 5j45Yw4AQAQPAQ(H@H4HAR 15Z45{45e45k45r_4L +4L^4H %]4Hy4H5{41H0H֒4H|L>!4L /!41 APh 5QZ45v4AQAQPAQrH@HD4H*H,y4H5z41h1Ha4HL 4L 41APh+5Y45&v4AQAQPAQH@HϤ4HL ?\4L \41H ]4H[Y4H5y4֜1Hב4HmL/ 4L 41APh>5Bi45u4AQAQPAQcH@HE4HL4L 41APh\5Pd45Bu4AQAQ54AQ H@H4HL4L w41APhx5h45t4AQAQ5ѐ4AQH@H4HmHx4H5P^411H4HBL4L 41APh5Gv45it4AQAQPAQ8H@H24HAQ 15]45\45^[45hr45\4L w4Lq4AQAQPAQ H@H?4HLX4H X41Hzj4H5#u41H4HLN4L ?41APh5Qs45p4AQAQPAQH@H4H:Ln4H `41Hi4H5t4j1H4HL4L 41APhU5f`45(p4AQAQPAQH@H94HLAn4H Y41HDi4H5 t4ߖ1H4HvL84L )41APh5CY45o4AQAQPAQlH@H4H$L4L 41APh5z45Ko4AQAQ5)4AQH@Hg4H1V5r4Lyw4L k4H W4Hk4H5s41H04_AXHLE4L 641APh5h]45n4AQAQPAQyH@Hӝ4H1L4L 41APh/5V45Xn4AQAQ54AQ"H@H4H5]T4 158h45Q454Y45q45W4L i4Ll4H q4HZ4H5W4ߔ1H0H$4HrL44L %41 APhn5^45m4AQAQPAQhH@HҜ4H H1L 311IHHHR1H4H5,p4 15gc45qW45S145e`45T4L V4Lf4H p4HCg4H5P41H0HC4HLC4L 441 APh5v]45l4AQAQPAQwH@H4H/H)U4H5s41m1HΈ4HL4L 41APh%5]45+l4AQAQPAQH@Ht4HH P4Hd41H5p41HR4HLB4L 341APhH5u45k4AQAQPAQvH@H4H.1AU5HP4H Q4H:o4L R4L1H4HL4L 41APh5W45d4AQAQPAQH@H4H5p4 15M45g45M\4L f[4Lg4H [4Hg4H5h41H HQ4H'L4L 41 APh5Z45Nd4AQAQPAQH@H4HH1L 11AUH[4AQHIH1HȀ4ZYH5f415f4L m4LI4H _4Hf4H5g41H4A[A\HDL4L 41APhF5Z45kc4AQAQPAQ:H@H44HL 4L 41APh5F45c4AQAQ54AQH@H4HHf4H5vF41ى1H4HpL2 4L # 41APh5}d45b4AQAQPAQfH@Hp4HAR158G45E45_45xH4L H4LW4H _4H4Z4H5h4χ1H H}4HbL$ 4L  4 1 APh5J45`4AQAQPAQXH@Hz4HH:[4H޿1R1HK}4HAP 15;Z45Y45^45O45kl45V45WZ4L C4LyY4H c4HX4H5 I41H@H|4HyL; 4L , 41 APhP5P45_4AQAQPAQoH@H4H'Hb4W PP15k45kY45Y4L B4LX4H c4H X4H5ZH441H0H9|4HL 4L z 41 APh5LP45^4AQAQPAQH@H4HuLj4H U41Hf4H5b41H{4H<L4L 41APh5R45c^4AQAQPAQ2H@Hl4H5@4 15O45Z45O4L 5f4L@4H P4H@4H5)b41H H{4HLP4L A41 APh5{@45]4AQAQPAQH@Hƍ4H<HFj4H5a41z1Hz4HL4L 41APj15U45C]4AQAQPAQ H@HT4HL j4LG41H C4H(J4H5X41Hz4HLD4L 541APj=5M45\4AQAQPAQ{H@H͌4H3L4L 41APj`5e45e\4AQAQ5v4AQ'H@H4HL4L 41APjs5J45\4AQAQ5q4AQH@H54HLM4L >41APh5H45[4AQAQ5p4AQ|H@H4H4L4L 41APj5d]45b4AQAQ5o4AQ(H@H4HL4L 41APj5(`45jb4AQAQ5n4AQH@HN4HH H4HV41H5C4Á1Hw4HZL4L 41APhD5K45Z4AQAQPAQPH@HҊ4HV15h45@45A4L E4LiG4H R_4HD4H5,E41H HSw4HLs4L d41APhV5J45Y4AQAQPAQH@H14H_5 D4 15 V45C45\45[g45-@4L 6A4LOD4H p]4HF4H5JU4d1H0Hv4HL4L 41 APh{5J45&Y4AQAQPAQH@H4HQ15HC45f45?4L @4LC4H _4HE4H5T41H Hv4HNL4L 41APh5I45}X4AQAQPAQDH@Hވ4HH .C4HC41H5T431Hu4HL4L }41APh5I45W4AQAQPAQH@Hb4HxH T4HT41H5[4~1H u4HFL4L 41APh5`45uW4AQAQPAQ4L @4LX4H G4HG4H5>4z1H Hq4HL3L 31 APh5dD45NS4AQAQPAQH@H4H5(G4 153G45`4594L :4L=4H V4H@4H5N4y1H Hsp4HqL33L $31 APh5D45R4AQAQPAQgH@Ha4HL3L 31APh5C45NR4AQAQ5o4AQH@H4HLN4H N41HN4H5&V4x1Ho4HLQ3L B31APh5$[45Q4AQAQPAQH@H4H=5T4 15E45E45<4L PN4LQN4H :N4H3<4H5|U4Nx1H Hn4HL3L 31 APh5Z45Q4AQAQPAQH@H4HLQ3L B31APh5Z45P4AQAQ531 APhq5?45N4AQAQ5@l4AQ|H@H4H4L3L 31 APh5?45cN4AQAQ5k4AQ%H@Hw4HL3L 31APh5?45 N4AQAQ5Rk4AQH@H(4HLH3L 931APh5W45M4AQAQ5Kk4AQwH@H~4H/5rP4 15}A45A45q84L BJ4LCJ4H ,J4H84H5nQ4@t1H Hj4HL3L 31 APh5V45M4AQAQPAQH@H3~4HLC3L 431APh5V45L4AQAQ5.j4AQrH@H}4H*L3L 31APh5;45YL4AQAQ5a4AQH@H}4HL3L 31APh"5(945L4AQAQ58a4AQH@HF}4H|L>3L /31APj5M45S4AQAQ5\_4AQpH@H|4H(L3L 31APj5pP45R4AQAQ5^4AQH@H|4HL3L 31APhV5 <45K4AQAQ5)h4AQH@H_|4H}H/.4HX4RRPPP15V145`245045t54574L /4LO4H >4H>4H554uq1HPH*h4HL3L 31APhh5M;457J4AQAQPAQH@H{4HLx3L i31APh5s;45I4AQAQ5+g4AQH@HQ{4H_L!3L 31APh5R45I4AQAQ5$g4AQPH@H{4HL3L 31APh5%S457I4AQAQ5f4AQH@Hz4HLs3L d31APh5745H4AQAQ5~^4AQH@Hdz4HZL3L 31APh5545H4AQAQ5]4AQKH@Hz4HL3L 31APj53J45O4AQAQ5[4AQH@Hy4HLq3L b31APj5L459O4AQAQ5w[4AQH@H}y4H[L311L 31APj25:453G4AQAQAQAQYH@H;y4HL311L 31APj>5W<45F4AQAQAQAQH@Hx4HL3L z31APjO5K45F4AQAQ5Z4AQH@Hx4HsL53L &31APh50545BF4AQAQ5PZ4AQdH@H^x4HLN4H O541H154H5zJ4Lm1H d4HL3L 31APh5545E4AQAQPAQH@Hw4HLS3L D31APh5VG45`E4AQAQ5nY4AQH@Hw4H:L3L 31APh5R45 E4AQAQ5Y4AQ+H@H=w4HL3L 31APh5(I45D4AQAQ5X4AQH@Hv4HLN3L ?31APh5:45[D4AQAQ5iX4AQ}H@Hv4H5L3L 31APh52445D4AQAQ5X4AQ&H@HPv4HL3L 31APh5,45C4AQAQ5W4AQH@Hv4HLI3L :31APh5'45VC4AQAQ5dW4AQxH@Hu4H0L3L 31APh5'45B4AQAQ5 W4AQ!H@Hcu4HL3L 31APh5^'45B4AQAQ5V4AQH@Hu4HLD3L 531APh5&45QB4AQAQ5_V4AQsH@Ht4H+H m54HG41H5F4bi1H+`4HL3L 31APh5G45A4AQAQPAQH@HIt4HL A54Lj;41H 44H(4H5E4h1H_4HgL)3L 31APh 5 ;456A4AQAQPAQ]H@Hs4HLN4H xB41H?4H5sE4Eh1H_4HL3L 31APh5AI45@4AQAQPAQH@H4AQAQPAQH@H q4H:S15eE45o145%4L :%4Ls)4H 84H%.4H5~B4Pe1H HM\4HL3L 31APh5P045=4AQAQPAQH@Hkp4H5t#4 15045945#4L $4L$4H 94H84H5A4d1H H[4H5L3L 31 APh5>45=4AQAQPAQ+H@Ho4HH74H5VA41!d1H2[4HLz3L k31APh5>45<4AQAQPAQH@HPo4HfL(3L 31APh5;>455<4AQAQ5CP4AQWH@Ho4H5R/415!4L &;4L!4H 8"4H 4H5Z@4,c1HEZ4AZA[HL3L r31APh5=45;4AQAQPAQH@Hgn4HmH7A4H5?41b1HY4HBL3L 31APh15/=45;4AQAQPAQ8H@Hm4HHr.4H5c?41.b1HWY4HL3L x31APh?5<45:4AQAQPAQH@H}m4Hs1W5-4L 74L 4H !4H54H5>4a1HX4AXAYH(L3L 31APhS5;4594AQAQPAQH@Hl4HL3L 31APhp5[D4594AQAQ5M4AQH@Hl4HH 14H141H5=4`1HW4HML3L 31APhv5*G4594AQAQPAQCH@Hl4HHE4H&"45h94545_C45F4RR5'45{&4PPP15?45F45+4L 4L4H 4H?4H5 =4_1HpHW4HnL03L !31APh5 &45=84AQAQPAQdH@HFk4HHf4V 545F4PP15'4L %4LE4H 4H?4H5W<4)_1H0HnV4HL~3L o31 APh5C4574AQAQPAQH@Hj4HjL,3L 31APj5945>4AQAQ5JK4AQ^H@HPj4HL3L 31APj5^<45>4AQAQ5J4AQ H@Hj4HHd54H55;41^1HQU4HLY3L J31APh5A45f64AQAQPAQH@Hi4HEL3L 31APh5284564AQAQ5T4AQ6H@H@i4HL3L 31APh574554AQAQ5;T4AQH@Hh4HLY3L J31APj5745!=4AQAQ5wI4AQH@Hh4HCL3L 31APj5:45<4AQAQ5 I4AQ7H@HYh4HP15*84H #(4H:4L 4L^4H524\1HjS4ZYHLh3L Y31APh545u44AQAQPAQH@Hg4HT1S54L '4L4H R.4H[24H584v[1HR4A\A]H L3L 31APh5~04534AQAQPAQH@H1g4HH5441Z1HeR4HH.4H5841Z1HBR4HhL*3L 31APh5u645734AQAQPAQ^H@Hf4HL3L 31APh5#4524AQAQ5F4AQH@HIf4HL3L r31APj5445I:4AQAQ5F4AQH@He4HkL-3L 31APj574594AQAQ53F4AQ_H@He4HH ),4H*,41H5,04NY1HP4HL3L 31APh 5Z+4514AQAQPAQH@H5e4HH]74H5/41X1HRP4HhL*3L 31APh45M45714AQAQPAQ^H@Hd4HL3L 31APh5!4504AQAQ5D4AQH@Hqd4HL3L r31APj5245I84AQAQ5D4AQH@H%d4HkL-3L 31APj554574AQAQ53D4AQ_H@Hc4HH !>4H*,41H5|44NW1HN4HL3L 31APh5445/4AQAQPAQH@H]c4HH =4H41H534V1H[N4HaL#3L 31APh5n4450/4AQAQPAQWH@Hb4HH =4H741H5t34FV1HM4HL3L 31APh5345.4AQAQPAQH@Heb4HLM3L >31APj504564AQAQ5kB4AQH@Hb4H7L3L 31APj534554AQAQ5A4AQ+H@Ha4HL3L 31APh58!45-4AQAQ5A4AQH@H~a4HLN3L ?31APhF5q345[-4AQAQ5F4AQ}H@H/a4H5L3L 31APhe545-4AQAQ5E4AQ&H@H`4HH 4H+41H5C14T1HK4HLn3L _31APh}5.45{,4AQAQPAQH@Hd`4HZL3L 31APh5'645),4AQAQ57@4AQKH@H`4HL 4H 141H&4H5a043S1HJ4HL3L }31APh5.45+4AQAQPAQH@H_4HxAQ154L C4L,4H .4H$4H514R1HJJ4AZA[H,L3L 31APh5945*4AQAQPAQ"H@H^4HL3L 31APh"545*4AQAQ5>4AQH@H^4HLE3L 631APj5,45 24AQAQ5c>4AQwH@Ha^4H/L3L 31APj5w/4514AQAQ5=4AQ#H@H^4HL3L 31APh55/45)4AQAQ5G4AQH@H]4HLF3L 731APj5+4514AQAQ5d=4AQxH@Hz]4H0L3L 31APj5x.4504AQAQ5<4AQ$H@H.]4HL3L 31APj5 +45f04AQAQ5<4AQH@H\4HLJ3L ;31APj5-4504AQAQ5P<4AQ|H@H\4H4L3L 31APj5d*45/4AQAQ5<4AQ(H@HJ\4HL3L 31APj5(-45j/4AQAQ5;4AQH@H[4HLN3L ?31APj5)45/4AQAQ5l;4AQH@H[4H8L3L 31APj5,45.4AQAQ5;4AQ,H@Hf[4HL6(4H 41H4H5B+4N1HE4HLm3L ^31APh5`45z&4AQAQPAQH@HZ4HYL3L 31APj5(45-4AQAQ59:4AQMH@HZ4HL3L 31APj5M+45-4AQAQ594AQH@HKZ4HLs3L d31APh"5f45%4AQAQ5D4AQH@HY4HZL3L 31APj5'45,4AQAQ5:94AQNH@HY4HL3L 31APj5N*45,4AQAQ584AQH@HdY4HLt3L e31APj5&45<,4AQAQ584AQH@HY4H^L 3L 31APj5)45+4AQAQ5&84AQRH@HX4H H 4HU41H5o(4AK1HC4HL3L 31APhc5=45#4AQAQPAQH@HPX4HH 4Ha 41H5'4J1HB4HTL3L 31APhg5 45##4AQAQPAQJH@HW4HL 4L)41H 4H)4H5*4+J1HA4HL3L u31APh5/-45"4AQAQPAQH@HJW4HpHR4H541I1HA4HEL3L 31APh5R!45"4AQAQPAQ;H@HV4H1V54LO4L 4H 4H'4H54I1H@4_AXHLk3L \31APh545x!4AQAQPAQH@HAV4HWL3L 31APh545&!4AQAQ5d54AQHH@HU4HH5*41EH1H.@4HL3L 31APh:5 45 4AQAQPAQH@HU4HL4H M41H+4H5)4G1H?4HQL3L 31APh545 4AQAQPAQGH@HU4HH.4H5J.41=G1H6?4HH5.41G1H?4HLr3L c31APh5!454AQAQPAQH@HhT4H^H4H5#41F1H>4H3L3L 31APh5X!454AQAQPAQ)H@HS4HL3L 31APh* 5 454AQAQ5=4AQH@HS4HHT)4H541E1H=4H_L!3L 31APhA 545.4AQAQPAQUH@H/S4H L3L 31APhS 5%454AQAQ5B=4AQH@HR4HLx3L i31APhb 5# 454AQAQ5<4AQH@HR4H_L!3L 31APhq 5445.4AQAQ5<4AQPH@HBR4HL3L 31APh 5]454AQAQ5=<4AQH@HQ4H54 15454545-45/4L 4L)4H 4HS4H5 4C1H0H;4HIL 3L 31 APh 5^454AQAQPAQ?H@HAQ4HHF1L%S11HL/C1HP;4Hƿ5A4 1545&4545J45<4L E4L4H 4Hh4H54B1H0H:4H^L 3L 31 APh 545-4AQAQPAQTH@H^P4H L3L 31APj5<45"4AQAQ5.4AQH@HP4HLz3L k31APj5 45B"4AQAQ5.4AQ謽H@HO4HdL 4L41H 3H4H5#4A1H94H$L3L 31APh 59454AQAQPAQH@H1HY74HHH531>1H:74HhH"74H531>1H74H=L3L 31APj5m454AQAQ5+4AQ1H@HsL4HL3L 31APj5145s4AQAQ5*4AQݹH@H'L4HLW3L H31APj5454AQAQ5u*4AQ艹H@HK4HAL3L 31APj5454AQAQ5 *4AQ5H@HK4HL3L 31APhH5454AQAQ5.4AQ޸H@H@K4H5I4154L 4L&4H 3H04H54<1H,54A[A\HFL3L 31APhY5454AQAQPAQL3L 31APj5454AQAQ5&4AQ2H@HG4HH,4H5]41(91H14HL3L r31APh5d454AQAQPAQ赴H@H_G4HmL 3L0 41H 3Hs3H5481H?14H-L3L 31APh5454AQAQPAQ#H@HF4H۴L3H  41H4H594 81H04HLd3L U31APh5g45y4AQAQPAQ蘳H@HRF4HPL3L 31APh5m35'4AQAQ5-$4AQAH@HF4HL3L 31APh5354AQAQ5#4AQH@HE4HL3H 31H3H5461H/4HiL+3L 31APh535@4AQAQPAQ_H@H1E4HLٹ3L ʹ31APj5G454AQAQ5"4AQ H@HD4HòL3L v31APj5 45M4AQAQ5"4AQ跱H@HD4HoL 3L 41H |4H4H5451HY.4H/L3L 31APh@5 454AQAQPAQ%H@HD4HݱL3L 31APj5 45g4AQAQ5!4AQѰH@HC4HLK3L <31APj5454AQAQ5Q!4AQ}H@HwC4H5H _4H 41H54l41H5-4HLŷ3L 3 1APhY545 4AQAQPAQH@HB4HH3H5$4131H,4HLH3L 931APh545] 4AQAQPAQ|H@HB4H4Hf3H541r31HK,4H L˶3L 31APh5 45 4AQAQPAQH@HB4HLy3L j3#1APh5\45 4AQAQ54AQ訮H@H,4H`H4H54121H+4H5L3L 31APh5 45 4AQAQPAQ+H@HEA4HL3L 31APh535 4AQAQ54AQԭH@H@4HLN3L ?31APh5I35c 4AQAQ5i4AQ}H@H@4H5L3L 31APh5R35 4AQAQ5*4AQ&H@HX@4HޭL 4L31H 3H44H55411H)4HL`3L Q31APh535u 4AQAQPAQ蔬H@H?4HLH 3H41H5 401Ht)4HLܳ3L ͳ31APh'5G354AQAQPAQH@HR?4HȬLB 4H {31H]3H53/1H(4HLQ3L B31APh:5\35f4AQAQPAQ腫H@H>4H=L w4L41H R3H# 4H5 4f/1Hg(4HL3L 31APhT5354AQAQPAQH@HE>4HL% 4H ^31H3H54.1H'4HrL43L %31APhs5G35I4AQAQPAQhH@H=4H L3H  41H- 4H5^3P.1Ha'4HL3L 31APh5354AQAQPAQݩH@H?=4HL  4LH31H j3H3H5 4-1H&4HUL3L 31APh5j35,4AQAQPAQKH@H<4HLŰ3L 31APj5345 4AQAQ54AQH@Hi<4HLq3L b31APj5 459 4AQAQ5w4AQ裨H@H<4H[L3H 41H 4H5 4,1H%4H"L3L կ31APh5454AQAQPAQH@H;4HШL3L 31APj545Z 4AQAQ54AQħH@HN;4H|L>3L /31APj5 45 4AQAQ5D4AQpH@H;4H(L3L ۮ31APh5454AQAQ5 $4AQH@H:4HѧL3L 31APj545[ 4AQAQ54AQŦH@Hg:4H}L?3L 031APj545 4AQAQ5E4AQqH@H:4H)Hs3H5L31g*1H#4HHH531@*1Hq#4HצAP15!3L r3Ls3H 3H 4H5&4)1H1#4AYAZHLM3L >31APh&535b4AQAQPAQ聥H@H394H9H [3H31H54p)1H"4HLɬ3L 31APhJ5,354AQAQPAQH@H84HH 3HH31H54(1H5"4HLE3L 631APhe535Z4AQAQPAQyH@H;84H1L 3L31H F 4HO3H54Z(1H!4HL3L 31APh5^454AQAQPAQH@H74HLa3L R31APh5\35v4AQAQ5|4AQ萣H@Hb74HHL 4H {31H]3H54x'1H 4HLѪ3L ª31APh5,353AQAQPAQH@H64HL3L p31APj545G4AQAQ54AQ豢H@H64HiL+3L 31APj5454AQAQ514AQ]H@HG64HH4H541S&1H4HL3L 31APh5_353AQAQPAQH@H54HHb4H531%1H?4HmL/3L 31APh535D3AQAQPAQcH@H]54HL ]3L31H 3H3H54D%1H4HۡL3L 31APh5 453AQAQPAQѠH@H44HL3H 31H4H54$1H24HPL3L 31APh5 35'3AQAQPAQFH@HP44HL3H !31H+4H5<4.$1H4HŠL3L x31APh5J353AQAQPAQ軟H@H34Hs53154H53L 3L,3H 3H3#1H4^_H%L3L ئ31APh%5353AQAQPAQH@H534Hӟ5n315Y4H *3H3L d3L3H53"1H4ZYHLG3L 831APhM5235\3AQAQPAQ{H@H24H3L3L 31APj5S353AQAQ54AQ'H@HQ24HߞL3L 31APj535i4AQAQ54AQӝH@H24HLM3L >31APj5C354AQAQ5s4AQH@H14H7L3L 31APj5354AQAQ54AQ+H@HHi14He[A\A]]UtLF|.H ^.H53|.LBf.L a~.Ls.HAWL=(@.AVH-L56.AUL-_].ATL%G.SH-HdH%(HE1HC~.fHnH:.fHnH)H.fHnH=H.fHnHO.fHnH].fHnHf.fLnH].fLnHa_.fLnH-fLnHn.fLnH .fLnH.fLnHo.fLnH.fHn1ƅY-H 3HDž -HDžH-HDžp-ƅ-HDž-ƅ-HDž-ƅ-HDž-ƅ-HDž.ƅ .HDž8.ƅH.HDž`.ƅp.HDž. ƅ.HDž.ƅ.HDž. ƅ.fk.HDž/f0f1fA1ƅ/HDž(/ƅ9/HDžP/*ƅ`/HDžx/;ƅ/HDž/ƅ/HDž/ƅ/HDž/ƅ0HDž0ƅ(0HDž@0ƅP0HDžh0oƅx0HDž0hƅ0HDž0:ƅ0HDž0HDž1HDž01HDžX1fi1f1f1f1f 2f12fY2f2f2f2f2f!3fI3fq3f3f3HDž1HDž1HDž1HDž1HDž 2HDžH2HDžp2HDž2HDž2HDž2HDž3HDž83HDž`3 HDž3ƅ3HDž3HDž3 HDž4f94fa4f4f4f4f)5fQ5fy5f5f5f5f6fA6fi6ƅ4HDž(4HDžP4 HDžx4HDž4 HDž4 HDž40ƅ5HDž5HDž@5HDžh5HDž5 HDž5 HDž5HDž6HDž06 HDžX6 HDž6 f6f6f6f 7f17fY7f7f7f7f7fq8f8f8f8f9HDž6 HDž6HDž6HDž 7HDžH7 HDžp7 HDž7HDž7HDž7 HDž8ƅ 8HDž880ƅH8HDž`8HDž8HDž8HDž8HDž9 HDž(9f99fa9f9f9f9f:f):fQ:fy:f:fA;fi;HDžP9 HDžx9HDž9HDž9HDž9HDž:HDž@:HDžh:HDž:HDž:ƅ:HDž:ƅ:ƅ:HDž;ƅ;ƅ;HDž0;HDžX;HDž;&f;f;f;f ƅ>HDž(> f9>fa>f>f>f>f?f)?fQ?fy?f?f?f?f@fA@fi@HDžP>HDžx>HDž>HDž> HDž>HDž?%HDž@?'HDžh?HDž? HDž?HDž?!HDž@HDž0@HDžX@ HDž@%ƅ@HDž@ f@f@f Af1AfYAfAfAfAfAf!BfIBfqBfCHDž@HDž@HDž AHDžHA&HDžpA(HDžAHDžAHDžA'HDžB)HDž8BHDž`B HDžB ƅBƅBHDžBƅBHDžBƅBƅBHDžCHDž(C$faCfCfCfCfDf)DfQDfyDfDfDfDfEfAEƅ8CHDžPCHDžxCHDžCHDžC&HDžC(HDžDHDž@DHDžhD HDžD HDžD HDžDHDžEHDž0E"HDžXEqƅhEHDžE,ƅEHDžE1ƅEfHHDžE3ƅEHDžE$ƅFHDž F>ƅ0FHDžHF,ƅXFHDžpFEƅFHDžF%ƅFHDžF"ƅFHDžF7ƅFHDžGlƅ GHDž8G'ƅHGHDž`GHƅpGHDžGdƅGHDžGOƅGHDžG4ƅGHDžH HDž(Hƅ8HHDžPHnfHfHfIf)IfQIfyIfIfIfIfAJfJƅ`HHDžxHƅHHDžH HDžHHDžH HDžIHDž@IHDžhIHDžI$HDžIHDžIHDžJƅJHDž0JHDžXJƅhJHDžJ(ƅJHDžJHDžJ'ƅJf1KfKfKf!LfqLfLfMfaMHDžJ ƅKHDž KHDžHK*ƅXKHDžpKHDžK#ƅKHDžKHDžKƅKHDžLHDž8L"ƅHLHDž`LHDžLƅLHDžLHDžL ƅLHDžMHDž(M>ƅ8MHDžPM#HDžxM.fMfNfQNfNfNfAOƅMHDžMHDžMƅMHDžMHDžNƅ(NHDž@NHDžhN ƅxNHDžNHDžN!ƅNHDžNHDžO$ƅOHDž0OHDžXOƅhOHDžO'ƅOHDžO+ƅOHDžO,ƅOHDžOf PfYPfPfPf!QfqQfQf9RfRHDž Pƅ0PHDžHPHDžpPHDžPƅPHDžPHDžP!ƅPHDžQHDž8Q#ƅHQHDž`QHDžQ"ƅQHDžQ$ƅQHDžQ HDžR,ƅRHDž(RHDžPRƅ`RHDžxRHDžRfRf)SfQSfySfSfSfSfTfATfiTfTfTHDžR%ƅRHDžRƅSHDžSHDž@SHDžhSHDžSHDžSHDžS HDžT HDž0THDžXT HDžT HDžT!HDžT#ƅTHDžTƅUHDž Uƅ0UfYUfUfUfUfUf!VHDžHUHDžpU(HDžU*HDžUHDžU)HDžV+HDž8V+ƅHVHDž`VeƅpVHDžVeƅVHDžVgƅVHDžVƅVHDžWƅWHDž(Wƅ8WHDžPWƅ`WHDžxWƅWHDžWdƅWHDžWƅWHDžWƅXHDžXwƅ(XHDž@X ƅPXHDžhXƅxXHDžXƅXHDžXW ƅXHDžXƅXHDžYƅYHDž0Yƅ@YHDžXYƅhYHDžYlƅYHDžY ƅYHDžYƅYHDžYkƅZHDž Zƅ0ZHDžHZƅXZHDžpZ ƅZHDžZ ƅZHDžZ^ƅZHDžZƅZHDž[8ƅ [HDž8[ƅH[HDž`[ƅp[HDž[ƅ[HDž[ƅ[HDž[/ƅ[HDž\Xƅ\HDž(\ƅ8\HDžP\ ƅ`\HDžx\ƅ\HDž\ƅ\HDž\ƅ\HDž\ƅ]HDž]ƅ(]HDž@]ƅP]HDžh]ƅx]HDž]-ƅ]HDž]*ƅ]HDž]ƅ]HDž^ƅ^HDž0^ƅ@^HDžX^lƅh^HDž^ƅ^HDž^ƅ^HDž^ƅ^HDž^ƅ_HDž _`ƅ0_HDžH_ZƅX_HDžp_ƅ_HDž_ƅ_HDž_ƅ_HDž_ƅ_HDž`ƅ `HDž8`ƅH`HDž``ƅp`HDž`ƅ`HDž`hƅ`HDž` ƅ`HDžaXƅaHDž(aƅ8aHDžPaƅ`aHDžxaƅaHDžavƅaHDža(ƅaHDžaƅbHDžbƅ(bHDž@bƅPbHDžhbƅxbHDžbgƅbHDžbKƅbHDžbƅbHDžcƅcHDž0cƅ@cHDžXcƅhcHDžcƅcHDžcƅcHDžcƅcHDžcƅdHDž dSƅ0dHDžHdƅXdHDžpd ƅdHDždfdfdfdf!efIefqefefef9ffff)gHDžd HDžd HDžeHDž8eHDž`eHDžeHDže HDže&ƅeHDžf'ƅfHDž(fHDžPfƅ`fHDžxfƅfHDžfHDžf(ƅfHDžf'ƅgHDžgHDž@g$fygfgfgfgfhfAhfihfhfhfhf if1ifYiƅPgHDžhg HDžg HDžgHDžg HDžh HDž0hHDžXhHDžhHDžhHDžhHDžhHDž iHDžHiHDžpi-ƅiHDži)ƅiHDžififif!jfIjfqjfkfakfkfkfkflf)lfQlHDžiHDžjHDž8jHDž`jHDžj-ƅjHDžj)ƅjHDžjƅjHDžkHDž(k ƅ8kHDžPkHDžxk"HDžk"HDžkHDžkHDžlHDž@lHDžhl!fylflflflfmfAmfimfmHDžl#HDžlHDžlHDžmHDž0mHDžXmHDžmHDžm-ƅmHDžm*ƅmHDžm-ƅnHDž nƅ0nHDžHnƅXnHDžpn ƅnHDžnUƅnHDžnWƅnHDžnGƅnfQqfyqHDžoIƅ oHDž8owƅHoHDž`owƅpoHDžowƅoHDžowƅoHDžoeƅoHDžpJƅpHDž(pPƅ8pHDžPpNƅ`pHDžxpmƅpHDžp@ƅpHDžp`ƅpHDžpXƅqHDžqeƅ(qHDž@q HDžhq HDžqfqfrfArfirfrfrf sfYsfsHDžq4ƅqHDžq\ƅqHDžrHDž0rHDžXrHDžr HDžr&ƅrHDžr HDžrHDž sƅ0sHDžHs HDžps HDžs8ƅsHDžs9ƅsHDžs8ƅsHDžt9ƅ tHDž8t9ƅHtHDž`tƅptHDžtƅtHDžtCƅtHDžtDƅtHDžuƅuHDž(uƅ8uHDžPu{ƅ`uHDžxuzƅuHDžu&ƅuHDžu+ƅuHDžuƅvHDžvƅ(vHDž@v!ƅPvHDžhv=ƅxvHDžv>ƅvHDžv#f xf1xfYxfxfxfxfxf!yƅvHDžv,ƅvHDžw?ƅwHDž0w)ƅ@wHDžXw)ƅhwHDžw)ƅwHDžwƅwHDžw&ƅwHDžw HDž xHDžHxHDžpxHDžxHDžx*HDžx,HDžy)HDž8yfIyfqyfyfyfyfzf9zfazfzf{f){fQ{fy{f{HDž`yHDžyHDžyHDžy"HDžz(HDž(z)HDžPz&HDžxzHDžzƅzHDžzƅzƅzHDžzHDž{HDž@{HDžh{HDž{HDž{f{f|f|fY}f}f}fI~HDž{$ƅ{HDž| HDž0|ƅ@|HDžX|%ƅh|HDž|HDž|ƅ|HDž|#ƅ|HDž|ƅ}HDž }#ƅ0}HDžH}HDžp}"ƅ}HDž}HDž} ƅ}HDž}HDž~ƅ ~HDž8~HDž`~ f~faffff)fQfyffɀƅp~HDž~HDž~ƅ~HDž~&ƅ~HDž]ƅHDž(`ƅ8HDžPHDžx)HDž5ƅHDž'HDž HDžHDž@ HDžh!HDžHDž)HDž0fffAfiffff f1fYfffтffqHDž HDž0HDžXHDžHDžHDžЁHDž(HDž *HDžH*HDžp,HDž,HDžHDžHDž+ƅ HDž8.ƅHHDž`HDžffff)fQfyfHDžHDž؃/ƅHDž1ƅHDž(0ƅ8HDžPwƅ`HDžxDƅHDžƅHDžȄƅ؄HDž HDž HDž@HDžhHDžHDžƅȅƅʅHDžƅHDžffAfiffffqHDž0HDžXHDžƅHDžHDžІHDž*ƅHDž }ƅ1HDžH~ƅYHDžpƅHDž HDžHƅчHDžwƅHDž5ƅ HDž8ƅHHDž`HDž fff9fafffىff)fQfyffɊffHDž'ƅHDž؈ƅHDž HDž( HDžPHDžx HDž HDžȉHDž HDž HDž@HDžh HDž HDžHDžHDžHDž0 fAfif!fIfqfHDžXHDžƅHDžƅHDžЋƅHDžƅHDž ƅ0HDžHƅXHDžpƅHDž$ƅHDž^ƅЌHDžƅHDžHDž8!HDž`#HDžHDž"fffff)fQfyffɏffHDž؍$HDžMƅHDž(Jƅ8HDžP7ƅ`HDžxcƅHDžHDžȎƅ؎ƅڎHDž HDžHDž@HDžhHDžHDžHDžHDžHDž0fAfiff1fYffff!fIHDžXHDžHDžƅƅHDžА ƅƅHDžƅƅ HDž HDžHHDžpHDžHDž*ƅБHDžHDžHDž8HDž`,ƅpHDžffffٓff)fɔffHDžHDžؒ'ƅHDžHDž(+ƅ8HDžP/ƅ`HDžx-ƅHDž.ƅHDžȓHDžHDžHDž@1ƅPHDžh0ƅxHDž)ƅHDžHDžHDžHDž0fffffіffqfƅ@HDžX:ƅhHDžƅHDž HDžЕHDž"ƅHDž &ƅ0HDžH%ƅXHDžp HDžHDž HDžHDž(ƅ HDž8'ƅHHDž`HDžHDž"ƅfff٘ffffAHDžؗHDž&ƅHDž(*ƅ8HDžP(ƅ`HDžx)ƅHDžHDžȘ HDžHDž,ƅ(HDž@+ƅPHDžh$ƅxHDžlƅHDž ƅșHDžHDžHDž0$HDžX&fiff fYfffћfHDžƅƅHDžƅHDžК HDžHDž !ƅ0HDžH HDžpHDžHDžHDžHDž&ƅ HDž8%ƅHHDž`%ƅpHDž$ƅHDž'ƅHDž؜%ff)fQfyffɞfffAƅHDžHDž(9ƅ8HDžPƅ`HDžx8ƅHDž-ƅHDžȝƅ؝HDžƅHDž HDž@ HDžhHDž#HDž%HDžHDžHDž0HDžXfiffff f1fYfffѠff!fIfqfffHDž HDžHDžПHDžHDž HDžHHDžpHDžHDž HDž"HDžHDž8 HDž`HDžHDžHDžءHDžff9fafff٢ff)fQfyffɣHDž( HDžPHDžxHDž#HDžȢ%HDžHDž HDž@HDžh$HDžHDž!HDž$ƅHDžƅHDž0!ƅ@HDžX+ƅhHDžBƅff fѥff!fIfqffffHDž.ƅHDžФHDžHDž ƅ0HDžHQƅXHDžpƅHDžƅHDž HDžHDžHDž8HDž`HDžHDžHDžئHDžHDž(f9fafff٧ff)fQfyffɨfffAfifHDžPHDžxHDžHDžȧHDžHDžHDž@HDžhHDžHDžHDžHDžHDž0HDžXHDžHDžfff f1fYfffѪfffHDžЩHDžHDž HDžHHDžpHDžHDžHDžHDžƅ HDž8ƅHHDž`ƅrHDžƅHDžHDžثHDžƅHDž(ƅ8HDžP fafff٬fAfiffHDžx HDž HDžȬHDž#ƅHDžƅ(HDž@ƅPHDžhƅxHDžƅHDžBƅȭHDžƅHDž ƅHDž0 HDžXHDžHDžHDžЮfffIfqfffff9HDžƅHDž 'ƅ0HDžHƅXHDžpƅHDžƅHDžƅЯHDž HDžƅ ƅ"HDž8HDž`!HDž#HDžHDžذ(HDžHDž(HDžPfafffٱff)fQfyffɲfffAfifffHDžxHDžHDžȱHDž(HDžHDž@HDžhHDžHDžHDžHDžHDž0HDžXHDž%HDž'HDžгHDž(fѴff!ffff9faƅHDž }ƅ0HDžH ƅXHDžp? ƅHDžh ƅHDž HDž HDž HDž8 ƅHHDž`;ƅpHDžƅHDžHDžص"HDž$HDž(HDžPHDžx*fffٶff)fQfyffɷfffAfiffff HDž(HDžȶHDžHDž HDž@HDžhHDžHDžHDžHDžHDž0HDžXHDžHDž!HDžи#HDžHDž f1fYffѹff!fIfqfff9fafHDžHHDžpHDžƅHDžHDž HDžHDž8HDž` HDž ƅƅHDž HDžغ ƅHDž HDž(HDžP!HDžx HDž)ffٻff)fQfyffɼfffAfiffff HDžȻ+HDžHDž!HDž@6HDžh HDžHDž)HDž+HDžHDž0HDžX%HDžHDžHDžн$HDž&HDž f1fYfffѾff!fIfqfffff9faffHDžH!HDžp)HDžHDžHDžHDžHDž84HDž`HDž"HDž7HDžؿHDž+HDž(-HDžPHDžxHDž+HDž-fff)fyfffffAfiffff f1HDžHDžHDž@ƅPƅRHDžhHDžHDžHDžHDžHDž0HDžXHDž HDžHDž#HDž"HDž HDžHfYfff!fqfffafHDžpHDž#ƅHDžHDžƅHDžHDž8ƅHHDž`HDžƅHDžHDž$ƅHDžHDž($ƅ8HDžPHDžx*ƅHDž"HDž.ƅfyfffff1HDž'ƅHDž,ƅ(HDž@)ƅPHDžhHDž&ƅHDžHDž'ƅHDž$HDž00ƅ@HDžX'ƅhHDžHDžƅHDžHDž"ƅHDž HDžH#ƅXHDžpffffIHDžƅHDžHDžHDž"ƅ HDž8HDž`ƅqHDžƅHDžƅHDžƅHDžƅHDž(<ƅ8HDžP@ƅ`HDžx ƅHDžƅHDž ƅHDžff)HDžHDž@HƅPHDžhƅxHDžƅHDžƅHDžƅHDžƅHDž0ƅ@HDžXGƅhHDžƅHDž&ƅHDžƅHDžƅHDž ƅ0HDžHnƅXHDžpƅHDžOƅHDžƅHDžƅHDžƅ HDž8ƅHHDž`ƅpHDžƅHDž3ƅHDžƅHDžEƅHDž(ƅ8HDžPƅ`HDžxƅHDžƅHDžƅHDžƅHDžf)fQfyfffffAfiffYfHDž@ HDžh(HDž(HDžHDž HDžHDž0HDžXHDžHDžJƅHDžNƅHDž]ƅHDž \ƅ0HDžH HDžp HDž ƅƅfff!fIfqffff9faffHDžHDžHDžHDž8HDž`HDžƅƅHDžHDžHDžHDž( HDžP HDžxƅHDžHDžƅHDžHDžƅ(fQfyfffffAfifff fYfHDž@HDžhHDžHDžHDžHDžHDž0HDžXHDžHDžHDž$ƅHDžHDž ƅ0HDžHHDžpƅHDžHDžff!fff9fƅHDž HDžHDž8ƅHHDž`5ƅpHDžHDžƅHDžHDž)ƅHDž(HDžP#ƅ`HDžxHDžƅHDž$ƅHDž-ƅHDž!ƅ(HDž@'ƅPffAfff1fHDžhMƅxHDž+ƅHDž_ƅHDžHDžƅHDž0HDžX#ƅhHDžHDžƅHDž HDžƅHDž HDžH!ƅXHDžpHDž!ƅHDžƅHDžff)fQƅHDžmƅ HDž8ƅHHDž`ƅpHDžƅHDžƅHDžYƅHDžƅHDž(iƅ8HDžPƅ`HDžxƅHDžHDž)ƅHDžƅHDžHDž@HDžhfyfffffiffff f1fYffffHDž$HDž&HDž HDž HDž0ƅ@HDžXHDž)HDž"HDž$HDžHDž HDžH!HDžpHDž#HDžHDžHDžf!fIfqffff9fafffff)fQfyHDž8!HDž`HDžHDžƅHDžHDž)HDž("HDžP$HDžxHDžHDž!HDžHDžHDž@HDžhHDžffffAfiffff f1fYffff!HDžHDžƅHDžHDž0)HDžX"HDž$HDžHDžHDž!HDž HDžHHDžpHDžHDžƅHDžHDž)HDž8"fIfqfffff9faffff)fQfyfHDž`$HDžHDžHDž!HDžHDž(HDžPHDžxHDžHDžHDžƅHDž HDž@HDžhHDž HDžfffAfiffff f1fYffff!fIHDžHDž=ƅHDž0 HDžXHDžHDž HDž"HDžHDž HDžHHDžp*ƅHDž HDžHDžHDžHDž8$HDž`&fqffff9fafffff)fQfyffHDžHDž HDžHDž.ƅHDž(HDžP HDžxHDžHDžHDžHDžHDž@ HDžhHDžHDžHDžfffAfifff1ffff!fIfqHDž HDž0HDžXHDžHDžƅHDž!HDž,ƅHDž HDžH&ƅXHDžp5ƅHDž HDžHDžHDž HDž8HDž`HDž#ffff9faffffQffƅHDžHDžHDžHDž(HDžPHDžxHDžHDžƅHDž HDžƅ(HDž@HDžh!ƅxHDž HDž HDžƅHDž ffiff fYfffIfHDž0ƅ@HDžXHDžƅHDžHDžƅHDžHDž ƅ0HDžHHDžpƅHDžHDž$ƅHDžHDž(ƅ HDž8HDž`ƅpHDž HDžffffQffHDžƅHDžƅHDž(!ƅ8HDžP&ƅ`HDžx#ƅHDžHDž ƅHDžHDž!ƅ(HDž@HDžh*ƅxHDž!ƅHDž HDžƅHDžHDž0ƅ@fiff fYffff!fIfqffHDžX HDžƅHDžHDžƅHDžHDž ƅ0HDžHHDžpHDž%ƅHDžHDž HDžHDž8HDž`HDžHDžHDžfff9faffQfffAHDžHDž(HDžPHDžx#ƅHDžHDžƅHDž*ƅHDž%ƅ(HDž@HDžh"ƅxHDžHDžƅHDžHDžƅHDž0HDžX!ƅhfff1fff!fqfHDžHDž!ƅHDžHDž!ƅHDž HDžH#ƅXHDžpHDžƅHDžHDžƅHDžHDž8ƅHHDž`HDžƅHDžHDžXƅHDžQfafffffyfƅHDž(Gƅ8HDžPHDžxHDžHDž%HDžHDžƅ(HDž@$ƅPHDžhHDžƅHDžHDžƅHDž(ƅHDž0%ƅ@HDžXƅhHDž ƅf fHDžƅHDž"ƅHDžHDž ƅ0HDžHƅXHDžp*ƅHDžVƅHDž HDž\ƅHDž^ƅ HDž8EƅHHDž`ƅpHDžƅHDžƅHDžƅHDžƅHDž(ƅ8HDžPƅ`HDžxƅHDžƅHDžƅHDžƅHDžƅ(HDž@-ƅPHDžhƅxHDž>ƅHDžƅHDžƅHDž>ƅHDž0ƅ@HDžXƅhHDžƅHDžƅHDžƅHDžƅ HDž $ƅ0 HDžH ƅX HDžp "ƅ HDž ƅ HDž Sƅ HDž Xƅ HDž <ƅ HDž8 ƅH HDž` Uƅp HDž iƅ HDž Lƅ HDž 8ƅ HDž ƅ HDž( ƅ8 HDžP fi f f ƅ` HDžx <ƅ HDž ƅ HDž 5ƅ HDž ƅ HDž ƅ( HDž@ ƅP HDžh ƅx HDž ƅ HDž ƅ HDž ƅ HDž ƅ HDž0 \ƅ@ HDžX HDž HDž HDž f f fYffffIfqfffff9HDž HDž $ƅ0HDžH HDžp HDžHDž HDž$ƅHDž-ƅ HDž8 HDž` HDžHDžHDžHDžHDž(HDžP%ƅ`HDžx'f)fQfyffffAfifffƅHDžƅHDžƅHDžOƅHDž HDž@HDžhHDžHDž'HDž)HDžƅƅHDž0 HDžX HDžHDž HDž HDž f f1fYfffff!fIfqfffff9faHDž HDžHHDžpHDžHDž HDžHDžHDž8HDž` HDžHDž HDž HDž HDž( HDžPHDžxfffff)fQfyfffffAfiffff HDžHDžHDžHDž HDž@HDžh HDžHDžHDžHDž HDž0HDžX HDž HDž HDžHDž HDž  f1fYfff!fIfqfffff9fafHDžH HDžp HDžOƅHDžPƅHDž HDž HDž8HDž` HDž HDžHDž HDž HDž(HDžP HDžx HDžfffAffff f1HDžƅHDžƅHDžXƅ(HDž@2ƅPHDžh(ƅxHDž'ƅHDžhƅHDžUƅHDž HDž0HDžX"ƅhHDžHDžHDžHDžHDž HDžH fYfffff!fffHDžpHDž HDžHDžHDžHDž8ƅHHDž`#ƅpHDž)ƅHDžHDž-HDžƅHDž(ƅ8HDžPqƅ`HDžxƅHDžHDž ff f) fQ fy f f f f!fi!f "HDžHDž HDž@ HDžh HDž HDž HDž HDž! HDž0! ƅ@!ƅB!HDžX! HDž! ƅ!HDž!ƅ!HDž!;ƅ!HDž! HDž " ƅ0"ƅ2"HDžH" fY"f9$HDžp"#ƅ"HDž"cƅ"HDž"ƅ"HDž"ƅ"ƅ"HDž#ƅ #HDž8#ƅH#HDž`#ƅp#HDž#ƅ#HDž#ƅ#ƅ#HDž#ƅ#HDž$ƅ$HDž($HDžP$ƅ`$HDžx$HDž$ƅ$HDž$f)%f%fA&fi&f&f&f 'ƅ$HDž$ƅ%HDž%HDž@%ƅP%HDžh%ƅx%HDž%ƅ%ƅ%HDž%ƅ%ƅ%HDž%HDž&%ƅ&HDž0&HDžX&HDž& HDž& HDž& ƅ&HDž&HDž 'f1'fY'f'f'f'f'f!(fI(fq(f(f(f)f9)fa)f)f)HDžH' HDžp' HDž' HDž'HDž' HDž(HDž8( HDž`( HDž( HDž(HDž(ƅ(HDž)HDž()HDžP)HDžx)HDž)HDž)f)f*f)*fQ*fy*f*f*f*f+fA+f+f+f ,HDž) HDž*HDž@*HDžh*HDž*HDž*HDž* HDž+HDž0+ HDžX+ƅh+HDž+HDž+HDž+ƅ+ƅ+HDž+HDž ,ƅ0,ƅ2,fY,f,f,f!-fI-fq-f-f-f.f9.fa.f.HDžH,HDžp,ƅ,ƅ,HDž,HDž,ƅ,ƅ,HDž,HDž-HDž8-HDž`-HDž- HDž-ƅ-HDž- HDž. HDž(.HDžP. HDžx.HDž.f.f/f)/fQ/fy/f/f/f/f0fA0fi0f0f 1HDž. ƅ.ƅ.HDž. HDž/HDž@/HDžh/HDž/ HDž/HDž/HDž0HDž00HDžX0 HDž0ƅ0HDž0HDž0 ƅ0HDž0HDž 1fY1f1f1f1f!2fI2fq2f2f2fa3ƅ01HDžH1 HDžp1 HDž1HDž1HDž1ƅ1ƅ1HDž2HDž82HDž`2HDž2 HDž2HDž2ƅ2ƅ2HDž3$ƅ3HDž(3ƅ83HDžP3 HDžx3 ƅ3f3f4f)4fQ4fy4f4fA5fi5f5f5ƅ3HDž3ƅ3HDž3 HDž3 HDž4HDž@4 HDžh4HDž4HDž4ƅ4ƅ4HDž4ƅ4HDž5ƅ5ƅ5HDž05"HDžX5HDž5HDž5HDž5f5f 6f16fY6f6f6f6f!7fq7f7f8f98HDž5 HDž 6 HDžH6HDžp6HDž6 HDž6 ƅ6ƅ6HDž6HDž7 HDž87ƅH7HDž`7HDž7 HDž7ƅ7HDž7ƅ7ƅ7HDž8HDž(8HDžP8fa8f8f8f8f9f)9fQ9fy9f9f9f9f:fA:fi:f:f:HDžx8HDž8 HDž8 HDž8HDž9 HDž@9HDžh9 HDž9HDž9 HDž9HDž: HDž0:HDžX:HDž:HDž: HDž: f:f ;f1;fY;f;f;f;f;f!f)>fQ>fy>f>f>f>f?fA?fi?f?f?f?HDž= HDž= HDž=HDž>HDž@>HDžh>HDž>HDž> HDž>HDž? HDž0?HDžX? HDž? HDž? HDž? HDž? f @f1@fY@f@f@f@f@f!AfIAfqAfAfAfAfBf9BfaBfBHDž @HDžH@HDžp@HDž@HDž@HDž@ HDžAHDž8A HDž`A HDžAHDžA HDžAHDžB HDž(B HDžPB HDžxB HDžBfBfCf)CfQCfyCfCfCfCfDfADfiDfDfDf EHDžB@ƅBHDžBHDžCHDž@CHDžhC HDžC HDžCHDžCHDžD HDž0DHDžXDHDžD HDžDƅDHDžD HDžDHDž Ef1EfYEfEfEfEf!FfIFfqFfFf9GfaGfGHDžHEHDžpEƅEHDžEHDžE HDžEHDžFHDž8FHDž`FHDžFƅFƅFHDžF ƅFHDžFHDžGƅGƅGHDž(G HDžPGHDžxGHDžGfGfGfHf)HfQHfHfHfHfIfAIfiIfIfIfIf JHDžGHDžGHDžHHDž@HHDžhHƅxHƅzHHDžH HDžHHDžH HDžIHDž0IHDžXIHDžIHDžIHDžIHDžI HDž Jf1JfYJfJfJfIKfqKfKfKfLfaLHDžHJHDžpJƅJƅJHDžJ HDžJ ƅJƅJHDžJHDžKƅ Kƅ"KHDž8K HDž`K HDžKHDžKƅKHDžKHDžLHDž(Lƅ8LHDžPLHDžxLfLfLfMf)MfQMfyMfMfMfNfANfiNfNfNfNƅLHDžLHDžL HDžLHDžMHDž@M HDžhMHDžMHDžM0ƅMHDžMHDžNHDž0NHDžXN HDžN HDžNHDžN HDžN f Of1OfYOfOfOfOf!PfIPfqPfPfQf9QfaQHDž OHDžHOHDžpOƅOHDžO HDžOHDžO HDžPHDž8PHDž`PHDžPƅPƅPHDžPƅPHDžPHDžQ HDž(QHDžPQHDžxQfQfQfQfyRfSfiSHDžQHDžQHDžQƅRƅRHDžR ƅ(RHDž@RƅPRHDžhRHDžRƅRƅRHDžRƅRHDžR ƅRHDžSHDž0Sƅ@SƅBSHDžXS HDžSƅSHDžSƅSHDžSƅSf1TfYTfTfTfTfTf!UfqUfUfUfUfVf9VfaVHDžSƅTHDž THDžHTHDžpTHDžT HDžT HDžTHDžU HDž8UƅHUHDž`U HDžUHDžUHDžU HDžV HDž(VHDžPVHDžxVfVfWf)WfyWfWfWfXfAXfiXfXƅVƅVHDžVƅVƅVHDžVHDžVHDžWHDž@WƅPWHDžhWHDžWƅWƅWHDžW HDžWHDžX HDž0XHDžXXHDžXHDžXƅXƅXfXf YfYYfYfYfYfYfIZfqZfZfZHDžXHDžXHDž Yƅ0Yƅ2YHDžHYHDžpYHDžY HDžYHDžY HDžZ ƅ Zƅ"ZHDž8Z HDž`Z HDžZHDžZHDžZƅZƅZHDž[ƅ[f9[fa[f[f[f\f)\fQ\fy\f\f\f]fA]fi]HDž([HDžP[HDžx[ HDž[ HDž[ƅ[HDž[ HDž\ HDž@\HDžh\HDž\HDž\ƅ\ƅ\HDž\ HDž]HDž0]HDžX]HDž]ƅ]f]f]f ^f1^fY^f^f_f_f_ƅ]HDž] HDž] HDž]HDž ^HDžH^HDžp^ HDž^ƅ^HDž^ ƅ^HDž^ ƅ^HDž_ ƅ _HDž8_ ƅH_HDž`_ƅr_HDž_HDž_HDž_HDž` f`f9`fa`f`f`faf)afQafyafafafafbfAbfibfbHDž(` HDžP`HDžx`ƅ`HDž`HDž`HDž`HDža HDž@aHDžha HDžaHDža HDžaHDžbHDž0bHDžXb HDžb HDžbfbfbf cf1cfYcfcfcfcfcf!dfIdfqdfdfdfeHDžbHDžb HDž cHDžHcHDžpcHDžc HDžcHDžc HDždHDž8d HDž`d HDžd ƅdHDžd HDždHDže HDž(e f9efaefefefefffQffffgfAgfigHDžPe HDžxeHDžeHDžeHDže HDžfƅ*fHDž@fHDžhfƅxfƅzfHDžfƅfƅfHDžfƅfƅfHDžfHDžgHDž0gHDžXgHDžg fgfgf hfYhfhfhfhfhf!ifIifqififififjHDžgHDžgƅgHDžg HDž h"ƅ0hHDžHhHDžphHDžh HDžhHDžh HDži HDž8iHDž`iHDžiHDžiHDžiHDžjHDž(jf9jfjfjfjfkf)kfykfkfilHDžPjƅ`jHDžxjHDžjHDžjHDžj HDžkHDž@kƅPkƅRkHDžhkHDžkƅkƅkHDžkƅkHDžkHDžlƅlƅlHDž0lƅ@lHDžXlHDžlflf mf1mfYmfmfmfmfInfqnfnfnƅlƅlHDžlƅlHDžlHDžlHDž mHDžHmHDžpmƅmHDžm HDžm HDžmHDžnƅ nƅ"nHDž8n HDž`n HDžn HDžn HDžnfnfof9ofaofofofpf)pfQpfypfpfpfpfqfAqfiqHDžoHDž(o HDžPoHDžxoƅoHDžoHDžo HDžoHDžp HDž@p HDžhp HDžp HDžp HDžp HDžq HDž0q HDžXqHDžq fqfqfqf rfYrfrfrfrf!sfIsfqsfsfsfsHDžq HDžqHDžqHDž rƅ0rƅ2rHDžHrHDžpr HDžrƅrHDžrHDžrHDžsHDž8sHDž`sHDžs HDžsHDžsHDžtftf9tfatftftftfuf)ufQufyufufufufvfAvfivHDž(tHDžPtHDžxtHDžtHDžtHDžt HDžuHDž@uHDžhu HDžu HDžu HDžu HDžv HDž0v HDžXvHDžvƅvfvfYwfwfwfqxfxfxƅvHDžvHDžvƅvƅvHDžvƅwHDž wƅ0wHDžHw HDžpw HDžwHDžwƅwHDžwƅwƅwHDžxƅ"xHDž8x ƅHxƅJxHDž`xHDžxHDžxHDžxfyf9yfayfyfyfyf)zfzƅxƅxHDžy HDž(y HDžPyHDžxyHDžyHDžyHDžyƅzHDžzHDž@zƅPzƅRzHDžhzƅxzƅzzHDžzƅzƅzHDžzƅzƅzHDžzHDž{!f{fA{fi{f{f{f{f |f1|fY|f|f|f|f|f!}fI}fq}HDž0{HDžX{HDž{HDž{ HDž{HDž{HDž |HDžH|HDžp| HDž|HDž|HDž|HDž}HDž8}HDž`}HDž}ƅ}f}f}f9~fa~f~f~f~ff)fQfyfffHDž} HDž}HDž~ƅ~ƅ~HDž(~ HDžP~HDžx~ HDž~ HDž~ HDž~HDžHDž@HDžhHDž HDž HDž HDžƅfAfiffYfсfIHDž0HDžXHDžƅƅHDžƅHDžЀHDžƅƅ HDž ƅ0HDžHHDžpƅƅHDžƅHDžHDžƅƅHDžƅ HDž8 HDž`fqffff9fafffكffQfyffɄHDžƅƅHDžHDž؂HDžHDž( HDžPHDžx HDžHDžȃHDžHDž ƅ(HDž@HDžhHDžHDžHDžfffAfifff f1ffцff!HDž HDž0 HDžX HDžHDžHDžЅ ƅHDž HDž HDžHƅXƅZHDžpHDžƅƅHDžHDžHDžHDž8ƅHfffffaffوf)fQfyfHDž`ƅpHDž HDž HDž؇HDž HDž(ƅ8HDžPHDžxHDž ƅHDžȈHDžƅƅHDž HDž@ HDžhHDžHDžfɉfffAfiffff f1fYffff!HDž HDž"HDž0 HDžX HDžHDžHDžЊHDž HDž HDžHHDžpHDžHDžƅЋƅҋHDž HDž HDž8fIfqffffffٍfQHDž`HDž HDžHDž،HDž HDž( ƅ8ƅ:HDžPƅ`HDžx HDžƅHDžȍHDžƅHDž ƅ(ƅ*HDž@ HDžh ƅxƅzHDžƅfɎfAffff f1fYfffHDž HDž ƅƅHDžƅHDž0HDžX ƅhHDž HDžHDžЏHDžHDž HDžHHDžpHDžHDžƅАHDž HDžf!fIfqffffafffْff)fQfyHDž8 HDž` HDž'ƅHDž HDžؑHDž HDž(ƅ8HDžPHDžxHDžHDžȒHDžHDžHDž@HDžhHDžƅƅfɓfAfifff f1fYffѕHDžHDžƅHDžƅƅHDž0HDžX HDž HDžƅƅHDžДHDžHDž HDžHHDžpƅHDžHDžHDžƅƅf!fff9fafffٗff)HDžHDž8ƅHƅJHDž`1ƅpHDžHDž,ƅHDžؖHDž ƅHDž( HDžPHDžxHDžHDžȗHDž HDž HDž@ƅPHDžh fyffɘfffAfiffff fYfffњHDžHDžHDžHDž HDž0HDžXHDž HDž HDžЙHDžHDž ƅ0ƅ2HDžHHDžpHDžHDž HDžff!fff9faffff)HDžHDž8ƅHƅJHDž`$ƅpHDžHDžƅƅ›HDž؛HDžƅƅHDž(HDžPHDžx HDžHDžȜƅ؜HDž HDžHDž@fQfyffɝfffAfiffff f1fYffџHDžh HDž HDž HDžHDžHDž0HDžXHDžHDž HDžО HDž HDž HDžHHDžpHDž3ƅHDžHDžf!fqfffafffƅƅHDžHDž8#ƅHHDž`HDžƅƅHDžƅƅ HDžؠ HDžHDž(ƅ8ƅ:HDžP HDžx HDžHDžȡƅءHDžHDž f)fQfyfɢfffAff HDž@ HDžhHDžƅHDž HDž HDž HDž0 HDžXƅhƅjHDžƅƅHDž HDžУ ƅƅHDžHDž ƅ0ƅ2HDžHƅXƅZfffѤff!fIfffff9HDžp HDž HDžHDžHDžHDž8HDž`ƅpƅrHDžHDžHDžإHDž HDž(HDžP"ƅ`HDžxƅHDžƅHDžȦƅئff)fQfyffɧffAfiffff f1HDžHDžHDž@ HDžhHDžHDžHDžHDžƅƅHDž0 HDžXHDžHDž HDžШ HDž HDž HDžHfYfffѩff!fIfqfffff9faff٫HDžp HDžHDž HDž HDž HDž8 HDž`HDž HDžHDžتHDžHDž(HDžP HDžxHDžƅHDžȫ HDž f)ffɬfffAfff f1fYƅHDž HDž@/ƅPHDžh0ƅxHDžHDžHDžHDžHDž0 HDžX ƅhƅjHDžƅHDžHDžЭ HDžHDž HDžH HDžpffff!fIfqffff9fafHDžHDžƅЮƅҮHDžHDžHDž8HDž`HDžHDžHDžدHDž ƅƅHDž( HDžPHDžxzƅHDžHDžȰƅذffQfyfffAfifffHDžHDžƅ(ƅ*HDž@HDžh HDž HDž%ƅȱHDž%ƅHDžHDž0 HDžXHDžHDž HDžвHDžƅƅ HDž :ƅ0HDžHfYfffѳff!fIfqffHDžp HDžHDž HDžHDž HDž8HDž` HDžHDžHDžش ƅHDžƅHDž(ƅ8HDžP@ƅ`HDžxƅHDžƅHDžȵ:ƅصffiffYHDž:ƅHDž:ƅ(HDž@:ƅPHDžhFƅxHDžHDžƅɶHDžƅHDžƅHDž0ƅAHDžXHDžƅHDžƅHDžзHDžƅ HDž ƅ1HDžH HDžpfѸf9fafffٺƅHDžƅHDžHDžƅHDžƅ HDž8?ƅHHDž`tƅpHDžƅHDžƅHDžعƅHDžƅHDž( HDžPHDžxHDž HDžȺHDžff)fQfyffɻfffAfiffff1fYfHDžHDž@!HDžhHDžHDž HDž HDž HDž0HDžX"HDžHDžHDžмHDžƅ HDž HDžH HDžpHDžfIfqfff9faffٿƅƅHDžƅнHDžƅHDž ƅ"HDž8HDž`HDžHDžHDžؾƅHDžƅƅHDž( HDžPHDžxHDžƅƅHDžȿHDž ff)fQfyfffffAfifff f1fYHDžHDž@ HDžh HDžHDž%HDžHDž HDž0HDžX HDž HDž HDžƅHDžHDž HDžH HDžp fffff!fIfffff9fafffHDž HDž HDžHDžHDž8 HDž`ƅpHDž HDžHDžHDž HDž( HDžP HDžxHDž$ƅHDžHDžHDžf)fQfyfffffifff f1fYfHDž@HDžhHDžHDžHDžHDžHDž0ƅ@HDžXHDžƅƅHDžHDž HDž HDž HDžHHDžpHDž ffff!fIfqfffff9faffffHDžHDž HDžHDž8HDž`HDž HDžHDžHDž HDž( HDžP HDžx HDž HDžHDž HDžf)fQfyffffff1fYHDž@HDžh HDžHDž HDž HDž HDž0ƅ@ƅBHDžX*ƅhHDžƅHDž ƅHDžHDžƅƅ HDž HDžHHDžpƅHDžffff!fqfffff9faffHDžHDžHDžHDž8ƅHHDž`HDžHDž HDžHDžHDž( HDžPHDžxHDžCƅHDžHDžƅƅHDžPffff f1fYfƅ)HDž@>ƅQHDžhNƅyHDž@ƅHDžSƅHDžKƅHDžDƅHDž0AƅAHDžX@ƅiHDžHDž HDž HDž HDž HDžHHDžpHDž ffff!fIfqfffff9fafffff)HDžHDžHDžHDž8HDž`HDžHDž HDžHDž HDž(HDžPHDžxHDžHDžHDžHDžHDž@fQfyffffiffff f1fYfHDžhHDž HDž ƅƅHDžHDžHDž0ƅ@HDžX HDžHDžHDžHDžHDž HDžHHDžp HDžƅƅff!fIfqfffff9fafffHDž HDž ƅƅHDžHDž8HDž` HDž HDžHDžHDž HDž(HDžP HDžx HDž HDžHDžƅƅHDž f)fQfyfffAfiffff f1fYHDž@ HDžh HDž ƅƅHDž HDž HDžƅƅHDž0HDžX HDžHDž HDžHDž HDž HDžHHDžpffff!fIfqfffffHDž HDžHDžƅƅHDžHDž8HDž`HDž HDžƅƅHDžHDžHDž(ƅ8ƅ:HDžPƅ`HDžxHDž HDž ƅf)fQffffifff ƅHDžƅHDžHDž@HDžhƅxƅzHDžƅHDžHDžHDž HDž0ƅ@HDžX HDžHDžƅƅHDžHDžHDž f1fYfffff!fqfffff9faffHDžHHDžpHDžHDž HDžHDžHDž8ƅHHDž`HDžHDžHDžHDžHDž(HDžPHDžx HDžHDžfff)fQfyfffffAfiff1HDž HDžHDž@ HDžh HDž HDž HDž HDž HDž0HDžXHDžHDžƅHDž ƅHDž ƅHDž HDžHƅXfffffHDžp ƅHDž ƅHDžHDž HDžƅ HDž8ƅHHDž`ƅpHDžƅHDžHDž HDžHDž(ƅ8ƅ:HDžPƅ`HDžxƅHDžƅHDž ƅf)fQfyfffffAfifff f1fYHDžƅHDž HDž@HDžh HDžHDž HDžHDž HDž0 HDžXHDžHDžƅHDž HDžHDž HDžH HDžp fff!fIfqfffff9fafffHDžƅHDžƅHDž HDž HDž8HDž` HDžHDž HDžHDžHDž(HDžPHDžxHDž HDž HDžff)fQfyffffAffff1fYfHDžHDž@HDžh HDžHDž$ƅHDžHDžHDž0HDžXƅhHDžHDžHDž HDž1ƅHDž HDžHHDžp HDžffffIfqf9HDž HDžHDžƅ ƅ"HDž8HDž`HDžƅƅHDžƅƅHDžƅƅHDžƅƅHDž(HDžPƅ`ƅbHDžxƅHDžff)fffAfiffHDžƅƅHDžƅHDžHDž@ƅPƅRHDžhƅxHDžHDžƅƅHDžƅHDžHDž0HDžXHDžHDžƅƅHDžHDžf1fYffff!fIffffƅƅ HDž HDžHHDžp&ƅHDžHDžHDžHDžHDž8HDž`ƅpƅrHDžHDžHDž HDž HDž( ƅ8ƅ:HDžP fafffQfyffffifHDžxHDžHDžƅƅHDžƅHDžƅ(HDž@HDžhHDž HDžHDžƅƅHDž HDž0 ƅ@ƅBHDžX HDžHDžfff f1fYffff!fqffHDžHDž HDž HDžHHDžpHDžHDžƅƅHDž HDž HDž8 ƅHƅJHDž`HDžHDžƅƅHDžHDž ffafffff)fQfyfffHDž(ƅ8ƅ:HDžPHDžxHDžHDž HDžHDž HDž@ HDžh HDžHDž HDžƅHDžHDž0ƅ@ƅBHDžX fiffff f1fYffffIHC2fHnHHDž flE)-fHnHflE8-fHnH,-flfHnHDž H13-HDž HDž HDž HDžHHDžpHDžHDžHDžƅƅHDž?ƅ HDž8HDž`ƅpHE)`-fHnHflfHnHT2--fHnHflfHnHB-)-fHnHflfHnHy-fHnH -HflfHnH-).fHnHflfHnH-(.fHnHflfHnH@T,)P.fHnHflfHnH p)x.fHnHflfHnHm)).fHnHflfHnH P,.fHnHflfHnH)).fHnHflfHnHM,/fHnHflfHnHG,)@/fHnHflfHnHA,h/fHnHflfHnH,)/fHnHflfHnH@)/fHnHflfHnH?,)/fHnHflfHnH>,0fHnHflfHnH`:,)00fHnHflfHnH6,X0fHnHflfHnH-)0fHnHflfHnHw-0fHnHflfHnH7-)0fHnHflfHnHq-0fHnHflfHnH6-) 1fHnHflfHnH~-H1fHnHflfHnHq-)p1fHnHflfHnHp-1fHnHflfHnHN-)1fHnHflfHnHN-1fHnHflfHnH0-)2fHnHflfHnH=-82fHnHflfHnH-)`2fHnHflfHnH -2fHnHflfHnH`-)2fHnHflfHnHv-2fHnHflfHnH-)3fHnHflfHnH-(3fHnHflfHnH%-)P3fHnHflfHnH-x3fHnHflfHnHpD-)3fHnHflfHnHh-3fHnHflfHnHP|-)3fHnHflfHnH0h-4fHnHflfHnH|-)@4fHnHflfHnH{-h4fHnHflfHnH ))4fHnHflfHnH0L-4fHnHflfHnHPU-)4fHnHflfHnH -5fHnHflfHnH-)05fHnHflfHnH{-X5fHnHflfHnHs-)5fHnHflfHnHpm-5fHnHflfHnH0-)5fHnHflfHnH-5fHnHflfHnH-) 6fHnHflfHnH0-H6fHnHflfHnHl-)p6fHnHflfHnH f-6fHnHflfHnHe-)6fHnHflfHnHy-6fHnHflfHnHy-)7fHnHflfHnHl-87fHnHflfHnHI-)`7fHnHflfHnH -7fHnHflfHnHk-)7fHnHflfHnH@)7fHnHflfHnHPk-)8fHnHflfHnH0-(8fHnHflfHnHI-)P8fHnHflfHnHj-x8fHnHflfHnH0x-)8fHnHflfHnH@d-8fHnHflfHnHw-)8fHnHflfHnHp-9fHnHflfHnH?-)@9fHnHflfHnHP[-h9fHnHflfHnH-)9fHnHflfHnH -9fHnHflfHnHi-)9fHnHflfHnHi-:fHnHflfHnHPi-)0:fHnHflfHnHנ-X:fHnHflfHnH-):fHnHflfHnH˟-:fHnHflfHnHo-):fHnHflfHnHPP-:fHnHflfHnH`)) ;fHnHflfHnH)H;fHnHflfHnHp-)p;fHnHflfHnHn-;fHnHflfHnH--);fHnHflfHnH`-;fHnHflfHnH ))fHnHflfHnH-)@>fHnHflfHnH0|-h>fHnHflfHnHC-)>fHnHflfHnH)>fHnHflfHnH ))>fHnHflfHnH-?fHnHflfHnHr-)0?fHnHflfHnH0,X?fHnHflfHnH,)?fHnHflfHnHB-?fHnHflfHnHPk-)?fHnHflfHnHP-?fHnHflfHnH ),) @fHnHflfHnHq-H@fHnHflfHnHK-)p@fHnHflfHnH d-@fHnHflfHnH9-)@fHnHflfHnH@)@fHnHflfHnH))AfHnHflfHnHP-8AfHnHflfHnH0-)`AfHnHflfHnH@)AfHnHflfHnH',)AfHnHflfHnH@-AfHnflfHnHA)BfHnH fl(BfHnHAH fAl)PBfHnHG&,fAlfHnHk-xBfHnHflfHnH}-)BfHnHflfHnH)BfHnHflfHnH&@-)BfHnHflfHnH-CfHnHflfHnHf -)@CfHnHflfHnH6%,hCfHnHflfHnH$,)CfHnHflfHnH-CfHnHflfHnHF-)CfHnHflfHnH6,DfHnHflfHnH,)0DfHnHflfHnHn-XDfHnHflfHnH&-)DfHnHflfHnH& -DfHnHflfHnH6))DfHnHflfHnH6#,DfHnHflfHnH)) EfHnHflfHnH",HEfHnHflfHnH[))pEfHnHflfHnH)EfHnHflfHnH))EfHnHflfHnHV)EfHnHflfHnH))FfHnHflfHnHv)8FfHnHflfHnH))`FfHnHflfHnH)FfHnHflfHnH!,)FfHnHflfHnH6)FfHnHflfHnHv,)GfHnHflfHnH,(GfHnHflfHnHV,)PGfHnHflfHnHv)xGfHnHflfHnHt-)GfHnHflfHnH ,GfHnHflfHnHv,)GfHnHflfHnH -HfHnHflfHnHVk-)@HfHnHflfHnH&+-hHfHnHflfHnHV,)HfHnHflfHnH&3-HfHnHflfHnHF#-)HfHnHflfHnH#-IfHnHflfHnH))0IfHnHflfHnH-XIfHnHflfHnHD-)IfHnHflfHnHf-IfHnHflfHnH:-)IfHnHflfHnH,IfHnHflfHnH)) JfHnHflfHnHv,HJfHnHflfHnHV))pJfHnHflfHnH6,JfHnHflfHnH,)JfHnHflfHnH)JfHnHflfHnH-)KfHnHflfHnH6)8KfHnHflfHnH0-)`KfHnHflfHnHF,KfHnHflfHnH-)KfHnHflfHnH,KfHnHflfHnHF0-)LfHnHflfHnH,(LfHnHflfHnH'-)PLfHnHflfHnH,xLfHnHflfHnH/-)LfHnHflfHnH)LfHnHflfHnHV))LfHnHflfHnH)MfHnHflfHnH7-)@MfHnHflfHnH,hMfHnHflfHnH.-)MfHnHflfHnHF,MfHnHflfHnH&-)MfHnHflfHnH,NfHnHflfHnH-)0NfHnHflfHnHv,XNfHnHflfHnH-)NfHnHflfHnH)NfHnHflfHnH--)NfHnHflfHnHv,NfHnHflfHnH)) OfHnHflfHnHv,HOfHnHflfHnH,)pOfHnHflfHnH5-OfHnHflfHnH,)OfHnHflfHnHF?-OfHnHflfHnH?-)PfHnHflfHnH6,8PfHnHflfHnH-)`PfHnHflfHnHv,PfHnHflfHnH-)PfHnHflfHnHv)PfHnHflfHnHf-)QfHnHflfHnHv,(QfHnHflfHnH))PQfHnHflfHnH,xQfHnHflfHnHQ))QfHnHflfHnH&+-QfHnHflfHnH,)QfHnHflfHnHf-RfHnHflfHnH&-)@RfHnHflfHnH)hRfHnHflfHnH-)RfHnHflfHnH-RfHnHflfHnH-)RfHnHflfHnH -SfHnHflfHnHFU-)0SfHnHflfHnHfF-XSfHnHflfHnHV+)SfHnHflfHnH&b-SfHnHflfHnH&2-)SfHnHflfHnH+SfHnHflfHnH+) TfHnHflfHnH)HTfHnHflfHnH+)pTfHnHflfHnHf1-TfHnHflfHnH,)TfHnHflfHnH-TfHnHflfHnH))UfHnHflfHnH)8UfHnHflfHnH-)`UfHnHflfHnH+UfHnHflfHnH))UfHnHflfHnH)UfHnHflfHnH+)VfHnHflfHnHv+(VfHnHflfHnH+)PVfHnHflfHnH+xVfHnHflfHnHvG))VfHnHflfHnHA)VfHnHflfHnH+)VfHnHflfHnH+WfHnHflfHnHV+)@WfHnHflfHnH+hWfHnHflfHnHv+)WfHnHflfHnH+WfHnHflfHnHv,)WfHnHflfHnHv,XfHnHflfHnHv+)0XfHnHflfHnH+XXfHnHflfHnH+)XfHnHflfHnH֩+XfHnHflfHnH+)XfHnHflfHnH9)XfHnHflfHnH6+) YfHnHflfHnH+HYfHnHflfHnHV,)pYfHnHflfHnH6+YfHnHflfHnHv+)YfHnHflfHnH6,YfHnHflfHnHV,)ZfHnHflfHnHv)8ZfHnHflfHnH+)`ZfHnHflfHnH5)ZfHnHflfHnH+)ZfHnHflfHnH6+ZfHnHflfHnH+)[fHnHflfHnH6}+([fHnHflfHnHv,)P[fHnHflfHnH{+x[fHnHflfHnH6{+)[fHnHflfHnHVN-[fHnHflfHnHw+)[fHnHflfHnHu+\fHnHflfHnHq+)@\fHnHflfHnHo+h\fHnHflfHnHk+)\fHnHflfHnHf+\fHnHflfHnHd+)\fHnHflfHnH2)]fHnHflfHnHvb+)0]fHnHflfHnH`+X]fHnHflfHnHV[+)]fHnHflfHnHJ+]fHnHflfHnHE+)]fHnHflfHnH66+]fHnHflfHnHV3+) ^fHnHflfHnHV2+H^fHnHflfHnH,)p^fHnHflfHnH,+^fHnHflfHnH%+)^fHnHflfHnH +^fHnHflfHnHV+)_fHnHflfHnHV,8_fHnHflfHnH,)`_fHnHflfHnH։,_fHnHflfHnH,)_fHnHflfHnH6,_fHnHflfHnH+)`fHnHflfHnHփ,(`fHnHflfHnH6+)P`fHnHflfHnHy,x`fHnHflfHnH+)`fHnHflfHnH+`fHnHflfHnHV+)`fHnHflfHnH+afHnHflfHnHw,)@afHnHflfHnH6+hafHnHflfHnHv +)afHnHflfHnH +afHnHflfHnHV-))afHnHflfHnH+)bfHnHflfHnH+)0bfHnHflfHnHV+XbfHnHflfHnHv+)bfHnHflfHnH*bfHnHflfHnHV*)bfHnHflfHnH*bfHnHflfHnH6*) cfHnHflfHnH*HcfHnHflfHnH*)pcfHnHflfHnH*cfHnHflfHnH*)cfHnHflfHnHV*cfHnHflfHnH*)dfHnHflfHnHF-8dfHnHflfHnHl-)`dfHnHflfHnHr-dfHnHflfHnHF,)dfHnHflfHnHF.-dfHnHflfHnH -)efHnHflfHnHf,(efHnHflfHnH*)PefHnHflfHnH*xefHnHflfHnH))efHnHflfHnH6@-efHnHflfHnH,)efHnHflfHnH,ffHnHflfHnHE-)@ffHnHflfHnH6)hffHnHflfHnH֢))ffHnHflfHnH,ffHnHflfHnHV))ffHnHflfHnH[-gfHnHflfHnHvR-)0gfHnHflfHnHF"-XgfHnHflfHnH6[-)gfHnHflfHnHR-gfHnHflfHnH!-)gfHnHflfHnHf>-gfHnHflfHnHVr-) hfHnHflfHnH>-HhfHnHflfHnHf5-)phfHnHflfHnHF-hfHnHflfHnHC-)hfHnHflfHnH&,hfHnHflfHnHVq,)ifHnHflfHnH6)8ifHnHflfHnH&=-)`ifHnHflfHnHf4-ifHnHflfHnHF-)ifHnHflfHnHB-ifHnHflfHnH&,)jfHnHflfHnH6p,(jfHnHflfHnH))PjfHnHflfHnHv*xjfHnHflfHnH.B-)jfHnHflfHnHt-jfHnHflfHnH;-)jfHnHflfHnH6)kfHnHflfHnH֝))@kfHnHflfHnHV,hkfHnHflfHnH&,)kfHnHflfHnH,kfHnHflfHnHA-)kfHnHflfHnHvn,lfHnHflfHnH֜))0lfHnHflfHnH2-XlfHnHflfHnH,)lfHnHflfHnH,lfHnHflfHnH,)lfHnHflfHnH.@-lfHnHflfHnHf,) mfHnHflfHnH*HmfHnHflfHnH))pmfHnHflfHnHv*mfHnHflfHnHV*)mfHnHflfHnHv*mfHnHflfHnHU-)nfHnHflfHnH)8nfHnHflfHnH))`nfHnHflfHnH*nfHnHflfHnHv))nfHnHflfHnH֘)nfHnHflfHnH6))ofHnHflfHnH)(ofHnHflfHnH))PofHnHflfHnHV)xofHnHflfHnH֕))ofHnHflfHnH(ofHnHflfHnH6()ofHnHflfHnH(pfHnHflfHnH6()@pfHnHflfHnH(hpfHnHflfHnH6()pfHnHflfHnH(pfHnHflfHnHS-)pfHnHflfHnHvJ-qfHnHflfHnH&-)0qfHnHflfHnH*XqfHnHflfHnH))qfHnHflfHnHvC-qfHnHflfHnH><-)qfHnHflfHnH -qfHnHflfHnHI-) rfHnHflfHnH)HrfHnHflfHnHf-)prfHnHflfHnHp-rfHnHflfHnH-)rfHnHflfHnH~f-rfHnHflfHnHNf-)sfHnHflfHnH)8sfHnHflfHnH6()`sfHnHflfHnH(sfHnHflfHnHv()sfHnHflfHnHV)sfHnHflfHnH-)tfHnHflfHnHF-(tfHnHflfHnH))PtfHnHflfHnHv(xtfHnHflfHnH,)tfHnHflfHnH,tfHnHflfHnH*)tfHnHflfHnH)ufHnHflfHnH6*)@ufHnHflfHnHv)hufHnHflfHnH,)ufHnHflfHnHf*-ufHnHflfHnH֎))ufHnHflfHnHV*vfHnHflfHnH))0vfHnHflfHnHv,XvfHnHflfHnH))vfHnHflfHnH)vfHnHflfHnHV))vfHnHflfHnH)vfHnHflfHnH)) wfHnHflfHnH&,HwfHnHflfHnH*)pwfHnHflfHnHU-wfHnHflfHnHn7-)wfHnHflfHnH,wfHnHflfHnH7-)xfHnHflfHnHf,8xfHnHflfHnH*)`xfHnHflfHnH*xfHnHflfHnHd,)xfHnHflfHnH0-xfHnHflfHnH,)yfHnHflfHnH,(yfHnHflfHnHF=-)PyfHnHflfHnH*xyfHnHflfHnHV))yfHnHflfHnH)yfHnHflfHnH))yfHnHflfHnH,zfHnHflfHnH6,)@zfHnHflfHnHi-hzfHnHflfHnHi-)zfHnHflfHnH&,zfHnHflfHnH4-)zfHnHflfHnH-{fHnHflfHnH-)0{fHnHflfHnH,X{fHnHflfHnHa,){fHnHflfHnHA-{fHnHflfHnH&,){fHnHflfHnHV,{fHnHflfHnH:-) |fHnHflfHnH,H|fHnHflfHnHV))p|fHnHflfHnH,|fHnHflfHnHֆ))|fHnHflfHnHf,|fHnHflfHnHv*)}fHnHflfHnH-8}fHnHflfHnH,)`}fHnHflfHnH,-}fHnHflfHnH,)}fHnHflfHnHF-}fHnHflfHnHv,)~fHnHflfHnHF,-(~fHnHflfHnHf,)P~fHnHflfHnH*x~fHnHflfHnH*)~fHnHflfHnHք)~fHnHflfHnH,)~fHnHflfHnHV)fHnHflfHnH()@fHnHflfHnHփ)hfHnHflfHnH*)fHnHflfHnH,fHnHflfHnHV*)fHnHflfHnH)fHnHflfHnH,)0fHnHflfHnH)XfHnHflfHnHV()fHnHflfHnH6,fHnHflfHnHF,)ЀfHnHflfHnH,fHnHflfHnH-) fHnHflfHnH,HfHnHflfHnHf,)pfHnHflfHnHV)fHnHflfHnH))fHnHflfHnH)fHnHflfHnH()fHnHflfHnHv(8fHnHflfHnH -)`fHnHflfHnH[,fHnHflfHnH))fHnHflfHnH*؂fHnHflfHnH-)fHnHflfHnH-(fHnHflfHnH,)PfHnHflfHnH~)xfHnHflfHnH,)fHnHflfHnHV~)ȃfHnHflfHnHV*)fHnHflfHnH6W,fHnHflfHnHQ,)@fHnHflfHnHM,hfHnHflfHnHfK-)fHnHflfHnHC-fHnHflfHnHf-)fHnHflfHnH -fHnHflfHnH3-)0fHnHflfHnH3c-XfHnHflfHnHd-)fHnHflfHnH,-fHnHflfHnH&-)ЅfHnHflfHnH,fHnHflfHnHGd-) fHnHflfHnH^-HfHnHflfHnHZ-)pfHnHflfHnH{)fHnHflfHnH6z))fHnHflfHnHv*fHnHflfHnH,)fHnHflfHnHA-8fHnHflfHnHVy))`fHnHflfHnH(fHnHflfHnH*)fHnHflfHnH,؇fHnHflfHnH^-)fHnHflfHnHvH-(fHnHflfHnHVx))PfHnHflfHnH,xfHnHflfHnHH-)fHnHflfHnHF@-ȈfHnHflfHnH-)fHnHflfHnHG-fHnHflfHnH?-)@fHnHflfHnH-hfHnHflfHnH&G-)fHnHflfHnHf?-fHnHflfHnH-)fHnHflfHnHVN-fHnHflfHnHF-)0fHnHflfHnH-XfHnHflfHnHV-)fHnHflfHnHV-fHnHflfHnH5-)ЊfHnHflfHnH.-fHnHflfHnHF,) fHnHflfHnH-HfHnHflfHnH,)pfHnHflfHnH-fHnHflfHnH,)fHnHflfHnHƾ,fHnHflfHnH,)fHnHflfHnH*8fHnHflfHnHt))`fHnHflfHnH,fHnHflfHnH^&-)fHnHflfHnHF,،fHnHflfHnHs))fHnHflfHnH& -(fHnHflfHnH*)PfHnHflfHnHVs)xfHnHflfHnH*)fHnHflfHnHv*ȍfHnHflfHnHr))fHnHflfHnH*fHnflfHnHA)@fHnH flhfHnHAH fAl)fHnHEP-fAlfHnH<-fHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnH<,XfHnHflfHnH|,)fHnHflfHnH,fHnHflfHnH,)ЏfHnHflfHnH,fHnHflfHnH,) fHnHflfHnHo[-HfHnHflfHnHJ-fHnHflfHnHo*)fHnHflfHnH n*fHnHflfHnHW))fHnHflfHnHV)8fHnHflfHnH0-)`fHnHflfHnH\,fHnHflfHnH,)fHnHflfHnHL,إfHnHflfHnH,)fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnHL,xfHnHflfHnH ,)fHnHflfHnH-ȦfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH|-)@fHnHflfHnH,hfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH -)fHnHflfHnH-fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnH\,)fHnHflfHnH,fHnHflfHnH,)ШfHnHflfHnH,fHnHflfHnH,) fHnHflfHnHl -HfHnHflfHnH<,)pfHnHflfHnH -fHnHflfHnH-)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH\,8fHnHflfHnH,)`fHnHflfHnH\,fHnHflfHnH,)fHnHflfHnH,تfHnflfHnHA)fHnH fl(fHnHAH fAl)PfHnH -fAlfHnH2,xfHnHflfHnH,)fHnHflfHnH2,ȫfHnHflfHnH"3-)fHnHflfHnH#-fHnHflfHnHB+-)@fHnHflfHnH,hfHnHflfHnHb,,)fHnHflfHnHe*fHnHflfHnHd*)fHnHflfHnHb*fHnHflfHnH*,)0fHnHflfHnH"`*XfHnHflfHnH]*)fHnHflfHnH\*fHnHflfHnH-)ЭfHnHflfHnHl>-fHnHflfHnH,) fHnHflfHnH,HfHnHflfHnH-)pfHnHflfHnH,fHnHflfHnHN))fHnHflfHnH,fHnHflfHnHX*)fHnHflfHnH®,8fHnHflfHnH,)`fHnHflfHnH!-fHnHflfHnH,)fHnHflfHnHR-دfHnHflfHnH,)fHnHflfHnHW*(fHnHflfHnHҹ,)PfHnHflfHnHW*xfHnHflfHnHr,)fHnHflfHnH,ȰfHnHflfHnH,)fHnHflfHnHҠ,fHnHflfHnHB,)@fHnHflfHnH,hfHnHflfHnHU*)fHnHflfHnH2,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnHr,XfHnHflfHnHr,)fHnHflfHnH«,fHnHflfHnH,)вfHnHflfHnH,fHnHflfHnHbT*) fHnHflfHnHT*HfHnHflfHnHB,)pfHnHflfHnHS*fHnHflfHnHBJ))fHnHflfHnHE*fHnHflfHnHB;*)fHnHflfHnH.*8fHnHflfHnH"-)`fHnHflfHnH2-fHnHflfHnH-)fHnHflfHnH,شfHnHflfHnHI))fHnHflfHnH+*(fHnHflfHnH,)PfHnHflfHnHB+*xfHnHflfHnHBH))fHnHflfHnHr,ȵfHnHflfHnH",)fHnHflfHnHG)fHnHflfHnHBG))@fHnHflfHnH,hfHnHflfHnH,)fHnHflfHnH"-fHnHflfHnHB,)fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnH2,)fHnHflfHnH-fHnHflfHnHr,)зfHnHflfHnH,fHnHflfHnH,) fHnHflfHnHš,HfHnHflfHnH!,)pfHnHflfHnH-fHnHflfHnHr,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH;-8fHnHflfHnH7-)`fHnHflfHnH -fHnHflfHnH,)fHnHflfHnHҟ,عfHnflfHnHA)fHnH fl(fHnHAH fAl)PfHnHI-fAlfHnH --xfHnHflfHnH-)fHnHflfHnHh,ȺfHnHflfHnH,)fHnHflfHnHx,fHnHflfHnH,)@fHnHflfHnHX&*hfHnHflfHnHh,)fHnHflfHnH%*fHnHflfHnH()fHnHflfHnHX,fHnHflfHnH(,)0fHnHflfHnH,XfHnHflfHnH$*)fHnHflfHnHh,fHnHflfHnH,)мfHnHflfHnHA)fHnHflfHnH(,) fHnHflfHnH(,HfHnHflfHnHA))pfHnHflfHnH@)fHnHflfHnH,)fHnHflfHnH8@)fHnHflfHnHع()fHnHflfHnHȻ,8fHnHflfHnHȳ,)`fHnHflfHnH,fHnHflfHnHȏ,)fHnHflfHnH8?)ؾfHnHflfHnH,)fHnHflfHnH>)(fHnHflfHnH()PfHnHflfHnHȲ,xfHnHflfHnH8"*)fHnHflfHnH!*ȿfHnHflfHnH,)fHnHflfHnH(,fHnHflfHnH8!*)@fHnHflfHnH *hfHnHflfHnH,)fHnH flfHnHAfHnfl)fHnHAH fAlfHnH,fAlfHnH,)0fHnHflfHnHn,XfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH^,fHnHflfHnHn*) fHnHflfHnH,HfHnHflfHnH;))pfHnHflfHnH;)fHnHflfHnH^,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH:)8fHnHflfHnH,)`fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHN,fHnHflfHnH>,)fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnH9)xfHnHflfHnH>,)fHnHflfHnH9)fHnHflfHnH~,)fHnHflfHnH8)fHnHflfHnH.8))@fHnHflfHnH(hfHnHflfHnHn*)fHnHflfHnH,fHnHflfHnHn7))fHnHflfHnHޡ,fHnHflfHnH6))0fHnHflfHnH,XfHnHflfHnHn*)fHnHflfHnHN6)fHnHflfHnH.()fHnHflfHnH*fHnHflfHnH,) fHnHflfHnHN,HfHnHflfHnH,)pfHnHflfHnH*fHnHflfHnH޳,)fHnHflfHnH5)fHnHflfHnH,)fHnHflfHnHޓ,8fHnHflfHnH,)`fHnHflfHnH,fHnHflfHnH*)fHnHflfHnH,fHnHflfHnH3))fHnHflfHnHn*(fHnHflfHnHn*)PfHnHflfHnH *xfHnHflfHnH *)fHnHflfHnH,fHnHflfHnH*)fHnHflfHnH*fHnHflfHnH))@fHnHflfHnHN)hfHnHflfHnH,)fHnHflfHnH^,fHnHflfHnH))fHnHflfHnH)fHnHflfHnHn ,)0fHnHflfHnH)XfHnHflfHnH()fHnHflfHnH(fHnHflfHnHn))fHnHflfHnH)fHnHflfHnH)) fHnHflfHnH(HfHnHflfHnH))pfHnHflfHnH)fHnHflfHnH))fHnHflfHnH(fHnHflfHnH))fHnHflfHnH)8fHnHflfHnH()`fHnHflfHnH)fHnHflfHnH))fHnHflfHnH)fHnHflfHnH.))fHnHflfHnH.,(fHnHflfHnH)))PfHnHflfHnH#)xfHnHflfHnHn))fHnHflfHnHN)fHnHflfHnH))fHnHflfHnH)fHnHflfHnH+)@fHnHflfHnHN)hfHnHflfHnH.))fHnHflfHnH,fHnHflfHnH))fHnHflfHnH)fHnHflfHnHn()0fHnHflfHnH~+XfHnHflfHnH.))fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnHΒ,) fHnHflfHnH)HfHnHflfHnH.))pfHnHflfHnH)fHnHflfHnHΨ()fHnHflfHnH,fHnflfHnHA)fHnH fl8fHnHAH fAl)`fHnH,fAlfHnHg!-fHnHflfHnHD-)fHnHflfHnH-fHnHflfHnH-)fHnHflfHnH -(fHnHflfHnH -)PfHnHflfHnHT,xfHnHflfHnHT,)fHnHflfHnH4,fHnHflfHnH-)fHnHflfHnHt,fHnHflfHnHd,)@fHnHflfHnH,hfHnHflfHnH,)fHnHflfHnHT,fHnHflfHnHr-)fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnHt,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHT,fHnHflfHnH|,) fHnHflfHnH,HfHnHflfHnH))pfHnHflfHnHL,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH,8fHnHflfHnH,)`fHnHflfHnH$,fHnHflfHnHԶ,)fHnHflfHnH,fHnHflfHnH$))fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnHD,xfHnHflfHnHd))fHnHflfHnHԥ,fHnHflfHnH))fHnHflfHnH,,fHnHflfHnHT,)@fHnHflfHnHD)hfHnHflfHnH))fHnHflfHnH+fHnHflfHnH))fHnHflfHnH)fHnHflfHnH$))0fHnHflfHnH)XfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH )fHnHflfHnH,) fHnHflfHnH,HfHnHflfHnH-)pfHnHflfHnH,fHnHflfHnH,)fHnHflfHnH+fHnHflfHnH,)fHnHflfHnHd+8fHnHflfHnHd))`fHnHflfHnH$)fHnHflfHnH$()fHnHflfHnH)fHnHflfHnHĴ))fHnHflfHnHİ)(fHnHflfHnH))PfHnHflfHnH)xfHnHflfHnH$+)fHnHflfHnHD)fHnHflfHnHD+)fHnHflfHnH)fHnHflfHnH-)@fHnHflfHnH)hfHnHflfHnH))fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnH))0fHnHflfHnH$)XfHnHflfHnH))fHnHflfHnHd,fHnHflfHnHĘ))fHnHflfHnH,fHnHflfHnHD)) fHnHflfHnH$)HfHnHflfHnH))pfHnHflfHnH,fHnHflfHnH4,)fHnHflfHnH$)fHnHflfHnH,)fHnHflfHnH)8fHnHflfHnH,)`fHnHflfHnHt,fHnHflfHnH,)fHnHflfHnH)fHnHflfHnHT,)fHnHflfHnHԊ,(fHnHflfHnH,)PfHnHflfHnH,xfHnHflfHnH))fHnHflfHnH)fHnHflfHnH))fHnHflfHnH4,fHnHflfHnH,)@fHnHflfHnH)hfHnHflfHnH,)fHnHflfHnHd,fHnHflfHnHt},)fHnHflfHnHT,fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnH,)fHnHflfHnH$,fHnHflfHnH()fHnHflfHnH)fHnHflfHnH() fHnHflfHnH,HfHnHflfHnH4,)pfHnHflfHnHd(fHnHflfHnH,)fHnHflfHnH{,fHnHflfHnH4,)fHnHflfHnHT,8fHnHflfHnHԌ,)`fHnHflfHnH,fHnHflfHnHD()fHnHflfHnH$)fHnHflfHnH()fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnH$(xfHnHflfHnH,)fHnHflfHnH,fHnHflfHnHy,)fHnHflfHnH4,fHnHflfHnH,)@fHnHflfHnHt,hfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHL,fHnHflfHnHD-)0fHnHflfHnH,XfHnHflfHnHx,)fHnHflfHnH)fHnHflfHnH$,)fHnHflfHnHT,fHnHflfHnH\,) fHnHflfHnHDl,HfHnHflfHnHč))pfHnHflfHnHT,fHnHflfHnHtw,)fHnHflfHnH,fHnHflfHnH()fHnHflfHnHT,8fHnHflfHnH4,)`fHnHflfHnHt,fHnHflfHnH,)fHnHflfHnH$(fHnHflfHnH()fHnHflfHnHt,(fHnHflfHnH$))PfHnHflfHnH,xfHnHflfHnH()fHnHflfHnH4,fHnHflfHnH,)fHnHflfHnHo,fHnHflfHnH,)@fHnHflfHnHt,hfHnHflfHnHԌ,)fHnHflfHnHT,fHnHflfHnH$,)fHnHflfHnHԪ,fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnHt,)fHnHflfHnH$,fHnHflfHnHT,)fHnHflfHnH,fHnHflfHnH,) fHnHflfHnH,HfHnHflfHnH()pfHnHflfHnHĐ(fHnHflfHnHDy,)fHnHflfHnH)fHnHflfHnH()fHnHflfHnH,8fHnHflfHnH-)`fHnHflfHnH -fHnHflfHnH,)fHnHflfHnHT,fHnHflfHnH,)fHnHflfHnH((fHnHflfHnHT,)PfHnHflfHnH,xfHnHflfHnH,)fHnHflfHnH,,fHnHflfHnHԘ,)fHnHflfHnH,fHnHflfHnHT,)@fHnHflfHnHw,hfHnHflfHnH,)fHnHflfHnH,fHnHflfHnHT,)fHnHflfHnH$+fHnHflfHnHd,)0fHnHflfHnH,XfHnHflfHnHt,)fHnHflfHnHT,fHnHflfHnH,)fHnHflfHnH\,fHnHflfHnHt,) fHnHflfHnHԸ,HfHnHflfHnHo,)pfHnHflfHnHt,fHnHflfHnHdi,)fHnHflfHnH4,fHnHflfHnHn,)fHnHflfHnHt,8fHnHflfHnH()`fHnHflfHnHtn,fHnHflfHnH()fHnHflfHnH,fHnHflfHnHT,)fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnHy,xfHnHflfHnH4,)fHnHflfHnH+fHnHflfHnHD()fHnHflfHnH(fHnHflfHnH,)@fHnHflfHnHa,hfHnHflfHnHT,)fHnHflfHnH+fHnHflfHnHa,)fHnHflfHnH(fHnHflfHnHD+)0fHnHflfHnH,XfHnHflfHnH4,)fHnHflfHnH,fHnHflfHnHq,)fHnHflfHnH,fHnHflfHnH,) fHnHflfHnHt,HfHnHflfHnH$q,)pfHnHflfHnH,fHnHflfHnHv,)fHnHflfHnH4,fHnHflfHnH{,)fHnHflfHnH(8fHnHflfHnHd,)`fHnHflfHnHD,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHu,(fHnHflfHnHt,)PfHnHflfHnH^,xfHnHflfHnH,)fHnHflfHnH,fHnHflfHnHt,)fHnHflfHnH)fHnHflfHnH,)@fHnHflfHnHy,hfHnHflfHnHd~))fHnHflfHnH~)fHnHflfHnH,)fHnHflfHnH$+fHnHflfHnHt,)0fHnHflfHnHh,XfHnHflfHnH,)fHnHflfHnHT,fHnHflfHnH,)fHnHflfHnH[,fHnHflfHnH4,) fHnHflfHnHd[,HfHnHflfHnHԜ,)pfHnHflfHnHZ,fHnHflfHnH,)fHnHflfHnH|)fHnHflfHnHT,)fHnHflfHnHTr,8fHnHflfHnH,)`fHnHflfHnHt,fHnHflfHnHT,)fHnHflfHnHe,fHnHflfHnH,)fHnHflfHnHe,(fHnHflfHnH,)PfHnHflfHnHdz)xfHnHflfHnH()fHnHflfHnH(fHnHflfHnH$,)fHnHflfHnH,fHnHflfHnHu,)@fHnHflfHnH(hfHnHflfHnHD,)fHnHflfHnHtu,fHnHflfHnHD()fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnHԬ,XfHnHflfHnHo,)fHnHflfHnHd(fHnHflfHnH()fHnHflfHnH],fHnHflfHnH$W,) fHnHflfHnH],HfHnHflfHnHw))pfHnHflfHnH,fHnHflfHnHTz,)fHnHflfHnHD,fHnHflfHnHD+)fHnHflfHnHs)8fHnHflfHnH,)`fHnHflfHnHD(fHnHflfHnH()fHnHflfHnHD(fHnHflfHnH$r))fHnHflfHnH$q)(fHnHflfHnH$p))PfHnHflfHnH$o)xfHnHflfHnHĖ()fHnHflfHnHn)fHnHflfHnH$m))fHnHflfHnH$l)fHnHflfHnH$k))@fHnHflfHnHD(hfHnHflfHnH$j))fHnHflfHnHD(fHnHflfHnHd()fHnHflfHnHh)fHnHflfHnH+)0fHnHflfHnH$(XfHnHflfHnHdg))fHnHflfHnH(fHnHflfHnH$+)fHnHflfHnH$f)fHnHflfHnH$e)) fHnHflfHnH$d)HfHnHflfHnH()pfHnHflfHnH+fHnHflfHnHb))fHnHflfHnH+fHnHflfHnHD+) fHnHflfHnH+8 fHnHflfHnH()` fHnHflfHnH{( fHnHflfHnHa)) fHnHflfHnH_) fHnHflfHnH() fHnHflfHnHd(( fHnHflfHnH()P fHnHflfHnH(x fHnHflfHnHd])) fHnHflfHnH[) fHnHflfHnHdY)) fHnHflfHnHX) fHnHflfHnH$W))@ fHnHflfHnHU)h fHnHflfHnHS)) fHnHflfHnHd( fHnHflfHnH() fHnHflfHnH+ fHnHflfHnHDR))0 fHnHflfHnH+X fHnHflfHnH$Q)) fHnHflfHnH+ fHnHflfHnHD() fHnHflfHnH}, fHnHflfHnH,) fHnHflfHnH,H fHnHflfHnHT,)p fHnHflfHnH, fHnHflfHnHD() fHnHflfHnH, fHnHflfHnH,)fHnHflfHnH,8fHnHflfHnHd,)`fHnHflfHnHd(fHnHflfHnH()fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH,(fHnHflfHnH,)PfHnHflfHnH,xfHnHflfHnH ,)fHnHflfHnHx,fHnHflfHnH()fHnHflfHnHN)fHnHflfHnH+)@fHnHflfHnH(hfHnHflfHnHD()fHnHflfHnHt,fHnHflfHnH,)fHnHflfHnHԠ,fHnHflfHnH,)0fHnHflfHnHd(XfHnHflfHnHL))fHnHflfHnHL,fHnHflfHnH,)fHnHflfHnH,fHnHflfHnHt,) fHnHflfHnH$,HfHnHflfHnH4,)pfHnHflfHnH,fHnHflfHnHD,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHIJ,8fHnHflfHnHT,)`fHnHflfHnH,fHnHflfHnHt,)fHnHflfHnHT,fHnHflfHnH,)fHnHflfHnH,(fHnHflfHnHt,)PfHnHflfHnHD,xfHnHflfHnH,)fHnHflfHnH,fHnHflfHnHDO,)fHnHflfHnHt,fHnHflfHnH,)@fHnHflfHnH,hfHnHflfHnH,)fHnHflfHnHD,fHnHflfHnH,,)fHnHflfHnH,fHnHflfHnH,)0fHnHflfHnH,XfHnHflfHnH,)fHnHflfHnH,fHnHflfHnHt,)fHnHflfHnHT,fHnHflfHnH$,) fHnHflfHnH,HfHnHflfHnHܶ,)pfHnHflfHnH,fHnHflfHnHt,)fHnHflfHnHD,fHnHflfHnHD,)fHnHflfHnH(8fHnHflfHnH()`fHnHflfHnH,fHnHflfHnHd,)fHnHflfHnH,fHnHflfHnHD,)fHnHflfHnH,(fHnHflfHnHt,)PfHnHflfHnH,xfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnHD,fHnHflfHnH,)@fHnHflfHnH(hfHnHflfHnHm()fHnHflfHnHl(fHnHflfHnH()fHnHflfHnH(fHnHflfHnHD()0fHnHflfHnHD<)XfHnHflfHnHv()fHnHflfHnHD,fHnHflfHnHT,)fHnHflfHnHD+fHnHflfHnH,) fHnHflfHnH4v,HfHnHflfHnHm,)pfHnHflfHnH,fHnHflfHnHT,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH4,8fHnHflfHnH,)`fHnHflfHnHԌ,fHnHflfHnH$,)fHnHflfHnHY,fHnHflfHnHļ()fHnHflfHnHd((fHnHflfHnH|,)PfHnHflfHnH(xfHnHflfHnH4|,)fHnHflfHnH+fHnHflfHnH4))fHnHflfHnH0)fHnHflfHnH{,)@fHnHflfHnH,hfHnHflfHnH,)fHnHflfHnH,fHnHflfHnH,)fHnHflfHnH\, fHnHflfHnH9,)0 fHnHflfHnHt,X fHnHflfHnH,) fHnH flfHnHA fHnfl) fHnHAH fAl fHnH,fAlfHnH-)) !fHnHflfHnHb,H!fHnHflfHnHZ()p!fHnH flfHnHA!fHnfl)!fHnHAH fAl!fHnH,fAlfHnHЕ+)"fHnHflfHnH*)8"fHnHflfHnH0,)`"fHnHflfHnH,"fHnHflfHnH,)"fHnHflfHnH,"fHnHflfHnH,)#fHnHflfHnHa,(#fHnHflfHnHQ,)P#fHnHflfHnH,x#fHnHflfHnH,)#fHnHflfHnH,#fHnHflfHnH,)#fHnHflfHnH,$fHnHflfHnH,)@$fHnHflfHnHg,h$fHnHflfHnH,)$fHnHflfHnH,$fHnHflfHnH,)$fHnHflfHnH,%fHnHflfHnH,)0%fHnHflfHnH>,X%fHnHflfHnH},)%fHnHflfHnHе(%fHnHflfHnH*,)%fHnHflfHnH,%fHnHflfHnH,) &fHnHflfHnHp,H&fHnHflfHnH0,)p&fHnHflfHnH,&fHnHflfHnHy,)&fHnHflfHnH,&fHnHflfHnHX,)'fHnHflfHnH,8'fHnHflfHnH,)`'fHnHflfHnH,'fHnHflfHnH@,)'fHnHflfHnH ,'fHnHflfHnHP,)(fHnHflfHnH ,((fHnHflfHnH`{,)P(fHnHflfHnH0%)x(fHnHflfHnH,)(fHnHflfHnH,(fHnHflfHnHB,)(fHnHflfHnH,)fHnHflfHnH,)@)fHnHflfHnH,h)fHnHflfHnH0,))fHnHflfHnH,)fHnHflfHnH,))fHnHflfHnHH,*fHnHflfHnH,)0*fHnHflfHnH~,X*fHnHflfHnH,)*fHnHflfHnHH,*fHnHflfHnH,)*fHnHflfHnH0,*fHnHflfHnHt,) +fHnHPflfHnHAH+fHnfl)p+fHnHAHPfl+fHnHfl)+fHnHfl+fHnHfl),fHnHfl8,fHnHfl)`,fHnHfl,fHnH,flfHnHi,),fHnHfHnflHa,,fHnHfHnflHw,)-fHnHfHnflH,(-fHnHfHnflH`Y,)P-fHnHfHnflH ,x-fHnHfHnflHH,)-fHnHfHnflH,-fHnHfHnflH,)-fHnHfHnflHP,.fHnHfHnflH(,)@.fHnHfHnflH,h.fHnHfHnflH,).fHnHfHnflH,.fHnHfHnflH,).fHnHfHnflH@,/fHnHfHnflH,)0/fHnHfHnflHm,X/fHnHfHnflH~,)/fHnHfHnflH&,/fHnHfHnflH,)/fHnHfHnflH,/fHnfHnHAfl) 0fHnH flH0fHnHAH fl)p0fHnH`,flfHnHX,0fHnHflfHnHh,)0fHnHflfHnH,0fHnHflfHnH,)1fHnHflfHnHt,81fHnflfHnHA)`1fHnH flfHn1fHnHAH fl)1fHnHt,flfHnHc,1fHnHflfHnH8,)2fHnHflfHnH,(2fHnflfHnHA)P2fHnH flfInx2fHnHAH flHQ )2fHnH(flfHnH:,2fHnfHnHAfl)2fHnfl3fHnHAfl)@3fHnHjd,flfHnH,h3fHnHQ(fHnflH,fHn)3fHnHfHnflH,3fHnHfHnflH,)3fHnHfHnflHf,4fHnHfHnflHBHr)04fHnflX4fHnHBfl)4fHnH,flfHnH],4fHnHr fHnflH)H0)4fHnHfHnflH,4fHnHfHnflH,) 5fHnHfHnflH,H5fHnHfHnflH,)p5fHnHfHnflH,fIn5fHnHfHnflHL,)5fHnHfHnflHD,5fHnHfHnflHP,)6fHnfHnHBfl86fHnH fl)`6fHnHBH fl6fHnHA,flfHnH,)6fHnHfHnflH<,6fHnHfHnflĤ,)7fHnHfHnflH,(7fHnHfHnflH,)P7fHnHfHnflH,x7fHnHfHnflH,)7fHnHfHnflH,7fHnHfHnflHd,)7fHnHfHnflH,8fHnHfHnflHT,)@8fHnHfHnflH,h8fHnHfHnflH,,)8fHnHfHnflH,8fHnHfHnflH,)8fHnHfHnflH,9fHnHfHnflHT,)09fHnHfHnflH,X9fHnHfHnflH,)9fHnHfHnflH,9fHnHfHnflH̓,)9fHnHfHnflH,9fHnHfHnflH,) :fHnHfHnflH,H:fHnHfHnflH,)p:fHnHfHnflH,:fHnHfHnflHd,):fHnHfHnflH,:fHnHfHnflH,);fHnHfHnflH,8;fHnHfHnflHd,)`;fHnHfHnflHD,;fHnHfHnflH,);fHnHfHnflH,;fHnHfHnflH,)fHnHfHnflHR,)0>fHnHfHnflH$,X>fHnHfHnflH,)>fHnHfHnflH,>fHnHfHnflH,)>fHnHfHnflH,>fHnHfHnflHd,) ?fHnHfHnflH4,H?fHnHfHnflHܤ,)p?fHnHfHnflHԼ,?fHnHfHnflHT,)?fHnHfHnflH̎,?fHnHfHnflH),)@fHnHfHnflH,8@fHnHfHnflHT,)`@fHnHfHnflH$,@fHnHfHnflH,)@fHnHfHnflH,@fHnHfHnflH,)AfHnHfHnflHz,(AfHnHfHnflH,,)PAfHnHfHnflH,xAfHnHfHnflH ,)AfHnHfHnflH,AfHnHfHnflH,)AfHnHfHnflHD,BfHnHfHnflHě,)@BfHnHfHnflH,(hBfHnHfHnflH,)BfHnHfHnflH,BfHnHfHnflH,)BfHnHfHnflHܡ,CfHnHfHnflH,)0CfHnHfHnflH`,XCfHnHfHnflH,)CfHnHfHnflH,,CfHnHfHnflH,)CfHnHfHnflH,CfHnHfHnflHܠ,) DfHnHfHnflH#,HDfHnHfHnflHԾ,)pDfHnHfHnflH&,DfHnHfHnflH<,)DfHnHfHnflH,DfHnHfHnflHF,)EfHnHfHnflH,8EfHnHfHnflH|,fIn)`EfHnHfHnflH,EfHnHfHnflHo,)EfHnHfHnflHWm,fInEfHnH fHnflHB)FfHnfl(FfHnHBfl)PFfHnH,flfHnHB xFfHnH fl)FfHnHB(H0flFfHnH,flfHnH,)FfHnHfHnflHʗ,GfHnHfHnflH,fHn)@GfHnHfHnflH<,hGfHnHfHnflH,)GfHnHfHnflH,HZGfHnfHnHBfl)GfHnflHfHnHBfl)0HfHnH,flfHnH,XHfHnHZ fHnflHi,)HfHnHfHnflH,HfHnHfHnflH1,)HfHnHfHnflHa,HfHnHfHnflH1,) IfHnHfHnflHɾ,fInHIfHnHfHnflH,,)pIfHnHfHnflH̺,fInIfHnHfHnflH`,)IfHnHfHnflHBpfInIfHnH8fl)JfHnHBxfl8JfHnHfl)`JfHnHflJfHnHfl)JfHnHflJfHnH,flfHnH,)KfHnHfHnflHr,(KfHnHfHnflHR`,)PKfHnHfHnflH:,xKfHnHfHnflHZ,)KfHnHfHnflHA,KfHnHfHnflH ,)KfHnHfHnflHRA,LfHnHfHnflH{,)@LfHnHfHnflH,hLfHnHfHnflHʌ,)LfHnHfHnflHr{,LfHnHfHnflH",)LfHnHfHnflH,MfHnHfHnflH,)0MfHnHfHnflH(XMfHnHfHnflHP,)MfHnHfHnflH*,MfHnHfHnflH,)MfHnHfHnflHj,MfHnHfHnflH,) NfHnHfHnflHz,HNfHnHfHnflHZ,)pNfHnHfHnflHҘ,NfHnHfHnflH,)NfHnHfHnflHo,NfHnHfHnflH',)OfHnHfHnflH2,8OfHnHfHnflHj,fIn)`OfHnHfHnflHݧ,OfHnHfHnflH͹,)OfHnHfHnflH,OfHnHfHnflH)PfHnHfl(PfHnHfl)PPfHnH`,flfHnH7,xPfHnHfHnflH,)PfHnHfHnflHw,PfHnHfHnflH[,)PfHnHfHnflHd,HpQfHnHfHnflH,)@QfHnHfHnflHhQfHnHfl)QfHnHL/QfHnH,L/fHnH$w,)QfHnHfHnflHRfHnHfl)0RfHnHvL/XRfHnHv,\L/fHnH,)RfHnHfHnflH0RfHnHfl)RfHnH8HxL/RfHnHp,K/fHnH],) SfHnHfHnflH-,HSfHnHfHnflH,)pSfHnHfHnflHݍ,SfHnHfHnflHk,)SfHnHfHnflH,SfHnHfHnflH,)TfHnHfHnflH%,8TfHnHfHnflHM,)`TfHnHfHnflH ,TfHnHfHnflH,)TfHnHfHnflH5R,TfHnHfHnflHe,)UfHnHfHnflH,(UfHnHfHnflHe,)PUfHnHfHnflH,xUfHnHfHnflHբ,)UfHnfHnH-j,flfHnHBUfHnH(fl)UfHnH(flVfHnHB H(}I/)@VfHnH,cI/fHnH`,hVfHnHfHnflH-,)VfHnHfHnflHW,VfHnHfHnflH,)VfHnHfHnflHٺ,WfHnHfHnflH,)0WfHnHfHnflH,XWfHnHfHnflH,)WfHnHfHnflH7,WfHnHfHnflH,)WfHnfHnHV,flfHnHBWfHnH8fl) XfHnH8flHXfHnHB H(G/)pXfHnH^,G/fHnHBXfHnfl)XfHnHBH(G/XfHnH,wG/fHnH},)YfHnHfHnflHk,8YfHnH(fHnflH>,)`YfHnfHnHBflYfHnH(fl)YfHnHBH(F/YfHnH,F/fHnH,)ZfHnH(fHnflHf,(ZfHnfHnHBfl)PZfHnH(flxZfHnHBH(eF/)ZfHnHM,KF/fHnHz,ZfHnHfHnflH',)ZfHnHfHnflH7,[fHnHfHnflHW,)@[fHnHfHnflHϰ,h[fHnHfHnflH?,)[fHnHfHnflHw,[fHnHfHnflHo,)[fHnHfHnflH,\fHnHfHnflH",)0\fHnHfHnflH,X\fHnHfHnflH,)\fHnHfHnflH.,\fHnH(fHnflHO4,)\fHnfHnHBfl\fHnH(fl) ]fHnHBH(_D/H]fHnHT,ED/fHnH,)p]fHnHfHnflH,]fHnHfHnflHǵ,)]fHnHfHnflH,]fHnHfHnflHɪ,)^fHnHfHnflHaZ,8^fHnHfHnflHQ,)`^fHnHfHnflH!,^fHnHfHnflH,)^fHnH(fHnflHѓ,^fHnH(fHnflHB)_fHnfl(_fHnHBH(B/)P_fHnH:,B/fHnHm,x_fHnHfHnflH3,)_fHnH(fHnflH,_fHnfHnHBfl)_fHnH(fl`fHnHBH(3B/)@`fHnH89,B/fHnHְ,h`fHnHfHnflHk,)`fHnHfHnflH,`fHnHfHnflH,)`fHnHfHnflH͢,afHnHfHnflH,)0afHnHfHnflH=,XafHnHfHnflHeH,)afHnHfHnflHt,afHnHfHnflHH,)afHnHfHnflHE,afHnHfHnflHU,) bfHnHfHnflHu,HbfHnHfHnflHN,)pbfHnHfHnflH},bfHnHfHnflH ,)bfHnHfHnflHϮ,bfHnHfHnflH,)cfHnHfHnflH,8cfHnHfHnflHa,)`cfHnHfHnflH],cfHnHfHnflHE,)cfHnHfHnflHm,cfHnHfHnflH,)dfHnHfHnflHͥ,(dfHnHfHnflHU,)PdfHnHfHnflHeU,xdfHnHfHnflH,)dfHnHfHnflH,dfHnHfHnflH,)dfHnHfHnflH^,efHnHfHnflHmy,)@efHnHfHnflH=y,hefHnH(fHnflHE,)efHnH(fHnflHBefHnfl)efHnHBH(=/ffHnHv,=/fHnH`,)0ffHnHfHnflH=,XffHnHfHnflH,)ffHnHfHnflH,ffHnHfHnflHǧ,)ffHnHfHnflH~,ffHnHfHnflH,) gfHnHfHnflHo~,HgfHnHfHnflHp,)pgfHnHfHnflH,gfHnHfHnflH()gfHnHfHnflH̪,gfHnHfHnflHwo,)hfHnHfHnflH,8hfHnHfHnflHe,)`hfHnHfHnflH,hfHnHfHnflHǓ,)hfHnHfHnflH,hfHnHfHnflH,)ifHnHfHnflH,(ifHnHfHnflHA,)PifHnHfHnflHA,xifHnHfHnflHW,)ifHnHfHnflHQ,ifHnHfHnflH' ,)ifHnHfHnflHW1,jfHnHfHnflHԫ,)@jfHnHfHnflHߤ,hjfHnHfHnflH,)jfHnHfHnflH7m,jfHnfHnHV,flfHnHB)jfHnH8flkfHnHHfl)0kfHnHB H(9/XkfHnH Y,m9/fHnHB)kfHnflkfHnHBE9/)kfHnHX,+9/fHnHB kfHnH(fl) lfHnHB(H88/HlfHnH0X,8/fHnH>,)plfHnHfHnflHUz,lfHnHfHnflHs,)lfHnHfHnflH,lfHnHfHnflH',)mfHnHfHnflH=,8mfHnH(fHnflHy,)`mfHnH(fHnflHBmfHnfl)mfHnHBH(7/mfHnHb,7/fHnH?,)nfHnHfHnflH,(nfHnHfHnflH_,)PnfHnHfHnflH`,xnfHnHfHnflH=,)nfHnHfHnflH,nfHnHfHnflH_`,)nfHnHfHnflHo,ofHnHfHnflHL,)@ofHnHfHnflH,hofHnHfHnflHL,)ofHnHfHnflH/,ofHnHfHnflHw,)ofHnHfHnflH߅,pfHnHfHnflHWw,)0pfHnHfHnflH,XpfHnHfHnflHo,)pfHnHfHnflH~,pfHnHfHnflH,)pfHnHfHnflHoT,pfHnHfHnflHτ,) qfHnHfHnflHǕ,HqfHnH(fHnflH,)pqfHnH(fHnflHBqfHnfl)qfHnHBH(4/qfHnH5,4/fHnH,)rfHnHfHnflH,8rfHnHfHnflH],)`rfHnHfHnflH,rfHnHfHnflH,)rfHnHfHnflHQ,rfHnHfHnflH3,)sfHnHfHnflHI,(sfHnHfHnflH!W+)PsfHnHfHnflH@,xsfHnHfHnflHQf,)sfHnHfHnflHf,sfHnHfHnflHH,)sfHnHfHnflHQ,tfHnHfHnflH[,)@tfHnHfHnflH1,htfHnHfHnflHA,)tfHnHfHnflHAe,tfHnHfHnflHQ1,)tfHnHfHnflHщ,ufHnHfHnflHy,)0ufHnHfHnflHy,XufHnHfHnflHI,)ufHnHfHnflH ,ufHnfHnH,flfHnHB)ufHnH8flufHnH8fl) vfHnHB H8I1/HvfHnH21/)pvfHnH#1/vfHnH, 1/fHnHc,)vfHnHfHnflH ,vfHnHfHnflHE,)wfHnHfHnflHmk,8wfHnHfHnflH,)`wfHnHfHnflH,wfHnHfHnflH ,)wfHnHfHnflH},wfHnHfHnflH@,)xfHnHfHnflH,(xfHnHfHnflH6,)PxfHnHfHnflH,xxfHnHfHnflHw,)xfHnHfHnflH~,xfHnHfHnflHu5,)xfHnHfHnflH,yfHnHfHnflHv,)@yfHnH(fHnflHua,hyfHnH(fHnflHB)yfHnflyfHnHBH(./)yfHnH,./fHnH,zfHnHfHnflHݣ,)0zfHnHfHnflH,XzfHnHfHnflH,)zfHnHfHnflH7s(zfHnHfHnflH,)zfHnHfHnflH},zfHnHfHnflH`,) {fHnHfHnflH,H{fHnHfHnflHn,)p{fHnHfHnflH,,{fHnHfHnflH'3,){fHnHfHnflH,{fHnHfHnflH,)|fHnHfHnflH'_,8|fHnHfHnflHGB,)`|fHnHfHnflHB,|fHnHfHnflHu,)|fHnHfHnflHl,|fHnHfHnflH_,)}fHnHfHnflH9,(}fHnfHnHO,flfHnHB)P}fHnH(flx}fHnH(fl)}fHnHB H(+/}fHnH,}+/fHnH,)}fHnHfHnflHz,~fHnHfHnflH,)@~fHnHfHnflHq,h~fHnHfHnflH),)~fHnHfHnflH s,~fHnHfHnflHk,)~fHnHfHnflHr,fHnHfHnflHy,)0fHnHfHnflHyy,XfHnHfHnflHIr,)fHnHfHnflH ,fHnH(fHnflH,)fHnfHnHBflfHnHHfl) fHnHB)/HfHnH[,)/fHnHB()pfHnHHflfHnHB0H8k)/)fHnHp[,Q)/fHnHBfHnfl)fHnHB))/8fHnH[,)/fHnHB )`fHnH8flfHnHB(H8(/)fHnHTc,(/fHnH,؁fHnH(fHnflHB)fHnfl(fHnHBH(y(/)PfHnH?,_(/fHnH,xfHnHfHnflHZ,)fHnHfHnflH,ȂfHnHfHnflHh,)fHnHfHnflHo,fHnHfHnflHsP,)@fHnHfHnflHb,hfHnHfHnflHCo,)fHnHfHnflH#,fHnHfHnflH,)fHnHfHnflHg,fHnHfHnflHg,)0fHnHfHnflH,XfHnHfHnflHa,)fHnHfHnflH{,fHnHfHnflHK,)ЄfHnHfHnflH,fHnHfHnflH`,) fHnHfHnflHN,HfHnHfHnflH},)pfHnfHnHm,flfHnHBfHnfl)fHnflfHnHB%/)fHnHH8%/H8H88fHnHm%/)`fHnHM,S%/fHnHl,fHnHfHnflHl,)fHnHfHnflHs,؆fHnHfHnflHs,)fHnHfHnflH,(fHnHfHnflH_,)PfHnHfHnflHd,xfHnHfHnflH,)fHnHfHnflHs,ȇfHnHfHnflHՑ,)fHnHfHnflHޔ,fHnfHnH,flfHnHB)@fHnH(flhfHnH(fl)fHnHB H(#/fHnHz,#/fHnH,)fHnHfHnflH*,fHnHfHnflH),)0fHnHfHnflH(,XfHnHfHnflH ,)fHnHfHnflHg(fHnHfHnflHy,)ЉfHnHfHnflHIq,fHnHfHnflH',) fHnHfHnflH ,HfHnHfHnflHA,)pfHnHfHnflH,fHnHfHnflHq,)fHnHfHnflHܔ,fHnH(fHnflHP,)fHnH(fHnflHB8fHnfl)`fHnHBH(!/fHnHƀ,g!/fHnH,)fHnHfHnflH),؋fHnHfHnflH,)fHnHfHnflHo,(fHnHfHnflHē,)PfHnH(fHnflHL,xfHnfHnHBfl)fHnH(flȌfHnHBH({ /)fHnH,a /fHnHw,fHnHfHnflHm%,)@fHnHfHnflHQ,hfHnHfHnflH,)fHnH(fHnflH,fHnfHnHBfl)fHnH8flfHnHB/)0fHnHv,/fHnHB(XfHnH(fl)fHnHB0H8O/fHnHD$,5/fHnH,)ЎfHnHfHnflHu,fHnHfHnflHQm,) fHnHfHnflH;,HfHnHfHnflHL,)pfHnHfHnflHI,fHnHfHnflH,)fHnHfHnflH,fHnHfHnflHƎ,)fHnHfHnflHʐ,8fHnHfHnflHQO,)`fHnHfHnflHe,fHnHfHnflH1*,)fHnHfHnflH|,ؐfHnHfHnflHk,)fHnHfHnflHa((fHnHfHnflH9|,)PfHnHfHnflH;,xfHnHfHnflH{,)fHnHfHnflH1),ȑfHnHfHnflH@,)fHnHfHnflHQ,fHnHfHnflH(,)@fHnHfHnflHIV,hfHnHfHnflHaD,)fHnHfHnflHU,fHnHfHnflHD,)fHnHfHnflH4,fHnHfHnflH:,)0fHnHfHnflH,XfHnHfHnflH,)fHnHfHnflHЋ,fHnHfHnflH;,)ГfHnHfHnflH,fHnHfHnflH1i,) fHnHfHnflHJ,HfHnHfHnflHL,)pfHnHfHnflHa+fHnHfHnflHZ,)fHnHfHnflH,fHnHfHnflHT,)fHnH8fHnflH,8fHnH8fHnflHB)`fHnflfHnHBH8/)fHnH/ؕfHnH/)fHnH(/fHnHY,(fHnHfHnflHE()PfHnHfHnflH%+xfHnHfHnflHo,)fHnHfHnflH`,ȖfHnHfHnflHJ,)fHnHfHnflH+fHnHfHnflHX,)@fHnHfHnflH_,hfHnHfHnflH,)fHnHfHnflHn,fHnHfHnflH,)fHnHfHnflH%f,fHnHfHnflHm,)0fHnHfHnflHT,XfHnHfHnflH}v,)fHnHfHnflH|,fHnHfHnflHuH,)ИfHnHfHnflH5?,fHnHfHnflH^,) fHnHfHnflH],HfHnHfHnflH,)pfHnHfHnflH+fHnHfHnflH,)fHnHfHnflH,fHnHfHnflH,)fHnHfHnflHʈ,8fHnHfHnflHm,)`fHnH(fHnflHF{,fHnH(fHnflHB)fHnflؚfHnHBH(/)fHnHY(/fHnHc,(fHnH(fHnflHn,)PfHnH(fHnflHBxfHnfl)fHnHBH(7/țfHnH],/fHnH,)fHnHfHnflH!,fHnHfHnflH,)@fHnHfHnflH́,hfHnHfHnflHj,)fHnHfHnflHƄ,fHnHfHnflHy,)fHnHfHnflH)b,fHnHfHnflHa,)0fHnHfHnflH!y,XfHnHfHnflH,)fHnHfHnflH!},fHnHfHnflHy,)НfHnHfHnflH,fHnHfHnflHq,) fHnHfHnflHq,HfHnHfHnflHIi,)pfHnHfHnflHx,fHnHfHnflHw,)fHnHfHnflH|,fHnHfHnflH},)fHnHfHnflH)V(8fHnHfHnflH_,)`fHnHfHnflH,fHnHfHnflHB,)fHnHfHnflH(؟fHnHfHnflH,)fHnHfHnflH,(fHnHfHnflHt,)PfHnH(fHnflHqv,xfHnfHnHBfl)fHnH(flȠfHnHBH(1/)fHnH^,/fHnH+g,fHnHfHnflHQ,)@fHnHfHnflH[J,hfHnHfHnflH +)fHnHfHnflHK^,fHnHfHnflHSu,)fHnHfHnflH,fHnHfHnflHI,)0fHnHfHnflHf,XfHnHfHnflH],)fHnHfHnflHe,fHnHfHnflHK],)ТfHnHfHnflH ,fHnHfHnflH+ ,) fHnHfHnflH;e,HfHnH(fHnflHU,)pfHnH(fHnflHBfHnfl)fHnHBH(/fHnH+/fHnHs,)fHnHfHnflH-\,8fHnHfHnflH~,)`fHnHfHnflH,fHnH(fHnflHL,)fHnH(fHnflHBؤfHnfl)fHnHBH(/(fHnHr, /fHnHz,)PfHnHfHnflHw,xfHnHfHnflH7l,)fHnHfHnflHG,ȥfHnHfHnflHP()fHnHfHnflH+fHnHfHnflH+)@fHnHfHnflH?+hfHnHfHnflH+,)fHnHfHnflHy,fHnHfHnflHZ,)fHnHfHnflHz,fHnHfHnflHu,)0fHnH(fHnflHj|,XfHnH(fHnflHB)fHnflfHnHBH( /)ЧfHnHR, /fHnH,fHnHfHnflH{,) fHnHfHnflHj,HfHnHfHnflHX,)pfHnHfHnflHX,fHnHfHnflHa,)fHnHfHnflH,fHnHfHnflHii,)fHnHfHnflHt,8fHnHfHnflHq`,)`fHnHfHnflHAo,fHnHfHnflHW,)fHnHfHnflH`,ةfHnHfHnflHYs,)fHnHfHnflH_,(fHnHfHnflHAC,)PfHnHfHnflHv,xfHnHfHnflHI,)fHnHfHnflHyI,ȪfHnHfHnflHV,)fHnHfHnflH)I,fHnHfHnflHa+)@fHnHfHnflH1V,hfHnHfHnflHm,)fHnHfHnflH9g,fHnHfHnflHK()fHnHfHnflHqK(fHnHfHnflHb|,)0fHnHfHnflHqx,XfHnHfHnflHMx,)fHnfHnH`z,flfHnHBfHnfl)ЬfHnflfHnHB /) fHnHH8/H8H8HfHnH/)pfHnH],/fHnH=G,fHnHfHnflHM,)fHnHfHnflHET,fHnHfHnflHs,)fHnHfHnflHp,8fHnHfHnflHv,)`fHnHfHnflHx,fHnHfHnflHU{,)fHnHfHnflHx,خfHnHfHnflHx,)fHnHfHnflHz,(fHnHfHnflHL,)PfHnHfHnflH$,xfHnHfHnflHU[,)fHnHfHnflHR,ȯfHnHfHnflH[r,)fHnHfHnflHu(fHnHfHnflHr,)@fHnH(fHnflH+hfHnfHnHBfl)fHnH(flfHnHBH( /)fHnHx,/fHnHWi,fHnHfHnflHb,)0fHnHfHnflH'G(XfHnHfHnflH'')fHnHfHnflH=,fHnHfHnflH7m,)бfHnHfHnflHm,fHnHfHnflH=,) fHnHfHnflH'J,HfHnHfHnflHu,)pfHnHfHnflHl,fHnHfHnflHE()fHnHfHnflH4,fHnHfHnflHg,)fHnHfHnflH!,8fHnHfHnflHWX,)`fHnHfHnflHo,fHnHfHnflH`,)fHnHfHnflH+سfHnHfHnflHf,)fHnHfHnflH?k,(fHnHfHnflHk,)PfHnHfHnflH?`,xfHnHfHnflH`,)fHnHfHnflHg2,ȴfHnHfHnflHg()fHnHfHnflH+fHnHfHnflHC()@fHnHfHnflH#+hfHnHfHnflH#+)fHnHfHnflH'#+fHnHfHnflH"+)fHnHfHnflH'(fHnHfHnflH_@,)0fHnHfHnflHg,XfHnHfHnflH+)fHnHfHnflH7 ,fHnHfHnflH,)жfHnHfHnflH0,fHnHfHnflHw,) fHnHfHnflH,HfHnHfHnflH+)pfHnHfHnflH8,fHnHfHnflH/,)fHnHfHnflH7L,fHnHfHnflH/,)fHnHfHnflHw,8fHnHfHnflHW+)`fHnHfHnflH+fHnHfHnflH\,)fHnHfHnflH?(ظfHnHfHnflH')fHnHfHnflH,(fHnHfHnflH,)PfHnHfHnflH7,xfHnHfHnflHg,)fHnHfHnflHD,ȹfHnHfHnflH+)fHnHfHnflHm,fHnHfHnflHg[,)@fHnHfHnflHi,hfHnHfHnflHi,)fHnHfHnflHm,fHnHfHnflH()fHnHfHnflH-,fHnHfHnflHB,)0fHnHfHnflHQ,XfHnHfHnflHWZ,)fHnHfHnflH`,fHnHfHnflHW+)лfHnHfHnflH+fHnHfHnflH7+) fHnHfHnflH+HfHnHfHnflHP,)pfHnHfHnflHp,fHnHfHnflHkk,)fHnHfHnflH_,fHnHfHnflH%k,)fHnHfHnflH6p,8fHnHfHnflHn,)`fHnHfHnflHn,fHnHfHnflH^,)fHnHfHnflHj,ؽfHnHfHnflH",)fHnHfHnflH9j,(fHnHfHnflH n,)PfHnHfHnflHn,xfHnHfHnflHl,)fHnHfHnflHW,ȾfHnfHnHen,flfHnHB)fHnH(flfHnH(fl)@fHnHB H(.hfHnH8i,m.fHnHi],)fHnHfHnflHb,fHnHfHnflHE,)fHnHfHnflH ?,fHnHfHnflH(,)0fHnHfHnflH9(XfHnHfHnflH,)fHnHfHnflH)E,fHnHfHnflHy,)fHnHfHnflHD,fHnHfHnflHD,) fHnHfHnflH[,HfHnHfHnflH +)pfHnHfHnflHy`,fHnHfHnflHd,)fHnHfHnflHi[,fHnHfHnflH9[,)fHnHfHnflHT,8fHnHfHnflHC,)`fHnHfHnflH)+fHnHfHnflH,)fHnHfHnflH<,fHnHfHnflH+)fHnHfHnflHb,(fHnHfHnflH5,)PfHnHfHnflHS,xfHnHfHnflHS,)fHnHfHnflHY,fHnHfHnflHY,)fHnHfHnflH+fHnHfHnflHY6()@fHnHfHnflH+hfHnHfHnflHI.,)fHnHfHnflH,fHnHfHnflH-,)fHnHfHnflHja,fHnHfHnflHA4,)0fHnHfHnflH$,XfHnHfHnflHi-,)fHnHfHnflH`,fHnfHnHQ$,flfHnHB)fHnH(flfHnH(fl) fHnHB H(!.HfHnH+.fHnHe,)pfHnHfHnflHKQ,fHnHfHnflH,)fHnHfHnflHK,,fHnHfHnflH\,)fHnHfHnflH;9,8fHnHfHnflHf,)`fHnHfHnflH{P,fHnHfHnflHN_,)fHnHfHnflH#2,fHnHfHnflH_,)fHnHfHnflHG,(fHnHfHnflH[,)PfHnHfHnflHf,xfHnHfHnflHU,)fHnHfHnflHG,fHnHfHnflHU,)fHnHfHnflH7,fHnHfHnflH>,)@fHnHfHnflHAe,hfHnHfHnflHN,)fHnHfHnflH),fHnHfHnflH !,)fHnHfHnflH6,fHnHfHnflH),)0fHnHfHnflH[T,XfHnHfHnflHE,)fHnHfHnflHK6,fHnHfHnflHxe,)fHnHfHnflH+fHnHfHnflHc,) fHnfHnH_,flfHnHBHfHnH(fl)pfHnH(flfHnHB H(.)fHnHa,.fHnH[,fHnHfHnflH,)fHnHfHnflH_,8fHnHfHnflH[,)`fHnHfHnflH-,fHnHfHnflH',)fHnHfHnflH,fHnHfHnflH]W,)fHnHfHnflHO^,(fHnHfHnflH-4,)PfHnHfHnflHV,xfHnHfHnflH'`,)fHnHfHnflHQ,fHnHfHnflHDZ,)fHnHfHnflH_,fHnH(fHnflH.()@fHnfHnHBflhfHnH(fl)fHnHBH(.fHnHb-({.fHnH-()fHnHfHnflH,(fHnHfHnflHϠ()0fHnHfHnflH+(XfHnHfHnflHo+()fHnHfHnflH*(fHnHfHnflHo*()fHnHfHnflH(fHnHfHnflH/^,) fHnHfHnflHI,HfHnHfHnflH1,)pfHnHfHnflH@,fHnHfHnflHX,)fHnHfHnflH[_,fHnHfHnflH*,)fHnHfHnflHO@,8fHnHfHnflHw*,)`fHnHfHnflH+fHnHfHnflH+)fHnHfHnflH+fHnHfHnflH+)fHnHfHnflH,(fHnHfHnflHM,)PfHnHfHnflH+xfHnHfHnflHM,)fHnHfHnflH,fHnHfHnflHY,)fHnHfHnflH_+fHnHfHnflH(,)@fHnHfHnflH+hfHnHfHnflH(,)fHnHfHnflH],fHnHfHnflHU,)fHnH(fHnflHaU,fHnH(fHnflHB)0fHnflXfHnHBH(.)fHnHZ,.fHnHZ,fHnHfHnflHT,)fHnHfHnflHT,fHnHfHnflHX,) fHnHfHnflHP,HfHnHfHnflH ,)pfHnHfHnflHT,fHnHfHnflHW,)fHnHfHnflH[W,fHnHfHnflH1W,)fHnH(fHnflHS,8fHnH(fHnflHB)`fHnflfHnHBH(i.)fHnHX,O.fHnHO,fHnHfHnflH,,)fHnHfHnflHD,(fHnHfHnflH3O,)PfHnHfHnflHR,xfHnHfHnflH2,)fHnHfHnflHZ,fHnHfHnflHcC,)fHnHfHnflH:,fHnfHnHC,flfHnHB)@fHnH(flhfHnH8fl)fHnHB H(.fHnH+,.fHnHMW,)fHnfHnHBflfHnH(fl)0fHnHBH(}.XfHnH:B,c.fHnHw*,)fHnHfHnflHP,fHnHfHnflHZT,)fHnHfHnflHW9,fHnHfHnflHT,) fHnHfHnflH9,HfHnHfHnflHS,)pfHnHfHnflH),fHnHfHnflHU,)fHnHfHnflHU,fHnHfHnflHOL,)fHnHfHnflH(,8fHnHfHnflH7W,)`fHnHfHnflHR,fHnHfHnflHKO,)fHnHfHnflH,fHnHfHnflHw,)fHnHfHnflHGF,(fHnHfHnflHN,)PfHnH(fHnflH'K,xfHnfHnHBfl)fHnflfHnHBH(.H8)fHnH, ,.fHnH,fHnH(fHnflHB)@fHnflhfHnHBU.)fHnH+;.fHnH/,fHnH(fHnflHB()fHnH(flfHnHB0H8.)0fHnH&,.fHnHLM,XfHnHfHnflHD,)fHnHfHnflHM&,fHnHfHnflH,)fHnfHnH5-,flfHnHBfHnH(fl) fHnH(flHfHnHB H(.)pfHnH,.fHnHO,fHnHfHnflH+)fHnHfHnflH+fHnHfHnflHS,)fHnHfHnflH+8fHnHfHnflHC,)`fHnHfHnflH&O,fHnHfHnflHH,)fHnHfHnflH,fHnHfHnflH,)fHnHfHnflH,(fHnHfHnflH+)PfHnHfHnflHJ,xfHnHfHnflHP,)fHnHfHnflHJ,fHnHfHnflH,)fHnHfHnflH*,fHnHfHnflHR,)@fHnHfHnflHO,hfHnHfHnflH;,)fHnHfHnflHI,fHnHfHnflHO,)fHnHfHnflH@,fHnHfHnflH2,)0fHnHfHnflH1,XfHnHfHnflHO:,)fHnHfHnflH:,fHnHfHnflHL,)fHnHfHnflHN,fHnHfHnflHH,) fHnHfHnflH/,HfHnHfHnflHo9,)pfHnHfHnflH?,fHnHfHnflHH,)fHnHfHnflH,fHnHfHnflH8,)fHnHfHnflH8,8fHnHfHnflHO+)`fHnHfHnflH_8,fHnHfHnflH,)fHnHfHnflH,fHnHfHnflHG,)fHnHfHnflH?,(fHnHfHnflHC,)PfHnH(fHnflH7,xfHnH(fHnflHB)fHnflfHnHBH(.)fHnH+.fHnH,fHnHfHnflHq,)@fHnHfHnflHq&,hfHnHfHnflH!,)fHnHfHnflH=,fHnHfHnflHLI,)fHnHfHnflH1.,fHnHfHnflH B,)0fHnHfHnflH<,XfHnHfHnflH+)fHnHfHnflH5,fHnHfHnflHA%,)fHnHfHnflHq,fHnHfHnflH+) fHnHfHnflH+HfHnHfHnflH-,)pfHnHfHnflH+fHnHfHnflH4,)fHnHfHnflH4,fHnHfHnflH4,)fHnHfHnflH!+8fHnHfHnflH+)`fHnHfHnflH:,fHnHfHnflH3,)fHnHfHnflH,fHnHfHnflH3,)fHnHfHnflH?,(fHnHfHnflHQ3,)PfHnHfHnflHB,xfHnHfHnflH+)fHnHfHnflHB,fHnHfHnflHa+)fHnHfHnflHA+fHnHfHnflHq",)@fHnHfHnflHa2,hfHnHfHnflHA+)fHnHfHnflH1*fHnHfHnflH ,)fHnHfHnflH8,fHnHfHnflHA+)0fHnHfHnflH1(XfHnHfHnflHI,)fHnHfHnflHH,fHnHfHnflHF,)fHnHfHnflH@,fHnHfHnflH@,) fHnHfHnflHm@,HfHnHfHnflH(,)pfHnHfHnflHч(fHnHfHnflHI ,)fHnHfHnflH<,fHnHfHnflH10,)fHnHfHnflHfC,8fHnH(fHnflH,)`fHnfHnHBflfHnH(fl)fHnHBH(.fHnH+?,.fHnHS+)fHnHfHnflHG,(fHnHfHnflHG,)PfHnHfHnflHG,xfHnH(fHnflHG,)fHnfHnHBflfHnHHfl)fHnHB7.fHnH\+.fHnHB()@fHnHHflhfHnHB0H8.)fHnH+.fHnHBfHnfl)fHnHB.fHnH+.fHnHB )0fHnH(flXfHnHB(H8a.)fHnH,G.fHnH;+fHnHfHnflHc,)fHnfHnH@,flfHnHBfHnH8fl) fHnH8flHfHnHB H8.)pfHnH.fHnH.)fHnH,,{.fHnH+fHnHfHnflH()fHnHfHnflH+8fHnHfHnflH;,)`fHnHfHnflH;,fHnHfHnflH+)fHnHfHnflHR?,fHnHfHnflHjD,)fHnHfHnflH?+(fHnHfHnflH+)PfHnfHnHg,flfHnHBxfHnH(fl)fHnH8flfHnHB H(.)fHnH ,.fHnHB,fHnfHnHBfl)@fHnH(flhfHnHBH(.)fHnH,o.fHnH=,fHnHfHnflHC,)fHnHfHnflHA,fHnH8fHnflH1,)0fHnH8fHnflHBXfHnfl)fHnHBH8.fHnH.)fHnH.fHnH,.fHnHo ,) fHnHfHnflH? ,HfHnHfHnflHg+)pfHnHfHnflH!,fHnHfHnflH,)fHnHfHnflH{8,fHnfHnH/,flfHnHB)fHnH8fl8fHnH8fl)`fHnHB H(w.fHnH|(,].fHnHB)fHnflfHnHBH(1.)fHnH=,.fHnH7,(fHnHfHnflH$A,)PfHnHfHnflH=,xfHnHfHnflH.,)fHnHfHnflH3@,fHnHfHnflH6,)fHnHfHnflH6,fHnHfHnflH ,)@fHnHfHnflH ',hfHnHfHnflH ,)fHnHfHnflH&,fHnHfHnflH&,)fHnHfHnflH,fHnHfHnflH2,)0fHnHfHnflH-,XfHnfHnH+flfHnHB)fHnH(flfHnH(fl)fHnHB H(.fHnH,.fHnH,) fHnHfHnflH},HfHnHfHnflH=,)pfHnHfHnflH-%,fHnHfHnflH},)fHnHfHnflH4,fHnHfHnflH,)fHnfHnH>,flfHnHB8fHnH(fl)`fHnH(flfHnHB H(.)fHnH(.fHnH&<,fHnHfHnflH<,)fHnfHnH9,flfHn(fHnH-fl)PfHnflxL#Mt^C C!H{HsHSK"t&t.#HHt 1.. H.I$Ht H.H(HEdH+%(te.H1[A\A]A^A_]H21U~y.fHnH-PflfHnH0)1fHnH~.fl)U1~.fl)1虹y=IO,S.H1Ht7O,9.H1Ht1.H1Ht.H1Ht.Hp1Ht.Ha1Hg.HN1HLr.H;1H1 W.H(1H <.H1H2!.H1H@.H1H.H1H.H1H.H1Htஂ.H1HY)/ .H1H> d.H}1H#A I.Hj1H> ..HW1HD ;.HD1HH.HH.1]UH51HATSF.HHt@1H.IHu"m.HuH.H5 H8.HLVjH[A\]UHAWAVAUIATE1SH(LPHA|$Ht HLcHcH.HNMcH5C KDHHIHPH.H81m.HL.Hk.H,.1H([A\A]A^A_]UHAVAUATIH=̒ SAIHMt$.L.HtLpLL.HHtaHKIT$ H@ uHX.LH5 H81.!H9tiH5.LH5 H81.HXh1AH.H8.t.LY.xLLL6.xI$LLIh[LA\A]A^]H=.HH1H1H1H1H1H1H1H1Hr1Hc1HT1HE1H61H'1H1H 1H1HWHBpHtH@HtH1UH.H5 HRH81He.1]HGHH;'.u 1%h.UHATSHtH /.HHu. .Hx`IH;.H0t111L蹚H[A\]UHATSHH51rHu 8.1*HIĺH.Åy.1Lrf[A\]UHAWAVAUIATSHH51.L%.HuH51Ln.HHuHH5_1LN.H9u+H5خ1LeHEHH9tHeE1=H51L.HHu11E1E1HUH5l1L|eHEHtH5\1H9uLFIHuDaH}AątH5+1LIHuUH}1E1E18ejIH51L.IH5֭1.Aąy.HE1H51LIHt0H51Hu1{H51LlHHuU-.H5v1LMHHtrIH5P1Hg.xWIH5=1.Aąy:0.Ht-L`.H}'d\11E1E1HEHE1E1.HuHB.IUH5 H81 .H}t H}cAH}t H}cMtLcMtLcHtHcHD[A\A]A^A_]UH=߳0HAUATSQH=U1?Ey}HT1H51H=1.xHT1Hu HǀH=<0H50H=T1DxHT1H5m1H=1.xH=T1Hu HLJZWHVT1H=/0H=XT1H!0qD.H=T1H56m1H=/1I. H=T1Hu HLJHS1H=Z0H=S1HL0CHS1H5p1H=1.H=S1Hu HLJpmHl41H=0Hf1HǡHP41HIS1H=bS1Hۦ0kC(H5/1H=@S18 H=,S1HS1H5o1H=1.H=R1Hu HLJH31HYH1H fHnH=k0fHnHR1H=R1flHM0)V31BSH5R1H=sR188H=_R1D$HKR1H5$c1H=%1?.H(R1Hu HǀH1H21H=V0H1HXjH=Q1H70)21H21AH51H=Q1Z7{H=Q1gHQ1H5_l1H=h1.DHsQ1Hu HǀH11H=@0H 1H1HiH=9Q1)11H11HQ1H0AH51H=Q16H=P1HP1H5j1H=1.HP1Hu HǀH#11H=0H=1HN1 HgH=}P1)%01H01HPP1H0Q@H51H=FP15H=2P1HP1H5?k1H=1.HO1Hu HǀHO01H=0Hq1H1(HfH=O1)-"01H01HO1H0?JH5)1H=O15/H=vO1;HbO1H5i1H=16.H?O1Hu HǀH{/1H=T0H1H10HLeH=O1)5N/1HG/1HN1H0>H5]1H=N1J4kH=N1wWHN1H5Oj1H=X1r.4HN1Hu HǀH.1H=0H1H18HdH=IN1)=z.1Hs.1HN1HՎ0>H51H=N13H=M1HM1H5d1H=1.pHM1Hu HǀH-1HbH 1H fHnH=0fHnHFH=M1H-1H9M1fl)v-1H_0/=H=KM1H5 &.HH.H9Xu$H9.Hp(HH w'HP(HJ H=L1H5 .HH9Xu$HR.Hp(HH ew'HP(HJ H=L1H5h .H:H9Xu$H;.Hv'Hp(HHZ HP(H51H=hL11H=TL1H@L1H5x1H=1.HL1Hu HǀHK1H=0H=K1H0;yHK1H5]1H=z1.VH=K1Hu HLJ0-H+1H=0H1H1H`H=vK1)_+1HX+1HK1Hʄ0;H51H=?K10H=+K1HK1H5X]1H=1.HJ1Hu HǀH*1H=0H1HS1H^H=J1)*1H*1HUJ1H0V:H51H=J1/H=oJ1H[J1H5}1H=1.H8J1Hu HǀH)1H=~0HF1H1 H]H=I1)%)1H)1HI1H~09OH51H=I1/4H=I1@ HI1H5{1H=!1;.H|I1Hu HǀH)1H=Y{0Hz1H1(HQ\H=BI1)-(1H(1HH1H|08H521H= I1O.pH=H1|\HH1H5T{1H=]1w.9HH1Hu HǀH<(1H=x0H1H10H [H=H1)5(1H(1H H1HZy0 8H5f1H=OH1-H=;H1H'H1H5P[1H=1.uHH1Hu HǀHh'1H=u0H1HC18HXH=G1)=;'1H4'1HEG1Hv0F7H51H=G1,H=G1HkG1H5]1H=1.HHG1Hu HǀH,1H&1H=r0H1HY H=G1Hs0) `&1HY&16FH51H=F1 ,+H=F17HF1H5W1H=12.HF1Hu HǀH%1H=o0HQ1HZ1HWH=YF1)%1H%1H4F1Hup05H5 1H="F1F+gH=F1sSHE1H5cW1H=T1n.0HE1Hu HǀH$1H=l0H1H1HVH=E1)$1H$1HE1HQm05H5=1H=fE1*H=RE1H>E1H5?k1H=1.lHE1Hu HǀH$1H=(i0H1H:1 H SH=D1)%#1H#1H1fl)1H0J0.}H51H=m?1A$bH=Y?1nNHE?1H5ve1H=O1i.+H"?1Hu HǀH>1H=F0H=?1HyG0).H>1H5^1H=1.H=>1Hu HLJH1HBH1HfHnH=x=0fHnH|H=m>1H1HW>1fl)l1HE>0u-2H=9>1H5C l.HH,.H9Xu$Hg.H&Hp(HHZ HP(H51H==1"H==1H=1H5MC1H=1л.H=1Hu HǀH1H=70H1HHq1HR<1H=[=1H80t,1H5`1H=9=1!H=%=1"H=1H5Jl1H=1.H=<1Hu HLJH1H=20H1HpH1H;1H=<1Hd30+qH51H=<15!VH=m<1bBHY<1H5Bf1H=C1].H=6<1Hu HLJH1H=N-0H1HH1H:1H=;1H$.0*H51H=;1u H=;1H;1H5d1H=1._H=~;1Hu HLJ96H51H='0H?1HH1H:1H=3;1Hd(04*H51H=;1H=:1H:1H5rc1H=1ݸ.H=:1Hu HLJyvHm1H="0Hw1H0HQ1HR91H={:1Hd#0t)1H5@1H=Y:1H=E:1"H1:1H5c1H=1.H=:1Hu HLJH1H=. 0H1H1()-v1H@H=91Ht1H91H 0(cH5j1H=91'HH=91T4Hk91H5|W1H=51O.HH91Hu HǀH1H=M0H1H10)51H@H= 91H1H81H0'H51H=81fH=81sH81H5@1H=t1.PH81Hu HǀH1H=0H1HV18)=1H@H=Q81H1H81H0$'H51H=!81H= 81H71H5ZV1H=1͵.H71Hu HǀHj1H1H=0HM1 ) 1HRH=71H1HZ0j&'H51H=o71 H=[71HG71H5N1H=1.H$71Hu HǀH1H=10H1H1)1H@H=61H1H61H0%fH5M1H=61*KH=61W7H61H5L1H=81R.Hk61Hu HǀH71H=0H1H1)1H@H=-61H1H61H0$H51H=51iH=51vH51H5L1H=w1.SH51Hu HǀHV1H=o0H1H1 )%71H@H=t51H51HN51H70'$H51H=D51H=051H51H5h1H=1в.H41Hu HǀHu1H=0H71HP1()-V1H@H=41HT1H41H0f##H51H=41H=w41Hc41H5,K1H=1.H@41Hu HǀH1H= 0Hn1H10)5u1H@H=41Hs1H31Hu 0"bH5)1H=31&GH=31S3H31H53g1H=41N.H31Hu HǀH1H=L 0H1H18)=1H@H=I31H1H 31H 0!H5`1H=31eH=31rH21H5I1H=s1.OH21Hu HǀH1H=0H1H 1) 1H@H=21H1HJ21H0#!H51H=`21H=L21H821H5e1H=1̯.H21Hu HǀH 1H=0H1HL1) 1H@H=11H 1H11HR0b H51H=11H=11H11H5XH1H=1 .H\11Hu HǀH 1H=)0HJ1H1) 1H@H=11H 1H01H0^H51H=01"CH=01O/H01H5_d1H=01J. H01Hu HǀH/ 1H=0H1H1 )% 1H@H=e01H 1H01H0H5<1H=501aH=!01nH 01H5F1H=o1.KH/1Hu HǀHN 1H=g/H1H1()-/ 1H@H=/1H- 1HN/1H//H5s1H=|/1H=h/1HT/1H5E1H=1Ȭ.H1/1Hu HǀHm 1H=/H1H@10)5N 1H@H=.1HL 1H.1H/^H51H=.1H=.1 H.1H5d@1H=1.Hx.1Hu HǀH 1H=/H&1HϾ18)=m 1H@H=:.1Hk 1H|-1Hm/ZH51H= .1?H=-1K+H-1H5B1H=,1F.H-1Hu HǀHs1H1H==/HV1 ) 1HRH=-1H1H/H51H=X-1dH=D-1qH0-1H51=1H=r1.NH -1Hu HǀH1H=/H1H1)1H@H=,1H1H,1H/"H5V1H=,1H=,1Hw,1H5<1H=1˩.HT,1Hu HǀH1H=/Hһ1H1)1H@H=,1H1H@+1H/aH51H=+1H=+1H+1H57V1H=1 .H+1Hu HǀH1H=/H 1Hһ1 )%1H@H=]+1H1H*1H/]H5ĺ1H=-+1!BH=+1N.H+1H511H=/1I. H*1Hu HǀHV1H'1H=/H91*)-1HRH=*1H 1H/H51H={*1gH=g*1tHS*1H54C1H=u1.QH0*1Hu HǀHT1H=/H~1H10)551H@H=)1H31H)1Hu/%H591H=)1 H=)1H)1H5A1H=1Φ.Hw)1Hu HǀHs1H=L/H1H18)=T1H@H=9)1HR1HC(1H/d!H5p1H= )1 H=(1H(1H5 B1H=1 .H(1Hu HǀH1H=/H1Hո1) s1H@H=(1Hq1H'1H/`H51H=P(1$ EH=<(1Q1H((1H5Q@1H=21L.H(1Hu HǀH1H=/H#1H<1)1H@H='1H1H'1H/H5޶1H='1c H='1pHo'1H58A1H=q1.MHL'1Hu HǀH1H=/HZ1H1)1H@H='1H1H&1H/!H51H=&1 H=&1H&1H5;1H=1ʣ.H&1Hu HǀH0H=H/H1H1 )%0H@H=U&1H0H?%1H/`H5L1H=%&1 H=&1H%1H5Y1H=1 .H%1Hu HǀH0H=/Hȴ1Hѵ1()-0H@H=%1H0H~$1Ho/\H51H=l%1 AH=X%1M-HD%1H5N1H=.1H. H!%1Hu HǀH-0H=/H1H10)50H@H=$1H 0H#1H/H51H=$1_H=$1lH$1H5t+1H=m1.IHh$1Hu HǀHL0H=/H61HO18)=-0H@H=*$1H+0H"1HM/H51H=#1H=#1H#1H5M1H=1Ơ.H#1Hu HǀHk0H=$/Hm1H1) L0H@H=q#1HJ0H;"1H/\H5(1H=A#1H=-#1 H#1H5*1H=1.H"1Hu HǀH0H=/H1HͲ1)k0H@H="1Hi0Hz!1Hk/XH5_1H="1=H=t"1I)H`"1H5q21H=*1D.H="1Hu HǀH0H=/H۰1H 1)0H@H=!1H0H 1Hj/H51H=!1[|H=!1hH!1H5@41H=i1.EH!1Hu HǀH0H=/H1HK1 )%0H@H=F!1H0H1H/H5ͯ1H=!1H=!1H 1H5<1H=1.H 1Hu HǀH0HPHI1HfHnH=f/fHnH:1H= 1flHH/)0QL%&h H=V 1LE.HH.H9Xu$H.Hp(HH K:%HP(HJ H51H= 1H=1H1H5(1H=1.kH1Hu HǀHN1H='/H=1H/i &H=}1Ld.H H9Xu$H.H$Hp(HHZ HP(H:1H5K1H=1.H=1Hu HLJH0H=ϭ/Hp1HH0H1H=1H/ RH591H=17H=1C#H1H5#I1H=$1>.H_1Hu HǀHc0H=/H1HnHG0H1H=!1H/ H5~1H=1cH=1萿pH1H5=1H=q1.MH1Hu HǀH0H9H1HfHnH=ϡ/fHnH1H=l1flH/)Z0 H51H=?1H=+1ȾH1H5$1H=1Ù.H1Hu HǀH@1H0H z5H=3/H1:H 0H=1H/)=}0] H51H=1H=v1 Hb1H5S?1H=1.H?1Hu HǀH0H4sHe1HfHnH fHnHHflfHnH)0fHnfHnHO1flH=/)m0fHnflH=1Hf/)_0O  H5ө1H=1H=p1H\1H561H=ޫ1.H=91Hu HLJHp0H5Q1 HH:1H1H=/H=1H|/IH51H=1 .H=1:H1H5!1H=15.H=~1Hu HLJѿHm0H51 HHo1H01H=/H=01H/H5=1H=1JkH=1wWH1H5O!1H=X1r.4H=1Hu HLJ Hj0H5˧1 HH1Hm1H=/H=u1H֏/H5r1H=S1H=?1贺H+1H5t$1H=1.qH=1Hu HLJKHHg0H51 HH٦1H1H=!/H=1H/CH51H=1H=1Hp1H5#1H=Ҩ1.H=M1Hu HLJ舽H1H=]/H=1HO/\H1H51H=]1w.9H=1Hu HLJH1H=/H=1H/*H1H571H=1.ĿH=s1Hu HLJ螼H0H sH,1HՠfHnH=/fHnH] H=1Ho0HX1fl)M0H/v3H5Ҥ1H=1H=1$H1H51H=1.H1Hu HǀH0H={/HN1HH0H1H=Z1H|/H51H=81DeH=$1qQH1H5 1H=R1l..H=1Hu HLJH0H=x/H1HH0H=1νH5]1H=1H=z1迶Hf1H5'>1H=1.|H=C1khH=u/H=01MH1H5;1H=N1h.*H=1H0H H1H hfHnH DrfHnH(HflfHnHш)J0fHnfHnH flH.H=m/)00fHnfHnHflHtH=U1)0fHnfHnH flH)0fHnfHnHflHww)0fHnfHnfl)0fHnfl)0;H51H=1ݻH=1ɻH1H501H=ʣ1.H=}1蕸H 0H=jj/H1H1HH=R1H0H1H=k/JH5ɠ1H="1/H=1;H1H5+1H=16.H=1Hu HLJҷϺH>0H=g/H81H1HH=1H0H1Hzh/H51H=g1KlH=S1xXH?1H501H=Y1s.5H=1Hu HLJ Hk0H Hm1Hַ fHnH=c/fHnH1H=1flHd/)%0H=1H5yK .HH.H9Xu$HǛ.Hp(HH 8'HP(HJ H=S1H5bK .HGH9Xu$H.Hp(HH 8'HP(HJ H= 1H5.X W.HH9Xu$HY.H5'Hp(HHZ HP(H5E1H=1H=1DZH1H5C1H=1Œ.Hs1Hu HǀHם1H0H=`/H12H=@1Ha/)50j'H51H=1 H=1H1H5p81H=1.շH1Hu HǀH0H=q^/H 1H18H1H=1HK_/)=0qH5М1H=i15VH=U1bBHA1H5z1H=C1].H1Hu HǀH20H=[/HL1HM1H 1H=1H\/) 0H51H=1H=1謯H1H5A1H=1.iHp1Hu HǀHL 1H=Y/H=N1HY/g$H31H5 1H=%1?.H=1Hu HLJ۲صH0H=V/H1H1)0H@H= 1H0H 1HxW/H5Ԛ1H= 1IjH= 1vVHm 1H5)1H=W1q.3HJ 1Hu HǀH0H=T/HP1HA1)0H@H= 1H0H 1HT/ĴH5 1H= 1H= 1赭H 1H51H=1.rH 1Hu HǀH%0H=nQ/H1H1 )%0H@H=S 1H0H 1H6R/FH5B1H=# 1H= 1ԳH 1H5$>1H=՛1.H 1Hu HǀHD0H=N/H1H1()-%0H@H= 1H#0HT 1HO/BH5y1H=j 1'H=V 13HB 1H5 1H=1..H 1Hu HǀHc0H=,L/H1H10)5D0H@H= 1HB0H1HL/H51H= 1EfH= 1rRH 1H5Z<1H=S1m./Hf 1Hu HǀH0H=I/H,1H=18)=c0H@H=( 1Ha0H1HSJ/H51H= 1H= 1豪H 1H51H=1.nH 1Hu HǀH0H=F/Hc1H|1) 0H@H=o 1H0H1HG/BH51H=? 1H=+ 1аH 1H5:1H=ј1.H1Hu HǀH0H=ID/H1H1)0H@H=1H0HP1HE/>H5U1H=1#H=r1/H^1H5g1H=1*.H;1Hu HǀH0H=A/Hє1H1)0H@H=1H0H1HpB/}H51H=1AbH=1nNH1H591H=O1i.+H1Hu HǀH0H=?/H1H91 )%0H@H=D1H0H1H?/H5Ó1H=1H=1譧H1H51H=1.jH1Hu HǀH0H=fH51H=[1H=G1̭H31H5t1H=͕1.H1Hu HǀH<0H=9/Hv1H10)50H@H=1H0HL1H:/}:H511H=1H=1+ Hz1H5{1H= 1&.HW1Hu HǀH[0H=$7/H1H18)=<0H@H=1H:0H1H7/yH5h1H=1=^H=1jJH1H51H=K1e.'H1Hu HǀHz0H=4/H1H51) [0H@H=`1HY0H1HK5/H51H=01|H=1詤H1H5Q1H=1.fH1Hu HǀH0H=1/H1Ht1)z0H@H=1Hx0H 1H2/:H5֏1H=w1ܪH=c1ȪHO1H51H=ɒ1~.H,1Hu HǀH0H=./HR1H1)0H@H=1H0HH1H//y6H5 1H=1H=1'H1H51H=1"~.Hs1Hu HǀH0H=+/H1H1 )%0H@H=51H0H0H,/uH5D1H=19ZH=1fFH1H51H=G1a}.#H1Hu HǀH0H=(/H1H11()-0H@H=|1H0H0H)/H5{1H=L1xH=81襡H$1H5E01H=1|.bH1Hu HǀH0H=%/H1Hp10)50H@H=1H0H0H&/6H51H=1اH=1ħHk1H5/1H=ŏ1{.HH1Hu HǀH40H="/H.1H18)=0H@H= 1H0HD0H#/u2H=0H5F ly.HH,|.H9Xu$H?.Hp(HH 0&HP(HJ H51H=0ɦH=x0՟Hd0H5.1H=1z.HA0Hu HǀH0H=n/H1H1) 0H@H=0H0H50H6 /f#H5Ҋ1H=0H=0H0H51H=1z.ѥH0Hu HǀH$0H=m/HN1Hߌ1)0H@H=J0H0Ht0H5/bH5 1H=0&GH=0S3H0H51H=41Ny.H0Hu HǀHC0H=/H1H1)$0H@H=0H"0H0Ht/H5@1H=a0eH=M0蒝rH90H51H=s1x.OH0Hu HǀH҈1H[0H=D/H1")%C0HRH=0HA0H/*H5~1H=0̣H=0؜H0H51H=1w.Hd0Hu HǀH0H=/H1H1()-i0H@H=&0Hg0H0H/i&H51H=0 H=0H0H51H=1w.ԢH0Hu HǀH0H=P/H11HB10)50H@H=m0H0HG0H/eH51H==0)JH=)0V6H0H5H=71Qv.H0Hu HǀH~1H 0H=/H a1:)=0HRH=0H0H/H5*1H=0oH=w0蜚|Hc0H51H=}1u.YH@0Hu HǀH0H= /H1HDž1) 0H@H=0H0H0H]/-H5a1H=0ϠH=0ۙH0H50H=1t.H0Hu HǀH 0H=4 /H݄1H1)0H@H=I0H0H#0H /l)H51H=0H=0H0H5:!1H=1t.ןH0Hu HǀH*0H=/H1H1) 0H@H=0H 0Hz0H{/hH=g0H5/1 q.HKL%bt.L9`u$H .HV&&Hp(HHZ HP(L-2> H=0LQq.HL9`u$H|.Hp(HH %&HP(HJ H0 H=0Hq.HL9`u$Hq}.Hp(HH 4%&HP(HJ H51H=~0JkH=j0wWHV0H5?1H=X1rr.4H30Hu HǀH1H `0H=i/H b1")%H0HRH=0HF0H?/̝H5+1H=0H=0轖H0H5 1H=1q.zH0Hu HǀH0H=/H1H1()-n0H@H=C0Hl0H%0H/N H5b1H=0H=0ܜH0H5 1H=݄1p.H0Hu HǀH0H=.Hހ1H10)50H@H=0H0Hd0H}/JH51H=Z0/H=F0;H20H5s 1H=16p.H0Hu HǀH0H=T.H1H.18)=0H@H=0H0H0H.H51H=0MnH=0zZHy0H5 1H=[1uo.7HV0Hu HǀH0H=s.HL1HE1) 0H@H0H0H1HC1)0H@H=0H0H0H.H=0Ll.HL%o.L9`u$HG{.Hp(HH &HP(HJ H=0Hl.H;L9`u$HTo.HM&Hp(HHZ HP(H5H~1H=90H=%0H0H5 1H=1m.H0Hu HǀH20H=;.H}1H̀1)0H@H=0H0Hb0H.PH5}1H=05H=l0A!HX0H51H="10HO0H0H.ΗH5{1H=0H=0运H0H5"1H=1k.|H0Hu HǀH0H=.Hi{1H~10)5p0H@H=0Hn0H0H.P H5${1H=U0H=A0ޖH-0H51H=~1j.H 0Hu HǀHz1H0H=0.Hz1:)=0HRH=0H0H.SH5bz1H=08H=0D$H{0H51H=%~1?j.HX0Hu HǀHԽ0H=.Hy1H|1) 0H@H=0H0H0H.H5y1H=0VwH=0胎cH0H50H=d}1~i.@H0Hu HǀH0H=|.Hy1H|1)Լ0H@H=a0HҼ0H30HD.єH5x1H=10H=0H 0H5:0H=|1h.H0Hu HǀH0H=.HLx1HE{1)0H@H=0H0Hr0H.SH5x1H=x0H=d0HP0H51H={1g.H-0Hu HǀH10H=.Hw1Hz1 )%0H@H=0H0H0H.OH5>w1H=04H=0@ H0H51H=!{1;g.Ht0Hu HǀHP0H=Y.Hv1Hy1()-10H@H=60H/0H0H!.H5uv1H=0RsH=0_H0H5?1H=`z1zf.0H=y1e.{H0Hu HǀH0H=W.H(u1Hx18)=o0H@H=0Hm0H.0H.O H5t1H=0H=0ݐHl0H5m1H=x1d.HI0Hu HǀH0H=.H_t1Hw1) 0H@H= 0H0Hm0H^.KH5t1H=00H=0<H0H50H=x17d.H0Hu HǀH̶0H=.Hs1Hv1)0H@H=R0H0H0H.H5Qs1H="0NoH=0{[H0H5 1H=\w1vc.8H0Hu HǀH0H=.Hr1Hu1)̵0H@H=0Hʵ0H#0H. ɎH5r1H=i0H=U0躇HA0H50H=v1b.wH0Hu HǀHZ0H=.H=0H.u2H0H51H=3v1Mb.H=0Hu HLJH0H=^.H=0HP.Ht0H51H=u1a.H=Q0Hu HLJtqH0H51s1 HHq1H0H=j.H=0H\.l)H5p1H=0H=0H0H5j1H=t1a.׌H=0Hu HLJ豉H 0H5nr1 HHGp1H0H='.H=H0H.fH5p1H=&0*KH=0W7H0H50H=8t1R`.H=0Hu HLJH 0Hc H5q1HHuo1fHnHfHnH30H=,.flH=q0H.)0H5'o1H=H0DeH=40qQH 0H51H=Rs1l_..H=0Hu HLJH0H5p1 HHn1Hg0H=.H=0H.H5\n1H=0H=y0讃He0H5V0H=r1^.kH=B0Hu HLJEBH0H5p1 HHm1H0H=.H=0H .=H5m1H=0߉H=0ˉH0H530H=q1].H=0Hu HLJ肆Hޮ0H5?o1 HHl1H0H=8.H=90H*.z7H5l1H=0H=0(H0H5X0H= q1#].H=0Hu HLJ迅Hۭ0H5|n1 HH-l1H0H=u.H=~0Hg.tH5k1H=\08YH=H0eEH40H50H=Fp1`\."H=0Hu HLJHج0H5m1 HHbk1H +H0HK0H=.H=0H.H5"k1H=0gH=0蔀tHk0H51H=uo1[.QH=H0Hu HLJ+(H'0H=.H=0H.BH0H50H=o1[.܆H=0Hu HLJ趃HZ0H=.H=0H}.H0H50H=n1Z.gH=n0Hu HLJA>H0H=.H=?0H.XH$0H5E0H=n10Z.H=0Hu HLĴɅH0H=.H=0H.H0H5(0H=m1Y.}H=0Hu HLJWTHS0H=,.H=e0H.n+HJ0H5k0H=,m1FY.H='0Hu HLJ߄H=.H=0ĄH=c.H=0H=.H=0H=.H=0sH=2.H=0XH=w.H=0=H=.H=0e"H=.H=z0JH=F.H=g0/H=.H=T0уH=Њ.H=A0H=.H=.0H=Z.H=0H=.H=0eH=.H=0JH=).H=0r/H=n.H=0WH=~.H=0<H=|.H=0!ނH=={.H=0ÂH=y.H=0H=w.H=p0H X.H=%u.H=V0HP0Hv.H8H5u.H6v.KH="0:Z[A\A]]UHAUIATSH(LdH%(HE1Mu].HUHuLHI$La.LH<HtHEdH+%(t^.H(H[A\A]]UH=0HHud1HuH=! 1HMd1HtH=0̑H-d1HtH=0贑H d1HtH= 1蜑Hc1HtH=a0脑Hc1HtH=90lHc1HdH=0PHc1HHH=114Hec1H,H=21HAc1HH=Q0Hc1HH=0Hb1HH= 1ĐHb1HH=0訐Hb1HH=a0茐Hb1HH=-+1pHib1HhH=0THEb1HLH=08H!b1H0H=71Ha1HH=0Ha1HH=a1HH=%1ϏHa1HH= 1賏H|a1HH=0藏HXa1HH=%1{H4a1HsH=\1_Ha1HWH=P0CH`1H;H=1'H&H=0HH`1]ÐIL\.H\.Lc.Q. Lua.8ILua.H}HuH)HtY.Hoc.L&\.H]c.L\.HKc.L\.H9c.L3Y.H"c.LY.H c.UHAWAVE1AUATIS1HLM9OlIEt HuP1H5!1LyHHt"LHE<9 H}IMu.`.HtLHR.IEu!H9].H5ʦ H8Y.LazHu-LIV.HHti1I9tITHKHHHHsLHHb_.x;LIHuI$*HP.HIHuE1 HHL[A\A]A^A_]UHAWAVMAUIATSH8HuL}HUdH%(HE1Ht[H5$1L@xIHt9~E1HLHuHEHEEe4 LH:"_.Hu-GX.HHtH51LHTY.y H13H5}&1LH1Y.xMtH51LHY.xHEdH+%(tW.H8H[A\A]A^A_]UHAWAVAUATSHxdH%(HE1[.H E0IH0H9Hu'H&0HEHtH;H= 0DHEH=0H0H50HEH}) a.HEHdH0H 0H9Hu"L50MtI7H=91̊IH=(1Hq0H5r0mIM!IuI}bIHH [.1E1I9Nu#M~MtI^ILHIXHMPfInLHMfInHcH)HEfl)EH4192 HMtL LHuE1E1E1A)vLH5q01H}HL.,HH0H ^0H9Hu"L5I0MtI7H=1苉IH=1H 0H5!0,IM Ic}Z.IH Ic}xZ.IHHEIc} ^Z.LEfHnIfInflH H Y.E11I9NuGM^Mt>I^ILLhHIL])pLhL](pPfInLH)HEfInHcflLpH41L])M)E0 L]LpHMtLLEOLELLE?H}6L.HuE1E1E1A*L H5.1H}HJ.YHH0H {0H9Hu"L5f0MtI7H=%1踇IH=1H=0H5>0YIM| Iu8I}0_IHy H W.1E1I9Nu#M~MtI^ILHIDPfInLH)HEfInHcflH41)E-/ HMtLLHuE1E1E1A-j LH5m&1H}HI. HH0H 20H9Hu"L50MtI7H=1IH=1H0H50 IMl IuXI}PW^IHi H V.1E1I9Nu#M~MtI^ILHI PfInLH)HEfInHcflH41)E- HMtLLHuE1E1E1A.1 LH5 1H}HH. HwH0H 0H9Hu"L50MtI7H=1FIH=1H0H50IM\ IuxI}p]IHY H U.1E1I9Nu#M~MtI^ILHIPfInLH)HEfInHcflH41)E, HMtLLHuE1E1E1A/ LbH51H}HNG. H>HW0H 0H9Hu"L50MtI7H=z1 IH=i1Hb0H5c0讄IML II[IHC H LT.1E1I9Nu+MNMt"I^ILLMHILMfInPLH)fInL}HcflI41LM)Ep+ LMHMtL<L4HuE1E1E1A0 LH51H}HE._ HH0H A0H9Hu"L5,0MtI7H=+1辂IH=1H0H50_IM&IIZIHH R.1E1I9Nu+MNMt"I^ILLMHI@LMfInPLH)fInHcLMflI41)E%* LMHMtLLHuE1E1E1A1^LH5I1H}HD.HH0H 0H9Hu"L50MtI7H= 1sIH= 1H0H50IMIIEYIHH Q.1E1I9Nu+MNMt"I^ILLMHILMfInPLH)fInHcLMflI41)E( LMHMtLLHuE1E1E1A2L}H5 1H}HiC.HYHr0H 0H9Hu"L5v0MtI7H= 1(IH= 1HM0H5N0ɀIMIIWIHH gP.1E1I9Nu+MNMt"I^ILLMHILMfInPLH)fInHcLMflI41)E' LMHMtL[LSHuE1E1E1A3L2H5 1H}HB.~HH'0H 00H9Hu"L50MtI7H=J 1~IH=9 1H0H50~IMIIVIHH O.1E1I9Nu+MNMt"I^ILLMHI_LMfInPLH)fInHcLMflI41)ED& LMHMtLLHuE1E1E1A4}LH501H}H@.3HH0H վ0H9Hu"H0HtH7H=1}HH=1H0H503~HHI8I0dUIHH M.1E1H9Ku$LkMtLsIEHILfInPHH)fInHcflI41)E% IMtLLMuIE1E11A5?HH51LzIHLHM.I9D$u*Ml$1Mt#I\$IELHIU1E111LLmHEH)HtŠH$ HMtLHuME1E1E1A5LH50H}H>.@HHUH50H}HH,H}H}E1E1E1E11A)E1E1E11A)E1E11A)E1E1E11A*E1E11A*E11A+1A,E1E1E11A-E1E11A-vE1E1E11A.`E1E11A.ME1E1E11A/7E1E11A/$E1E1E11A0E1E11A0E1E1E11A1E1E11A1E1E1E11A2E1E11A2E1E1E11A3E1E11A3E1E1E11A4mE1E11A4]E1E1E1E1A5IIE1E11A56E1E11A5&A)E1E1E1E1E1E1E1E1A)H}LEeH}LEtH}LEMLEHtHLE8LEMtLLE#LEMtLLELEMuMtLLA)Hf DH=o 1HEdH+%(tD.HxH[A\A]A^A_]L6F.HmM.L$F.H[M.H}F.HHM.LE.H6M.LE.H$M.LE.HM.H}E.HL.LE.HL.LE.HL.UHHfHnfHnflHATMSH0dH%(HU11HMHUEEuHuLH HFHt HGtVHHt'LHuHH HIaLHEdH+%(t9C.H0H[A\]H D.H}D.H`D.HK.UHAWAVAUE1ATSHdH%(HE1HN1LLLLLLHHt.1H91/HD.H5 H8B./HH=%N1_9.H0HHH=,Hq0HH= H]0HiHE0H=M1H5!D.FHbA.H0HH u19.H 0HulHHATHHHA L f RHѷ PH* P1A.1HH 8.yHR+1H:.H0H1HvL.Hw0HkH=?Q._Hc0HOHH.H=|M.HN.HN4.H?N.^H30HlZ HH.8uc:.HHt!oH1H51H=DL1^8.yE1E1E1E1L1E11L1E1E1LE1E1E1H5_AZ&L% HL4.Ht xHK1LH8.r4c^薠QOcD豮7֬*L5=F.H=>F1IL54F1H=s01V5*.1L0L 0H0IH_AXHHƽ0HH51H36.HH=0B;.1ɾH=dr0P5-1Hf0L 0Lp03IHZYHHB0HH51H5.5HH= 0:.K6.HHNL=E.HLxI*HH~g>.HIHuM1E11E1H1E11H11E1HE1E1E1H5A#H,0SIHH=J0jL Q0HHHA\XHsHH9uRH1ɾH=p0AQ51,1L 0L0H0GHAZA[H!HH5 H5.H1HHH5p1HHHHH=p01V15+1LY0L 0H0GH_AXHL9sH5 1uHNHH2.HH=.Hh11H=]o0P5+1H0L h0L0FHZYHL9sH51uHNHHm2.HHC=.HHMH50H=G0AHHH(H=:B1H3B1H1LHH1Hp]8.HHuN1E11E1H1E11L1E1E1HE1E1E1H5-A+!HH=0HQHHHH=Y0HHHHH50H>HHIHH=(A1H!A1HHx52.HHHuO1E1E1E1HE11E1L11E1LE1E1E1H50A/ LxHIHHt:.HIHuW11E11HE11E1HE111LE1E1E1HE10H5cAH0ATIHH=0jL 0HHA_ZHkHH9H21ɾH=l0AQ5'1L 50L0H0RCHHAZA[H1E11E1H1E1E1L11E1HE1E1E1H5}1AHHH5< 1.DE1E11E1L1E1E1L11E1HE1E1E1H50AGH1H51HHHHH11H=j0P5&1Hg0L 0LY0BHHZYHuM1E11E1H1E11H11E1HE1E1E1H5N>AHH51L9puHNHH-.HH8.uH(AV115%1L 50H=i0L0H0KAIHA_XMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5y@AHH=.H5$ 1H9XuHNLH,.HL7.kLLHMHH50H=0AHIH7H=<1H<1HE1LL޼HE1Lȼ2.HHuM111E1H1E1E1H11E1HE1E1E1H5DDAHH0HQHHHH=0HHHHH5[0H IHJHH=y;1Hr;1LݻHѻ11H=Fg0P5#1H 1L Ѭ0L20>IHXZMuN1E11E1H1E11L1E1E1HE1E1E1H5&AfH51H=X0L/+.xL#L%<0It$I$LH9t L;%H,.u,H=w:1L%p:119.HHHtx_H= u1E1E1E1HE11E1L11E1LE1E1E1H5SLA0+.HHuO1E1E1E1HE11E1L11E1LE1E1E1H5QA-H0fHnۿfHnHfl@8.HHHuYE1E1E11LE11E1LE111LE1E1E1HE1RH5YA;*.~H)HvH^0fHnfHnHfl@^7.HIHuM111E1H1E1E1H11E1HE1E1E1H5SA).HHHtH0fInfHnHfl@6.HIHuM1E11E1H1E11H11E1HE1E1E1H5 TAE(.fHnfHnIfl)HtH&0fInfHnHfl@6.HIHuK1E11E1H1E11L1E1E1HE1E1H5dUAA(.HHHtH0fInݿfHnHfl@;-.HIHH@(E1(LHX  h'.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5PAH0fInfHnHflC/.HIHuOE1E11E1L1E1E1L11E1HE1E1E1H5WA?H=51L%Y'.L9u%H]'.HH5g; H81*.yHh0H5y0ӊIHZH5i0HL%.L襵H=&51LH賸IHH1LHn1H=0LHH5D0N%.L>HW0H Hn0H9Hu%L53n0MtIH=0 bIH=0Hn0H5n0bIMuM11E11H1E1E1H1E1E1HE11E1H5v^AH50LgHHH)LhHH *#.H9HuH8tH0.IHRHHIH1H m0HH0H9Hu,Hl0HtHHH=0`HH=0Hl0H5l0saHHHuK1E1E11H1E1E1L11E1HE1E1H59^AyH5Z0LHxL;H11HJ0HH l0H9Hu,Hk0HtHHH=0_HH=0Hk0H5k0`HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5Q`AH5z0HBIHtHR1HH=11L9uaH..H5K H8+.111E1H1E1E1H11E1HE1E1`H5AH50L!.xLH٢0H j0H9Hu%L5j0MtIH=0^IH=p0HYj0H5Zj0-_IMuM1E11E1H1E11H11E1HE1E1E1H5aA8H5)0LHHHLH=c01L9ufH-.E1E1E1H5?J 1E1H8*.1L1LE11E1HE1E1E1H5XaAHH50d .H1NHg0HH i0H9Hu,Hh0HtHHH=0 ]HH=0Hh0H5h0]HHHuPE1E1E1E1L1E11L1E1E1LE1E1E1H5nbAH50H_IHtHoH=.1E1LL9uaH,.H5H H8(.111E1H1E1E1H11E1HE1E1H5bAH50L.xLܮH0H g0H9Hu%L5g0MtIH=0[IH=0HUg0H5Vg0I\IMuM11E11H1E1E1H1E1E1HE11E1H5cAT H5U0LHHHpLH=-1L9udH*.E11E1H5\G E1E1H85'.1E11H11E1H1E1H5}HAc HH50.HE1kH0LH f0H9Hu,He0HtHHH=0)ZHH= 0He0H5e0ZHHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5dA H50H|IHtH茬H=,1E1LL9ucH/).H5E H8%.E1E11E1L1E1E1L11E1HE1E1H5dA3 H5<0L.xLH=H01yHHHuM111E1H1E1E1H11E1HE1E1E1H5A H50H=0Hv.xHjH= 01xHHHuM1E11E1H1E11H11E1HE1E1E1H5%A H50H=0H.xHݪH=v01wHHHuN1E11E1H1E11L1E1E1HE1E1E1H5A H5+0H=0H[.xHOH=01qwHHHuO1E1E1E1HE11E1L11E1LE1E1E1H5A H540H=0H.xHH=I01vHHHuPE1E1E1E1L1E11L1E1E1LE1E1E1H5xAsH50H=e0H<.xH0E1LH.͵!..HHHuM111E1H1E1E1H11E1HE1E1E1H5*AH50H=0H.xH腨11H=S0P5c1H0L 0L0+HHXZHuM1E11E1H1E11H11E1HE1E1E1H5 -AH5d0H= 0H.xHاAU1151L 0H=R0L?0HP0*HHA^A_HuPE1E1E1E1L1E1E1L11E1LE1E1E1H5m?AhH50H=Z0H1.xH%1U%.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5RAH50H=ї0H.xH蜦$.HHHuM111E1H1E1E1H11E1HE1E1E1H5ZSAUH50H=G0H.xH?$.HHHuM1E11E1H1E11H11E1HE1E1E1H5TAH540H=0H.xH舥#.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5DUA?H50H=10H.xH)#.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5VAH50H=0H{.xHo".HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5+WA&H50H=0H.xH".HHHuM111E1H1E1E1H11E1HE1E1E1H5XAH50H=0He.xHY!.HHHuM1E11E1H1E11H11E1HE1E1E1H5YAH50H=0H.xHϢ .HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5ZAH50H=x0HO.xHC p .HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5[AH50H=0H.xH趡 .HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5r\AmH50H=_0H6.xH* W.HHHuM111E1H1E1E1H11E1HE1E1E1H5]AH5|0H=Ց0H.xH蠠 .HHHuM1E11E1H1E11H11E1HE1E1E1H5^^AYH50H=K0H".xHC.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5_AH5&0H=0H.xH芟.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5E`A@H50H=20H .xH*.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5aAH50H=0H}.xHq.HHHuM111E1H1E1E1H11E1HE1E1E1H5/bA*H5{0H=0H .xH.HHHuM1E11E1H1E11H11E1HE1E1E1H5cAH50H=0Hi .xH].HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5dAH5M0H=0H .xHќ.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5eAH50H=y0HP .xHD!q.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5fAH5|0H=0H .xH踛%.HHHuM111E1H1E1E1H11E1HE1E1E1H5vgAqH5:0H=c0H: .xH.[.HHHuM1E11E1H1E11H11E1HE1E1E1H5hAH50H=ً0H .xH褚 .HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5`iA[H5|0H=M0H$ .xH#E.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5jAH50H=0H .xH苙".HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5GkABH5+0H=40H .xH,.HHHuM111E1H1E1E1H11E1HE1E1E1H5lAH5A0H=0H.xHu(.HHHuM1E11E1H1E11H11E1HE1E1E1H53mA.H5g0H= 0H.xH'.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5nAH5˽0H=0Hk.xH_.HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5oAH50H=0H.xHҖ$.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5pAH5b0H={0HR.xHF)s.HHHuM111E1H1E1E1H11E1HE1E1E1H5qAH50H=0H.xH輕*.HHHuM1E11E1H1E11H11E1HE1E1E1H5zrAuH5V0H=g0H>.xH2_.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5sAH50H=ۅ0H.xH覔 .HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5atA\H50H=N0H%.xHF.HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5վuAH50H=„0H.xH荓.HHHuM111E1H1E1E1H11E1HE1E1E1H5KvAFH5_0H=80H.xH0.HHHuM1E11E1H1E11H11E1HE1E1E1H5wAH5%0H=0H.xHy.HHHuN1E11E1H1E11L1E1E1HE1E1E1H56xA1H50H=#0H.xH&.HHHuO1E1E1E1HE11E1L11E1LE1E1E1H5yAH50H=0Hn.xHb1.HHHuPE1E1E1E1L1E11L1E1E1LE1E1E1H5 {AH5t0H= 0H.xHؐ.HHHuN1E11E1H1E1E1L11E1HE1E1E1H5|AH50H=0HY.xHMH.1H50HH=O0H&.rH5G0H=00H.AW11H=;050H0L 0L_0"HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5AH5"0H=0HZ-xHN11H=C:0AP5C0L T0L0H^0qHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5AH50H=0H-xH螎11H=s90P50H%0L 0L0HHXZHuN1E1E11H1E1E1L11E1HE1E1E1H58A3H50H=%0H-xHS1150L ~0H=80LX0H0HHA\A]HuPE1E1E1E1L1E11L1E1E1LE1E1E1H5eAH5j0H=s~0HJ-xH>E1H .H5}0LH=?~0-#.HHHxxHHZH=˼01 ZHIHH=}0H50H-Lxa覌1HHHt茌H1HHtrH1HHtX1HEH{`H5 11H "H%H=\jHHHHCH=01 YIHH50H=|0H-L見HHt蕋H1HHt{H1HHtaH{xHHH胸E1%E1&'H{xHE11HHE1E1䉅61L1H1E1E1H1E1E1ɋH5AS11H=I5050L {0L0HV0 HHA\A]HuPE1E1E1E1L1E1E1L11E1LE1E1E1H5**AFH50H=8{0H-xH.HHHuM111E1H1E1E1H11E1HE1E1E1H59AH.H50H-xP11H=3050H0L z0L0 IHXZMWI$HH21H50LHH=8z0-L-HHHuOE1E11E1L1E1E1L11E1HE1E1E1H5ZAH0HSHHHH=0YHIHtHbH5k0LHHHXH5I0H=Ry0H)-9H1LHH=Y01*UHHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5\AH50H=x0H-xHxH=01THHHuM111E1H1E1E1H11E1HE1E1E1H5϶A.H5'0H= x0H-xHH=$01 THHHuM11E11H1E1E1H1E1E1HE11E1H5BAH50H=w0Hj-xH^K-HHHuM1E11E1H1E11H11E1HE1E1E1H5AH@0HSHHHH=0跪HIHtHH5 0LAHHHZH50H=v0H-;HE1tLLe"-HIHuO1E1E1E1HE11E1L11E1LE1E1E1H5 AH -LHHID$诣HHHtH1\HIHkH~0AQE1HjL &0HHԤAZA[IH8I9u4LvE1E1LH5>~0HLXHIHtbLH5H-y1E11E1H1E1E1L11E1HE1E1H5 AH5}0H=t0H-xLŃ1LH贃L謃1HH蛃Ht01HH ,<0H9Hu,H<0HtHHH=4}0W0HH=#}0H;0H5;00HHHuM1E11E1H1E11H11E1HE1E1E1H5$A-HIHtH+1fHnLHfHnflAD$iHHH`H1HZIHJH\|0AUE1HjL 0HH葢IHA_XMuME1E1E1E1L1E1E1L11E1LE1E1$H5AI9u8LE1E1LH5{0HLƶHIHxLH5ZL1-[H5{0H=r0H-:Lp1HL_E1LLMHEH^r0E1H 90LH9Hu,H90HtHHH=z0.HH=z0H}90H5~90.HHHuM111E1H1E1E1H11E1HE1E1E1H5F(AB-IHtH0fHnLHfHnflAFHHHuJ1E11E1H1E11H11E1HE1E1H5(AH1WHIHtHy0ASE1HjL K0HHIHA_XMdI9u1LE1E1LH5y0HLwIHLH5L-H5Py0H=ap0H8-mL(L E1LLE1HL~Hp0E1H s70LH9Hu,HW70HtHHH=x0+HH=x0H$70H5%70X,HHHuM111E1H1E1E1H11E1HE1E1E1H5,A\-HIHtH{0fHnLHfHnflAD$ɜHHH`H1UHIHCHw0PE1HjL =0LHIXZMI9L}AT1150L n0H='0L0Hcw0IHA_XME1E1E1E1L1E11L1E1E1LE1E1H5-ALH5Lx-ME1E1E1E1L1E1E1L11E1LE1E1,H5OAH-I9FH5!0uHNLL- LL-LQ|E1E1LH59v0HL3HIHuK1E11E1H1E1E1L11E1HE1E1H5,AH5u0H=l0H-xL{1LH{L{1HH{Hl01HH 30H9Hu,H30HtHHH=(u0K(HH=u0H30H530(HHHuM1E11E1H1E11H11E1HE1E1E1H52A-HIHtH0fHnLHfHnflAD$]HHH`H1A-HIHtH&0fHnLHfHnflAD$茒HHH`H1kKIHJHwm0PE1HjL 0LH赓IHXZMuK1E11E1H1E11L1E1E1HE1E1H5٢>A8I9u8L sE1E1LH5l0HLHIHzLH5LX-]H5l0H=c0H-01LHHXZHuM1E11E1H1E11H11E1HE1E1E1H5'>A鰹H5Ѥ0H=K0Hy-xHmZAU1150L zK0H=S0LԠ0H0HHA^A_HuPE1E1E1E1L1E1E1L11E1LE1E1E1H5tPAH50H=J0H-xHYH=01&HHHuOE1E11E1L1E1E1L11E1HE1E1E1H50AnH5'0H=`J0H7-xH+Y-HHHuM111E1H1E1E1H11E1HE1E1E1H5AHe0HSHHHH=0}IHtHXH5e0LHHHuJ1E11E1H1E11H11E1HE1E1H5ALH5ee0H=>I0H-xHE1XLLWAV1ɾ5x0L I0H=0L[0Hj0A_IXMjH)L0H5*0LH-kLWH=K0-1ɾH=/0AS50L H0L0H"i0[A\IHkHK0H50LH -BkLWH=K03-1ɾH=/AP50L H0Lg0Hh0#AYAZIH8kH4K0H50LH-hkLVH=K0-H=/Q150L G0L0Hh0^_IH`kHJ0H50LH--kLVH=J0@-1ɾH=b/P50H4h0L G0Ln01IXZMkHDJ0H50LH-kLUH=J0-AU1ɾ5A0L F0H=/L0Hg0A_IXMkHI0H50LH;-kL+UH=I0N-1ɾH=0/AS5Ƚ0L !F0L0Hg0>[A\IHkHPI0H5!0LH-lLTH="I0-1ɾH=/AP5V0L E0L0Hif0AYAZIHkHH0H5F0LHF-"lL6TH=H0Y-H=/Q150L -E0L0He0J^_IHlH]H0H50LH-KlLSH=/H0-1ɾH=c/P5t0He0L D0L0IXZMClHG0H50LHV-plLFSH=G0i-AU1ɾ5 0L CD0H=/L0He0YA_IXMglHkG0H5$0LH-lLRH==G0-1ɾH=1/AS50L C0L#0Hd0[A\IHlHF0H5B0LHb-lLRRH=F0u-1ɾH=/AP50L HC0L0Hd0eAYAZIHlHvF0H50LH-lLQH=HF0-H=/Q150L B0L/0Hc0^_IHlHE0H5G0LHo-mL_QH=E0-1ɾH=d/P5=0H^c0L OB0L0sIXZMlHE0H5_0LH-.mLPH=XE0 -AU1ɾ5ӹ0L A0H=/L>0Hb0A_IXM$mH E0H50LH}-QmLmPH=D0-1ɾH=2/AS5Z0L cA0LĖ0H b0[A\IHHmHD0H5 0LH-xmLOH=dD0-1ɾH=/AP50L @0LJ0H{a0AYAZIHmmHD0H5h0LH-mLxOH=C0-H=/Q15v0L o@0LЕ0H a0^_IHmHC0H50LH-mLOH=qC0#-H=dC0gHHmH=0H0NH0H5&0H=?0-mP1ɾH=/50H ]0L ?0L0IXZM nH@0H5v0LHF-=nL6NH=?0Y-AV1ɾ5J0L 3?0H=l/L0Hv\0IA_IXM3nH?0H5z0LH̽-`nLMH=u?0-S1ɾ5ٶ0L >0H=/L0H\0A\A]IHWnH)?0H5J0LHR-nLBMH=>0e-1ɾH=G/AQ5_0L 8>0L0Hr[0UAZA[IHuN1E11E1H1E1E1L11E1HE1E1E1H5yA˫H5t0H==0H-nLL1ɾH=v/P50HZ0L =0L0ZYIHuM1E11E1H1E11H11E1HE1E1E1H5xAH5ߗ0H==0H-mLKAU1ɾ50L <0H=/L;0HX0A_IXMH5a0H=<0L-mLqK1ɾH=#/AS50L t<0LՑ0HX0[A\IHH50H=D<0H-|mL K1ɾH=/AP5U0L <0Lo0H]0+AYAZIHH50H=;0H-amLJH=/Q150L ;0L 0HZ]0^_IHH5H0H=y;0HP-GmL@J11H=/P50H?}0L @;0L0dIXZMOmH5}0H=;0L-mLIAU115K0L :0H=/LF0HO0A_IXMmH540H=:0L-mL|I11H=/AS50L :0L0HT0[A\IHmH590H=R:0H)-mLI11H=/AP50L :0L0H0<AYAZIHnH5m0H=90HŸ-8nLH1H=/Q1530L 90L0H0^_IH@nH5ԅ0H=90Hd-unLTH11H= /P5ڱ0H0L T90L0xIXZMnH50H=,90L-nLGAU1150L 90H=y/LZ0H0A_IXMnH5x0H=80L-nLGH80H /H9Hu%L5/MtIH=yz0\IH=hz0H/H5/IMuPE1E1E1E1L1E1E1L11E1LE1E1E1H50rAL]HHHknLFL%-L9u:HH=0H0FH70E1H /LH9HtHH5-80Hau#H/HtHHH=601HH=%0H/H5/HHHuN1E11E1H1E1E1L11E1HE1E1E1H5qAդH50HIHtHE1HM9uOH=q0L5j0uE11H=/P5 0HT0L u60L֋0IXZM3m^H590L*u1E11E1HE11E1L11E1HE1E1H5&pAHl0H5Ń0LH=50IH-lLD11H=/AQ5@0L 50L 0Hk0AZA[IHlH5O0H=x50HO-3mL?DH=x/1V150L0L ?50H0c_AXIH:mH5e0H=50H-omLC11H=/P50HD0L 40L>0ZYIHzmH50H=40H-mL|CH=n01IHmH5n0H=t40HK- nL;C(-IHuM1E11E1H1E11H11E1HE1E1E1H59nAHP0IVLHHH=s0gHHHmLBH5uP0HIHUH5VP0H=30Hf-mLVBHJBH=301lHHHuOE1E11E1L1E1E1L11E1HE1E1E1H5HHtLE1>LLHH-MjL>H>-HHijH/0H /H9Hu-H/HHtHDH=04H!H=0H}/H5~/HHRjH5j0H=HIHjH=HLH-jH=L=Ȼ-HIHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5h'AQHJ.0H {/H9Hu-Hf/HHtHDH=~0H!H=~0H2/H53/HH@H5Zx0H<IHiH<LLH-(jLw<E1LLe<-IHAjHg-0H /H9Hu-L-s/MtIELH=}0IH=}0H?/H5@/ILMuK1E11E1H1E1E1L11E1HE1E1H5f(A鹚H5v0Lj;HHtLv;1LHHHZ-iLJ;H>;k-HHiH<,0H M/H9Hu-H8/HHtHDH=|0H!H=|0H/H5/HHiH5v0H:HIHiH:HLHn-iHZ:LR: -HIHuM11E11H1E1E1H1E1E1HE11E1H5Ie*A H+0H /H9Hu-H/HHtHDH=x{0H!H=c{0H/H5/PHHCH5t0HH9IHCiHP9LLHA-viL191LH 9M-IHiH"*0H /H9Hu-L-/MtIELH=z0IH=z0H/H5/nILMuK1E11E1H1E11L1E1E1HE1E1H5c+AtH5}0L%8HHtLE1.8LLHH-hL8H7%-HHiH(0H /H9Hu-H/HHtHDH=jy0H!H=Uy0H/H5/BHHhH5N0H:7HIHiH;7HLH(-LiH7L 79-HIHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5b-A•H'0H /H9Hu-Hw/HHtHDH=/x0jH!H=x0HC/H5D/HH@H5+0H5IHhH6LLH-hL5E1LL5-IHhH&0H /H9Hu-L-/MtIELH=Hw0IH=7w0HP/H5Q/$ILMuK1E11E1H1E1E1L11E1HE1E1H5h`.A*H50L4HHtL41LHHHˤ-1hL4H4 ܲ-HHMhH%0H ^/H9Hu-HI/HHtHDH=!v0\H!H= v0H/H5/HH6hH5Ek0H3HIHfhH3HLHߣ-hH3L3 -HIHuM11E11H1E1E1H1E1E1HE11E1H5^0A|Hu$0H /H9Hu-H/HHtHDH=t0$H!H=t0H/H5/HHCH5%j0H2IHgH2LLH-hL21LH2 -IH)hH#0H $/H9Hu-L-/MtIELH=t0>IH=s0H/H5/ILMuK1E1E11H1E11L1E1E1HE1E1H5#]1AH5i0L1HHtLE11LLHH-gLu1Hi1-HHgHg"0H /H9Hu-H/HHtHDH=r0H!H=r0H/H5/HHgH5We0H0HIHgH0H5U01L3IHgL0E1LHLHj- hHV0LN0{-IH+hHP!0H /H9Hu-H/HHtHDH=q0H!H=q0Hx/H5y/HHhH5@d0H/HIH>hH/H5>01L2HHohLl/E1LHLHO-hL?/H3/`-HHh2-IHhH 0H /H9Hu-L-k/MtIELH=p0IH=~p0H7/H58/kILMuLE1E11E1L1E1E1L11E1HE1E1H5Y6AH5b0L!.HIHtL*.H5ۉ01L91HIHjL-1LLHH58x0->L-H01HH 3/H9Hu-L-/MtIELH=Ro0IH=Ao0H/H5/.ILMuJ11E11H1E1E1H1E1E1HE11Ҹ7H5nXA鱋H5a0L,HIHtL,H501L/HIHlL,1LLHH5}m0-fL,1H /HH0H9Hu-L-/MtIELH=n0RIH=n0H/H5/ILMuK1E11E1HE11E1L11E1HE1E1H57W8AuH5R`0L+HIHtL+H5l01L.HIHkL+E1LLLH50~0j-eLZ+Hs0E1H /LH9Hu-L-/MtIELH=l0IH=l0HQ/H5R/ILMuLE1E11E1L1E1E1L11E1HE1E1H5U9A6H5_0Lk*HIHtLt*H5501L-HIHjLH*1LLHH5k0,-eL*1LHHH-!eH)L)!-IH@e-HH}eH0H /H9Hu-L-/MtIELH=@k0{IH=/k0H/H5/ILMuJ11E11H1E1E1H1E1E1HE11Ҹ<H5\TA韇H5|]0L(HIHtL(H501L+HIHlL(1H5r0LHH-AH5Z0LQ&HIHtLZ&H5301Li)HIHjL.&1HLHH5x0-nbL%H01HH /H9Hu-L-/MtIELH=~g0IH=mg0H/H5/ZILMuJ11E11H1E1E1H1E1E1HE11Ҹ?H5PA݃H5Y0L%HIHtL%H501L*(HIHlL$1H5f0LHHϔ-yaL$1LHHH-aL$H$-HHaH0H f/H9Hu-HQ/HHtHDH=e04H!H=e0H/H5/HHaH5f0H#HIHaH#HLH-aH#L#ȡ-HIHuOE1E11E1L1E1E1L11E1HE1E1E1H5NBARHK0H /H9Hu-H/HHtHDH=d0H!H=d0H/H5/HHAH5e0H"IH@aH"LLH-saLE1u"LLf" -IHaHh0H )/H9Hu-L-/MtIELH=c0IH=c0H/H5/ILMuME1E1E1E1L1E1E1L11E1LE1E1H5LCA鸀H5Yd0Li!HHtLu!1HLHHY-`LI!H=!j-HH`H;0H /H9Hu-H/HHtHDH=b0H!H=b0H/H5/HH`H5sc0H HIHaH HLHm-GaHY LQ ~-HIHuM111E1H1E1E1H11E1HE1E1E1H5HKEA H0H /H9Hu-H/HHtHDH=wa0H!H=ba0H[/H5\/OHHCH5;b0HGIH`HOLLH@-`L01LHL-IH`H!0H /H9Hu-L-/MtIELH=`0IH=`0Hi/H5j/mILMuJ1E11E1H1E11H11E1HE1E1H5IFAt}H5a0L%HHtLE1.LLHH-/`LHH=ٝ0Hҝ0 Ҡ-HHHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5HJA|-HHtHH0HHQ-_H=j-HH_HV0HH-`H(-HHC`HV0HH͌-`H -HH`Hd0HH-`Hw-HH aH0HHI-KaH5b-HHsaHS%0HH-aH -HHaH1%0HHŋ-bHޙ-HHA|rH0HH5M0H:- ^H&H=0I-H5M0H=0l-HHJH='0HWkHHH]HH0HH5M0H-HH=O0-1ɾH=3/AP5|0L 0LX0H0衕HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5O=AqH0H5k<0HHӁ-xHH=0-1ɾH=L/P5{0Hn0L 0LX0۔HHXZHuN1E11E1H1E11L1E1E1HE1E1E1H5<ALpH0H5K0HH-xHH=0%-H5fK0H=0H+HHHUH=0H0iHH6HE1Hf0LHH5K0H|-[HhH=)0-S1ɾ5uz0L f0H=/LV0HY0|HA\A]H[H5X0H=*0H-\H1ɾH=?/AQ5z0L 0LQV0H0 HAZA[H\H5[0H=0H-Q\H~H=/1V5y0LU0L {0H0蟒H_AXHS\HE0HH5W0H-\HH=0)-1ɾH= /P5$y0H0L /LWU0HZYHr\H0HH5kW0H~-\HH=K0-AU1ɾ5x0L /H=X/LT0H0蕑HA^A_H\H0HH5V0H~-\H H=/-1ɾH=/AS5(x0L /LRT0H0H[A\H\H/HH5^V0H}-\Hu H=N/-1ɾH=/AP5w0L k/LS0HU0舐HAYAZH\H/HH5U0H}-\H H=/-H=u/Q15,w0L /LFS0H_0H^_H\H/HH5SU0H~|-]Hj H=S/-AV1ɾ5v0H0H=/L Y/LR0}HA_ZH]H /HH5T0H{->]H H=/-S1ɾ59v0L /H=/Ls0L /LO0Hq-0ԋHAYAZH]H/HH5#Q0HNx-^H:H=K/]}-H=/Q15r0L 1/LN0Hk0NH^_H^H/HH5P0Hw-<^HH=/|-AV1ɾ5Jr0H 0H=/L /LN0ɊHA_ZH-^H/HH5P0HDw-[^H0H=i/S|-S1ɾ5q0L ./H=G/LM0H 0DHA\A]HO^H!/HH5O0Hv-{^HH=/{-1ɾH=/AQ5Gq0L /LM0H0轉HAZA[Hk^H/HH5 O0H7v-^H#H=l/F{-H=/1V5p0LL0L /HL07H_AXH^H%/HH5g00Hu-^HH=/z-1ɾH=c/P5Lp0H/L /LK0貈HZYH^H/HH5/0H.u-^HH=k/=z-AU1ɾ5o0L /H=/LqK0HR/-HA^A_H^H/HH5X0Ht-^HH=/y-1ɾH=/AS5Po0L /LJ0HK 0覇H[A\H^H/HH5.0H!t-_H H=f/0y-1ɾH=r/AP5n0L /LdJ0H 0 HAYAZHuM111E1H1E1E1H11E1HE1E1E1H5.AbH/HH5.0HQs-^H=H=/`x-H5-0H=/HHMHHHH^HH;/HH5}-0Hr-HH=/w-AW1ɾ5m0H 0H=/L /LH0迅HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5l-A.aHg/H5J0HHq-xHH==/w-1ɾH= /AP5l0L /L;H0H0HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5,Ag`H/H5+0HH)q-xHH=v/@v-AW1ɾ5l0H0H=/L /LmG00HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5+A_H/H5)+0HHap-xHUH=/xu-1ɾH=:/AP5:k0L K/LF0HU0hHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5+A^H/H5R*0HHo-xHH=/t-AW1ɾ5j0H0H=D/L }/LE0衂HHXZHuO1E1E1E1HE11E1L11E1LE1E1E1H5O*A^HR/H5G0HHn-xHH=(/s-1ɾH=l/AQ5i0L /LE0H0ځHHAZA[HuN1E11E1H1E1E1L11E1HE1E1E1H5)AI]H/H5(0HH n-xH1H=^/Hs-H|-H5C0H=/m-YAW1ɾH=V/5h0H0L /L#D0HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5(#AU\H/H5E0HHm-xH H=t/.r-1ɾH=p/AP5h0L /LbC0H0HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5''A[H/H5 '0HHPl-xHDH=/gq-AW1ɾ5Xg0H0H=z/L 3/LB0WHHXZHuO1E1E1E1HE11E1L11E1LE1E1E1H5'*AZH/H5I&0HHk-xH}H=/p-H5&0H=/HHHTHHH0H@0P{HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5"rAVH5@0H=/Hg-xH}11H=R/P5b0HI0L }/L=0zHHXZHuOE1E11E1L1E1E1L11E1HE1E1E1H5O"AVH5rI0H=/Hf-xH1H/HH _/H9Hu0HJ/HHtHH=o0芣H!H=Z0H/H5/'HHuPE1E1E1E1L1E11L1E1E1LE1E1E1H5d!A&Uf-IH'SH`/fHnHAFn-HHIS{f-HHH}SH0H1fInLHHfHnHflHH(@Hwg-IH8HHHIVH9toHJHPE11Hq-E1E1E1H5H81$i-1E11HE111LE1E1H5! HAXSHHI1H/H=1.HL5s.H=/H.EH5Yx0H=/H=/H/H5C/H=x0d-H/Hu HǀLAW1ɾ5;_0H/H=/L /LO:0wHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5ARH/H5(0HHCc-xH7H=/Zh-1ɾH=/AP5t^0L -/L90H/JvHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H53AQH/H5T;0HH|b-xHpH=/g-AW1ɾ5]0H/H=Ə/L _/L80uHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H506APHK/H5\/0HHa-xHH=!/f-1ɾH=/AP5\0L /L70H/tHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5iBA+PH/H55D0HH`-xHH=Z/f-AW1ɾ5T0H/H=/L /L170sHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5NAcOH/H5-0HH%`-xHH=/H/H5P0HHXO-xHLH=/oT-1ɾH=y/AP51K0L B/L%0H/_bHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5  A=H:0H5)0HHHH/HN-xHtH=/S-AW1ɾ5hJ0H/H=x/L c/L$0aHHXZHuO1E1E1E1HE11E1L11E1LE1E1E1H55  A<HX80H50HHHHE/HM-zHH=!/R-H50H= /HIH?H2HHH$LBH/E1HLH5/0HM-H H=/-R-1ɾH=Ow/AP5H0L /La#0Hz/`HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H58 A;H/H50HHOL-xHCH=/fQ-AW1ɾ5GH0Hp/H=Yv/L 2/L"0V_HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5` A:H./H50HHK-xH{H=/P-1ɾH=u/AP5G0L q/L!0H/^HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5<o A9Hg/H5(0HHJ-xHH==/O-AW1ɾ5F0Hy/H=t/L /L!0]HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5t A69H/H50HHI-xHH=u/O-1ɾH=s/AP5F0L /LC 0H/\HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5 Ao8H/H50HH1I-xH%H=/HN-AW1ɾ5IE0Hz/H=r/L /Lu08\HHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5 A7H/H5i0HHiH-xH]H=/M-1ɾH=q/AP5D0L S/L0H/p[HHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5 A6HI/H50HHG-xHH=/L-AW1ɾ5C0H/H=p/L /L0ZHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5V A6H/H5 0HHF-xHH=W/K-1ɾH=p/AP5C0L /L%0H/YHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5< AQ5H/H5!0HHF-xHH=/*K-AW1ɾ5KB0HL/H=o/L /LW0YHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5a A4H/H5/HHKE-xH?H=/bJ-1ɾH=Dn/AP5A0L 5/L0H/RXHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5n A3H+/H5(0HHD-xHxH=/I-AW1ɾ5@0H/H=Nm/L g/L0WHHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H58 A2H[.0H540HHHHH/HC-yHH=$/H-1ɾH=`l/AP5?0L /L0H/VHHAYAZHuM111E1H1E1E1H11E1HE1E1E1H5\ A2H/H5(0HHB-xHH=]/G-AW1ɾ58?0H1/H=jk/L /L$0UHHXZHuO1E1E1E1HE11E1L11E1LE1E1E1H5 AW1H/0H5"0HHHH/HB-zHH=/G-1ɾH=}j/AQ5]>0L /LO0H/ UHHAZA[HuN1E11E1H1E1E1L11E1HE1E1E1H5 Az0H/H50HH-xHHH=/kC-H50H=/HIHVH=FM0Hv&HHH4L1H5Z0HHHa/H=-HH==/B-F-HHHuM1E11E1H1E11H11E1HE1E1E1H5@ Ae,H>0H50H.=-xAV11H==e/590L 0/L0HR0MPIHA_XMUI$HE1HLH50LH=ٽ/<-LE-HHH*H=WL0HPL0s11H=hd/AR590L y/L0H0OHHA[A\HuOE1E11E1L1E1E1L11E1HE1E1E1H5B A+H!0H50HH=/HH;-xH1I-HHHuM111E1H1E1E1H11E1HE1E1E1H5 Ah*uI-HxHHtWI-HH)-LLHxHhL`xHLhHpMHHq>-LLHxHpLh2'HLpHxHLLHL>-LLHHxLp&LLxHHHH=-LLHHLx&LLHHL$HHH=-LHHLo&LLHHHH<-HHLA&HLHHH<-HL$&HRHHHHl<-HL &HLHH3<-L&LHH<-&HHL;-#&LHH;-@&H|HL;-_&L\H=%B0HHB0B11H=Z/AP5.0L H/L0H0eEHAYAZH=&H50H=/H1-k&H1H=Y/Q15.0L ݲ/L>0H/DH^_Hg&H5/H=/H1-&HmAV115).0H0H=X/L l/L0DHA_ZH&H5V0H=?/H1-&HS115-0L /H=iX/Lj0H{/&DHA\A]H&H5[/H=Ա/H0-&H11H=W/AQ5\-0L /L0HW0CHAZA[H&H570H=h/H?0- 'H+H=dW/1V15,0L0L +/H\/OCH_AXH'H5=/H=/H/-4'H11H=V/P5,0H0L /L"0BHZYH0'H5|0H=/Hl/-`'HXAU115<,0L e/H=>V/L0H/{BHA^A_HW'H5/H=)/H/-'H11H=U/AS5+0L /LS0H0BH[A\H}'H50H=/H.-'H聾11H=6U/AP5n+0L /L0H/AHAYAZH'H5/H=R/H).-'H/7-HH'H==0H=07-HH%(H==0H=0ý6-HHU(H=^=0HW=0蚽1H=1T/Q15*0L /L0H30@H^_HZ(H50H=n/HE--(H1AV1155*0H 0H=S/L 0/L0T@HA_ZH(H5 0H=/H,-(HƼS115)0L ԭ/H= S/L.0Hw 0?HA\A]H(H0HH5I 0H=/HHHW,-(HC11H=xR/AQ5P)0L I/L0H{ 0f?HAZA[H(H5[ 0H=/H+-(H׻H=Q/1V15(0LF0L ׬/H0 0>H_AXH(H5 0H=/H+-)Hm11H=bQ/P5(0H/L m/L0>HZYH )H5/H=A/H+-9)HAU1150(0L /H=P/Lk0H/'>HA^A_H2)H5l/H=ի/H*-b)H蘺11H=MP/AS5'0L /L0Hx/=H[A\HZ)H5Y/H=j/HA*-)H-11H=O/AP5b'0L 3/L0H-/P=HAYAZH)H5 /H=/H)-)H1H=8O/Q15&0L Ȫ/L)0H/<H^_H)H5/H=/Hl)-)HXAV115&0H/H=N/L W/L/{<HA_ZH)H5Q/H=*/H)-*HS115B&0L /H=N/LU/H/<HA\A]H)H5/H=/H(-**H肸11H=M/AQ5%0L /L/H /;HAZA[H!*H0HH5/H=E/HHH(-7*HH=L/1V15\%0Lm/L /H/";H_AXH0*Hh0HH5j/H=è/HHH'-I*H|11H=QL/P5$0HC0L |/L/:HZYHE*H50H=P/H''-s*HAU115$0L /H=K/Lz/H 06:HA^A_Hj*H5 0H=/H&-*H觶H4-HH*J'-IH*H11H=K/IGAS5#0L q/L/H/9H[A\H*LLIHH5/H=$/%-+H11H=_J/AP5g#0L /LQ/H/ 9HAYAZH*H5/H=/H%--+H~1H=I/Q15#0L /L/Hw/8H^_H'+H5Y/H=R/H)%-U+HAV115"0H:/H=;I/L /Lu/88HA_ZHM+H5/H=/H$-~+H誴S115G"0L /H=H/L/Hs/7HA\A]Hx+H5S/H=|/HS$-+H?11H=4H/AQ5!0L E/L/H0b7HAZA[H+H50H=/H#-+HӳH2-HH+v$-IH$,HH=tG/1IGV15=!0L/L /H/6H_AXH$,LLIFHH5/H=Q/+#-9,H11H=F/P5 0H/L /Lx/;6HZYH3,H5/H=/H"-d,H讲AU115j 0L /H=4F/L/Hf/5HA^A_H],H5F/H=/HV"-,HB11H=E/AS50L H/L/H"/e5H[A\H,H5/H=/H!-,Hױ11H=,E/AP50L ݢ/L>/H7/4HAYAZH,Hg0HH5 /H=/HHHg!-,HS1H=D/Q15!0L Z/L/H/w4H^_H,H0HH5/H=/HHH -,HҰAV1150H/H=C/L ѡ/L2/3HA_ZH,H5/H=/H{ -,HgS115L0L u/H=NC/L/HX/3HA\A]H,H 0HH5*/H=+/HHH- -H11H=B/AQ50L /LK/H/3HAZA[H-H$ 0HH5/H=/HHHt--H`H=B/1V15N0L/L `/H02H_AXH-H 0HH5d0H=%/HHH-+-Hޮ11H=sA/P50H/L ޟ/L?/2HZYH%-H5/H=/H-S-HuAU115y0L /H=@/L/H5/1HA^A_HJ-HE 0HH5/H=8/HHH-c-H70-HH-H/H `c/H9Hu-HKc/HHtHDH=/ZH!H=n/Hc/H5c/;[HH_-HH5@/HK--H7HP/H b/H9Hu-Hb/HHtHDH=,/YH!H=/Hhb/H5ib/ZHHU-HH5/H--H蘬H/H b/H9Hu-Ha/HHtHDH=/`YH!H=x/Ha/H5a/YHHP-HH5b/H -z-HH/H Sa/H9Hu-H>a/HHtHDH=f/XH!H=Q/H a/H5 a/^YHHF-HH5/Hn-p-HZHs/H `/H9Hu-H`/HHtHDH=/"XH!H=/H[`/H5\`/XHH>-HH5/H-j-H軪Hԛ/H _/H9Hu-H_/HHtHDH=/WH!H=/H_/H5_/ XHH6-HH5e/H0-`-HH5/H F_/H9Hu-H1_/HHtHDH=A/VH!H=,/H^/H5^/WHH,-HH5/H-Y-H}H/H ^/H9Hu-H^/HHtHDH=/EVH!H=/HN^/H5O^/VHH'-HH5//H-Q-HިH/H ]/H9Hu-H]/HHtHDH=/UH!H=/H]/H5]/CVHH-HH5/HS-G-H?HX/H 9]/H9Hu-H$]/HHtHDH=/UH!H=/H\/H5\/UHH-HH5/H-A-H蠧H/H \/H9Hu-Hu\/HHtHDH=/hTH!H=/HA\/H5B\/UHH -HH5/H-7-HH/H [/H9Hu-H[/HHtHDH=/SH!H=/H[/H5[/fTHH-HH5K/Hv-0-HbH{/H ,[/H9Hu-H[/HHtHDH=_/*SH!H=J/HZ/H5Z/SHH,HH5/H-(-HåHܖ/H }Z/H9Hu-HhZ/HHtHDH=x/RH!H=c/H4Z/H55Z/(SHH,HH5/H8--H$H=/H Y/H9Hu-HY/HHtHDH=/QH!H=/HY/H5Y/RHH,HH5/H--H腤H/H Y/H9Hu-H Y/HHtHDH=R/MQH!H==/HX/H5X/QHH,HH5/H--HH/H pX/H9Hu-H[X/HHtHDH=/PH!H=/H'X/H5(X/KQHH,HH5p/H[--HGH`/H W/H9Hu-HW/HHtHDH=,/PH!H=/HxW/H5yW/PHH,HH5/H-,H訢H/H W/H9Hu-HV/HHtHDH=/pOH!H=x/HV/H5V/ PHH,HH5J/H-,H H"/H cV/H9Hu-HNV/HHtHDH=/NH!H=/HV/H5V/nOHH,HH5/H~-,HjH/H U/H9Hu-HU/HHtHDH=_/2NH!H=J/HkU/H5lU/NHH,HH5/H-,HˠH/H U/H9Hu-HT/HHtHDH=/MH!H=/HT/H5T/0NHH,HH5}/H@-,H,HE/H VT/H9Hu-HAT/HHtHDH=9/LH!H=$/H T/H5T/MHH,HH5/H-,H荟H/H S/H9Hu-HS/HHtHDH=/ULH!H=/H^S/H5_S/LHH,HH5//H-,HH/H R/H9Hu-HR/HHtHDH=/KH!H=/HR/H5R/SLHH,HH5/Hc-,HOHh/H IR/H9Hu-H4R/HHtHDH=t/KH!H=_/HR/H5R/KHH,HH5 /H -,H谝HɎ/H Q/H9Hu-HQ/HHtHDH=/xJH!H=/HQQ/H5RQ/KHH,HH5j/H% -,HH*/H P/H9Hu-HP/HHtHDH=6/IH!H=!/HP/H5P/vJHH,HH5/H -,HrH/H

H!H=/HC/H5C/>HH],H55/H1IH,HאH5`/HL-,L谐HɁ/H ZC/H9Hu-HEC/HHtHDH=U/x=H!H=@/HC/H5C/>HHx,H5Y/H1KHH,HH5/HH,,HH/H B/H9Hu-HqB/HHtHDH=/<H!H=|/H=B/H5>B/Q=HH,H5/H1臒IH,HOH5@/HL8,,L(HA/H A/H9Hu-HA/HHtHDH=/;H!H=/HiA/H5jA/<HH,H5/H1ÑHH,H苎H5/HHt,-HdH}/H @/H9Hu-H@/HHtHDH= /,;H!H=/H@/H5@/;HH,H5%/H1IH,HǍH5/HL,!-L蠍H~/H @/H9Hu-H?/HHtHDH=/h:H!H=/H?/H5?/;HH,H5I/H1;HH-HH5/HH,=-H܌H}/H 6?/H9Hu-H!?/HHtHDH=9/9H!H=$/H>/H5>/A:HH-H5/H1wIH4-H?H5/HL(,X-LH1}/H b>/H9Hu-HM>/HHtHDH=u/8H!H=`/H>/H5>/}9HH#-H5/H1賎HHR-H{H5,/HHd,v-HTHm|/H =/H9Hu-Hy=/HHtHDH=/8H!H=/HE=/H5F=/8HH@-H5/H1IHl-H跊H5X/HL,-L萊H{/H H=y/HH.H5+/H=4v/H ,+/H11H= /AP5L/L u/L^/H/HAYAZH"/H5/H=u/H,P/H苄1H=/Q15/L u/L/H/H^_HJ/H5/H=_u/H6,{/H"H;u/H <6/H9Hu-H'6/HHtHDH=/0H!H=/H5/H55/1HHE/H&HH/H芃H~Ht/H 5/H9Hu-Hs5/HHtHDH=/F0H!H=~/H?5/H5@5/0HHR/H5/HۂHH/H߂Hs/H 4/H9Hu-H4/HHtHDH=D//H!H=//H4/H54/D0HH_/HHHH/H@H4H(H=a/1JO IH/H5G/H= s/H,/L1ɾH=/P5J/H/L r/LE/IXZM/H+/H5/LIHH t/H{,/LkH=s/,x,IH0Hu,H5/H5,yK1E11E1H1E11L1E1E1HE1E1H5;_AAS1ɾH=/5P/L q/LJ/H˙/A\A]HHuLLI蔀H%s/H5/HHv,/HfH=r/,1ɾH=/AP5/L \q/L/H./yAYAZHH/Hr/H5/HH,/HH=|r/,H=R/Q15Y/L p/LC/H/^_HH/H2r/H5[/HH,/HsH=r/,1ɾH=/P5/H2/L cp/L/HXZH/Hq/H5/HH , 0H~H=q/,H/,H5X/H=o/H,yN1E1E11H1E1E1L11E1HE1E1E1H5ӫAH5/H=o/Hg,xS1ɾH=/5/L go/L/HA/A\A]HHw/Hr/H5/HH,/H}H=r/,H=r/]HH/H5B/H=n/H,yNE1E1E1E1LE1E11L1E1E1LE1E1H5AH5ь/H=zn/HQ,xHE}H=Y /Q15/L In/L/Hx/f^_HHS/Hq/H5/HH,/H|H=q/,1ɾH= /P5h/H/L m/L+/HXZH|/HQq/H5j/HHr,/Hb|H=#q/,AV1ɾ5/L _m/H= /L/Hb/uA_HXH/Hp/H5/HH,/H{H=p/ ,1ɾH= /AS5/L l/L?/Hh/A\A]HH/Hlp/H5u/HH},/Hm{H=>p/,1ɾH= /AP5/L cl/L/H%/AYAZHH/Ho/H5/HH,0HzH=o/,H=Y /Q15/L k/LJ/H/^_HH0Ho/H5/HH,K0HzzH=[o/,1ɾH= /P50/HY/L jk/L/HXZHD0Ho/H5 /HH,r0HzH=n/%,AV1ɾ5/L j/H= /LY/H/A_HXHk0Hn/H5/HH,0HyH=yn/,1ɾH=/AS5M/L ~j/L߿/Hp/A\A]HH0H4n/H5/HH,0H yH=n/0,1ɾH=/AP5/L j/Ld/H-/ AYAZHH0Hm/H5/HH,0HxH=m/,H=Y/Q15h/L i/L/H{/^_HH0HQm/H5"/HH*,1HxH=#m/=,1ɾH=/P5/H!}/L i/Lk/.HXZH 1Hl/H5/HH,<1HwH=l/,AV1ɾ5/L h/H=/L/Hz/A_HXH31Hol/H50/HH8,a1H(wH=Al/K,1ɾH=/AS5/L h/L/Hz/;A\A]HHY1Hk/H5/HH,1HvH=k/,1ɾH=/AP5/L g/L/Hy/AYAZHH1Hk/H5:/HHB,1H2vH=[k/U,H=Y/Q150/L )g/L/HKy/F^_HH1Hk/H5 /HH,1HuH=j/,11H=/P5/H/L f/L/HXZH1H/H5/HH={f/HHK,1H;uAV1ɾ5\/L Ef/H=/L/H/[A_HXH2H5j/H5֟/HH,12HtH=j/,1ɾH=/AS5/L e/L%/H./A\A]HH'2Hi/H5[/HHc,U2HStH=i/v,1ɾH=/AP5p/L Ie/L/H/fAYAZHHM2HOi/H5/HH,~2HsH=!i/,H=_/Q15/L d/L0/Hx/^_HHw2Hh/H5h/HHp,2H`sH=h/,1ɾH=/P5/Ho/L Pd/L/tHXZH2Hoh/H5/HH,2HrH=Ah/ ,AV1ɾ5$/L c/H=/L?/Hpm/A_HXH2Hg/H5n/HH~,2HnrH=g/,1ɾH=.AS5/L dc/LŸ/Hl/A\A]HH2Hg/H5/HH,3HqH=Tg/,1ɾH=.AP58/L b/LJ/H[/AYAZHH3Hg/H5/HH,C3HxqH=f/,H=_.Q15/L ob/Lз/H}/^_HH>3Hf/H5/HH,o3HqH=f/#,H b/H "/H9Hu-H"/HHtHDH=/H!H=/H"/H5"/XHH53H5/HPpHHh3HTpH5-~/H!pIH3H)p,HH3Hb/fInfHnHflC,IH3,IH4H/fHn1LHfHnH,flM}(AEHH8IH"4HHSH9tdHJHPE1E1H#,H5^H81y,11E1H1E1E1HH5rAH14L oLoHHa/H=Sj-Hk-H=d/H>k-PyK1E1E1E1H111H1E1E1HE1E1H5ߛAH=Kd/H55^,HtH ",H9Hu$HM,Hp(HH $HP(HJ H5/H=c/EPH=c/,2H.kH=`/Q,H5/H=`/tHHYH=3/HcIH2HjH`/H5q/LH,2LjH=`/,AU1ɾ55/L [/H=.L/Hn/A^A_IH2H5`/H5/LHF,2L6jH=`/Y,1ɾH=;.AS5/L ,[/L/HNn/I[A\IH2H_/H5Ĕ/LH,3LiH=_/,1ɾH=.AP5I/L Z/L/Hԁ/AYAZIH3HH_/H5I/LHQ,43LAiH=_/d,H=.Q15/L 8Z/L/H/U^_IH,3H^/H5ѓ/LH,Z3LhH=^/,AW1ɾ5m/Hfo/H=_.L Y/L/IXZMS3Hg^/H5X/LH`,3LPhH=9^/s,S1ɾ5/L NY/H=.L/Hn/dA\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5 AH]/H5/LH,3LgH=s]/,H5/H=_]/ЁIHVH=/H迿HH3L;gH$]/H5u/HH,'3H gH=\/0,H=t.Q15/L X/Le/Hn/!^_HH#3H\/H5/HH,Q3HfH=\/,AW1ɾ5Q/Hn/H=.L W/L/HXZHI3H;\/H5/HH,,y3HfH= \/?,(,HH3%,IH3HZ/HH,4Le,IH94HhZ/HH,r4Le,IH4H>Z/HHZ,4LJew,IH4HZ/HH ,55Le=,IH^5HY/HH,5Ld,IH5HY/HH,5Ld,IH!6H^Y/HHr,W6Lbd,IH6H4Y/HH8,6L(d U,IH6H Y/HH,7Lc ,IHE7HX/HH,~7Lc ,IH7HX/HH,7Lzc ,IH8HtX/HHP,A8L@cm,IHj8HBX/HH,8Lc3,IH8HX/HH,9Lb,IH-9HW/HH,c9Lb,IH9HW/HHh,9LXb,IH9HzW/HH.,&:LbK,IHQ:HHW/HH,:La,IH:HW/HH,:La!,IH;HV/HH,M;Lpa,IHv;HV/HHF,;L6a#c,IH;HV/HH ,<L`),IH9<HVV/HH,o<L`(,IH<H4V/HH,<L` ,IH<HU/HH^,2=LN`"{,IH]=HU/HH$,=L`'A,IH=HU/HH,=L_,IH >H\U/HH,Y>L_$,IH>H2U/HHv,>Lf_ ,IH>HT/HH<,?L,_)Y,IHE?HT/HH,{?L^*,IH?HT/HH,?L^,IH@HbT/HH,>@L~^,IHi@H0T/HHT,@LD^q,IH@HS/HH,AL ^&7,IH,AHS/HH,eAL],IHAHS/HH,AL],IHAH`S/HHl,(BL\]%,IHQBHR/HH2,BL"]O,IHBHR/HH,BL\H=/H/\,HHCL%,H5/HL,yNE1E1E1E1LE1E11L1E1E1LE1E1yH5AwH5x/LHD,xAP11H=s.5 /L FM/L/H/cAYAZIH^H/HIHIH[H5a/H=L/L,WBL[H= /1( IHBH5/H=L/H,BLx[H=Q/1( IHBH57/H=pL/HG,CL7[1H=N.Q15 /L >L/L/H`/[^_IHCH5F/H=L/H,RCLZ11H=.P5/H/L K/L7/IXZMZCH5]/H=K/L,CLuZL-,IE',HHCfInI$11H=.@@(fInfl@8AU5/L 6K/L/H@/SA^A_IHCHHHYH5/H=J/L,CLY11H=o.AS5/L J/L!/H/[A\IHCH/H5h/LH=J/IHV,CLFY11H=.AP5;/L LJ/L/HF/iAYAZIHCH/H5#/LH=J/IH,DLX1H=H.Q15/L I/L9/H/^_IH&DH/H5ѡ/LH=I/IHo,MDL_X11H=.P5e/HF/L _I/L/IXZMWDHf/H5/LH=-I/IH,zDLWAU115/L H/H=.LT/Hu/A^A_IHDH5Y/H=H/H,DLW11H=.AS5/L H/L/H/[A\IHDH5/H=_H/H6,DL&W1ɾH=.AP5@/L )H/L/Hki/FAYAZIHDHG/H5(/LIHHdI/H,ELVH=@I/,H=n.Q15/L G/L/Hh/^_IHEHH/H5/LH?,>EL/VH=H/R,1ɾH=.P5M/Hnh/L G/L/CIXZM7EH~H/H5/LH,hELUH=PH/,AU1ɾ5/L F/H=-.L/HM/A^A_IH_EH H/H5/LHL,EL/X,H=.Q15˼/L ,=/L/HE/I^_HH6EH>/H5e/HHͻ,fEHKH=^>/,1ɾH=b.P5[/HD/L /H5/HHU,EHEKH==/h,R,HHEH5O/LH,yL1E1E1E1H111LE1E1E1HE1E1H5ax)AAS1ɾH=S.5u/L ;/L'/HC/A\A^IHtHHHqJH=/H5/LHS,/ELCJH=/A\A^IHtH/HIHIHlEH 8/H5/LHN,(EL>EH=7/a,1ɾH=.AP5+/L 46/L/H^>/QAYAZIH EH/H5/LIHHw7/H´,H=w1/,AW1ɾ5/H7/H=.L //L&/IXZMAEH /H5h/LIHH1/H\,^ELL>H=0/o,S1ɾ5/L J//H=C.L/H6/`A\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5RkAԜHU0/H5x/LH,DL=H='0/,H5w/H=0/WIHVH=/H軕HHDL7=H//H5w/HH,EH =H=//,,H=.Q15W/L ./La/Hb5/^_HHDHh/H5r/HHHHE//H, EH<H=!//,1ɾH=E.P5֭/H4/L p-/Lт/蔿HXZHuOE1E11E1L1E1E1L11E1HE1E1E1H5iA H./H5]v/HHͫ,DH;H=^./,H5)v/H=J./VHHWH=º/HIHDHn;H./H5u/LHP,DL@;H=-/c,1ɾH=.AS5/L 6,/L/H`3/S[A\IHDHu/H5n/LIHHz-/HŪ,DL:H=V-/د,1ɾH=:.AP5/L +/L /H2/ȽAYAZIHDH -/H5zn/LHJ,EL::H=,/],H=.Q15/L 1+/L/H+I/N^_IHDH-/H5j/LHҩ,(EL9H=-/,1ɾH=.P58/H4/L */L/ּIXZM EH,/H5rp/LHZ,NELJ9H=,/m,AU1ɾ5Ϊ/L G*/H=`.L/H3/]A^A_IHEEHn,/H5_/LHߨ,sEL8H=@,/,1ɾH=Լ.AS5T/L )/L&/H3/[A\IHiEH+/H5Ջ/LHe,ELU8H=+/x,1ɾH=:.AP5/L K)/L~/HD/hAYAZIHuM111E1H1E1E1H11E1HE1E1E1H5]eAߖH/H5Io/LIHH+/H,EL7H=*/,H5o/H=*/QIHHH=/H赏HH EL17H*/H5n/HH,/EH7H=|*/&,1ɾH=Ⱥ.P5/HrB/L '/LT}/HXZHuN1E11E1H1E11L1E1E1HE1E1E1H5 d[ A鏕HP/H5m/HHHH)/H@,DH06H=)/S,H5m/H=)/vPHHGH=5/HeIHDH5Hb)/H5km/LHå,DL5H=4)/֪,S1ɾ5X/L &/H=J.L |/HTB/ǸA\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5b A;H/H5l/LIHH(/H,>DL4H=e(/,H5`l/H=Q(/"OIHEH=/HHH9DL4H(/H5l/HHo,^DH_4H='/,H=.Q15/L V%/Lz/H@/s^_HHZDH/H5k/HHHH'/H,wDH3H=_'/,1ɾH=;.P5/H?/L $/L'z/HXZHuOE1E11E1L1E1E1L11E1HE1E1E1H5` AaH"/H5j/HHHH&/H,CH3H=&/%,H5j/H=&/HMHHFH=/H7IHCH2HD&/H5=j/LH,DL2H=&/,1ɾH=ʵ.AS5:/L {#/Lx/H>/蘵[A\IHDH/H53i/LIHH%/H ,#DL1H=%/,1ɾH=.AP5/L "/LQx/H:?/ AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5_# A鄐Hu/H5h/LIHH$/H5,CL%1H=$/H,H5h/H=$/kKIHHH=*/HZHHCL0Ho$/H5`h/HH,CH0H=A$/˥,1ɾH=.P5n/H'9/L !/Lv/輳HXZHuM1E11E1H1E11H11E1HE1E1E1H5] A5H/H5g/HHHH#/H,4CH/H=w#/,H5Zg/H=c#/JHHHH=ۮ/H IH1CH/H(#/H5g/LHi,XCLY/H="/|,AU1ɾ5-/L V /H=/.Lu/HqV/lA^A_IHOCH"/H5V^/LH,|CL.H="/,1ɾH=.AS5/L /L5u/HV/[A\IHsCH;"/H5d/LHt,CLd.H= "/,1ɾH= .AP5A/L Z/Lt/HU/wAYAZIHuM111E1H1E1E1H11E1HE1E1E1H5l[i AHߋ/H5e/LIHH\!/H,CL-H=8!/,H5Ke/H=$!/GIHHH=/HąHHCL@-H /H5e/HH",9CH-H= /5,AW1ɾ5/H_T/H=.L /Lbs/%HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5Z A雋H/H5d/HHHH /HL,BH<,H=/_,H5hd/H=/FHHEH=A/HqIHBH+H/H5d/LHϛ,BL+H=h/,1ɾH=$.AP5/L /Lr/HH/ҮAYAZIHuM111E1H1E1E1H11E1HE1E1E1H5X AIH/H5b/LIHH/H,GBL*H=/ ,H5b/H=/0EIHHH=/HHHCBL*HL/H5Mb/HH},jBHm*H=/,1ɾH=.P5c/H6/L ]/Lp/聭HXZHuM1E11E1H1E11H11E1HE1E1E1H5xW AH/H5a/HHHH/H,AH)H=/,H5Ga/H=/CHHHH=/HЁIHAHL)Hu/H5`/LH.,BL)H=G/A,AU1ɾ5"/L /H=4.Luo/H,/1A^A_IHAH/H5\/LH,(BL(H=t/Ɲ,1ɾH=.AS5/L /Ln/Hc,/趫[A\IHBH(/H5\/LH9,LBL)(H=/L,1ɾH=.AP56/L /Ln/H,/<AYAZIHuM111E1H1E1E1H11E1HE1E1E1H51UFA鳆Hl/H5E_/LIHHI/Hd,ALT'H=%/w,H5_/H=/AIHHH=Y/HHHAL'H/H5^/HH,AH&H=/,AW1ɾ5/H*/H=.L /L'm/HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5SuA`H)/H5]/HHHH/H,YAH&H=/$,H5]/H=/G@HHEH=/H6~IHVAH%H/H5<]/LH,}AL%H=U/,1ɾH=).AP5/L z/Lk/HLC/藨AYAZIHtAH/H5i[/LH,AL %H=r/,,H=.Q15//L /Lak/HB/^_IHAH@/H5[/LIHH /H,AL$H=/,AW1ɾ5/HeB/H=֥.L o/Lj/蓧IXZMAH~/H5/[/LIHH/H,AL#H=_/,S1ɾ53/L /H=-.LNj/HA/ A\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5PA~H߁/H5Z/LIHH/H/,CAL#H=/B,H5Z/H=t/e=IHEH=$/HT{HH@AL"H9/H5ZZ/HH,fAH"H= /ŗ,H=ɣ.Q15/L /Lh/H@/趥^_HH_AHq/H5n/HHHH/H),|AH"H=/<,AW1ɾ5e/H:/H=.L /Lih/,HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5 OA颀H/H5Y/HHd,AHT!H=/w,H5X/H=y/;HHVH=Y/HyIHAH!H>/H5X/LH,&AL H=/,1ɾH=.AP5$/L /L.g/H8/AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5MAaHz/H5W/LIHH_/H,@L H=;/%,H5W/H='/H:IHHH=/H7xHH@LH/H5=W/HH,@HH=/,AW1ɾ5/H7/H=;.L t/Le/蘢HXZHuO1E1E1E1HE11E1L11E1LE1E1E1H5LA~H~/H5V/HHHH /H,4@HH=/ӓ,H5\V/H=/8HHFH=/HvIH0@HaH/H5V/LHC,T@L3H=l/V,1ɾH=؞.AQ5/L )/Ld/H#6/FAZA[IHK@H/H5T/LHȍ,y@LH=/ے,H=D.1V5/Ld/L /H5/̠_AXIHp@H/H5/T/LHO,@L?H=x/b,1ɾH=.P5/H#/L //Lc/SZYIHuM1E11E1H1E11H11E1HE1E1E1H5JJ:A{H/H5T/LH,)@L~H=W/,H5T/H=C/6IHYH=/HtHH'@L/H/H5iT/HH,J@HH=/$,AV1ɾ5}/L /H=7.LXb/H#/A_HXHA@H/H5k/HH,q@HH=/,1ɾH=.AS5/L } /La/H_#/蚞A\A]HHj@H{/H5k/HH,@H H=M//,1ɾH=.AP5/L  /Lca/H"/AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5HAyH/H5hR/HHX,@HHH=/k,H54R/H=u/4HHYH=M/H}rIH@HH:/H5Q/LHۉ,A@LH= /,),IH_@1ח,HH@Hd /HL,@Hp,HHAH2 /HLF,:AH6c,HHdAH /HL ,AH),HHAH /HL҈,AH,HH)BH /HL,cBH,HHBH /HL^,BHN{,HHBH0 /HL$,(CHA,HHSCH /HL,CH,HHCH /HL,CH ͕,HHDH /HLv,NDHf,HHxDHH /HL<,DH,Y,HHDH /HL,EH,HH=EH /HLȆ,wEH,HHEH /HL,EH~,HHFH /HLT,OH`/HL,xOH&,HHOH/HL,OHǎ,HHPH\/HLp,=PH`H=/L-/M11H=B.P5/HA/L M/LV/qIXZM2PHj/H5@/LH=/IH,UPLAU115/L /H=.LBV/HD/A^A_IH[PH5wD/H=/H,PLw1ɾH=).AS5!/L z/LU/H,/藒[A\IHPH/H59/LH,PL H=S/-,1ɾH=.AP5/L /LaU/HR,/AYAZIHPH/H58/LH~,PLH=/,H=.Q15=/L .LT/H+/裑^_IHPH/H5E/LH'~,QLH=`/:,1ɾH=\.P5̀/HV+/L .LhT/+IXZMuN1E11E1H1E11L1E1E1HE1E1E1H5c:AlH5LV/H=.Ll},PL\ S1ɾ5./L g.H=.LS/H*/}A\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H59AkH5X/H=.H|,=PL H=.Q15}/L .LS/HH*/ˏ^_IHEPH/H5O6/LHO|,rPL? H=/b,1ɾH=$.P5 /H)/L /.LR/SIXZMjPHF/H55/LH{,PL H=/,AU1ɾ5~/L .H=}.LR/Hg)/ڎA^A_IHPH/H5LB/LH\{,PLL H=/o,1ɾH=.AS5)~/L B.LQ/H(/_[A\IH/H5S/H=.Hz,PL 1ɾH=k.AP5}/L .L=Q/Hn(/AYAZIHxH5zV/H=.Hz,yPLr H=K^/1 IHPH51^/H=j.HAz,PL1 H=.Q15,}/L 5.LP/H'/R^_IHPH.H5R/LHy, QL H=.~,1ɾH= .P5|/H/L .LP/ڌIXZMQHe.H5G/LH^y,5QLN H=7.q~,[,IHXQH^/H5C/Hy,yJ1E11E1H111H1E1E1HE1E1H5q4tAgH]/H5\/Lx,xH]/H59/Lx,xL%,H5T/LLzx,^AS1ɾH=.5|{/L u.LN/H/蒋A^A_HHLLIEH.H5X/HHx,uPHH=.},1ɾH=.AP5z/L .LHN/H/AYAZHHmPH.H5?/HHw,PHvH=_.|,,HHPH5?/LHDw,yK111E1HE1E11H1E1E1HE1E1H52AfAW1ɾH=.5z/H/L .LRM/IXZMwIHHH.H5Y/LHv,=PLwH=`.{,1ɾH=<.AS5y/L m.LL/H//芉[A^IH5PH.H5=?/LH v,cPLH=. {,1ɾH=.AP5y/L .LTL/H/AYAZIHXPH.H5Q/LHu,PLH=k.z,~,IHPH5F/LHPu,yJ111E1H1E11H1E1E1HE1E1H507A"dP1ɾH=.55x/H/L .L`K/#HXZHyLLIEH.H54A/HHt,*PHH=m.y,AV1ɾ5w/L .H=ځ.LJ/H\/藇A_HXH!PH!.H5@/HHt,OPH H=.-y,1ɾH=O.AS5?w/L .LaJ/H/A\A]HHGPH.H5@/HHs,xPHH=x.x,1ɾH=.AP5v/L .LI/H/袆AYAZHHnPH+.H59/HH$s,PHH=.7x,H=.Q15Zv/L .LlI/H}/(^_HHPH.H5D8/HHr,PHH=.w,1ɾH=.P5u/H.L .LH/谅HXZHPH\/H5R/HHHH .H#r,PHH=.6w,AV1ɾ5ou/L .H=~.LjH/H{/&A_HXHPH]/H5Q/HHHH.Hq,PHH=q.v,1ɾH=-~.AS5t/L ~.LG/HX.蛄A\A]HHPHL_/H5m+/HHHH .H q, QHH=.v,1ɾH=}.AP5at/L .LSG/H.AYAZHHQHV/H5//HHHH}.Hp,"QHpH=Y.u,H=|.Q15s/L g.LF/H.脃^_HHQH_/H53/HHHH.Ho,8QHH=. u,1ɾH=,|.P5]s/H.L .L8F/HXZH3QHZ/H56/HHHHk.Hno,SQH^H=G.t,AV1ɾ5r/L [.H=t{.LE/H.qA_HXHJQHY/H5t//HHHH.Hn,gQHH=.s,1ɾH=z.AS5Xr/L .L*E/H.A\A]HH_QHo.H5P/HHhn,QHXH=A.{s,1ɾH==z.AP5q/L N.LD/H .kAYAZHHQH.H5EQ/HHm,QHH=.s,H=y.Q15sq/L .L5D/H.^_HHQHt]/H5 J/HHHHa.Hdm,QHTH==.wr,1ɾH=x.P5p/H.L D.LC/hHXZHQH^/H53/HHHH.Hl,QHH=.q,u,HH RHm,H5/R{IXZMLRHuR/H5~"/LIHH.Hg,kRLH=.l,S1ɾ5k/L .H= r.L >/H.zA\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5"dA=VH.H51/LHf,QLH=.l,H5S1/H=.5IHVH=u/H$OHHQLH.H5 1/HHf,RHrH=[.k,1H=p.Q15cj/L l.LRH|.H5/HHmZ,lRH]H=N._,H=Db.Q15^/L T.L0/H /qm^_HHgRH.H5e/HHY,RHH=._,AW1ɾ5q^/H /H=a.L .L50/lHXZHRH.H5/HH|Y,RHlH=].^,1ɾH=a.AS5]/L b.L//H /lA]A^HHRH.H51/HHY,RHH=.^,1ɾH=v`.AP5]/L .LH//H /lAYAZHHRH.H5~4/HHX, SHvH=g.],H=_.Q15]/L m.L./H.k^_HHSH-.H5~$/HHX,7SHH=.!],1ɾH=C_.P5\/H.L .LO./kHXZH0SH.H5n0/HHW,^SHH=.\,`,HHSH5/LHTW,yL1E1E1E1H111LE1E1E1HE1E1H5 A$FAS1ɾH=4^.5[/L .Lh-/H.$jA\A^IHtHHHH.H59/LHV,RLH=.[,1ɾH=].AP59[/L z.L,/H.iAYAZIHRH@/H5//LIHH.HV,SLH=.[,H=\.Q15Z/L .LP,/H. i^_IH SH.H5/LHU,8SLH=.Z,1ɾH=E\.P5FZ/HW.L p.L+/hIXZM0SH7.H5H/LHU,^SLH= .+Z,AU1ɾ5Y/L .H=[.L_+/H.hA^A_IHUSH.H51/LHT,SLH=.Y,1ɾH=[.AS5bY/L .L*/H.g[A\IHySHB.H5 /LH#T,SLH=.6Y,1ɾH=xZ.AP5X/L .Lj*/H.&gAYAZIHSH.H5,/LHS,SLH=.X,H=Y.Q15~X/L .L)/H.f^_IHSHO.H5./LH0S,SL H=!.CX,1ɾH=EY.P5X/H/.L .Lq)/4fIXZMSH.H5,/LHR,TLH=.W,AU1ɾ5W/L .H=X.L(/H.eA^A_IHTH A:H.H5/LHK,SLH=.P,H5/H=.IHVH=Z/H3HHSLNHO.H5/HH0K,SH H=!.CP,H=O.Q15fP/L .Lx!/H.4^^_HHSH.H5+/HHJ,SHH=.O,1ɾH=MO.P5O/H'.L .L /]HXZHSH=/H5+/HHHHD.H/J,SHH= .BO,AV1ɾ5{O/L .H=N.Lv /H?.2]A_HXHSH|7/H5/HHHH.HI,THH=.N,S1ɾ5N/L .H=M.L/H.\A\A]HHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5 A8H.H5^/HHH,SHH=.M,H5*/H=.HHVH=W/H1IHSHH.H5/LHaH,SLQH=R.tM,H=L.Q15M/L H.L/HJ.e[^_IHSH2/H5q/LIHH.HG,SLH=.L,AW1ɾ5H=?.aL,S1ɾ5L/L <.H=5K.L/Hw.RZA\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5M>A5H.H5/LHF,XSLxH=y.K,H5/H=e.IHVH=}U/H.HHSSL)H*.H5/HH F,xSHH=.K,H=I.Q15yK/L .LS/H$.Y^_HHtSHZ3/H5{ /HHHH.HE,SHrH=s.J,AW1ɾ5J/H.H=(I.L a.L/XHXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5xA3H.H5M/HHD,SHH=.I,H5/H=.HHVH=S/H,IHSH^H_.H5/LH@D,;SL0H=1.SI,11H=G.AP5I/L ).L/H/FWAYAZIH5SH5/H=.HC,iSL1H=VG.Q15eI/L .L'/H`"/V^_IHrSH5F"/H=.HnC,SL^1ɾH=F.P5 I/H2.L [.L/VIXZMSH.H5s/LHC,SLH=.H,L,IHTHP,H56 /HB,yK1E11E1H111LE1E1E1HE1E1H5=A1AS1ɾH=E.5 H/L q.L/H.UA\A^HHuLLIEH.H5=%/HHA,zSHH=.G,1ɾH=E.AP5G/L .LD/H.UAYAZHHrSH.H5Z/HHA,SHrH=k.F,H=yD.Q15(G/L i.L/H.T^_HHSH 1/H5/HHHH.H@,SHH=. F,1ɾH=C.P5F/H.L .L:/SHXZHSHH2/H5I/HHHH}.Hp@,SH`H=Y.E,AV1ɾ5,F/L ].H=C.L/H`.sSA_HXHSH .H5/HH?,SHH=. E,1ɾH=B.AS5E/L .L=/H.RA\A]HHSH*/H5/HHHHw.Hj?,THZH=S.}D,1ɾH=A.AP5/E/L P.L/Hz.mRAYAZHH TH)/H5/HHHH.H>,*THH=.C,H=5A.Q15D/L ſ.L&/H.Q^_HH%TH)/H5.HHHHb.HU>,BTHEH=>.hC,1ɾH=@.P5+D/H.L 5.L/YQHXZH.AP5B/L .L/H.OAYAZHHuM111E1H1E1E1H11E1HE1E1E1H5nA/+H0//H5 /HHHH.H;,THH=ɿ.@,H5/H=.HHHHqIHTHH.H5/LHj;,(TLZH=S.}@,AW1ɾ5fA/H.H==.L I.L/mNIXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5jA)H /H5M/LIHH.H:,SLH=}.?,H5/H=i.IHEH=I/H"HHSL5H..H5/HH:,SHH=.*?,1ɾH=;.AP5@/L .L^/Hg.MAYAZHHuM111E1H1E1E1H11E1HE1E1E1H5%A(Hj.H5/HHS9,FSHCH=<.f>,H5/H=(.HHYH=HH/Hx!IHDSHH.H5>/LH8,hSLH=.=,AW1ɾ5>/H+.H=<:.L .L/KIXZM_SH"/H5%/LIHHY.HL8,|SL<H=5._=,S1ɾ5a>/L :.H=9.L/H}.PKA\A^IHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5K[A&H%"/H5N.LIHH.Hu7,RLeH=^.<,H5 .H=J.IHEH=jF/HHHRLH.H5.HH6,SHH=. <,H=/8.Q15=/L ߷.L@ /H.I^_HH SH!/H5/HHHH|.Ho6,-SH_H=X.;,1ɾH=7.P5+.5h3/L .L/H.?A\A^IHgVH/L%;,H5A.IHL9cuHNLHy+, LHS6,oVL11H=*.AP52/L .Lb/H.?AYAZIHuVL9cH5C.uHNHH+, HH5,VL胻E1E1HH.H5.H=-,]IHVH5.H=f.H=+,VL-H%H=V /1G HHVH5D /H=.H*,.WHH=X).Q151/L .LI/H.>^_HH4WHP.H5.HH*,dWHyH="./,AW1ɾ5e1/H.H=(.L h.L/=HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5=AHc/H5.HHHHp.H),VH裹H=L..,H5w.H=8.HHEH=8/HIHVHTH.H5..LH6),VL&H=ϫ.I.,1ɾH=K'.AP50/L .L}.Hf.9<AYAZIHVH.H5 /LH(,$WL諸H=T.-,H=&.Q15//L .L.H.;^_IHWH .H5.LHC(,KWL3H=ܪ.V-,1ɾH=&.P51//H.L #.L.G;IXZMDWH.H5.LH',uWL軷H=d.,,AU1ɾ5./L .H=q%.L.H{.:A^A_IHlWH57/H=.HW',WLG1ɾH=$.AS5a./L J.L.H.g:[A\IHWH5/H=.H&,WL1ɾH=s$.AP5./L .LE.H>.:AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5DAxH/H5".LIHH.H)&,`WLH=ڨ.<+,H5.H=ƨ._IHHH躚HHbWLѵH.H5.HH%,WH裵H=d.*,AW1ɾ5,/H.H=".L .L.8HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5VA,H/H5.HHHH.H$,VHʹH=.),H5.H=z.HHEH=3/H IHVH~H?.H5X.LH`$,WLPH=.s),1ɾH=!.AP5u+/L F.L.H.c7AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5s{AH;/H5.LIHH`.H#,VL{H=<.(,H5.H=(.IHHH=2/H HHVL,H.H5V.HH#,VHH=.!(,AW1ɾ52*/Hs.H= .L .LN.6HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5 AH /H5i.HHHH .H8",+VH(H=.K',H5$.H=դ.nHHEH=-1/H] IH'VHٱH.H5.LH!,KVL諱H=l.&,1ɾH=.AP5(/L .L.H.4AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5A5H֣.H5g.LH ,ULH=. &,H53.H=.-IHYH=//H HHUL蘰HY.H5.HHz ,UHjH=+.%,1ɾH=O.P5'/H.L Z.L.~3HXZHUH.H5J/HH ,VHH=.%,AV1ɾ5>'/L .H=.LI.Hz.3A_HXHVHg.H5@/HH,EVHxH=9.$,1ɾH=.AS5&/L n.L.H.2A\A]HH;VH.H5.HH ,kVHH=. $,1ɾH=.AP5R&/L .LT.H.2AYAZHHdVHq.H5b.HH,VH肮H=C.#,H=.Q15%/L y.L.H.1^_HHVH.H5.HH,VH H=ˠ.-#,1ɾH=O.P5p%/HY.L .L[.1HXZHVH.H5.HH,VH蒭H=S.",AV1ɾ5%/L .H=.L.H.0A_HXHVH5.H=X.H/,WH1ɾH=1.AS5$/L ".L.Hd.?0A\A]HHWH5.H=.H,NWH踬1ɾH=.AP5B$/L .L.He./AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5qAO H/H5.HHHHŞ.H,VHH=.!,H5.H=.6HHHH葐IHVH訫HY.H5r.LH,VLzH=+. ,AW1ɾ5#/H7.H=0.L i.L..IXZMuO1E1E1E1HE11E1L11E1LE1E1E1H5A He/H5.LIHHz.H,qVL襪H=V.,H5y.H=B.IHFH=)/HHHlVLVH.H50.HH8,VH(H=ٜ.K,1ɾH=.AQ5!/L .L.H.;-AZA[HHuN1E11E1H1E1E1L11E1HE1E1E1H5JAH/H5.HHHH'.Hb,VHRH=.u,H5v.H=.HHGH=W(/HIHVHH.H5-.LH,)VLըH=.,1ɾH=Z.P5k /H.L ř.L&.+ZYIHuM1E11E1H1E11H11E1HE1E1E1H5AbH.H5.LH$,ULH=Ś.7,H5`.H=.ZIHYH='/HIHHULŧHv.H5.HH,UH藧H=H.,AV1ɾ5;/L .H=.L.HW.*A_HXHUH.H5u.HH-,UHH=Ι.@,1ɾH=b.AS5/L .Lt.H.0*A\A]HHUH.H5j.HH,$VH袦H=S.,1ɾH=.AP5O/L .L.Hr.)AYAZHHVH.H5.HH7,HVH'H=ؘ.J,H=..Q15/L .L.H.;)^_HHCVH.H5W.HH,tVH该H=`.,1ɾH=.P5m/HN.L .L.(HXZHmVH.H5O.HHG,VH7H=.Z,AV1ɾ5/L 4.H=.L.H.J(A_HXHVH5.H=.H,VHĤ1ɾH=v.AS5/L Ǖ.L(.HY.'A\A]HHVH5e.H=.Hm,WH]1ɾH=.AP5?/L `.L.H:.}'AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5_AHU.H5.HHHHr.H,VH蕣H=N.,H5Y.H=:.۽HHHH6IHVHMH.H5.LH/,VLH=ؕ.B,AW1ɾ5/H .H=u .L .Lo.2&IXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5AqAH .H5b.LIHH&.HY,&VLIH=.l,H5.H=.菼IHEH=N!/H~HH"VLH.H5.HH,IVH̡H=.,1ɾH= .AP5/L ’.L#.H.$AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5AVH.H5`.HHHHԓ.H,UHH=.,H5.H=.=HHHH=/H,IHUH訠Ha.H5.LH,ULzH=3.,AW1ɾ5n/Hw.H= .L i.L.#IXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5AH.H55.LH,gUL赟H=n.,H5.H=Z.IHVH=/HHHdULfH.H5.HHH,UH8H=.[,1ɾH== .AP5-/L ..L.H(.K"AYAZHH~UH.H5.HH,UH轞H=v.,H=.Q15/L .L.H.!^_HHUH,.H5 .HHU,UHEH=.h,1ɾH= .P5K/HT.L 5.L.Y!HXZHUH.H5.HH ,VH͝H=.,AV1ɾ5/L ʎ.H=c.L$.H. A_HXHUH:.H5.HHc ,,VHSH= .v,1ɾH=.AS5h/L I.L.H.f A\A]HH#VH.H5.HH ,UVH؜H=.,1ɾH==.AP5/L ΍.L/.H.AYAZHHMVH5T.H=.Ht ,VHdH=.Q15/L h.L.H*.^_HHVH5.H=9.H ,VHAW1ɾ5A/Hb.H=#.L .L]. HXZHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5/VAH.H5@.HHHH$.HG ,DVH7H=.Z,H5.H=.}HHEHIHGVHH.H5.LH ,kVLH=.,1ɾH=.AP5/L .L.H .AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5hAKH<.H5.LIHHٌ.H ,ULH=.,H5.H=.2IHHH=/H!HHUL蝙Hf.H5w.HH ,VHoH=8.,1ɾH=T.P5/Hε.L _.L.HXZHuM1E11E1H1E11H11E1HE1E1E1H5AH.H5..HH,UH讘H=w. ,H5.H=c.HHYH=/HIHUH_H(.H5.LHA,UL1H=.T ,AU1ɾ5}/L ..H=.L.H.DA^A_IHUH.H5.LH,UL趗H=. ,1ɾH=[.AS5/L .L .H.[A\IHUH3.H5.LHL,UL<H=._ ,1ɾH=-AP5/L 2.L.Ht.OAYAZIHUH.H5i.LH,VLH=. ,H=(-Q15/L .L.H.^_IHVH@.H5a.LHY,CVLIH=.l ,1ɾH=-P5/Hx.L 9.L.]IXZM;VH5.H=.L,pVLؕAV1ɾ5Y/L .H=-L<.H .A_IXMuVH5z.H=.L,VLrH=s.1b IHVH5Y.H=j.HA,WL1H=.1Sb IH1WH5.H=).H,fWLH=.1b IHWH5.H=.H,WL诔H=8.1a IHWH5.H=.H~,"XLnH=.1a IHJXH5.H=f.H=,XL-H=~.1Oa IHXH5d.H=%.H,XLH=.1a IHYH5.H=.H,;YL諓 ,IHhYH.H@LHHH.HHPH .HHPH].HHPH='.*HHhYL6H5.HIHYH5.H=-.H,YLH5U.HuIHYH5:.H=.H,$ZL貒H5K.H3IHIZH50.H=.H,}ZLpH5i.HIHZH5N.H=g.H>,ZL.H&,HHZH.HSHHHHl.HHBH=v.IH[H͑H5f.LNHHE[H5K.H=Ă.H,w[H苑H5.L HH[H5.H=.HY,[HILA.,IH[H.IULHHH'.HHBH=.ܵHH\LH5.HiIH?\H5n.H=߁.H,t\L覐H5.H'IH\H5.H=.Ht,\LdH\H.H5.H=g.A,\AS11H=L-5/L ?.L.H.\A\A]HH\H5.H=.H+*]HՏ11H=-AP5b/L ۀ.L<.H.AYAZHH0]H5q.H=.H+g]Hq1H=H-Q15/L x.L.H.^_HHs]H5.H=I.H +]HH).HB.H5.H+]H=. ,1ɾH=-P5t/He.L .L7.HXZH]H.H5.HH~+]HnH=.,AV1ɾ5 /L k.H=-L.H.A_HXH]H;.H5.HH+^HH= .,1ɾH=Y-AS5/L ~.LK.H.A\A]HH^H.H5.HH+G^HyH=.,1ɾH=-AP5/L o~.L.Hٝ.AYAZHH?^HE.H5V.HH+m^HH=.!,H=%-Q15/L }.LV.H7.^_HHg^H̀.H5.HH+^H膌H=.,1ɾH=-P5</Hm.L v}.L.HXZH^HU.H5.HH+^HH='.1,AV1ɾ5/L }.H=-Le.HΛ.!A_HXH^H.H5T.HH+^H蔋H=.,1ɾH=Y-AS5Y/L |.L.HD.A\A]HH^H`.H5.HH)+_HH=2.<,1ɾH=-AP5/L |.Lp.H.,AYAZHH _H~.H5ε.HH+;_H螊H=~.+H=%-Q15t/L {.L.H7. ^_HH6_Hm~.H5^.HH6+d_H&H=?~.I+1ɾH=-P5/Hݙ.L {.Lw.: HXZH^_H}.H5.HH+_H讉H=}.+AV1ɾ5/L z.H=-L.HN. A_HXH_H{}.H5t.HHD+_H4H=M}.W+1ɾH=Y-AS5!/L *z.L.Ht.G A\A]HH_H}.H5!.HH+_H蹈H=|.+1ɾH=-AP5/L y.L.H. AYAZHH_H|.H5.HHN+`H>H=W|.a+H=%-Q15</L 5y.L.H.R ^_HH`H |.H5.HH+2`HƇH={.+1ɾH=-P5/H.L x.L. HXZH-`H%.H5N.HHHHz{.HM+J`H=H=V{.`+AV1ɾ5Q/L :x.H=-L.H.P A_HXHB`H {.H5S.HH+t`HÆH=z.+1ɾH=H-AS5.L w.L.H+. A\A]HHl`Hz.H5.HHX+`HHH=az.k+1ɾH=-AP5e.L >w.L.H0.[ AYAZHH`H|.H5.HHHHy.H+`H輅H=y.+H=-Q15.L v.L.H.^_HH`Hy.H5t.HHT+`HDH=]y.g+1ɾH=i-P5r.HK.L 4v.L.XHXZH`Hy.H5.HH+aH̄H=x.+AV1ɾ5.L u.H=-L#.H.A_HXHaH.H5z.HHHH~x.HQ+aHAH=Zx.d+1ɾH=&-AS5~.L 7u.L.HQ.TA\A]HHaH x.H5^.HH+FaHƃH=w.+1ɾH=-AP5 .L t.L.Hޓ.AYAZHH>aHw.H5.HH[+laHKH=dw.n+H=-Q15.L Bt.L.Hl._^_HHfaH.H5.HHHHv.H+aH‚H=v.+1ɾH=G-P5.H.L s.L.HXZHaH.H5.HHHHvv.HI+aH9H=Rv.\+AV1ɾ5.L 6s.H=-L.H9.LA_HXHaHn.H5.HHHHu.H+aH讁H=u.+1ɾH=-AS5.L r.L.H.A\A]HHaHzu.H5{.HHC+aH3H=Lu.V+1ɾH=X-AP5.L )r.L.H.FAYAZHHaHt.H5X.HH+bH踀H=t.+H=-Q15..L q.L.Hq.^_HHbH.H5.HHHHlt.H?+bH/H=Ht.R+1ɾH=-P5.H.L q.L.CHXZHbHf.H5.HHHHs.H+:bHH=s.+AV1ɾ52.L p.H=-L.H&.A_HXH3bH5#.H=lp.HC+hbH31ɾH=e-AS5.L 6p.L.HȎ.SA\A]HHlbH5.H=p.H+bH~Ho.H 0.H9Hu-H0.HHtHDH=z.+H!H=y.H]0.H5^0.1,HHzbH5E.H)~IHbH1~H5Jr.LHHbL ~H}1ɾH=-AP5.L o.Lb.H.AYAZHHbH?.H5.HHHHs.H+bH}H=s.+H=f-Q15.L vn.L.Hx.^_HHbH.H5.HHHHss.H+cH|H=Os.+1ɾH=-P5.H.L m.LG. HXZHcH-.H56.HHHHr.H}+0cHm|H=r.+AV1ɾ5!.L jm.H=-L.HU.A_HXH'cH5.H=3m.H +^cH{1ɾH=-AS5.L l.L^.H.A\A]HHdcH5.H=l.H+cH{1ɾH=-AP5].L l.L.H.AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5A*Hkq.H5|.HH+-cHzH==q.+H5H.H=)q."HHYH=.HIH*cHzHp.H5.LHo+QcL_zH=p.+1ɾH=-P5%.Hv.L Ok.L.sIXZMKcHN.H5.LIHH[p.H+gcLyH=7p.+AU1ɾ5.L j.H=-L-.H.A^A_IH]cHo.H5+.LHk+cL[yH=o.~+1ɾH=`-AS50.L Qj.L.Hs.n[A\IHcHpo.H5.LH+cLxH=Bo.+1ɾH=-AP5.L i.L8.Hن.AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5+AkH5.H=]i.H4+AcL$x1ɾH=-P5.H@.L !i.L.EIXZMuOE1E11E1L1E1E1L11E1HE1E1E1H5|AH5}.H=h.L+bLuw11H=*-AS5r.L {h.Lܽ.He.[A\IHbHr.H5C.LH=Ah.IH+ cLw11H=-AP5.L h.Lh.Ha.$AYAZIHcH5E.H=g.H+EcLvH=-Q15.L g.L.H.^_IHMcHl.H5.LHB+zcL2vH=l.U+1ɾH=w-P58.Hi.L "g.L.FIXZMPH5.H=f.L+ScLuAU1ɾ5.L f.H=-L%.H.A^A_IHH5b.H=f.Hj+7cLZu1ɾH=l-AS5|.L ]f.L.H}.z[A\IHcH5.H=V.Hn+tcH^eLVe11H=+-P5.HM.L VV.L.zIXZMtcH5%.H=.V.L+cLdAU1159.L V.H=-L\.H}.A^A_IHcH5a.H=U.H+cLd11H=&-AS5.L U.L.H.[A\IHcH־.H5Ǜ.LH=]U.IH-+dLd11H=-AP5j.L #U.L.HՎ.@AYAZIHdH5.H=T.H+OdLc1H=-Q15.L T.L!.H.^_IHYdH5.H=T.Hh+dLXc1ɾH=-P5.H_.L UT.L.yIXZMuN1E1E11H1E1E1L11E1HE1E1E1H5AH.H5.LIHHW.H+dLbH=V.+H5^.H=V.|IHGH=.HǺHHdLCbHV.H5.HH%+5dHbH=fV.8+S1ɾ5r.L S.H= -Lm.H&^.)A\A]HHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5eAHU.H5.HH_+cHOaH=U.r+H5۩.H=U.{HHVH=T.H脹IHcHaHQU.H5.LH+cL`H=#U.+H=-Q150.L Q.L*.H\.^_IHcHT.H5B.LHj+dLZ`H=T.}+AW1ɾ5.H_.H=-L IQ.L.mIXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5* AHT.H5.LH+cL_H=S.+H5.H=S.yIHVH=.HʷHHcLF_HS.H5.HH(+cH_H=qS.;+1ɾH=-AP5.L P.Lo.H0^.+AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5jA A颽HR.H5l.HHd+2cHT^H=R.w+H58.H=R.xHHYH=Y.H艶IH.cH^H^R.H5.LH+RcL]H=0R.+AW1ɾ5S.H].H=M-L N.L'.IXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5(S A`HQ.H5.LH"+bL]H=kQ.5+H5.H=WQ.XwIHVH=.HGHHbL\HQ.H5=.HH+bH\H=P.+1ɾH=-AP5.L M.L.H[.AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5b AHXP.H5.HH+bH[H=*P.+H5͎.H=P.vHHYH=.HIH~bH[HO.H5.LHd+bLT[H=O.w+AW1ɾ5.HZ.H=-L CL.L.gIXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5q AݹHO.H5'.LH+*bLZH=N.+H5.H=N.tIHVH=.HIJHH%bL@ZHN.H5.HH"+IbHZH=kN.5+1ɾH=7-AP5.L K.Li.H2Y.%AYAZHHuM111E1H1E1E1H11E1HE1E1E1H5d A霸HM.H5f.HH^+aHNYH=M.q+H52.H=M.sHHYH=S.H胱IHaHXHXM.H5.LH+aLXH=*M.+1ɾH=ֳ-P5g.HW.L I.L".IXZMaHx.H51.LIHHL.HX+ bLHXH=L.k+AU1ɾ5.L EI.H=-L.HHW.[A^A_IHbH\.H5͊.LIHH9L.H+ bLWH=L.+1ɾH=-AS5a.L H.L.HV.[A\IHbH59.H=H.HY+ObLIW1ɾH=-AP5.L LH.L.HFV.iAYAZIHSbH5.H=H.H+bLV1H=y-Q15.L G.LJ.Hs.^_IHbH.H5R.LH=G.IH+bLpV11H=-P5>.H7.L pG.Lќ.IXZMbHO.H5.LH=>G.IH+bLUAU115.L G.H=D-Le.H.!A^A_IHbH.H5.LH=F.IH+cLU11H=-AS5f.L F.L.HY.[A\IHcHv.H57.LH=UF.IH%+.H9Hu-H).HHtHDH=Ad.lH!H=,d.H.H5. HHd+IHeHLIEnIHKeH1'HHeHc.QLH5PY.L Q.E1Ho^_HHeM9u+LOL9cH@.H5!.ubHNH+\LH5&H+y1E111H1E1E1HE1E1H5e{YAMHl+JeH5b.E1E1HHLIH^eH5b.H=@.Hھ+eLNHNHNLNHW@.H?.H5).H+veH=-@.+AW1ɾ5`.HY.H=z-L s?.LԔ.IXZMuPE1E1E1E1L1E1E1L11E1LE1E1E1H5zA H5.H=>.Lֽ+eLM1ɾH=-AP5.L >.L*.H[X.AYAZIHuM111E1H1E1E1H11E1HE1E1E1H5zA]H5.H=O>.H&+dLMH>.H@>.H5.H+dH=>.+ŭ HHdH>.1ɾH=-P5.HrW.L =.L.IXZMDH5J.H==.Lj+dLZLAU1ɾ5[.L d=.H=-L.HW.zA^IXMH5.H=-=.L+dLK1ɾH=-AS5.L <.LX.HZ.[A\IHdHf=.H5/.LH+dLKH=8=.+1ɾH=-AP5.L }<.Lޑ.H/Z.AYAZIHdH.H5.LIHH<.H + eLJH=<.+H=B-Q15.L ;.LS.HY.^_IHeH.H5 .LIHHG<.H+eLrJH=#<.+AV1ɾ5.HY.H=-L a;.L.IXZMH5.H=9;.L+dLJS1ɾ5*.L ;.H=-Le.HX.!A\A]IH7H5.H=:.H+dLI1ɾH=-AQ5.L :.L.HX.AZA[IHuN1E11E1H1E1E1L11E1HE1E1E1H5HuA0H?.H5".LH+rdLHH={?.+H5.H=g?.(cIHXH=.HHHidLHH(?.HH5.Hm+dHYHH=>.|+1ɾH=-P5.HV.L I9.L.mHZYHmdH>.HH56.H+dHGH=n>.+AU1ɾ5 .L 8.H=k-L,.H=V.HA^A_HydH5M.H=8.Hm+dHYG1ɾH=-AS5.L \8.L.HU.yH[A\HdH5.H=(8.H+dHF1ɾH=]-AP55.L 7.LO.HB. HAYAZHdH:.HH5ښ.H+dHqFH=:.+H=-Q15.L h7.LɌ.HbB.H^_HdH.HH5f.HHHHB:.H+dHEH=:.+AV1ɾ5-.HA.H=-L 6.L).HA_ZHdH .HH5ܙ.HHHH9.HS+dH?EH=9.b+S1ɾ5.L =6.H=6-L.H A.SHA\A]HdH09.HH5t.Hʹ+dHDH=8.ܹ+1ɾH=-AQ5.L 5.L.Hy@.HAZA[HdH8.HH5y.HF+dH2DH=s8.U+H=-1V5.L.L "5.H?.FH_AXHdH$8.HH5y.H+dHCH=7.и+1ɾH=R-P5.H?.L 4.L.HZYHuN1E1E11H1E1E1L11E1HE1E1E1H5oA5HH5׋.H= 4.+|dHBS1ɾ5p.L 3.H=j-LK.H>.HA\A]HuPE1E1E1E1L1E1E1L11E1LE1E1E1H57oAwHH51.H=b3.<+dH(BH=3.蜜HuM111E1H1E1E1H11E1HE1E1E1H5nAAH=k3.HD.7HHth+IHcH1ɾH=-IGAU5.H|V.L 2.L.HA^ZHcLLI4AH7.HH57.H+cH@H=7.!+1ɾH=C-AS5.L 1.LU.HU.H[A\HKH5w.H=1.H+}cH@1ɾH=-AP5.L 1.L.H`U.HAYAZHH5 .H=Q1.H(+RcH@HS.1E1QjH561.HL |.0`H^_HUcAV11H=-5.H{S.L 0.L=.IXZMjcHH $+H5Շ.H9HuHNLH׮+HL+ocLU?HE1H|0.H5R.H=V+E1+tIHucH5R.H=40.H +cL>H>S1ɾ5.L /.H=ӕ-LT.HS.HA\A]HcH2.HH5dž.H+cHv>H=2.+1ɾH=;-AQ5.L l/.L̈́.HS.HAZA[HcHn2.HH5H.H+cH=H=82.+H=-1V5m.LN.L ..HpR.H_AXHcH1.HH5z.H~+cHj=H=1.+1ɾH=-P5.HR.L Z..L.~HZYHcHe1.HH5.H+cH<H=/1. +H-.H c-H9Hu-HN-HHtHDH=VP.H!H=AP.H-H5->HHcH5.H6<IHcH><H0.H5x.LH +cL<H=Y0.3+AU1ɾ5̸.L -.H=f-Lg.HxP.#A^IXMcH 0.H5vk.LH+dL;H=/.+1ɾH=ۑ-AS5S.L ,.L.HO.詾[A\IHdH/.H5p.LH,+AdL;H=e/.?+1ɾH=A-AP5.L ,.Ls.HLO./AYAZIH8dH/.H5p.LH+edL:H=..į+H=-Q15o.L +.L.HO.赽^_IH]dH..H5ai.LH9+dL):H=r..L+AV1ɾ5.HN.H=-L +.Ly.<IXZMdH'..H5o.LH+dL9H=-.Ӯ+S1ɾ5.L *.H=g-L.HN.ļA\A]IHuPE1E1E1E1L1E11L1E1E1LE1E1E1H5Pe:A8Ha-.H5zs.LH+8dL8H=3-. +H5Fs.H=-.0SIHVH=.HHH1dL8H,.HH5r.Hu+KdHa8H=,.+H= -1V5G.L~.L Q).HL.uH_AXH4dH.HH5b.HHHH9,.Hܧ+CdH7H=,.+AW1ɾ5.H5L.H=>-L (.L~.ۺHZYHuO1E1E1E1HE11E1L11E1LE1E1E1H5fcsANHw+.HH5q.H +cH6H=A+.+H5\q.H=-+.>QHHKH=.H)IHcH6H*.H5 q.LH+cLs6H=*.+1ɾH=؋-AQ5h.L i'.L|.HJ.膹AZA[IHuN1E11E1H1E1E1L11E1HE1E1E1H5bAH].H5n.LIHH *.H+4cL5H=).+H5m.H=).OIHGH=.HҍHH+cLJ5H).HH5m.H(+EcH5H=]).7+AW1ɾ5.HI.H=J-L &.Ld{.'HZYHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5`A陓H(.HH5l.HW+bHC4H=(.f+H5k.H=x(.NHHJH=D.HtIHbH3H5(.H5k.LHΣ+bL3H=(.+1ɾH=-AP5ð.L $.Lz.HG.ѶAYAZIHuM111E1H1E1E1H11E1HE1E1E1H5`AHH5{.H=:$.H+NbL31ɾH=-P5.H]G.L #.L_y."IXZMuN1E11E1H1E11L1E1E1HE1E1E1H5Z_A隑H5[~.H=#.Lc+aLS2H=#.njHuPE1E1E1E1L1E1E1L11E1LE1E1E1H5*^AH=#.Ht._IHt+HHaLxHH=-H+HA 1HAP5Ů.L ".Lx.HhF.ôAYAZIHaHIHHJ1H'.H5Tt.LH,+aL1H='.?+H=-Q15:.L ".Ltw.HE.0^_IH]H5y.H=!.H+oaL01ɾH=]-P5ޭ.HwE.L !.L w.̳IXZMH5O|.H=!.LW+UaLG0H=!.車HuOE1E11E1L1E1E1L11E1HE1E1E1H5\AH=!.Hq.TIHt+HHaLx1ɾH=?-AS5.L .Lv.HC.Ͳ[A\IH aHIHHU/H&.H5_r.LH7+aL'/H=%.J+1ɾH=-AP5\.L  .L~u.H'C.:AYAZIHeH5w.H=.HÞ+`L.H=-Q15.L .Lu.HB.Ա^_IHH5Wz.H=.H_+`LO.Hh.H -H9Hu-H-HHtHDH=,o.H!H=o.H-H5-HH`H5.H10HH`H-HH5B.H=.+`H-H.H -H9Hu-H-HHtHDH=tB.GH!H=_B.H-H5-HH`̝+IHMaIGLKHH7aH1HHkaHA.PE1H5kC.HL %s.HLIXZM{aHI9u2Ls,H+I9EH.H5x.udHNL+^LH5nLƝ+yE1E111LE1E1E1LE1E1H5,X AL3+aHE1E1LH5@.H`IH4aH5@.H=.H+ZaL+L+H+Hu+H=.HuPE1E1E1E1L1E1E1L11E1LE1E1E1H5LW'A4H=.H.聅HHt+HH`HH+H=-HK 1HHCHC(AP5.L .L$q.H>.HAYAZH`HHHj*H#!.HH5mm.HH+`H4*H= .W+H=;~-Q15.L +.Lp.H>.HH^_H`H .HH5b.Hę+`H)H=i .Ӟ+H5db.H=U .CHH`H9 .HH5kb.H^+aHJ)H= .m+AV1ɾ5.HG=.H= }-L 9.Lo.]HA_ZH`H.HH5a.Hؘ+aH(H=}.+S1ɾ5).L .H={|-Lo.H<.ثHA\A]HaH-.HH5op.HR+*aH>(H=.a+HJ.H -H9Hu-Hv-HHtHDH=;.H!H=;.HB-H5C-HH`H5p.H'HH aH'HO.H5o.HHx+GaHh'H=!.+1ɾH= {-AQ5ͤ.L ^.Lm.H ;.{AZA[HH=aH.H5m\.HH+maH&H=.+H=yz-1V5[.LLm.L .H:._AXHHhaH[.H5T\.HH+aHt&H=-.+1ɾH=y-P5.HK:.L d.Ll.舩ZYHHuM1E11E1H1E11H11E1HE1E1E1H5RAH5n.H=.Hʕ+'aH%AV1ɾ5K.L .H=x-Ll.H9.ڨA_HXH,aH5\q.H=.Hd+eaHT%11H=x-AS5.L Z.Lk.HD^.wA\A]HHmaH5(^.H=).H+aH$11H=x-AP5.L .LWk.H].AYAZHHaH5].H=.H+aH$1H=w-Q152.L .Lj.Hx.谧^_HHaH5x.H=d.H;+"bH+$11H=w-P5١.Hrl.L +.Lj.OHXZH,bH5Jl.H=.Hړ+ebH#AV115.L .H=pv-L1j.Hk.A_HXHnbH5k.H=.Hw+bHg#11H=u-AS5$.L m.Li.Hk.芦A\A]HHbH}.H5tk.HH=2.HH+bH"11H=gu-AP5.L .LYi.Hk.AYAZHHbH6}.H5j.HH=.HH+bH}"1H=t-Q15K.L .Lh.HN.补^_HHcH5M.H=U.H,+AcH"11H=Qt-P5.HSi.L .L}h.@HXZHuOE1E11E1L1E1E1L11E1HE1E1E1H5wNA鷀H5h.H=.H+bHp!11H=s-AR5M.L v.Lg.Hh.蓤A[A\HHMH5h.H=E.H+bH !11WH=s-5.L .Ltg.HEh.0AXAYHHH5)h.H=.H+bH +HHbHK8.H5S.H{+yK111E1HE1E11HE11E1HE1E1H5LKALH.H5".H+x6H.H5".H+xHC.H5".Hߏ+yK1E1E1E1H111H1E1E1HE1E1H5JA~H0.H5".Hy+xQHf7.H5W".H^+x6H6.H5l".HC+xHG.H5#.H(+yME1E11E1LE1E11L1E1E1HE1E1H5IA}HA.H51#.H+\aHA.H5"#.H+yNE1E1E1E1LE1E11L1E1E1LE1E1H5oIAo}HA.H5*.H8+x6HA.H5*.H+xH@.H5).H+yME1E11E1LE1E11L1E1E1HE1E1H5HA|H"A.H5*.H+H@.H5*.H{+H@.H5).H\+mH5@.H5n).H=+H@.H5*.H+H?.H5*.H+xH@@.H5*.H+_H?.H5*.H+_H7.H5{*.H+!`H@.H5l%.H+bHT?.H5U%.Hd+CHU@.H5&%.HE+$H3.H5:.H&+x6H6.H5|:.H +xH3.H5q:.H+yK111E1HE1E11HE11E1HE1E1H5FAzH2?.H5SC.H+V_H?.H5.H5" .H+`H=.H5+.H+`H=.H5.Hs+`Hl=.H5%.HT+aH]=.H5.H5+KaH0.H5c.H+yaHA.H5l.H+aHx<.H51!.H؉+aHY=.H5B!.H+aH2=.H5!.H+.bH =.H5 .H{+\bH|.H5!.H\+bH.H5 .H=+bH.H5 .H+bH.H5 .H+cH.H5 .H+?cH!.H52 .H+kcH 0.H52.H+cHs<.H52.H+cH\<.H52.Hd+cHE<.H5V2.HE+"dH*.H51.H&+NdH:.H51.H+zdH0.H51.H+dH2.H51.Hɇ+dH"/.H51.H+eH#/.H5t1.H+1eH.H50.Hl+]eHU.H5V1.HM+eH..H51.H.+eH..H51.H+eH.H51.H+fHy?.H5B1.Hц+@fH".H51.H+nfH.H5 M.H+fHd.H5X.Ht+fH5.H5B.HU+fHV.H5h.H6+#gH'.H5Q.H+QgH .H5g.H+gH.H5JQ.Hم+gH.H5g.H+gH.H5$Q.H+hH.H5g.H|+4hHe.H5P.H]+chHV.H57e.H>+hH.H5d.H+hH.H5d.H+hH.H5I.H+iH.H5U.H„+FiHS.H5F.H+tiHD.H5F.H+iH .H5K.He+iH.H5K.HF+iH.H5K.H'+)jH.H5F.H+WjH.H5a.H+yK1E1E1E1H111H1E1E1HE1E1H5>ArH .H5f.H+yME1E11E1LE1E11L1E1E1HE1E1H5W>ARrH.H5 ?.H+iHD.H5P.H+H.H5Q.H݂+iH .H5Q.H+H.H5>.H+H.H5`.H+Hq.H5 Q.Ha+xlHF.H5OP.HF+xQHC.H5Q.H++x6H.H5)P.H+xH.H5Q.H+yNE1E1E1E1LE1E11L1E1E1LE1E1H5<ApH.H5uE.H+H.H5_.Hm+H6.H5GH.HN+H.H5[.H/+H.H5I.H+HQ.H5[.H+xlHV.H57;.Hր+xQH+.H5R.H+x6H .H5Y.H+xHe.H5#.H+yME1E11E1LE1E11L1E1E1HE1E1H5Y;AToH.H5 .H+OgH>'.H5 .H+wH_'.H5x .H+_gH.H5A.H+gHy'.H52.H+gH*'.H5 .H+gHC'.H5 .Hc+hHl.H5-.HD+BhH '.H5&.H%+phH&.H5.H+hH&.H5.H~+hH6.H5.H~+hH.H5.H~+%iHz&.H5.H~+xH/&.H5h.Ho~+yK111E1HE1E11HE11E1HE1E1H5@9A@mH%.H5 .H ~+xH5.H5.H}+yK1E1E1E1H111H1E1E1HE1E1H58AlH .H5.H}+xQH} .H5&.Hm}+x6HJ%.H5.HR}+xH .H5@.H7}+yME1E11E1LE1E11L1E1E1HE1E1H5 8AlH .H5=.H|+H$.H5 .H|+xlH$.H5 .H|+xQHr#.H5K .Hz|+x6H.H5 .H_|+xHT1.H5 .HD|+yNE1E1E1E1LE1E11L1E1E1LE1E1H57AkH/.H5 .H{+x6H#.H5 .H{+xH".H5 .H{+yME1E11E1LE1E11L1E1E1HE1E1H5y6AtjH".H5~ .H={+xH:".H5.H"{+yK111E1HE1E11HE11E1HE1E1H55AiH".H5m.Hz+eH!.H5N .Hz+eHN.H5.H~z+x6H; .H5.Hcz+xHH.H5).HHz+yK1E1E1E1H111H1E1E1HE1E1H55AiH.H5[.Hy+xH .H5.Hy+yME1E11E1LE1E11L1E1E1HE1E1H54AhH/ .H5p.H_y+dH( .H5.H@y+dH!$.H5.H!y+eH.H5.Hy+IeH#.H5.Hx+ueH.H5e.Hx+xH/.H5.Hx+yNE1E1E1E1LE1E11L1E1E1LE1E1H5w3AwgH.H5.H@x+eH".H5.H!x+H.H5k.Hx+H.H54.Hw+H.H5.Hw+H.H5>7.Hw+yH~".H5(.Hw+ZH.H5(.Hgw+H.H5i(.HHw+H.H5j(.H)w+#H!.H5[(.H w+HK.H5L(.Hv+yME1E11E1LE1E11L1E1E1HE1E1H51AeH.H5'.Hv+yK111E1HE1E11HE11E1HE1E1H5T1ATeH%.H5'.Hv+yK1E1E1E1H111H1E1E1HE1E1H50AdHO .H5H'.Hu+H .H59'.Hu+~HY .H5*'.Hyu+_H .H5'.HZu+@H.H5 '.H;u+!Hd .H5M&.Hu+HE-H5%.Ht+Hf.H57.Ht+H.H5.Ht+H.H5.Ht+H.H5.Ht+gHz).H5{.Hbt+HHk(.H5T.HCt+)H4-H5.H$t+ HU.H5>.Ht+H.H5w.Hs+H/.H5.Hs+HH,.H5).Hs+Hy.H5*.Hs+oHR.H5;.Hjs+PHC.H5 .HKs+1H.H5.H,s+H-H5.H s+H.H5.Hr+H.H5.Hr+H.H5.Hr+HY.H5J.Hr+wHj.H5".Hrr+XHC.H5!.HSr+9H .H5}".H4r+H.H5F".Hr+H .H5_!.Hq+HO.H5!.Hq+H).H5Y".Hq+H-H5 .Hq+HZ-H5 .Hzq+`H.H5 .H[q+AH.H5 .H.Hj+H5J.H=-Hj+GHD`11E11H1E1E1H1E1E1HE11E1H5%E1Y1E11E1H1E11H11E1LE1E1E1HE1H5&A:Y1E1E1E1HE11E1L11E1LE1E1E1H5&AgXE1E1E11LE11E1LE111LE1E1E1HE1H5M&AX111E1H1E1E1H11E1HE1E1E1H5&AW1E11E1H1E11H11E1H1E1E1HE1H5%AW1E1E11HE11E1LE111LE1E1E1HE1H5R%AW11E11HE11E1HE111LE1E1E1HE1H5$A8V11E11H1E1E1H1E1E1HE11E1H5$AU1E11E1H1E11H11E1LE1E1E1HE1'H5W$AU1E1E1E1HE11E1L11E1LE1E1E1H5 $'AEU11E11HE11E1HE111LE1E1E1HE1)H5#AT11E11H1E1E1H1E1E1HE11E1H5i#)AT1E11E1H1E11H11E1LE1E1E1HE1H5 #AJTE1E1E1E1LE11E1LE111LE1E1E1LH5"E1-AS111E1H11E1HE11E1HE11E1H0H5`"AS1E11E1H1E11H11E1LE1E1E1HE1DH5 "AFSE1E1E11LE11E1LE111LE1E1H5!HPAR111E1H1E1E1H11E1HE1E1WH5l!AS1E11E1H1E11H11E1HE1E1H5'!PAgSE1E1E11LE11E1LE111LE1E1E1HH5 ^AS1111HE11E1HE111HE1E1E1HH5~ aAR1E11E1H1E11H11E1H1E1E1HH5+ cAkRE1E11E1L1E1E1L11E1HE1E1E1H5!$ARE1E1E1E1L1E1E1L11E1LE1E1E1H5'AQE1E11E1L1E1E1L11E1HE1E1E1H5~A}Q111E1H1E1E1H11E1HE1E1E1H55A0Q1E11E1H1E11H11E1HE1E1E1H5/!AP1E1E11H1E11L1E1E1HE1E1H59!(APE1E11E1L1E1E1L11E1HE1E1H5 6ALP111E1H1E1E1H11E1HE1E1BH5 AP1E11E1H1E11H11E1H1E1E1HH5P GAO1E11E1HE11E1L11E1HE1E1H5 GANE1E1E11LE11E1LE111LE1E1E1HH5FAO111E1H1E1E1H11E1HE1E1E1H5bMAN1E11E1H1E11H11E1H1E1E1HH5AnNE1E1E11LE11E1LE111LE1E1E1HH5AN1111HE11E1HE111HE1E1E1HH5<+AM1E11E1H1E11H11E1HE1E1H5*A{M11E1E1H1E1E1H111LE1E1E1HH5*A'ME1E1E1E1L1E1E1L11E1LE1E1H5Q*AL1111HE11E1HE111HE1E1E1HH5) AL1E11E1H1E11H11E1HE1E1H5):A=L11E1E1H1E1E1H111LE1E1E1HH5`);AKE1E1E11LE11E1LE111LE1E1E1HH5UrAK111E1H1E1E1H11E1HE1E1rH5AIK1E11E1H1E11H11E1H1E1E1HH5vAJ1E11E1HE11E1L11E1HE1E1H5mvAJE1E1E11LE11E1LE111LE1E1E1HH5AUJ111E1H1E1E1H11E1HE1E1不H5A J1E11E1H1E11H11E1H1E1E1HH5zAI1E11E1HE11E1L11E1HE1E1H5/AmIE1E1E11LE11E1LE111LE1E1E1HH5AI111E1H1E1E1H11E1HE1E1严H5AH1E11E1H1E11H11E1H1E1E1HH5<AzH1E11E1HE11E1L11E1HE1E1H5A/HE1E1E11LE11E1LE111LE1E1E1HH5AG111E1H1E1E1H11E1HE1E1中H5LAG1E11E1H1E11H11E1H1E1E1HH5A1E11E1H1E11H11E1H1E1E1HH5 6A>1E11E1HE11E1L11E1HE1E1H5d 6A9>E1E1E11LE11E1LE111LE1E1E1HH5 =A=111E1H1E1E1H11E1HE1E1=H5 A=1E11E1H1E11H11E1HE1E1H5 AO=1E1E11H1E11L1E1E1HE1E1H5 A=E1E1E1E1L1E1E1L11E1LE1E1H5r A<E1E11E1L1E1E1L11E1HE1E1H5+ Ak<111E1H1E1E1H11E1HE1E1H5 A!<1E11E1H1E11H11E1HE1E1H5 A;11E1E1H1E1E1H111LE1E1E1HH5|A;E1E1E1E1L1E1E1L11E1LE1E1H5a|A6;1111HE11E1HE111HE1E1E1HH5A:1E11E1H1E11H11E1HE1E1H5A:11E1E1H1E1E1H111LE1E1E1HH5pAE:E1E1E1E1L1E1E1L11E1LE1E1H5#A91111HE11E1HE111HE1E1E1HH5A91E11E1H1E11H11E1HE1E1H5A[911E1E1H1E1E1H111LE1E1E1HH52A9E1E1E1E1L1E1E1L11E1LE1E1H5A81111HE11E1HE111HE1E1E1HH5Ag81E11E1H1E11H11E1HE1E1H5HA8E1E1E11LE11E1LE111LE1E1E1HH5A7E1E11E1L1E1E1L11E1HE1E1H5A{711E11H1E1E1H111HE1E1E1HH5NA(711E1E1H1E1E1H111LE1E1E1HH5A6E1E1E1E1L1E1E1L11E1LE1E1H5A61111HE11E1HE111HE1E1E1HH5_A461E11E1H1E11H11E1HE1E1H5A511E1E1H1E1E1H111LE1E1E1HH5A5E1E1E1E1L1E1E1L11E1LE1E1H5tAI51111HE11E1HE111HE1E1E1HH5! A41E11E1H1E11H11E1HE1E1H5 A411E1E1H1E1E1H111LE1E1E1HH5AX4E1E1E1E1L1E1E1L11E1LE1E1H5IA 41111HE11E1HE111HE1E1E1HH5A31E11E1H1E11H11E1HE1E1H5An31E11E1HE11E1L11E1HE1E1H5a%A2E1E1E11LE11E1LE111LE1E1E1HE1&H5A2111E1H11E1HE11E1HE11E1HE1H5&A11E1L1H1E1E1H111LE1E1E1HH5Z&A11E1E1E1HE11E1L11E1LE1E1E1H5 %AI1E1E1E11LE11E1LE111LE1E1E1HH5'A0111E1H1E1E1H11E1HE1E1%H5fA-11E11E1H1E11H11E1H1E1E1HH5(A01E11E1HE11E1L11E1HE1E1H5%A 0E1E1E11LE11E1LE111LE1E1E1HE1)H5oA60111E1H11E1HE11E1HE11E1HE1H5)A\/1E1L1H1E1E1H111LE1E1E1HH5)A/1E1E1E1HE11E1L11E1LE1E1E1H5w%A.E1E1E11LE11E1LE111LE1E1E1HH5!*A_.111E1H1E1E1H11E1HE1E1%H5A.1E11E1H1E11H11E1H1E1E1HH5+AF.1E11E1HE11E1L11E1HE1E1H59%Aw-E1E1E11LE11E1LE111LE1E1E1HE1,H5A-111E1H11E1HE11E1HE11E1HE1H5,A,1E1L1H1E1E1H111LE1E1E1HH52,Ap,1E1E1E1HE11E1L11E1LE1E1E1H5%A!,E1E1E11LE11E1LE111LE1E1E1HH5-A+111E1H1E1E1H11E1HE1E1%H5>A,1E11E1H1E11H11E1H1E1E1HH5.A+1E11E1HE11E1L11E1HE1E1H5%A*E1E1E11LE11E1LE111LE1E1E1HE1/H5GA+111E1H11E1HE11E1HE11E1HE1H5/A4*1E1L1H1E1E1H111LE1E1E1HH5/A)1E1E1E1HE11E1L11E1LE1E1E1H5O%A)E1E1E11LE11E1LE111LE1E1E1HH50A7)111E1H1E1E1H11E1HE1E1%H5Aq)1E11E1H1E11H11E1H1E1E1HH5\1A)1E11E1HE11E1L11E1HE1E1H5%AO(E1E1E11LE11E1LE111LE1E1E1HE13H5Az(11E11H1E1E1H111HE1E1E1HE1H5b3A'11E1E1H1E1E1H111LE1LE1HH5 3AH'E1E1E11LE11E1LE111LE1E1E1HH53A&111E1H1E1E1H11E1HE1E1%H5eA&1E11E1H1E11H11E1H1E1E1HH54A&E1E1E11LE11E1LE111LE1E1E1HH54A&H1111HE11HE1E11H1E1E1HH5j4AH%1E11E1H1E11H11E1H1E1E1HH54A%1E11E1HE11E1L11E1HE1E1H5%A%E1E1E11LE11E1LE111LE1E1E1HE15H5gA.%11E11H1E1E1H111HE1E1E1H6H5AW$1E1E11H1E11L1E1E1HE1E1H56A $E1E1E1E1L1E1E1L11E1LE1E16H5|A#E1E11E1L1E1E1L11E1HE1E1H556As#111E1H1E1E1H11E1HE1E1%H5A)#1E11E1H1E11H11E1H1E1E1HH5;AZ#E1E1E11LE11E1LE111LE1E1E1HH5B<A#E1E11E1L1E1E1L11E1HE1E1H5<A4"111E1H1E1E1H11E1HE1E1<H5A!1E11E1H1E11H11E1HE1E1H5b<A!1E11E1HE11E1L11E1HE1E1H5%AU!E1E1E11LE11E1LE111LE1E1E1HE1AH5A!111E1H11E1HE11E1HE11E1HE1H5hAA 1E1L1H1E1E1H111LE1E1E1HH5AAN 1E1E1E1HE11E1L11E1LE1E1E1H5%AE1E1E11LE11E1LE111LE1E1E1HH5kBA111E1H1E1E1H11E1HE1E1%H5A1E11E1H1E11H11E1H1E1E1HH5CA1E11E1HE11E1L11E1HE1E1H5%AE1E1E11LE11E1LE111LE1E1E1HE1DH5%A111E1H11E1HE11E1HE11E1HE1H5DA1E1L1H1E1E1H111LE1E1E1HH5|DA1E1E1E1HE11E1L11E1LE1E1E1H5-%Ak11E11HE11E1HE111LE1E1E1HH5EA11E11H1E1E1H1E1E1HE11Ҹ%H5AQ1E1E11H1E11H11E1LE1E1E1HH5;FAE1E1E1E1L1E1E1L11E1LE1E1%H5A,E1E11E1L1E1E1L11E1HE1E1E1H5JA11E11H1E1E1H111HE1E1E1HE1H5IKA 1E11E1H1E11L1E1E1HE1E1E1H5JA9E1E1E1E1LE11E1LE111LE1E1E1LH5E1LAcE1E11E1L1E1E1L11E1HE1E1E1H5RJA11E11H1E1E1H111HE1E1E1HE1H5MA1E11E1H1E11L1E1E1HE1E1E1H5JAE1E1E1E1LE11E1LE111LE1E1E1LH5WE1NAE1E11E1L1E1E1L11E1HE1E1E1H5JAC11E11H1E1E1H111HE1E1E1HE1H5OAq1E11E1H1E11L1E1E1HE1E1E1H5aJAE1E1E1E1LE11E1LE111LE1E1E1LH5 E1PAE1E11E1L1E1E1L11E1HE1E1E1H5JA11E11H1E1E1H111HE1E1E1HE1H5bQA$1E11E1H1E11L1E1E1HE1E1E1H5JARE1E1E1E1LE11E1LE111LE1E1E1LH5E1RA|E1E11E1L1E1E1L11E1HE1E1E1H5kJA11E11H1E1E1H111HE1E1E1HE1H5SA1E11E1H1E11L1E1E1HE1E1E1H5JAE1E1E1E1LE11E1LE111LE1E1E1LH5pE1TA/111E1H1E1E1H11E1HE1E1E1H5 JA^11E11H1E1E1H1E1E1HE11E1H5RA1E11E1H1E11H11E1LE1E1E1HE1pH5wA>1E1E1E1HE11E1L11E1LE1E1E1H5-pAkE1E1E11LE11E1LE111LE1E1E1HE1|H5A111E1H1E1E1H11E1HE1E1E1H5|A1E11E1H1E11H11E1HE1E1E1H5:Ax1E1E11HE11E1LE111LE1E1E1HE1H5A E1E1E1E1L1E1E1L11E1LE1E1E1H5A1111HE11E1HE111HE1E1E1HE1H5A1E11E1H1E11H11E1HE1E1E1H5qA-1E1E11HE11E1LE111LE1E1E1HE1H5AYE1E1E1E1L1E1E1L11E1LE1E1E1H5A1111HE11E1HE111HE1E1E1HE1H5A1E11E1H1E11H11E1HE1E1E1H5A1E1E11HE11E1LE111LE1E1E1HE1H5GAE1E1E1E1L1E1E1L11E1LE1E1E1H5A:1111HE11E1HE111HE1E1E1HE1EH5Ah1E11E1H1E11H11E1HE1E1E1H5YEA1E1E11HE11E1LE111LE1E1E1HE1kH5AE1E1E1E1L1E1E1L11E1LE1E1E1H5kA1111HE11E1HE111HE1E1E1HE1H5VA1E11E1H1E11H11E1HE1E1E1H5AL1E1E11HE11E1LE111LE1E1E1HE1H5AxE1E1E1E1L1E1E1L11E1LE1E1E1H5fA 1111HE11E1HE111HE1E1E1HE1"H5 A 1E11E1H1E11H11E1HE1E1E1H5"A 1E1E11HE11E1LE111LE1E1E1HE1H5fA- E1E1E1E1L1E1E1L11E1LE1E1E1H5AY 1111HE11E1HE111HE1E1E1HE1H5A 1E11E1H1E11H11E1HE1E1E1H5xA 1E1E11HE11E1LE111LE1E1E1HE1H5A E1E1E1E1L1E1E1L11E1LE1E1E1H5A 1111HE11E1HE111HE1E1E1HE1H5uA< 1E11E1H1E11H11E1HE1E1E1H5-Ak 1E1E11HE11E1LE111LE1E1E1HE1VH5A E1E1E1E1L1E1E1L11E1LE1E1E1H5VA 1111HE11E1HE111HE1E1E1HE1vH5*A 1E11E1H1E11H11E1HE1E1E1H5vA 1E1E11HE11E1LE111LE1E1E1HE1H5AL E1E1E1E1L1E1E1L11E1LE1E1E1H5:Ax1111HE11E1HE111HE1E1E1HE1TH5A1E11E1H1E11H11E1HE1E1E1H5TA1E1E11HE11E1LE111LE1E1E1HE1iH5:AE1E1E1E1L1E1E1L11E1LE1E1E1H5iA-1111HE11E1HE111HE1E1E1HE1H5A[1E11E1H1E11H11E1HE1E1E1H5LA1E1E11HE11E1LE111LE1E1E1HE1H5AE1E1E1E1L1E1E1L11E1LE1E1E1H5A1111HE11E1HE111HE1E1E1HE1H5IA1E11E1H1E11H11E1HE1E1E1H5A?1E1E11HE11E1LE111LE1E1E1HE1H5AkE1E1E1E1L1E1E1L11E1LE1E1E1H5YA1111HE11E1HE111HE1E1E1HE1H5A1E11E1H1E11H11E1HE1E1E1H5A1E1E1E1H11E1L1E11LE1E1E1HE1H5YA E1E1E1E1L1E1E1L11E1LE1E1E1H5ALE1E11E1L1E1E1L11E1HE1E1E1H5A11E11H1E1E1H111HE1E1E1HE1H5iA1E11E1H1E11L1E1E1HE1E1E1H5A1E1E1E1HE11E1L11E1LE1E1E1H5*A E1E1E1E1L1E1E1L11E1LE1E1E1H5|HA1111HE11E1HE111HE1E1E1HE1HH5!Ad1E11E1H1E11H11E1H1E1E1HH5AE1E1E11LE11E1LE111LE1E1E1HH5}A?1111HE11E1HE111HE1E1E1HH5*Ah1E11E1H1E11H11E1H1E1E1HE1w H5A1E1E11HE11E1LE111LE1E1E1HE1 H5wA>E1E1E11LE11E1LE111LE1E1E1HH5& Ad11E11H1E1E1H111HE1E1E1H H5A11E1E1H1E11H1E1E1HH1H5AH 8E11E1E1L1E1E1H11E1L*111E1H1E1E1H1H1HH5AH 1E11E1H1E11H1H11E1E1HE11H1HH5AH JE1E1E11LE1E1L11E11HE1HHH57AH 1E11E1HHH5AH 1E11AHHH5H 1E1H5AHHH P1H5}AHHH "1HHH5AAH HE1H5AH E1H5 AE1E1 AH5E1E1E1 H5A}E1E1E1E1H5 AZ1E1E1E1E1H5s A5E11E1E1LE1E1 H5?AE111E1LE1E1E1H5 AE1E111LE1E1E1E1H5 AE11E11L1E1E1HE1E1H5 Ad11E1E1H11E1HE1E1E1H5c A%11E1E1H111HE1E1E1E1H5" A1E11E1H1E11H11E1HE1E1E1H5 AE1E1E11LE1E11L1E1E1E1E1 AH5QE1E11E1L1E1E1L11E1HE1E1E1H5@ A11E11HE1E11H1E1E1E1E1 AH51E11E1H1E11H11E1HE1E1E1H5Aq1E11E1H1E11H1E1E1E1E1AH5`-E1E1E1E1L1E1E1L11E1LE1E1E1H5-AE1E1E11LE1E11L1E1E1E1E1-AH5111E1H1E1E1H11E1HE1E1E1H5HAJ1E11E1H1E11H1E1E1E1E1HAH591E11E1H1E11H11E1HE1E1E1H5cAE1E1E11LE1E11L1E1E1E1E1cAH5sE1E11E1L1E1E1L11E1HE1E1E1H5b~A$11E11HE1E11H1E1E1E1E1~AH51E11E1H1E11H11E1HE1E1E1H5A1E11E1H1E11H1E1E1E1E1AH5OE1E1E1E1L1E1E1L11E1LE1E1E1H5=AE11E11LE1E11H1E1E1E1E1AH511E11H1E1E1H11E1HE1E1E1H5Am1E11E1H1E11H1E1E1E1E1AH5\)1E1E11HE11E1LE111LE1E1E1HE1H5 AE1E1E11LE11E1LE111LE1E1E1HE1H5Ax111E1H11E1HE11E1HE11E1HE1H5`A"1E11E1H1E11H11E1HE1E1E1H5AE1E1E11LE1E11L1E1E1E1E1AH5¿E1E11E1L1E1E1L11E1HE1E1E1H5~A@11E11HE1E11H1E1E1E1E1AH5/1E11E1H1E11H11E1HE1E1E1H53A1E11E1H1E11H1E1E1E1E13AH5kE1E1E1E1L1E1E1L11E1LE1E1E1H5YnAE1E1E11LE1E11L1E1E1E1E1nAH5111E1H1E1E1H11E1HE1E1E1H5ƽA1E11E1H1E11H1E1E1E1E1AH5wD1E11E1H1E11H11E1HE1E1E1H55AE1E1E11LE1E11L1E1E1E1E1AH5E1E11E1L1E1E1L11E1HE1E1E1H5Ab11E11HE1E11H1E1E1E1E1AH5Q1E11E1H1E11H11E1HE1E1E1H5 A1E11E1H1E11H1E1E1E1E1 AH5E1E1E1E1L1E1E1L11E1LE1E1E1H5{ A=E1E1E11LE1E11L1E1E1E1E1 AH5*111E1H1E1E1H11E1HE1E1E1H55A1E11E1H1E11H1E1E1E1E15AH5f1E11E1H1E11H11E1HE1E1E1H5WSAE1E1E11LE1E11L1E1E1E1E1SAH5E1E11E1L1E1E1L11E1HE1E1E1H5¹nA11E11HE1E11H1E1E1E1E1nAH5s@1E11E1H1E11H11E1HE1E1E1H51A1E11E1H1E11H1E1E1E1E1AH5E1E1E1E1L1E1E1L11E1LE1E1E1H5A_E1E1E11LE1E11L1E1E1E1E1AH5L111E1H1E1E1H11E1HE1E1E1H5 A1E11E1H1E11H1E1E1E1E1AH51E11E1H1E11H11E1HE1E1E1H5yA;E1E1E11LE1E11L1E1E1E1E1AH5(E1E11E1L1E1E1L11E1HE1E1E1H5A111E1H1E11H1E1E1HE1E1H5A\11E1E1H111H1E1E1HE1E1H5PAE1E1E11LE1E11L1E1E1E1E1AH5E1E11E1L1E1E1L11E1HE1E1E1H5EA}11E11HE1E11H1E1E1E1E1EAH5l91E11E1H1E11H11E1HE1E1E1H5*aA1E11E1H1E11H1E1E1E1E1aAH5۴E1E1E1E1L1E1E1L11E1LE1E1E1H5AXE1E1E11LE1E11L1E1E1E1E1AH5E111E1H1E1E1H11E1HE1E1E1H5A1E11E1H1E11H1E1E1E1E1AH51E11E1H1E11H11E1HE1E1E1H5rA4E1E1E11LE1E11L1E1E1E1E1AH5!E1E1E1E1L1E1E1L11E1LE1E1E1H5ܲA1111HE1E11H1E1E1HE1E1H5AT11E1E1H111H1E1E1HE1E1H5HA 1E11E1H1E11H1E1E1E1E1AH5E1E1E1E1L1E1E1L11E1LE1E1E1H5AvE1E1E11LE1E11L1E1E1E1E1AH5c0111E1H1E1E1H11E1HE1E1E1H5!3A1E11E1H1E11H1E1E1E1E13AH5Ұ1E11E1H1E11H11E1HE1E1E1H5YARE1E1E11LE1E11L1E1E1E1E1YAH5? E1E11E1L1E1E1L11E1HE1E1E1H5A11E11HE1E11H1E1E1E1E1AH5y1E11E1H1E11H11E1HE1E1E1H5jA,1E11E1H1E11H1E1E1E1E1AH5E1E1E1E1L1E1E1L11E1LE1E1E1H5֮AE1E1E11LE1E11L1E1E1E1E1AH5R111E1H1E1E1H11E1HE1E1E1H5CYA1E11E1H1E11H1E1E1E1E1YAH51E11E1H1E11H11E1HE1E1E1H5{AtE1E1E11LE1E11L1E1E1E1E1{AH5a.E1E11E1L1E1E1L11E1HE1E1E1H5A11E11HE1E11H1E1E1E1E1AH5ά1E11E1H1E11H11E1HE1E1E1H5AN1E11E1H1E11H1E1E1E1E1AH5= E1E1E1E1L1E1E1L11E1LE1E1E1H5AE1E1E11LE1E11L1E1E1E1E1AH5t111E1H1E1E1H11E1HE1E1E1H5eIA'1E11E1H1E11H11E1HE1E1E1H5IA1L1E1H1E11H1E1E1E1E1IAH5ŪE1E1E1E1L1E1E1L11E1LE1E1E1H5JABE1E1L1LE1E11L1E1E1E1E1IAH5+111E1H1E1E1H11E1HE1E1E1H5KA1L1E1H1E11H1E1E1E1E1IAH5c1E11E1H1E11H11E1HE1E1E1H5TLAE1E1L1LE1E11L1E1E1E1E1IAH5E1E11E1L1E1E1L11E1HE1E1E1H5MA}11L1HE1E11H1E1E1E1E1IAH5h51E11E1H1E11H11E1HE1E1E1H5&NA1L1E1H1E11H1E1E1E1E1IAH5ӧE1E1E1E1L1E1E1L11E1LE1E1E1H5OAPE1E1L1LE1E11L1E1E1E1E1IAH59111E1H1E1E1H11E1HE1E1E1H5PA1L1E1H1E11H1E1E1E1E1IAH5q1E11E1H1E11H11E1HE1E1E1H5bQA$E1E1L1LE1E11L1E1E1E1E1IAH5 E1E11E1L1E1E1L11E1HE1E1E1H5ɥRA11L1HE1E11H1E1E1E1E1IAH5vC1E11E1H1E11H11E1HE1E1E1H54SA1L1E1H1E11H1E1E1E1E1IAH5E1E1E1E1L1E1E1L11E1LE1E1E1H5TA^E1E1L1LE1E11L1E1E1E1E1IAH5G111E1H1E1E1H11E1HE1E1E1H5UA1L1E1H1E11H1E1E1E1E1IAH51E11E1H1E11H11E1HE1E1E1H5pVA2E1E1L1LE1E11L1E1E1E1E1IAH5E1E11E1L1E1E1L11E1HE1E1E1H5עWA11L1HE1E11H1E1E1E1E1IAH5Q1E11E1H1E11H11E1HE1E1E1H5BXA1L1E1H1E11H1E1E1E1E1IAH5E1E1E1E1L1E1E1L11E1LE1E1E1H5YAlE1E1L1LE1E11L1E1E1E1E1IAH5U"111E1H1E1E1H11E1HE1E1E1H5ZA1L1E1H1E11H1E1E1E1E1IAH51E11E1H1E11H11E1HE1E1E1H5~[A@E1E1L1LE1E11L1E1E1E1E1IAH5)E1E11E1L1E1E1L11E1HE1E1E1H5\A11L1HE1E11H1E1E1E1E1IAH5_1E11E1H1E11H11E1HE1E1E1H5P]A1L1E1H1E11H1E1E1E1E1IAH5E1E1E1E1L1E1E1L11E1LE1E1E1H5^AzE1E1L1LE1E11L1E1E1E1E1IAH5c0111E1H1E1E1H11E1HE1E1E1H5!_A1L1E1H1E11H1E1E1E1E1IAH5Ν1E11E1H1E11H11E1HE1E1E1H5`ANE1E1L1LE1E11L1E1E1E1E1IAH57E1E11E1L1E1E1L11E1HE1E1E1H5aA11L1HE1E11H1E1E1E1E1IAH5m1E11E1H1E11H11E1HE1E1E1H5^bA 1L1E1H1E11H1E1E1E1E1IAH5 E1E1E1E1L1E1E1L11E1LE1E1E1H5ƛcAE1E1L1LE1E11L1E1E1E1E1IAH5q>111E1H1E1E1H11E1HE1E1E1H5/dA1L1E1H1E11H1E1E1E1E1IAH5ܚ1E11E1H1E11H11E1HE1E1E1H5eA\E1E1L1LE1E11L1E1E1E1E1IAH5EE1E11E1L1E1E1L11E1HE1E1E1H5fA11L1HE1E11H1E1E1E1E1IAH5{1E11E1H1E11H11E1HE1E1E1H5lgA.1L1E1H1E11H1E1E1E1E1IAH5E1E1E1E1L1E1E1L11E1LE1E1E1H5ԘhAE1E1L1LE1E11L1E1E1E1E1IAH5L111E1H1E1E1H11E1HE1E1E1H5=iA1L1E1H1E11H1E1E1E1E1IAH51E11E1H1E11H11E1HE1E1E1H5jAjE1E1L1LE1E11L1E1E1E1E1IAH5S E1E11E1L1E1E1L11E1HE1E1E1H5kA11L1HE1E11H1E1E1E1E1IAH51E11E1H1E11H11E1HE1E1E1H5zlA<1L1E1H1E11H1E1E1E1E1IAH5'E1E1E1E1L1E1E1L11E1LE1E1E1H5mAE1E1L1LE1E11L1E1E1E1E1IAH5Z111E1H1E1E1H11E1HE1E1E1H5KnA 1L1E1H1E11H1E1E1E1E1IAH51E11E1H1E11H11E1HE1E1E1H5oAxE1E1L1LE1E11L1E1E1E1E1IAH5a.E1E11E1L1E1E1L11E1HE1E1E1H5pA11L1HE1E11H1E1E1E1E1IAH5ʓ1E11E1H1E11H11E1HE1E1E1H5qAJ1L1E1H1E11H1E1E1E1E1IAH55E1E1E1E1L1E1E1L11E1LE1E1E1H5rAE11L1LE1E11H1E1E1E1E1IAH5i11E11H1E1E1H11E1HE1E1E1H5ZsA1LE1E1H111H1E1E1HE1E1H5 sAE1E1E1E1LE111LE1E1E1E1H5ǑIAE1E1E1E1L1E1E1L11E1LE1E1E1H5wtA9111HHE1E11H1E1E1HE1E1H5(tA11E1E1H111HE1E1E1E1H5IA1E11E1H1E11H11E1HE1E1E1H5uA\E1E1E1LLE1E11L1E1E1LE1E1uH5CA E11E1E1LE111HE1E1E1E1H5IA11E11H1E1E1H11E1HE1E1E1H5vAz1HE1E1H111H1E1E1HE1E1H5ivA+E1E11E1LE111LE1E1E1E1H5&IAE1E1E1E1L1E1E1L11E1LE1E1E1H5֎wA111LHE1E11H1E1E1HE1E1H5wAI1E1E1E1H111HE1E1E1E1H5EIA1E11E1H1E11H11E1HE1E1E1H5xAE1E1E1HLE1E11L1E1E1LE1E1xH5AhE111E1LE111HE1E1E1E1H5dIA&11E11H1E1E1H11E1HE1E1E1H5yA1LE1E1H111H1E1E1HE1E1H5ȌyAE1E1E1E1LE111LE1E1E1E1H5IAFE1E1E1E1L1E1E1L11E1LE1E1E1H54zA111HHE1E11H1E1E1HE1E1H5zA駿11E1E1H111HE1E1E1E1H5IAf1E11E1H1E11H11E1HE1E1E1H5W{AE1E1E1LLE1E11L1E1E1LE1E1{H5AǾE11E1E1LE111HE1E1E1E1H5ŠIA鄾11E11H1E1E1H11E1HE1E1E1H5u|A71HE1E1H111H1E1E1HE1E1H5&|AE1E11E1LE111LE1E1E1E1H5IA饽E1E1E1E1L1E1E1L11E1LE1E1E1H5}AU111LHE1E11H1E1E1HE1E1H5D}A1E1E1E1H111HE1E1E1E1H5IAļ1E11E1H1E11H11E1HE1E1E1H5~AwE1E1E1HLE1E11L1E1E1LE1E1~H5^A%E111E1LE111HE1E1E1E1H5!IA11E11H1E1E1H11E1HE1E1E1H5ԇA閻1LE1E1H111H1E1E1HE1E1H5AGE1E1E1E1LE111LE1E1E1E1H5AIAE1E11E1L1E1E1L11E1HE1E1E1H5A鴺11E11HE1E11H1E1E1E1E1AH5p1E11E1H1E11H11E1HE1E1E1H5aA#1E11E1H1E11H1E1E1E1E1AH5߹E1E1E1E1L1E1E1L11E1LE1E1E1H5ͅA鏹E1E1E11LE1E11L1E1E1E1E1AH5|I111E1H1E1E1H11E1HE1E1E1H5:A1E11E1H1E11H1E1E1E1E1AH5鸸1E11E1H1E11H11E1HE1E1E1H5AkE1E1E11LE1E11L1E1E1E1E1AH5X%E1E11E1L1E1E1L11E1HE1E1E1H5Aַ11E11HE1E11H1E1E1E1E1AH5Ń钷1E11E1H1E11H11E1HE1E1E1H5 AE1E11E1H1E11H1E1E1E1E1 AH54E1E1E1E1L1E1E1L11E1LE1E1E1H54A鱶E1E1E11LE1E11L1E1E1E1E14AH5k111E1H1E1E1H11E1HE1E1E1H5ރA1E11E1H1E11H1E1E1E1E1AH5ڵ1E11E1H1E11H11E1HE1E1E1H5MA鍵E1E1E11LE1E11L1E1E1E1E1AH5GE1E11E1L1E1E1L11E1HE1E1E1H568A11E11HE1E11H1E1E1E1E18AH5鴴1E11E1H1E11H11E1HE1E1E1H5;Ag1E11E1H1E11H1E1E1E1E1;AH5V#E1E1E1E1L1E1E1L11E1LE1E1E1H5JAӳE1E1E11LE1E11L1E1E1E1E1JAH5鍳1111HE11E1HE111HE1E1E1HE1XH5pA7HE11E1E111E1H1E1E1H1E1H5*H1AHXܲ1E1E11HE11E1LE111LE1E1E1HE1YH5~A鄲E1E11HLE1E1E1L11E1HE1E1E1H5n~YA0111E1H1E1E1H11E1HE1E1E1H5!~YA1HE1E1H1E11H11E1HE1E1E1H5}YA鑱1E1E11HE11E1L11E1LE1E1E1HH5qA<E1E1E11LE1E11L1E1E1LE1E1H5$A1111H1E1E1H11E1HE1E1E1HH5~GA霰11E1E1H111H1E1E1HE1E1H5~GAR1E1E11HE11E1L11E1LE1E1E1HH52~_AE1E1E1E1LE1E11L1E1E1LE1E1H5}_A鯯1111HE1E1E1H11E1HE1E1E1HH5}~A[1E1E1E1H111H1E1E1HE1E1H5E}~A1E1E1E1H1E1E1L11E1LE1E1E1HH5|A麮E1E1E1E1LE1E11L1E1E1LE1E1H5|Al1111HE1E1E1H11E1HE1E1E1HH5M|A1E1E1E1H111H1E1E1HE1E1H5|Aͭ1E1E1E1H1E1E1L11E1LE1E1E1HH5{AwE1E1E1E1LE1E11L1E1E1LE1E1H5^{A)1111HE1E1E1H11E1HE1E1E1HH5 {Aլ1E1E1E1H111H1E1E1H1E1E1HH5zA遬E1E11E1LE1E11L1E1E1HE1E1H5izA4E1E1E11LE1E1E1L11E1LE1E1E1HH5zAݫ111E1HE1E11HE11E1HE1E1H5yA钫1E1E1E1H111H1E1E1H1E1E1HH5syA>E1E11E1LE1E11L1E1E1HE1E1H5&yAE1E1E11LE1E1E1L11E1LE1E1E1HH5xA險111E1HE1E11HE11E1HE1E1H5xAO1E1E1E1H111H1E1E1H1E1E1HH50xAE1E11E1LE1E11L1E1E1HE1E1H5wA鮩E1E1E11LE1E1E1L11E1LE1E1E1HH5wAW111E1HE1E11HE11E1HE1E1H5x11E11HE11E1H11E1LE1E1E1HH5hFAw11E11H1E11HE1E1E1HE11ҸH5FAw1E11E1H111H1E1E1LE1E1E1HH5EALw1E1E1E1HE111L1E1E1LE1E1H5yEAw11E11HE11E1H11E1LE1E1E1HH5*E:Av11E11H1E11HE1E1E1HE11Ҹ:H5DAbv1E11E1H111H1E1E1LE1E1E1HH5DkAv1E1E1E1HE111L1E1E1LE1E1kH5;DAuE1E1E11LE1E11L1E1E1LE1E1H5CHyAnu11E1E1H111H1E1E1HE1E1yHAH5Cu1E11E1H111LE1E1E1HE1E1H5PCyAtE1E1E1E1LE11E1L11E1LE1E1H5CLjE1A{tE1E11E1L1E11L1E1E1HE1E1H5BjA/t11E11H1E11H11E1HE1E1E1HH5UBAs1E11E1H111LE1E1E1HE1E1H5BAsE1E1E1E1LE11E1L11E1LE1E1H5ALE1A:sE1E11E1L1E11L1E1E1HE1E1H5lAAr11E11H1E11H11E1HE1E1E1HH5AAr1E11E1H111LE1E1E1HE1E1H5@APrE1E1E1E1LE11E1L11E1LE1E1H5@L+E1AqE1E11E1L1E11L1E1E1HE1E1H5+@+Aq11E11H1E11H11E1HE1E1E1HH5?AZq1E11E1H111LE1E1E1HE1E1H5?AqE1E1E1E1LE11E1L11E1LE1E1H5@?LE1ApE1E11E1L1E11L1E1E1HE1E1H5>Alp11E11H1E11H11E1HE1E1E1HH5=Ap1E11E1H111LE1E1E1HE1E1H5=AoE1E1E1E1LE11E1L11E1LE1E1H5A=LE1AwoE1E11E1L1E11L1E1E1HE1E1H5<A+o11E11H1E11H11E1HE1E1E1HH5Q=An1E11E1H111LE1E1E1HE1E1H5 =AnE1E1E1E1LE11E1L11E1LE1E1H5<LE1A6nE1E11E1L1E11L1E1E1HE1E1H5h<Am11E11H1E11H11E1HE1E1E1HH5<Am1E11E1H111LE1E1E1HE1E1H5;ALmE1E1E1E1LE11E1L11E1LE1E1H5};LE1AlE1E11E1L1E11L1E1E1HE1E1H5';Al11E11H1E11H11E1HE1E1E1HH5:AVl1E11E1H111LE1E1E1HE1E1H5:A lE1E1E1E1LE11E1L11E1LE1E1H5<:LE1AkE1E11E1L1E11L1E1E1HE1E1H59Ahk11E11H1E11H11E1HE1E1E1HH59Ak1E11E1H111LE1E1E1HE1E1H5H9AjE1E1E1E1LE11E1L11E1LE1E1H58L"E1AsjE1E11E1L1E11L1E1E1HE1E1H58"A'j111E1H1E11HE11E1HE1E1-H5V8Ai1E1E11H11E1H1E1E1H1E1H58HA-iE1E11E1LE1E11L1E1E1HE1E1H57-A?iE1E1E11LE1E1E1L11E1LE1E1E1HH5f7SAh111E1HE1E11HE11E1HE1E1SH57Ah1E1E1E1H111H1E1E1HE1E1H56WARh1E1E1E1H1E11L1E1E1LE1E1H56HAWgE1E1E1E1L1E11L1E1E1LE1E1H506WAg1111HE11E1H11E1HE1E1E1HH55A_g1E11E1H111H1E1E1HE1E1H55Ag1E1E11HE11E1L11E1LE1E1E1HH5>5AfE1E1E1E1L1E11L1E1E1LE1E1H54Asf1111HE11E1H11E1HE1E1E1HH54A f1E1E1E1H111H1E1E1HE1E1H5S4Ae1E1E1E1H1E1E1L11E1LE1E1E1HH53AeE1E1E1E1LE1E11L1E1E1LE1E1H53A1e1111HE1E1E1H11E1HE1E1E1HH5[3Ad1E1E1E1H111H1E1E1HE1E1H53Ad1E1E1E1H1E1E1L11E1LE1E1E1HH52)A_E1E1E1E1L1E11L1E1E1LE1E1H5o-A^1111HE11E1H11E1HE1E1E1HH5-A^1E11E1H111H1E1E1HE1E1H5,AT^1E1E11HE11E1L11E1LE1E1E1HH5},A]E1E1E1E1L1E11L1E1E1LE1E1H50,A]1111HE11E1H11E1HE1E1E1HH5+A_]1E11E1H111H1E1E1HE1E1H5+A]1E1E11HE11E1L11E1LE1E1E1HH5>+A\E1E1E1E1L1E11L1E1E1LE1E1H5*As\1111HE11E1H11E1HE1E1E1HH5*?A \1E11E1H111H1E1E1HE1E1H5T*?A[1E1E11HE11E1L11E1LE1E1E1HH5)IA[E1E1E1E1L1E11L1E1E1LE1E1H5)IA4[1111HE11E1H11E1HE1E1E1HH5_)OAZ1E1E1E1H111H1E1E1HE1E1H5)OAZ1E1E1E1H1E1E1L11E1LE1E1E1HH5(qA@ZE1E1E1E1LE1E11L1E1E1LE1E1H5p(qAY1111HE1E1E1H11E1HE1E1E1HH5(}AY1E1E1E1H111H1E1E1HE1E1H5'}ASYE1E11E1LE1E11L1E1E1HE1E1H5'AY11E11HE1E11H1E1E1LE1E1H59'HAX11E11H1E11HE1E1E1HE11ҸH5&AjX1E1E11H1E11H11E1LE1E1E1HH5&AXE1E1E1E1L1E11L1E1E1LE1E1为H5B&AWE1E11E1L1E11L1E1E1HE1E1H5%A}W11E1E1H111H1E1E1HE1E1HAH5%,W1E1E1E1H111LE1E1E1HE1E1H5^%AVE1E1E1E1LE1E1E1L11E1LE1E1H5%LE1AV111E1HE1E11H1E1E1HE1E1H5$A=V11E1E1H1E11HE1E1E1HE11ҸH5k$AU1E1E11H11E1H1E1E1LE1E1H5%$HAU1E1E1E1HE111L1E1E1LE1E1H5#ATU11E11HE11E1H11E1LE1E1E1HH5~#8AU11E11H1E11HE1E1E1HE11Ҹ8H5/#AT1E11E1H111H1E1E1LE1E1E1HH5"PAbT1E1E1E1HE111L1E1E1LE1E1PH5"AT11E11HE11E1H11E1LE1E1E1HH5@"'AS11E11H1E11HE1E1E1HE11Ҹ'H5!AxS1E11E1H111H1E1E1LE1E1E1HH5!`A$S1E1E1E1HE111L1E1E1LE1E1`H5Q!AR11E11HE11E1H11E1LE1E1E1HH5!qAR11E11H1E11HE1E1E1HE11ҸqH5 A:R1E11E1H111H1E1E1LE1E1E1HH5d AQ1E1E1E1HE111L1E1E1LE1E1H5 AQE1E1E1E1L1E11L1E1E1LE1E1H5AMQ1111HE1E11HE11E1HE1E1H5HAP1E1E1E1H111H1E1E1HE1E1H5/APE1E11E1LE1E11L1E1E1HE1E1H5[ AdPE1E1E11LE1E11L1E1E1LE1E1H5H[ AP111E1H1E11HE11E1HE1E1[ H5?AO1E11E1H111H1E1E1HE1E1H5 A|O1E1E1E1H1E11L1E1E1LE1E1H5HA )OE1E1E1E1LE1E11L1E1E1LE1E1H5Y AN1111HE1E1E1H11E1HE1E1E1HH5 AN1E1E1E1H111H1E1E1HE1E1H5 A1E11E1H111H1E1E1HE1E1H5 A>1E11E1H111LE1E1E1HE1E1H5 AD>E1E1E1E1LE1E11L1E1E1LE1H5w LAE1=111E1HE1E11H1E1E1HE1E1H5" A=11E1E1H1E11HE1E1E1HE11ҸH5 AY=1E1E11H11E1H1E1E1LE1E1H5 HA=1E1E1E1HE111L1E1E1LE1E1H54 A<11E11HE11E1H11E1LE1E1E1HH5 Ag<11E11H1E11HE1E1E1HE11ҸH5 A<1E11E1H111H1E1E1LE1E1E1HH5G  A;1E1E1E1HE111L1E1E1LE1E1 H5 A};E1E1E1E1L1E11L1E1E1LE1E1H5 :A0;1111HE1E11HE11E1HE1E1H5d H:A:1E1E1E1H111H1E1E1HE1E1H5 :A:1E1E1E1H1E1E1L11E1LE1E1E1HH5~A>:E1E1E1E1LE1E11L1E1E1LE1E1H5n~A91111HE1E1E1H11E1HE1E1E1HH5A91E1E1E1H111H1E1E1HE1E1H5AQ9E1E11E1LE1E11L1E1E1HE1E1H5A911E11HE1E11H1E1E1LE1E1H57HA811E11H1E11HE1E1E1HE11ҸH5Ah81E11E1H111H1E1E1LE1E1E1HH5A8E1E1E1E1LE1E11L1E1E1LH5JE1LAE17E1E11E1LE111LE1E1E1HE1H5Au711E1E1H111H1E1E1HE1E1HAH5$71E1E11H11E1LE1E1E1HE1H5TA6E1E1E1E1LE1E11L1E1E1LH5E1LAE16E1E11E1LE111LE1E1E1HE1H5A<611E1E1H111H1E1E1HE1E1HAH5^51E1E11H11E1LE1E1E1HE1H5A5E1E1E1E1LE1E11L1E1E1LH5E1LAE1M5E1E11E1LE111LE1E1E1HE1H5|A511E1E1H111H1E1E1HE1E1HAH5%41E1E11H11E1LE1E1E1HE1H5Ai4E1E1E1E1LE1E11L1E1E1LH5E1LAE14E1E11E1LE111LE1E1E1HE1H5CA311E1E1H111H1E1E1HE1E1HAH5y31E1E11H11E1LE1E1E1HE1H5A03E1E1E1E1LE1E11L1E1E1LH5fE1LAE12E1E11E1LE111LE1E1E1HE1H5 A211E1E1H111H1E1E1HE1E1HAH5@21E1E11H11E1LE1E1E1HE1H5pA1E1E1E1E1LE1E11L1E1E1LH5-E1LAE11E1E11E1LE111LE1E1E1HE1H5AX111E1E1H111H1E1E1HE1E1HAH5z11E1E11H11E1LE1E1E1HE1H57A0E1E1E1E1LE1E11L1E1E1LH5E1LAE1i0E1E11E1LE111LE1E1E1HE1H5A011E1E1H111H1E1E1HE1E1HAH5A/1E1E11H11E1LE1E1E1HE1H5A/E1E1E1E1LE1E11L1E1E1LH5E1LAE10/E1E11E1LE111LE1E1E1HE1H5_A.11E1E1H111H1E1E1HE1E1HAH5.1E1E11H11E1LE1E1E1HE1H5AL.E1E1E1E1LE1E11L1E1E1LH5E1LAE1-E1E11E1LE111LE1E1E1HE1H5&A-11E1E1H111H1E1E1HE1E1HAH5\-1E1E11H11E1LE1E1E1HE1H5A-E1E1E1E1LE1E11L1E1E1LH5IE1LAE1,E1E11E1LE111LE1E1E1HE1H5At,11E1E1H111H1E1E1HE1E1HAH5#,1E1E11H11E1LE1E1E1HE1H5SA+E1E1E1E1LE1E11L1E1E1LH5E1LAE1+E1E11E1LE111LE1E1E1HE1H5A;+11E1E1H111H1E1E1HE1E1HAH5]*1E1E11H11E1LE1E1E1HE1H5A*E1E1E1E1LE1E11L1E1E1LH5E1LAE1L*E1E11E1LE111LE1E1E1HE1H5{A*11E1E1H111H1E1E1HE1E1HAH5$)1E1E11H11E1LE1E1E1HE1H5Ah)E1E1E1E1LE1E11L1E1E1LH5E1LAE1)E1E11E1LE111LE1E1E1HE1H5BA(11E1E1H111H1E1E1HE1E1HAH5x(1E1E11H11E1LE1E1E1HE1H5A/(E1E1E1E1LE1E11L1E1E1LH5eE1LAE1'E1E11E1LE111LE1E1E1HE1H5 A'11E1E1H111H1E1E1HE1E1HAH5?'1E1E11H11E1LE1E1E1HE1H5oA&E1E1E1E1LE1E11L1E1E1LH5,E1LAE1&E1E11E1LE111LE1E1E1HE1H5AW&11E1E1H111H1E1E1HE1E1HAH5y&1E1E11H11E1LE1E1E1HE1H56A%E1E1E1E1LE1E11L1E1E1LH5E1LAE1h%E1E11E1LE111LE1E1E1HE1H5A%11E1E1H111H1E1E1HE1E1 HAH5@$1E1E11H11E1LE1E1E1HE1H5A$E1E1E1E1LE1E11L1E1E1LH5E1 LAE1/$E1E11E1LE111LE1E1E1HE1H5^A#11E1E1H111H1E1E1HE1E1 HAH5#1E1E11H11E1LE1E1E1HE1H5AK#E1E1E1E1LE1E11L1E1E1LH5E1 LAE1"E1E11E1LE111LE1E1E1HE1H5%A"11E1E1H111H1E1E1HE1E1 HAH5["1E1E11H11E1LE1E1E1HE1H5A"E1E1E1E1LE1E11L1E1E1LH5HE1LAE1!E1E11E1LE111LE1E1E1HE1H5As!11E1E1H111H1E1E1HE1E1HAH5"!1E1E11H11E1LE1E1E1HE1H5RA E1E1E1E1LE1E11L1E1E1LH5E1LAE1 E1E11E1LE111LE1E1E1HE1H5A: 11E1E1H111H1E1E1HE1E1HAH5\1E1E11H11E1LE1E1E1HE1H5AE1E1E1E1LE1E11L1E1E1LH5E1LAE1KE1E11E1LE111LE1E1E1HE1H5zA11E11H1E11H11E1HE1E1E1HDH5'A1E11E1H111LE1E1E1HE1E1H5DAcE1E1E1E1LE11E1L11E1LE1E1H5LxE1A E1E11E1L1E11L1E1E1HE1E1H5>xA11E11H1E11H11E1HE1E1E1H!H5Am1E11E1H111LE1E1E1HE1E1H5!A"E1E1E1E1LE11E1L11E1LE1E1H5[L4E1AE1E11E1L1E11L1E1E1HE1E1H54A11E11H1E11H11E1HE1E1E1H@H5A,1E11E1H111LE1E1E1HE1E1H5g@A1E1E1E1HE111L1E1E1LE1E1H5PAE1E1E1E1L1E11L1E1E1LE1E1H5AH1111HE11E1H11E1HE1E1E1HH5{cA1E11E1H111H1E1E1HE1E1H51cA1E1E11HE11E1L11E1LE1E1E1HH5vAVE1E1E1E1L1E11L1E1E1LE1E1H5vA 1111HE11E1H11E1HE1E1E1HH5<A1E11E1H111H1E1E1HE1E1H5Al1E11E1H111LE1E1E1HE1E1H5A!1E1E1E1HE111L1E1E1LE1E1H5A11E11HE11E1H11E1LE1E1E1HH5A11E11H1E11HE1E1E1HE11ҸH5A71E11E1H111H1E1E1LE1E1E1HH5jGA1E1E1E1HE111L1E1E1LE1E1GH5AE1E1E11LE11E1L11E1LE1E1E1HH5`AA111E1H1E11HE11E1HE1E1`H5yA1E11E1H111H1E1E1H1E1E1HH5+tAE1E11E1LE1E11L1E1E1HE1E1H5tAW11E11HE1E1E1H11E1LE1E1E1HH5A11E1E1H1E11HE1E1E1HE11ҸH59A1E1E1E1H111H1E1E1LE1E1E1HH5Ab1E1E1E1HE111L1E1E1LE1E1H5A11E11HE11E1H11E1LE1E1E1HH5IA11E11H1E11HE1E1E1HE11ҸH5Ax1E11E1H111H1E1E1LE1E1E1HH5A$1E1E1E1HE111L1E1E1LE1E1H5ZAE1E1E11LE11E1L11E1LE1E1E1HH5 7A111E1HE1E11HE11E1HE1E17H5A71E1E1E1H111H1E1E1H1E1E1HH5jXAE1E11E1LE1E11L1E1E1HE1E1H5XAE1E1E11LE1E1E1L11E1LE1E1E1HH5pA?111E1HE1E11HE11E1HE1E1pH5vA1E1E1E1H111H1E1E1H1E1E1HH5'AE1E11E1LE1E11L1E1E1HE1E1H5ASE1E1E11LE1E1E1L11E1LE1E1E1HH5A111E1HE1E11HE11E1HE1E1H53A1E1E1E1H111H1E1E1H1E1E1HH5A]E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E11LE1E1E1L11E1LE1E1E1HH5@A111E1HE1E11HE11E1HE1E1H5An1E1E1E1H111H1E1E1H1E1E1HH5AE1E11E1LE1E11L1E1E1HE1E1H5TAE1E1E11LE1E1E1L11E1LE1E1E1HH5Av111E1HE1E11HE11E1HE1E1H5A+1E1E1E1H111H1E1E1H1E1E1HH5^@A E1E11E1LE1E11L1E1E1HE1E1H5@A E1E1E11LE1E1E1L11E1LE1E1E1HH5A3 111E1HE1E11HE11E1HE1E1H5jA 1E1E1E1H111H1E1E1H1E1E1HH5A E1E11E1LE1E11L1E1E1HE1E1H5AG E1E1E11LE1E1E1L11E1LE1E1E1HH5wA 111E1HE1E11HE11E1HE1E1H5'A 1E1E1E1H111H1E1E1H1E1E1HH5AQ E1E11E1LE1E11L1E1E1HE1E1H5A E1E1E11LE1E1E1L11E1LE1E1E1HH54GA 111E1HE1E11HE11E1HE1E1GH5Ab 1E1E1E1H111H1E1E1H1E1E1HH5A E1E11E1LE1E11L1E1E1HE1E1H5HA 11E11HE1E1E1H11E1LE1E1E1HH5Al 11E11H1E11HE1E1E1HE11ҸH5A" 1E11E1H111H1E1E1LE1E1E1HH5UA1E1E1E1HE111L1E1E1LE1E1H5A11E11HE11E1H11E1LE1E1E1HH5A.11E11H1E11HE1E1E1HE11ҸH5fA1E11E1H111H1E1E1LE1E1E1HH5EA1E1E1E1HE111L1E1E1LE1E1EH5AD11E11HE11E1H11E1LE1E1E1HH5w\A11E11H1E11HE1E1E1HE11Ҹ\H5(A1E11E1H111H1E1E1LE1E1E1HH5AR1E1E1E1HE111L1E1E1LE1E1H5A11E11HE11E1H11E1LE1E1E1HH59A11E11H1E11HE1E1E1HE11ҸH5Ah1E11E1H111H1E1E1LE1E1E1HH5*A1E1E1E1HE111L1E1E1LE1E1*H5JAE1E1E11LE11E1L11E1LE1E1E1HH5:Ar111E1H1E11HE11E1HE1E1:H5A(1E11E1H111H1E1E1HE1E1H5edA1E1E1E1H1E11L1E1E1LE1E1H5HAdE1E1E1E1LE1E11L1E1E1LE1E1H5dA=1111HE1E1E1H11E1HE1E1E1HH5pA1E1E1E1H111H1E1E1HE1E1H5%A1E1E1E1H1E1E1L11E1LE1E1E1HH5%AHE1E1E1E1LE1E11L1E1E1LE1E1H5%A1111HE1E1E1H11E1HE1E1E1HH5-8A1E11E1H111H1E1E1HE1E1H58A\1E1E11HE11E1L11E1LE1E1E1HH5yAE1E1E1E1L1E11L1E1E1LE1E1H5AyA1111HE11E1H11E1HE1E1E1HH5|Ag1E11E1H111H1E1E1HE1E1H5|A1E1E11HE11E1L11E1LE1E1E1HH5OAE1E1E1E1L1E11L1E1E1LE1E1H5A{1111HE11E1H11E1HE1E1E1HH5A(1E11E1H111H1E1E1HE1E1H5eA1E1E11HE11E1L11E1LE1E1E1HH5AE1E1E1E1L1E11L1E1E1LE1E1H5A<1111HE11E1H11E1HE1E1E1HH5p%A1E11E1H111H1E1E1HE1E1H5&%A1E11E1H111LE1E1E1HE1E1H5BATE1E1E1E1LE1E11L1E1E1LE1H5BLAE1111E1HE1E11H1E1E1HE1E1H5;BA11E1E1H1E11HE1E1E1HE11ҸH5Ai1E1E11H11E1H1E1E1LE1E1H5HA1E1E1E1HE111L1E1E1LE1E1H5MA11E11HE11E1H11E1LE1E1E1HH5Aw11E11H1E11HE1E1E1HE11ҸH5A-1E11E1H111H1E1E1LE1E1E1HH5`A1E1E1E1HE111L1E1E1LE1E1H5A11E11HE11E1H11E1LE1E1E1HH51A911E11H1E11HE1E1E1HE11Ҹ1H5qA1E11E1H111H1E1E1LE1E1E1HH5"WA1E1E1E1HE111L1E1E1LE1E1WH5AOE1E1E11LE11E1L11E1LE1E1E1HH5qA111E1H1E11HE11E1HE1E1qH51A1E11E1H111H1E1E1H1E1E1HH5A\E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E11LE1E1E1L11E1LE1E1E1HH5?A111E1HE1E11HE11E1HE1E1H5Am1E1E1E1H111H1E1E1H1E1E1HH5AE1E11E1LE1E11L1E1E1HE1E1H5SAE1E1E11LE1E1E1L11E1LE1E1E1HH5Au111E1HE1E11HE11E1HE1E1H5A*1E1E1E1H111H1E1E1H1E1E1HH5]AE1E11E1LE1E11L1E1E1HE1E1H5AE1E1E11LE1E1E1L11E1LE1E1E1HH5A2111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1H1E1E1HH5SAE1E11E1LE1E11L1E1E1HE1E1H5AFE1E1E11LE1E1E1L11E1LE1E1E1HH5v A111E1HE1E11HE11E1HE1E1 H5&A1E1E1E1H111H1E1E1H1E1E1HH5׿ APE1E11E1LE1E11L1E1E1HE1E1H5 A11E11HE1E1E1H11E1LE1E1E1HH55 A11E11H1E11HE1E1E1HE11Ҹ H5Ad1E11E1H111H1E1E1LE1E1E1HH5 A1E1E1E1HE111L1E1E1LE1E1 H5FA11E11HE11E1H11E1LE1E1E1HH5 Ap11E11H1E11HE1E1E1HE11Ҹ H5A&1E11E1H111H1E1E1LE1E1E1HH5YI A1E1E1E1HE111L1E1E1LE1E1I H5A11E11HE11E1H11E1LE1E1E1HH5d A211E11H1E11HE1E1E1HE11Ҹd H5jA1E11E1H111H1E1E1LE1E1E1HH5g A1E1E1E1HE111L1E1E1LE1E1g H5ʻAH11E11HE11E1H11E1LE1E1E1HH5{ A11E11H1E11HE1E1E1HE11Ҹ H5,A1E11E1H111H1E1E1LE1E1E1HH5ݺ AV1E1E1E1HE111L1E1E1LE1E1 H5A 11E11HE11E1H11E1LE1E1E1HH5= A11E11H1E11HE1E1E1HE11Ҹ H5Al1E11E1H111H1E1E1LE1E1E1HH57 A1E1E1E1HE111L1E1E1LE1E17 H5NA11E11HE11E1H11E1LE1E1E1HH5g Ax11E11H1E11HE1E1E1HE11Ҹg H5A.1E11E1H111H1E1E1LE1E1E1HH5a A1E1E1E1HE111L1E1E1LE1E1 H5A11E11HE11E1H11E1LE1E1E1HH5 A:11E11H1E11HE1E1E1HE11Ҹ H5rA1E11E1H111H1E1E1LE1E1E1HH5# A1E1E1E1HE111L1E1E1LE1E1 H5ҶAP11E11HE11E1H11E1LE1E1E1HH5: A11E11H1E11HE1E1E1HE11Ҹ: H54A1E11E1H111H1E1E1LE1E1E1HH5y A^1E1E1E1HE111L1E1E1LE1E1y H5AE1E1E1E1L1E11L1E1E1LE1E1H5L| A1111HE1E11HE11E1HE1E1H5H| At1E1E1E1H111H1E1E1HE1E1H5| A)E1E11E1LE1E11L1E1E1HE1E1H5c AE1E1E11LE1E11L1E1E1LE1E1H5H A111E1H1E11HE11E1HE1E1 H5A>1E11E1H111H1E1E1HE1E1H5{> A1E1E1E1H1E11L1E1E1LE1E1H5/HA> E1E1E1E1LE1E11L1E1E1LE1E1H5ڲ> AS1111HE1E1E1H11E1HE1E1E1HH5` A1E1E1E1H111H1E1E1HE1E1H5;` A1E1E1E1H1E1E1L11E1LE1E1E1HH5n A^E1E1E1E1LE1E11L1E1E1LE1E1H5n A1111HE1E1E1H11E1HE1E1E1HH5C A1E1E1E1H111H1E1E1HE1E1H5 AqE1E11E1LE1E11L1E1E1HE1E1H5 A$11E11HE1E11H1E1E1LE1E1H5`H A11E11H1E11HE1E1E1HE11Ҹ H5 A1E11E1H111H1E1E1LE1E1E1HH5 A41E1E1E1HE111L1E1E1LE1E1 H5jAE1E1E11LE11E1L11E1LE1E1E1HH5+A111E1H1E11HE11E1HE1E1+H5ʮAH1E11E1H111H1E1E1HE1E1H5>A1E1E1E1H1E11L1E1E1LE1E1H59HA>E1E1E1E1LE1E11L1E1E1LE1E1H5>A]1111HE1E1E1H11E1HE1E1E1HH5\A 1E1E1E1H111H1E1E1HE1E1H5E\A1E1E1E1H111LE1E1E1HE1E1H5xArE1E1E1E1LE1E11L1E1E1LH5E1xLAE1E1E11E1L1E11L1E1E1HE1E1H5XxA11E11H1E11H11E1HE1E1E1HH5A~1E11E1H111LE1E1E1HE1E1H5A3E1E1E1E1LE11E1L11E1LE1E1H5mLE1AE1E11E1L1E11L1E1E1HE1E1H5A11E11H1E11H11E1HE1E1E1H:H5A=1E11E1H111LE1E1E1HE1E1H5y:AE1E1E1E1LE11E1L11E1LE1E1H5,L=E1AE1E11E1LE1E11L1E1E1HE1E1H5թ=AN11E1E1H1E11H11E1HE1E1E1HTH5|A1E1E1E1H111LE1E1E1HE1E1H55TAE1E1E1E1LE1E1E1L11E1LE1E1H5LZE1AVE1E11E1LE1E11L1E1E1HE1E1H5ZA 11E1E1H1E11H11E1HE1E1E1HH57A1E1E1E1H111LE1E1E1HE1E1H5AiE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AE1E11E1LE1E11L1E1E1HE1E1H5KA11E1E1H1E11H11E1HE1E1E1HH5Ap1E1E1E1H111LE1E1E1HE1E1H5A$E1E1E1E1LE1E1E1L11E1LE1E1H5]LUE1AE1E11E1LE1E11L1E1E1HE1E1H5UA11E1E1H1E11H11E1HE1E1E1HH5A+1E1E1E1H111LE1E1E1HE1E1H5fAE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AE1E11E1LE1E11L1E1E1HE1E1H5A:11E1E1H1E11H11E1HE1E1E1HH5hA1E1E1E1H111LE1E1E1HE1E1H5!AE1E1E1E1LE1E1E1L11E1LE1E1H5ӣL/E1AB111E1HE1E11H1E1E1HE1E1H5~/A11E1E1H1E11HE1E1E1HE11ҸnH5.A1E1E11H11E1H1E1E1LE1E1H5HAnZ1E1E1E1HE111L1E1E1LE1E1nH5AE1E1E1E1L1E11L1E1E1LE1E1H5HA1111HE1E11HE11E1HE1E1H5HAp1E1E1E1H111H1E1E1HE1E1H5A%E1E11E1LE1E11L1E1E1HE1E1H5_%A11E11HE1E11H1E1E1LE1E1H5H%A11E11H1E11HE1E1E1HE11Ҹ%H5A<1E1E11H1E11H11E1LE1E1E1HH5oHAE1E1E1E1L1E11L1E1E1LE1E1HH5AE1E11E1L1E11L1E1E1HE1E1H5֟[AO11E1E1H111H1E1E1HE1E1[HAH5z1E1E1E1H111LE1E1E1HE1E1H59[AE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AZE1E11E1LE1E11L1E1E1HE1E1H5A 11E1E1H1E11H11E1HE1E1E1HH5;A1E1E1E1H111LE1E1E1HE1E1H5AmE1E1E1E1LE1E1E1L11E1LE1E1H5L%E1AE1E11E1LE1E11L1E1E1HE1E1H5O%A11E1E1H1E11H11E1HE1E1E1HFH5At1E1E1E1H111LE1E1E1HE1E1H5FA(E1E1E1E1LE1E1E1L11E1LE1E1H5aLE1AE1E11E1LE1E11L1E1E1HE1E1H5 A11E1E1H1E11H11E1HE1E1E1HH5A/1E1E1E1H111LE1E1E1HE1E1H5jAE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AE1E11E1LE1E11L1E1E1HE1E1H5ŚA>11E1E1H1E11H11E1HE1E1E1HH5lA1E1E1E1H111LE1E1E1HE1E1H5%AE1E1E1E1LE1E1E1L11E1LE1E1H5יL2E1AFE1E11E1LE1E11L1E1E1HE1E1H52A11E1E1H1E11H11E1HE1E1E1HmH5'A1E1E1E1H111LE1E1E1HE1E1H5mAYE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AE1E11E1LE1E11L1E1E1HE1E1H5;A11E1E1H1E11H11E1HE1E1E1HH5A`1E1E1E1H111LE1E1E1HE1E1H5AE1E1E1E1LE1E1E1L11E1LE1E1H5MLE1AE1E11E1LE1E11L1E1E1HE1E1H5Ao11E1E1H1E11H11E1HE1E1E1HFH5A1E1E1E1H111LE1E1E1HE1E1H5VFAE1E1E1E1LE1E1E1L11E1LE1E1H5LE1AwE1E11E1LE1E11L1E1E1HE1E1H5A*11E1E1H1E11H11E1HE1E1E1HH5XA1E1E1E1H111LE1E1E1HE1E1H5AE1E1E1E1LE1E1E1L11E1LE1E1H5ÔLE1A2E1E11E1LE1E11L1E1E1HE1E1H5lA11E1E1H1E11H11E1HE1E1E1H[H5A1E1E1E1H111LE1E1E1HE1E1H5̓[AEE1E1E1E1LE1E1E1L11E1LE1E1H5~LE1AE1E11E1LE1E11L1E1E1HE1E1H5'A11E1E1H1E11H11E1HE1E1E1HPH5ΒAL1E1E1E1H111LE1E1E1HE1E1H5PAE1E1E1E1LE1E1E1L11E1LE1E1H59LE1AE1E11E1LE1E11L1E1E1HE1E1H5A[11E1E1H1E11H11E1HE1E1E1HH5A1E1E1E1H1E11L1E1E1LE1E1H5BHAE1E1E1E1LE111LE1E1E1LE1H5Ai1111HE1E11HE11E1HE1E1H5HA1E1E11H11E1H1E1E1HE1H5RA1E1E1E1H1E11L1E1E1LE1E1H5 HA}E1E1E1E1LE111LE1E1E1LE1H5A21111HE1E1E1H11E1HE1E1E1HH5wA1E1E1E1H111H1E1E1HE1E1H5,A1E1E1E1H1E11L1E1E1LE1E1E1HH5֎1A=E1E1E1E1LE1E11L1E1E1LE1E11H5AE1E11E1LE1E11L1E1E1HE1E1H5;=A11E1E1H111H1E1E1HE1E1=HAH5ߍQ1E11E1H111LE1E1E1HE1E1H5=AE1E1E1E1LE11E1L11E1LE1E1H5RL`E1AE1E11E1L1E11L1E1E1HE1E1H5`Ac11E11H1E11H11E1HE1E1E1HsH5A1E11E1H111LE1E1E1HE1E1H5^sAſE1E1E1E1LE11E1L11E1LE1E1H5LE1AnE1E11E1L1E11L1E1E1HE1E1H5A"11E11H1E11H11E1HE1E1E1HH5AϾ1E11E1H111LE1E1E1HE1E1H5DA鄾E1E1E1E1LE11E1L11E1LE1E1H5LE1A-E1E11E1L1E11L1E1E1HE1E1H5A111E1H1E11HE11E1HE1E1DH5+A闽1E1E11H11E1H1E1E1H1E1H5HADFE1E11E1LE1E11L1E1E1HE1E1H5DAE1E1E1E1LE1E11L1E1E1LE1E1H5DVA髼1111HE1E11HE11E1HE1E1H5HVAZ1E11E1H111H1E1E1HE1E1H5VA1E11E1H111LE1E1E1HE1E1H5^{AŻE1E1E1E1LE1E11L1E1E1LE1H5{LAE1p111E1HE1E11H1E1E1HE1E1H5{A%11E1E1H1E11HE1E1E1HE11ҸH5nAں1E1E11H11E1H1E1E1LE1E1H5(HA鈺1E1E1E1HE111L1E1E1LE1E1H5ІA<E1E1E1E1L1E11L1E1E1LE1E1H5A1111HE1E11HE11E1HE1E1H5>HA鞹1E1E1E1H111H1E1E1HE1E1H5AS1E1E1E1H1E1E1L11E1LE1E1E1HH5AE1E1E1E1LE1E11L1E1E1LE1E1H5HA鯸1111HE1E1E1H11E1HE1E1E1HH5A[1E1E1E1H111H1E1E1HE1E1H5A1E1E1E1H1E1E1L11E1LE1E1E1HH5SA麷E1E1E1E1LE1E11L1E1E1LE1E1H5Al1111HE1E1E1H11E1HE1E1E1HH5A1E1E1E1H111H1E1E1HE1E1H5fAͶ1E1E1E1H1E1E1L11E1LE1E1E1HH5AwE1E1E1E1LE1E11L1E1E1LE1E1H5‚A)1111HE1E1E1H11E1HE1E1E1HH5n;Aյ1E1E1E1H111H1E1E1HE1E1H5#;A銵1E1E1E1H1E1E1L11E1LE1E1E1HH5A4E1E1E1E1LE1E11L1E1E1LE1E1H5A1111HE1E1E1H11E1HE1E1E1HH5RA钴1E1E1E1H111H1E1E1HE1E1H5AG1E1E1E1H111LE1E1E1HE1E1H5qAE1E1E1E1LE1E11L1E1E1LE1H5IqLAE1馳111E1H1E11H1E1E1HE1E1H5qA\11E11H1E11HE1E1E1HE11ҸH5A1E1E1E1H111H1E1E1LE1E1H5`HAE1E1E1E1LE1E11L1E1E1LE1E1七H5ArE1E11E1LE1E11L1E1E1HE1E1H5~A%11E1E1H111H1E1E1HE1E1HAH5b~Ա1E11E1H111LE1E1E1HE1E1H5"~A鉱1E1E1E1HE111L1E1E1LE1E1H5}A=E1E1E11LE1E11L1E1E1LE1E1H5}HA111E1HE1E11HE11E1HE1E1H52}A鞰1E1E1E1H111H1E1E1H1E1E1HH5|AJE1E11E1LE1E11L1E1E1HE1E1H5|AE1E1E11LE1E1E1L11E1LE1E1E1HH5?|A馯111E1HE1E11HE11E1HE1E1H5{A[1E1E1E1H111H1E1E1H1E1E1HH5{AE1E11E1LE1E11L1E1E1HE1E1H5S{A麮E1E1E11LE1E1E1L11E1LE1E1E1HH5zAc111E1HE1E11HE11E1HE1E1H5zA1E1E1E1H111H1E1E1H1E1E1HH5]z3AĭE1E11E1LE1E11L1E1E1HE1E1H5z3AwE1E1E11LE1E1E1L11E1LE1E1E1HH5zA 111E1HE1E11HE11E1HE1E1H5zAլ1E1E1E1H111H1E1E1H1E1E1HH5AzA遬E1E11E1LE1E11L1E1E1HE1E1H5yA4E1E1E1E1LE1E11L1E1E1LE1E1H5x_A1111HE1E11HE11E1HE1E1H55xH_A镫1E11E1H111H1E1E1HE1E1H5w_AK1E11E1H111LE1E1E1HE1E1H5wqAE1E1E1E1LE1E11L1E1E1LE1H5NwqLAE1髪111E1HE1E11H1E1E1HE1E1H5vqA`11E1E1H1E11HE1E1E1HE11ҸH5vA1E1E1E1H111H1E1E1LE1E1H5cvHAéE1E1E1E1L1E11L1E1E1LE1E1丝H5 vAvE1E11E1L1E11L1E1E1HE1E1H5uA*11E1E1H111H1E1E1HE1E1HAH5gu٨1E1E1E1H111LE1E1E1HE1E1H5&uA鍨E1E1E1E1LE1E1E1L11E1LE1E1H5tLE1A5E1E11E1LE1E11L1E1E1HE1E1H5tA11E1E1H1E11H11E1HE1E1E1HH5(tA锧1E1E1E1H111LE1E1E1HE1E1H5sAHE1E1E1E1LE1E1E1L11E1LE1E1H5sLE1AE1E11E1LE1E11L1E1E1HE1E1H5A2E1E1E1E1LE1E11L1E1E1LE1E1H5]>A1111HE1E1E1H11E1HE1E1E1HH5X]OA鐐1E1E1E1H111H1E1E1HE1E1H5 ]OAE1E11E1H1E11L1E1E1HE1E1E1H5\xAE1E1E1E1LE1E1E1L11E1LE1E1H5q\LE1A韏E1E11E1LE1E11L1E1E1HE1E1H5\AR11E1E1H1E11H11E1HE1E1E1HH5[A1E1E1E1H111LE1E1E1HE1E1H5z[A鲎E1E1E1E1LE1E1E1L11E1LE1E1H5,[LE1AZE1E11E1LE1E11L1E1E1HE1E1H5ZA 11E1E1H1E11H11E1HE1E1E1HH5|ZA鹍1E1E1E1H111LE1E1E1HE1E1H55ZAmE1E1E1E1LE1E1E1L11E1LE1E1H5YLE1AE1E11E1LE1E11L1E1E1HE1E1H5YAȌ11E1E1H1E11H11E1HE1E1E1HH57YAt1E1E1E1H111LE1E1E1HE1E1H5XA(E1E1E1E1LE1E1E1L11E1LE1E1H5XLE1AЋE1E11E1LE1E11L1E1E1HE1E1H5KXA郋11E1E1H1E11H11E1HE1E1E1HH5WA/1E1E1E1H111LE1E1E1HE1E1H5WAE1E1E1E1LE1E1E1L11E1LE1E1H5]WLE1A鋊E1E11E1LE1E11L1E1E1HE1E1H5WA>11E1E1H1E11H11E1HE1E1E1HH5VA1E1E1E1H111LE1E1E1HE1E1H5fVA鞉E1E1E1E1LE1E1E1L11E1LE1E1H5VLE1AFE1E11E1LE1E11L1E1E1HE1E1H5UA11E1E1H1E11H11E1HE1E1E1HH5hUA饈1E1E1E1H111LE1E1E1HE1E1H5!UAYE1E1E1E1LE1E1E1L11E1LE1E1H5TLE1AE1E11E1LE1E11L1E1E1HE1E1H5|TA鴇11E1E1H1E11H11E1HE1E1E1H H5#TA`1E1E1E1H111LE1E1E1HE1E1H5S AE1E1E1E1LE1E1E1L11E1LE1E1H5SLE1A鼆E1E11E1LE1E11L1E1E1HE1E1H57SAo11E1E1H1E11H11E1HE1E1E1H.H5RA1E1E1E1H111LE1E1E1HE1E1H5R.AυE1E1E1E1LE1E1E1L11E1LE1E1H5IRLYE1AwE1E11E1LE1E11L1E1E1HE1E1H5QYA*11E1E1H1E11H11E1HE1E1E1HfH5QAք1E1E1E1H111LE1E1E1HE1E1H5RQfA銄E1E1E1E1LE1E1E1L11E1LE1E1H5QL|E1A2E1E11E1LE1E11L1E1E1HE1E1H5P|A11E1E1H1E11H11E1HE1E1E1HH5TPA鑃1E1E1E1H111LE1E1E1HE1E1H5 PAEE1E1E1E1LE1E1E1L11E1LE1E1H5OLE1AE1E11E1LE1E11L1E1E1HE1E1H5hOA頂11E1E1H1E11H11E1HE1E1E1HH5OAL1E1E1E1H111LE1E1E1HE1E1H5NAE1E1E1E1LE1E1E1L11E1LE1E1H5zNLE1A騁E1E11E1LE1E11L1E1E1HE1E1H5#NA[11E1E1H1E11H11E1HE1E1E1HH5MA1E1E1E1H111LE1E1E1HE1E1H5MA黀E1E1E1E1LE1E1E1L11E1LE1E1H55ML1E1AcE1E11E1LE1E11L1E1E1HE1E1H5L1A11E1E1H1E11H11E1HE1E1E1H?H5LA1E1E1E1H111LE1E1E1HE1E1H5>L?AvE1E1E1E1LE1E1E1L11E1LE1E1H5KLSE1AE1E11E1LE1E11L1E1E1HE1E1H5KSA~11E1E1H1E11H11E1HE1E1E1HpH5@KA}~1E1E1E1H111LE1E1E1HE1E1H5JpA1~E1E1E1E1LE1E1E1L11E1LE1E1H5JLvE1A}E1E11E1LE1E11L1E1E1HE1E1H5TJvA}11E1E1H1E11H11E1HE1E1E1HH5IA8}1E1E1E1H111LE1E1E1HE1E1H5IA|E1E1E1E1LE1E1E1L11E1LE1E1H5fILE1A|E1E11E1LE1E11L1E1E1HE1E1H5IAG|11E1E1H1E11H11E1HE1E1E1HH5IA{1E1E1E1H111LE1E1E1HE1E1H5gIA{E1E1E1E1LE1E1E1L11E1LE1E1H5ILE1AO{E1E11E1LE1E11L1E1E1HE1E1H5HA{111E1H11E1HE11E1HE11E1HE1H5tGAz1E1HE1H111H1E1E1LE1E1E1HH5GASzE1E1E1E1LE11E1L11E1LE1E1H5FLE1Ay1111HE1E1E1H11E1HE1E1E1HH5pFAy1E1E1E1H111H1E1E1HE1E1H5%FA]y1E1E1E1H1E1E1L11E1LE1E1E1HH5EAyE1E1E1E1LE1E11L1E1E1LE1E1H5EAx1111HE1E1E1H11E1HE1E1E1HH5-EAex1E1E1E1H111H1E1E1HE1E1H5DAx1E1E1E1H1E1E1L11E1LE1E1E1HH5EAwE1E1E1E1LE1E11L1E1E1LE1E1H56EAvw1111HE1E1E1H11E1HE1E1E1HH5DA"w1E1E1E1H111H1E1E1HE1E1H5DAv1E1E1E1H111LE1E1E1HE1E1H5SCAvE1E1E1E1LE1E11L1E1E1LH5 CE1LAE16vE1E11E1L1E11L1E1E1HE1E1H5BAu11E11H1E11H11E1HE1E1E1HH5ZBAu1E11E1H111LE1E1E1HE1E1H5BALuE1E1E1E1LE11E1L11E1LE1E1H5ALE1AtE1E11E1L1E11L1E1E1HE1E1H5qAAt11E11H1E11H11E1HE1E1E1HH5AAVt1E11E1H111LE1E1E1HE1E1H5@A t1E1E1E1HE111L1E1E1LE1E1H5zAAsE1E1E1E1L1E11L1E1E1LE1E1H52AArs1111HE11E1H11E1HE1E1E1HH5? As1E11E1H111H1E1E1HE1E1H5? Ar1E1E11HE11E1L11E1LE1E1E1HH5H?4ArE1E1E1E1L1E11L1E1E1LE1E1H5>4A3r1111HE11E1H11E1HE1E1E1HH5>Aq1E11E1H111H1E1E1HE1E1H5^>Aq1E11E1H111LE1E1E1HE1E1H5 ?AKq1E1E1E1HE111L1E1E1LE1E1H5>Ap11E11HE11E1H11E1LE1E1E1HH5s=Ap11E11H1E11HE1E1E1HE11ҸH5$=Aap1E11E1H111H1E1E1LE1E1E1HH5<A p1E1E1E1HE111L1E1E1LE1E1H5<AoE1E1E11LE11E1L11E1LE1E1E1HH53<Ako111E1H1E11HE11E1HE1E1H5;A!o1E11E1H111H1E1E1HE1E1H5<An1E11E1H111LE1E1E1HE1E1H5L<AnE1E1E1E1LE11E1L11E1LE1E1H5;LE1A5nE1E11E1L1E11L1E1E1HE1E1H5:Am11E11H1E11H11E1HE1E1E1HFH5Y:Am1E11E1H111LE1E1E1HE1E1H5:FAKmE1E1E1E1LE11E1L11E1LE1E1H59LeE1AlE1E11E1L1E11L1E1E1HE1E1H5p9eAl11E11H1E11H11E1HE1E1E1H}H59AUl1E11E1H111LE1E1E1HE1E1H58}A lE1E1E1E1LE11E1L11E1LE1E1H58LE1AkE1E11E1L1E11L1E1E1HE1E1H5/8Agk11E11H1E11H11E1HE1E1E1HH57Ak1E11E1H111LE1E1E1HE1E1H57AjE1E1E1E1LE11E1L11E1LE1E1H5D7LE1ArjE1E11E1L1E11L1E1E1HE1E1H56A&j11E11H1E11H11E1HE1E1E1H"H56Ai1E11E1H111LE1E1E1HE1E1H5P6"Ai1E1E1E1HE111L1E1E1LE1E1H56Ac11E11H1E11HE1E1E1HE11ҸH50Ab1E11E1H111H1E1E1LE1E1E1HH5h/aAbE1E1E1E1LE1E11L1E1E1LH5 /E1cLAE1KbE1E11E1LE111LE1E1E1HE1cH5.Ab11E1E1H111H1E1E1HE1E1gHAH5m.a1E1E11H11E1LE1E1E1HE1gH5*.AgaE1E1E1E1LE1E11L1E1E1LH5-E1aLAE1aE1E11E1LE111LE1E1E1HE1aH5-A`11E11H1E11H11E1HE1E1E1HH58-Au`1E11E1H111LE1E1E1HE1E1H5,A*`E1E1E1E1LE11E1L11E1LE1E1H5,LE1A_E1E11E1L1E11L1E1E1HE1E1H5O,A_11E11H1E11H11E1HE1E1E1HH5+A4_1E11E1H111LE1E1E1HE1E1H5+A^E1E1E1E1LE11E1L11E1LE1E1H5d+LE1A^E1E11E1L1E11L1E1E1HE1E1H5+AF^11E11H1E11H11E1HE1E1E1H:H5*A]1E11E1H111LE1E1E1HE1E1H5p*:A]1E1E1E1HE111L1E1E1LE1E1H5*A\]E1E1E11LE1E11L1E1E1LE1E1H5)HA]111E1HE1E11HE11E1HE1E1H5)A\1E1E1E1H111H1E1E1HE1E1H5:)Ar\1E1E1E1H1E11L1E1E1LE1E1H5(HA\E1E1E1E1L1E11L1E1E1LE1E1H5(A[1111HE11E1H11E1HE1E1E1HH5G(A[1E11E1H111H1E1E1HE1E1H5'A5[1E11E1H111LE1E1E1HE1E1H5'* AZE1E1E1E1LE1E11L1E1E1LE1H5g'* LAE1Z111E1HE1E11H1E1E1HE1E1H5'* AJZ11E1E1H1E11HE1E1E1HE11ҸA H5&AY1E1E11H11E1H1E1E1LE1E1H5|&HAA Y1E1E1E1HE111L1E1E1LE1E1A H5$&AaYE1E1E1E1L1E11L1E1E1LE1E1H5%S AY1111HE1E11HE11E1HE1E1H5%HS AX1E1E1E1H111H1E1E1HE1E1H5@%S AxX1E1E1E1H111LE1E1E1HE1E1H5$b A,XE1E1E1E1LE1E11L1E1E1LE1H5$b LAE1W111E1H1E11H1E1E1HE1E1H5U$b AW11E11H1E11HE1E1E1HE11Ҹq H5$ACW1E1E1E1H111H1E1E1LE1E1H5#HAq VE1E1E1E1LE1E11L1E1E1LE1E1q H5f#AVE1E11E1LE1E11L1E1E1HE1E1H5# AVV11E1E1H111H1E1E1HE1E1 HAH5"V1E11E1H111LE1E1E1HE1E1H5" AUE1E1E1E1LE11E1L11E1LE1E1H55"L E1AcUE1E11E1L1E11L1E1E1HE1E1H5! AU11E11H1E11H11E1HE1E1E1H H5!AT1E11E1H111LE1E1E1HE1E1H5A! AyTE1E1E1E1LE11E1L11E1LE1E1H5!LE1A"TE1E11E1L1E11L1E1E1HE1E1H5!AS11E11H1E11H11E1HE1E1E1HH5>!AS1E11E1H111LE1E1E1HE1E1H5 A8SE1E1E1E1LE11E1L11E1LE1E1H5L E1ARE1E11E1L1E11L1E1E1HE1E1H5] AR11E11H1E11H11E1HE1E1E1H( H5ABR1E11E1H111LE1E1E1HE1E1H5( AQE1E1E1E1LE11E1L11E1LE1E1H5rLD E1AQE1E11E1L1E11L1E1E1HE1E1H5D ATQ11E11H1E11H11E1HE1E1E1H H5AQ1E11E1H111LE1E1E1HE1E1H5~ APE1E1E1E1LE11E1L11E1LE1E1H5LE1A_P1111HE1E11HE11E1HE1E1H5-HAP1E1E11H11E1H1E1E1H1E1H5HAOE1E11E1LE111LE1E1E1HE1H5AsO11E11HE1E1E1H11E1LE1E1E1HH56AO11E1E1H1E11HE1E1E1HE11ҸH5AN1E11E1H1E11H11E1LE1E1E1HE13H5A|NE1E1E1E1LE1E11L1E1E1L3E1HLH56E1A NE1E11E1L1E11L1E1E1HE1E1H53AM111E1H11E1HE11E1HE11E1HE1H59A~M1E1LE1H111H1E1E1LE1E1E1HH5=9A%ME1E1E1E1LE1E11L1E1E1LE1H59LAE1L111E1H1E1E1HE11E1H9H5AHL1E111H1E1E1H1E1E1HH5W9A?LE1E11E1L11E1LE1E1E1H5IAKE1E11E1L11E1LE1E1E1HH59AK11E11HE11E1HE1E19H5A|K1E11E1H1E11H11E1H1E1E1HE1SH59A&K1E1E11HE111L1E1E1LLE1E1HH5SAJE1E1E11LE1E11L1E1E1LE1E1H5SAJ1111HE11E1HE111HE1E1E1HE1YH5=A*J1L1E1H111H1E1E1H1E1E1HH5YAI1E1E11HE111L1E1E1LE1E1H5HAYIE1E1E11LE111LE1E1E1LE1YH5JA7I111E1H11E1HE1E1E1HH5 YAH1E1E11H11E1HE1E1E1H5iAHE1E11E1L11E1LE1E1E1HH5YAlHE1E111LE1E1E1LE1E1H5FYA.HE1E11E1L1E1E1L11E1HE1E1E1H5AG1111HE1E11HE11E1HE1E1H5PAG11E1E1H111H1E1E1HE1E1H5 AKG1E11E1H1E11L1E1E1HE1E1E1H5AFE1E1E1E1LE11E1LE111LE1E1E1LH5E1AFE1E111LE1E11L1E1E1HE1E1H5AWF1111HE1E11HE11E1HE1E1H5A F11E1E1H111H1E1E1H1E1E1HH5HAE1E1E1E1HE111L1E1E1LE1E1HH5AnEE1E1E11L1E1E1L11E1LE1E1E1HH50YAE1111HE1E11HE11E1HE1E1YH5AD11E1E1H111H1E1E1H1E1E1HH5pA{D1E1E1E1HE111L1E1E1LE1E1pH5BA/DE1E1E11LE1E11L1E1E1LE1E1H5AC1111HE1E11H1E1E1HE1E1H5XAC111E1H1E11HE1E1E1HE11ҸH5aANC11E1E1H111H1E1E1HE1E1H5ACE1E1E11LE1E11L1E1E1E1E1AH5BE1E11E1L1E1E1L11E1HE1E1E1H5AoB11E11HE1E11H1E1E1E1E1AH58+B1E11E1H1E11H11E1HE1E1E1H5AA1E11E1H1E11H1E1E1E1E1AH5OAE1E1E1E1L1E1E1L11E1LE1E1E1H5 AJAE1E1E11LE1E11L1E1E1E1E1AH5A111E1H1E1E1H11E1HE1E1E1H5 A@1E11E1H1E11H1E1E1E1E1AH5 s@1E11E1H1E11H11E1HE1E1E1H5> A&@E1E1E11LE1E11L1E1E1E1E1AH5 ?E1E11E1L1E1E1L11E1HE1E1E1H5 A?11E11HE1E11H1E1E1E1E1AH5Z M?1E11E1H1E11H11E1HE1E1E1H5 A?1E11E1H1E11H1E1E1E1E1AH5 >E1E1E1E1L1E1E1L11E1LE1E1E1H5 Al>E11E11LE1E11H1E1E1E1E1AH54 '>111E1H1E1E1H11E1HE1E1E1H5 A=1E11E1H1E11H1E1E1E1E1AH5 =1E11E1H1E11H1E1E1E1E1AH5 R=E1E1E11LE1E11L1E1E1E1E1AH5  =E1E111LE1E11L1E1E1HE1E1H5 @A<1111HE1E11HE11E1HE1E1@H5 Av<1E11E1H1E11H1E1E1E1E1@AH5? 2<1E11E1H1E11H1E1E1E1E1AH5 ;E1E1E11LE1E11L1E1E1E1E1AH5] ;E1E1E1E1L1E1E1L11E1LE1E1E1H5pWAX;1111HE1E11H1E1E1HE1E1H5&YA;11E1E1H111HE1E1E1E1H5YA:11E1E1H111H1E1E1HE1E1H5WA:E1E11E1LE111LE1E1E1E1H5XWA@:E1E11E1L1E1E1L11E1HE1E1E1H5 A911E11HE1E11H1E1E1E1E1AH591E11E1H1E11H11E1HE1E1E1H5xA`91E11E1H1E11H1E1E1E1E1AH5)9E1E1E1E1L1E1E1L11E1LE1E1E1H5A8E1E1E11LE1E11L1E1E1E1E1AH58111E1H1E1E1H11E1HE1E1E1H5QA981E11E1H1E11H1E1E1E1E1AH571E11E1H1E11H11E1LE1E1E1HE1H5A7HE1E1E1L1E1E1H11E1LE1E1H5eLE1AC7E1E111LE1E11L1E1E1HE1E1H5A61111H1E1E1H1E1E1HE11E1HH5A611E1E1H111H1E1E1HE1E1H5rAZ6E1E1E1E1L1E1E1L11E1LE1E1H5%LE1A6E1E111LE1E11L1E1E1HE1E1H5A51111H1E1E1H1E1E1HE11E1HH5wAd511E1E1H111H1E1E1HE1E1H52A5E1E1E1E1L1E1E1L11E1LE1E1H5LE1A4E1E111LE1E11L1E1E1HE1E1H5Aw41111H1E1E1H1E1E1HE11E1H'H57A$411E1E1H111H1E1E1HE1E1H5'A31E1E1E1HE111L1E1E1LE1E1:H5A3E1E111LE1E11L1E1E1HE1E1H5Z:AB311E11HE1E11H1E1E1E1E1:AH5 21E11E1H1E11H11E1HE1E1E1H5TA21E11E1H1E11H1E1E1E1E1TAH5zm2E1E1E11LE1E11L1E1E1E1E1sAH54'2E1E111LE1E11L1E1E1HE1E1H5sA11111HE1E11HE11E1HE1E1sH5A111E1E1H111H1E1E1HE1E1H5_AG1E1E111LE1E11L1E1E1HE1E1H5A0E1E1E11LE1E11L1E1E1E1E1AH50E1E1E11LE1E11L1E1E1E1E1AH5|o01111HE1E11H1E1E1HE1E1H5=A%0111E1H1E11HE1E1E1HE11ҸH5A/11E1E1H111H1E1E1HE1E1H5QA/1E1E1E1HE111L1E1E1LE1E1H5AE/E1E1E11LE1E11L1E1E1LE1E1H5A.1111HE1E11H1E1E1HE1E1H5A.111E1H1E11HE1E1E1HE11ҸH5wAd.11E1E1H111H1E1E1HE1E1H5A.1E1E1E1HE111L1E1E1LE1E1H5A-E1E1E11LE1E11L1E1E1LE1E1H5A-1111HE1E11H1E1E1HE1E1H5OA7-111E1H1E11HE1E1E1HE11ҸH5A,11E1E1H111H1E1E1HE1E1H5cA,1E1E1E1HE111L1E1E1LE1E1H5AW,E1E1E11LE11E1LE111LE1E1E1HE1H5A+11L1H1E1E1H11E1HE1E1E1H5A+1E11E1H1E11H1E1E1E1E1AH5vi+1E1E11HE11E1LE111LE1E1E1HE1 H5$A+E1E11HL1E1E1L11E1HE1E1E1H5 A*1111HE1E11HE11E1HE1E1 H5At*1E1E11H11E1H1E1E1HE1 H5?A,*E1E11E1L11E1LE1E1E1HH5 A)E1E1E1E1L11E1LE1E1E1H5A)111E1H11E1HE1E1E1HH5w A_)1E111H1E1E1HE1E1H5; A#)1E1E1E1H111H1E1E1HE1E1H5&A(E1E1E1E1LE1E11L1E1E1LE1E1&H5A(E1E1E11LE1E11L1E1E1E1E1&AH5QD(111E1H1E1E1H11E1HE1E1E1H5JA'1E11E1H1E11H1E1E1E1E1JAH5'1E11E1H1E11H11E1HE1E1E1H5~cAf'E1E1E11LE1E11L1E1E1E1E1cAH5- 'E1E11E1L1E1E1L11E1HE1E1E1H5eA&11E11HE1E11H1E1E1E1E1eAH5&1E11E1H1E11H11E1HE1E1E1H5XA@&1E11E1H1E11H1E1E1E1E1AH5 %E1E1E1E1LE11E1LE111LE1E1E1LH5E1A%H111HE1E1E1H11E1HE1E1E1HH5aAI%1E1E1E1H111H1E1E1HE1E1H5A$1E1E1E1H1E1E1L11E1LE1E1E1HH5A$E1E1E1E1LE1E11L1E1E1LE1E1H5rAZ$1111HE1E1E1H11E1HE1E1E1HH5A$1E1E1E1H111H1E1E1HE1E1H5A#1E1E1E1H111LE1E1E1HE1E1H5/Ao#E1E1E1E1LE1E1E1L11E1LE1E1H5LE1A#E1E11E1LE1E11L1E1E1HE1E1H5A"11E1E1H1E11H11E1HE1E1E1HH5Av"1E1E1E1H111LE1E1E1HE1E1H5BA*"E1E1E1E1LE1E1E1L11E1LE1E1H5LE1A!E1E11E1LE1E11L1E1E1HE1E1H5A!11E1E1H1E11H11E1HE1E1E1HH5DA1!1E1E1E1H111LE1E1E1HE1E1H5A E1E1E1E1LE1E1E1L11E1LE1E1H5LE1A E1E11E1LE1E11L1E1E1HE1E1H5XA@ 11E1E1H1E11H11E1HE1E1E1HH5A1E1E1E1H111LE1E1E1HE1E1H5AE1E1E1E1LE1E1E1L11E1LE1E1H5jL%E1AHE1E11E1LE1E11L1E1E1HE1E1H5%A11E1E1H1E11H11E1HE1E1E1HMH5A1E1E1E1H111LE1E1E1HE1E1H5sMA[E1E1E1E1LE1E1E1L11E1LE1E1H5LE1AE1E11E1LE1E11L1E1E1HE1E1H5lA111E1HE1E11HE11E1HE1E1H5&Ak1E1E1E1H111H1E1E1HE1E1H5A 1E1E1E1H111LE1E1E1HE1E1H5AE1E1E1E1LE1E1E1L11E1LE1E1H5LE1A|E1E11E1LE1E11L1E1E1HE1E1H54A/111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5QALE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5fAf1E1E1E1H111H1E1E1HE1E1H5 AE1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H58A3111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5UAPE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5jAj1E1E1E1H111H1E1E1HE1E1H5$AE1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5<A7111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5YATE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5nAn1E1E1E1H111H1E1E1HE1E1H5(A#E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5@A;111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5]AXE1E1E1E1LE1E11L1E1E1LE1E1H5 A E1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5rAr1E1E1E1H111H1E1E1HE1E1H5,A'E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5DA?111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5aA\E1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5vAv1E1E1E1H111H1E1E1HE1E1H50A+E1E11E1LE1E11L1E1E1HE1E1H5A E1E1E1E1LE1E11L1E1E1LE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5HAC 111E1HE1E11HE11E1HE1E1H5A 1E1E1E1H111H1E1E1HE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5eA` E1E1E1E1LE1E11L1E1E1LE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5A 111E1HE1E11HE11E1HE1E1H5zAz 1E1E1E1H111H1E1E1HE1E1H54A/ E1E11E1LE1E11L1E1E1HE1E1H5A E1E1E1E1LE1E11L1E1E1LE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5LAG 111E1HE1E11HE11E1HE1E1H5A 1E1E1E1H111H1E1E1HE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5iAd E1E1E1E1LE1E11L1E1E1LE1E1H5A E1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5~A~1E1E1E1H111H1E1E1HE1E1H58A3E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5PAK111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5mAhE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5<A7E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5TAO111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5qAlE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5A111E1HE1E11HE11E1HE1E1H5A1E1E1E1H111H1E1E1HE1E1H5@A;E1E11E1LE1E11L1E1E1HE1E1H5AE1E1E1E1LE1E11L1E1E1LE1E1H5AE1E11E1LE1E11L1E1E1HE1E1H5XAS111E1HE1E11HE11E1HE1E1H5AHtzHHPXL`HhHpLxLD蛡HPXL`HhHpLxLDHtzHHX`LhHpHxLLDHX`LhHpHxLLDHHtsH`hLpHxHLLD蘠H`hLpHxHLLDHHtsH`hLpHxHLLDH`hLpHxHLLDMtvLH`hLpHxHLLD螟H`hLpHxHLLDHHtsH`hLpHxHLLDH`hLpHxHLLDHtzHH`hLpHxHLLD蛞H`hLpHxHLLDMtvLHhpLxHHLLD HhpLxHHLLDMthLHpxLHHLD謝HpxLHHLDMtZLHxLHHDFHxLHHDHtLHHLHDHLHDHtPHHLHD蔜HLHDHt>HHLDJHLDMt0LHDHDHt4HHDЛHDMt0LHD蛛HDHt0HHDfHDMt0LHD1HDHt4HHDHDH=,tY H-(MtNE1L%Y MtLY L(MtLY MHtMILMX MuH(MuH(MuH(Mt*LME1E1X H}tL薕IiH}(H}tLvMM:LY(M(HG(M.E1E1GMH!(MH(MH(H}HtW H(H}HtW H(Hu L(HW L(HtHW L(HtHW L(H{(H}t HppMtLcW HR(Mt0LMW H<(MtL7W H&(H(H(H}HtW H(LV H{HtV L((+O%(AE11Mt51LV HtdHV ZI I(MuL(Hz(H}t HxoMtMuHR(Mt!LMV L<(H}Ht6V H#(MtLV M_RH}HtV H(H}HtU H((I$LP(L0(H(H(H(I}HtU H(I}Ht~U Hm(H$(H@LO(H}HtIU H}H/(L(H@rH(LaH}HtU L (H((Mt I$LP(H(H(HtEHMT I|$HtF1T HtHT L(I|$HuHm(I|$MHuLW(LN(I|$HuH;(I|$HuH((I|$HuH(I|$HjH(I|$HSH(I|$HH((I$LP(H}HtJ XL(H(H}HtJ LpH}Mu H}LLHHHH@HhHt/J HXHtJ H (LH{Ht0E1I MtLI L(H{HuH(H(Mt+LMI H{HuL(H{IHtH{MHuL|(L%H{HmL^(H}tHWHXt HXAI L0(HhtHh"I HXtHX I L(H(HhtaHhH H(HhH81HhH8t H8H Hht HhH H(H|(H}tLuH}HtDE1bH MtLUH HD(Hxt Hx6H H}HtE1H(H}HuH(ME1MtLG H}HuH(H}tLՄE1E1HxtHxG L#H}HtG HxHtG H{(L#E1MtLiG MtL\G H}HtNG H=(MtL8G ME1MtLME1G LQ#MtE1L=#LH@HtHF HIHHtF L(HtHHF HL"LH}Ht HEF HEHo(HtHHEfF HEL"H}HtJF H9(MtL4F L*lLH}Ht HEF HEH(HtHHEE HELkLH}Ht HEE HEH(HtHHEE HELkLH}Ht HEE HEH~(HtHHEuE HELgkLH}Ht HERE HEH=(HtHHE4E HEMtL!E MMHhHt E MtLD L(MtLD LE1HtHD MH@jHhHtE1HhHuH(H(MfLD HtHpD H}HtbD H}HtTD LC(LCjL9jHxHt(D L jH(L((8H((L(LiMtLC H}HtC H(MtLC L覕H(L蕕H(HdH`/(H@"(LY(L9H`(H;(HtHHE2C HEH}Ht HEC HEH(LwLLjHH}Ht HEB HEH(HtHHEB HEL詷LH}Ht HEB HEH(HtHHEB HELhHH}Ht HEcB HEHN(HtHHEEB HELI$H}Ht&B H}HtB L(HtHB LLH}Ht HEA HEH(HtHHEA HELI褶H}HtA H}HtA L(HtHA LhLH`L(L`H}HtPA H}HtBA L1(HtHH`%A H`맾pH(HxHt@ L(LH}Ht@ H(L}H}Ht@ H(L^H}Ht@ H(LHH}Ht HEz@ HEHe(HtHHE\@ HEL^LH}Ht HE9@ HEH$(HtHHE@ HEHLH}HubHH}HtHh? HhHxHtHh? HhMtLHh? HhH(? HhtHhHP{? HPqHt HY? HXHtH? HtH;? HHHt*? H8Ht? Ht H? L(HXE1LHt1HHhHt> 1cHt H> IHHtH> L.E1HXH71HHXHtI> E1MtL9> HHHt(> H(E1HXHtL8M> ML8MtL= ML=VHhHt= Ld, HXHn= dHXHT1H8HXH61E1H8hME1GLUL+ HXH;E1H}HtIL%= ILHtH= LHtH= HxHt< L(L@UH}Ht< H}Ht< H}HtE1I1qH}Ht< 1H}Ht1E1[LTH}HDZLTH(HtP< HHt?< HHt.< H((L7(pL(렾HL(H8(LH}EPHXHt; H`(PH(HhTLKHxHt; MtL}; Hl(MtLg; MtLZ; ME1MtLC; HxHtE1H(LH}HXHt; H`H(8H(LLgH}Ht: H(H}tHwH7HhHudHxHu_LHXHtHHt: HHH\(HHHHtHHH@@: H@2: +: LLH}Ht HE : HEH(HtHHE9 HEHpHhHudHxHu_LHXHtHH9 HHH(HHHHtHHH@y9 H@k9 d9 LLH}Ht HEE9 HEH0(HtHHE'9 HELLH}Ht HE9 HEH(HtHHE8 HELhLH}Ht HE8 HEH(HtHHE8 HEE1H8 MtL8 Lv(LIH}HuJH߾Z(LA(0(!H(H'(H'8 L(8 H8 L(HI7 gMtL7 H}It H}7 Hpt Hp7 Hxt Hx7 L(HxtHxI7 1HxHpIt 1HxHT(HxuHpIuH4(I^HptHpHE1HpHx+HpuH(H`IHpjH(Ht'H6 L(HtmH6 L(H(Ht5H6 L~(H}Htx6 HtHk6 LZ(HQ(H}HtK6 H8(Ht'H36 L"(HtH6 L (H(H(L%H}Ht5 LMt>E1L5 MtL5 H(MtME1E1L5 MuL(MtLHEE15 H]HMuHj(H}HuMtLHEV5 HEHMu1H8H}(H`(H8t H85 H(H ]11H8H8tH84 HHHt4 HHHx4 nHtH4 1H}L(LC(HtH~4 Lm(H810L`1HHHtI4 L`1L`1HHHt 4 Ht]H4 L(Ht"H3 L(LjNH(H(HucL(HufL(L(H}Ht3 HtH3 L(HtH3 Lq(Hh(Hh3 LW(HW3 LF(L6SH5(Ht'H03 L(Ht6H3 L (H(HucL(HufL(H(HtH2 L(L(H}Ht2 HtH2 L(H2 L(H2 Lv(LfRLQHHZ(MtLHEQ2 HEMtL>2 H}Ht02 H(H`[MtL 2 LhHhtHh1 Mt I$LPH}Ht1 H(Hx{[H}Ht1 H(MtLE11 MtL1 MtLx1 HxtHxb1 HxtE1H}HtC1 HxurH}Ht'1 H(H}Ht1 MMMtL0 MtL0 MtLE10 MtL0 H(HxHt0 MtL0 LyHxHt0 H|(MuHn(MuH`(LHxHtO0 H>(MtL90 H((H(HxHt0 MuMtLE1/ MtL/ MuH(LjH}Ht/ H}3H((Ht HHPp(H(LH}Hs(LLٙHHU(MtHEL(L=(HEMMtL~(L(MtL / H(H}iMtL. HHt. H(LXH(Ht. H8Ht. MtL. MtL. LΤMtLq. MtLb. sLH8HtD. H(Ht3. H"(LrMtL. MtL. MtL- MtL- MtL- L菆HPH0H(LLS(HHsHr(HLF(Ѿ8L^((LHL3LH}Ht+- MtL- H (MtLE1- Mu&MtL, MtL, L: LMtME1E1MtL, H(Mu(H(H(MtL, Hz(Lz, Hi(H} LHM(MtLH, H7(Mu(H)(H (MtL, H (L , H(H}X LH(L= L+ HtH+ L(MtL+ MtL+ MuMtLE1+ L$H}Htv+ H}HuHtIE11L L,H<(H ,H'(HL Hm MtL+ H@HHt,L(H ?HtH* LhH(Ht* H@',H(HBH8Htq* H(Ht`* HO(HBHHHt:* 1L H H8Ht* MtL * H t H ) HHBH(H((\(H8HuHHHt) HXHt) H8HuHXHt}) LJ(랾8Lb((H(ALF) LHUHzHt HE-) HEH(HtHHE) HEH]AH1HHt( H(Ht( HHt( HHt( HHt( HHt( Hz(H@H誫H(HtY( H@H?HHt.( H(Ht( HHt ( HHt' HHt' HHt' H(H$@H踫H(Ht' L?HQHHH(Htp' HHt_' HHtN' HHt=' HHt,' H(L{?HϫH(Ht& & qLJ?H^HHH(Ht& HHt& HHt& HHt& HHty& Hh(L>HܫH(HtK& D& qH1HtHH& HHHHIHt% Ht H% H8Ht% L(H1H1xH蒽HHHt1HHXHtz% L`HHHu1KHAH1HHH7MH1HHt% HHuLk9H _9H($ HHt$ HHuHjH ^L($ HXHLHHHtHs$ HH8HtHT$ HHtHH9$ HMtLH$ HH`H$ H(I>(LE11V˶(& (Dž{xE1,# E1H}X(MtFL# LBHDH(ILH5DtH81Iy(1]DHytHʁ(IH5sH81y(H(IH5x)H81x(fHys%|(D%|(D%u|(D%e|(D%U|(D%E|(D%5|(D%%|(D%|(D%|(D%{(D%{(D%{(D%{(D%{(D%{(Dh%{(D@%{(D%u{(D%e{(DP%U{(D`%E{(D%5{(D%%{(D%{(D%{(D%z(D%z(D%z(D%z(D%z(D%z(D%z(D%z(D%uz(D%ez(D%Uz(D%Ez(D%5z(D%%z(D%z(D%z(D%y(D%y(D%y(D%y(D%y(D%y(D%y(D%y(D%uy(D%ey(D%Uy(D%Ey(D`%5y(DP%%y(D%y(D%y(D%x(DUHSHH_HtHuo(H߾H]%x(fH]f.UHATS(A(DH{(1H[A\]ÐHGH Ht~HHHt H@fD1DUHv(Hu 1]DHHtH@]f.HGHHtfD%p(fHGHHtfD%2m(fHt f.%*u(f.HwPH1H=$%q(f.UHAWAVL}LuAUIATASHH(dH%(HE1HGHEHEtBHAHOHMH|(LH5nH81as(1rD1LLH_r(t;HEH@uH{(LH5AnH81s(1'f.EuHMHyfDHUdH+%(uH([A\A]A^A_]w(f.DUHATIHSHHHGL@@tY'HLH[A\]A@ukHHNHHvHL[A\]AfHHN1HtHCH5mHHz(H81r(!f.HA(H5"H8w(H1[A\]fDHtLEHHU~(LEHuHa)LEHHU~(LEHuH$HCH5;lHHz(H81^q(xLEHHU~(LEHuH@HCH5lHHy(H81q(*UHAVIAUIATSHGHLMt\H=}l{(u6HLLAHNr(HtH[A\A]A^]Dj|(Ht#1H[A\A]A^][A\A]A^]%i(H~(H5,l1H8u(HHtHUHHH}.u(HtH}HHfHG@HtHUHHH}t(H}HG@HtHf.fHW HGHHH9}H9~HWHH4HHG1%rm(f.HGH9G ~HWHH4HHG1Ð%Bm(f.Ht %=l(Df.DUHHSHH X*uW~SH *H։q*HH1 HHHh(H ~(HC(fCHH]1H0HHuf.UHAWMAVIAUATISHHMLmLMx(HHtzHMLMLpHHHtHfCtCpHC@C C0MtILKPMtIL{HMtIELkXMtI$Lc`HHCh"}(HH[A\A]A^A_]UHAWAVIAUATSHHL'A|$ { Ml$ML{MHL)L9I<$uxH9p(I9D$ujAT$ u_H9CuYs 8rA@t@t7K4/LBp(uxI>M1HLg(M&I$(fHHL[A\A]A^A_]%^|(fDHIHL[A\A]A^A_]Lv(E1fHwv(@H n(H5iH8q(f.DHGhHtHUHAUATSHHLg`Mt7L-@*Br(1LHLYy(HtHChHH[A\A]]fH1i(H@UHSHHHGHu&H{t H~u(HCHH]H@@u'HH9P0uHc(tH]g(uHCHGHu mDUHH@uHH9P0t*D@H}ng(H}uHGH}c(H}tf.fUHSHHHGHu&H{(HtHPHH]f.@u'HH9P0uHb(tH]f(uHCUHSHHHGHu&H{HtHPHH]Jf.@u'HH9P0uHb(tH]Bf(uHCUHSHHHGHu&H{HtHPHH]f.@u'HH9P0uHa(tH]e(uHCUHh(Hf(H]f.Ht %r(DHHfHGHHtHUHSHHHGH8e(HCHHtHH]DHb(HH%w(f.fUHSHHHb(HHpw(H߾H]%ml(f.%b(f.H;=b(H;=r(uH;=e(t%r(ff.fH9t*HXHt'HJH~F1 fHH9t8H9tuHH9tHu1H;5fp(f.1f.H9tCH9t>LXMt:IxH~ 1ILH9tH9tHH9u1fDfHDHH9tHuH o(H9tfDHH9tHu1H9fDUHAVAUIATISHAƅu3H{HHt LAԅuH{PHt[LLA\A]A^]@A[DA\A]A^]ÐUHATIS o(HHtHH[A\]Dr(HuID$uH_(LH8e(L1*`(IHtH_(LH8e(L7DH H*uc~_UH*fHHSH`*HHHCC C0HC@`(Hu(HH]H01DHGXHtHUHSHHHGHxHtg(HCXHtHH]Hb(Hf.fHGH;q(u#HWHt*Hu+HcH%_o(H;r(t'%b(HHHu&HcH%*o(f@%XG%Me(Ht HtH@` GWHH HH놋GWHH HquH01@H m(H5*1H8f.Dttt"1HIn(H1H71fHHttt"1H!q(H1H71fHHH9UHAVAUATISHHVL6H?HCIM)H)L9r*HKHH)L9r{MH{[A\A]A^]DML&g(LLHIp(H;Ht HsH)'g(K<,L#H{H{[A\A]A^]1Hu1LH)uLH{[A\A]A^]ÐHn(H;LZ@LHn(HKH;IT$M4$HH)@LLn(H;Lf^(f.UHSHHL_(DHL9tHu#HHuHHH;H]HHPHHHHa(HH;H]UHj(HHNH5H81H$b(1]UH;5@n(HATSHt"H~H5f(H9tE1?*twj(uLcL[A\]f.DH^(HDHHfHGPHHt HZ(HHk(HD%%e(Dh%e(D@%e(DUHSHHHGHuFH{(HtHHH@H9ud(HH]fHH]@u'HH9P0uHX(tH]\(uHCUHFHATISH?IL$HHVH6H9t;H9tfI4$IL$IT$HSIT$Ht1H;HKHC[A\]H9t+I4$IT$HSIT$HHHC[A\]I9tKHtHt0m(HSI<$IT$H;HC[A\]fDHSI<$ϐHeUH)HAUIATISHHdH%(HE1HUHwNH?Hu5H]I<$I\$HEdH+%(uRH[A\A]]DHt%fHu1m(I$HHEID$HL l(H]I<$c(UHSH"HHtDf@@(@8@HH%HC5j(HC0HC(HC8CHCPHH]f.UHATSHGHHu0LcMtLn[(Lb(H[A\]<@@uHH9P0uH V(t[A\]BZ(uHCUHSHHHGHu&H{HtHPHH]f.@u'HH9P0uHU(tH]Y(uHCH9UHAWAVAUATISHHL~L6H?HCMM)H)L9r|HCHH)L9rM9tELL i(L+7HtLh(HCH;M|$M4$HH)I4L9u{ILkH[A\A]A^A_]DHL9riLH`(IM9tLLHi(H;Ht HsH)D`(ML#LkLHH)Ih(L+qf](f.H?LOIЃtHIALfHtIH>IAHLUHd(H5Z IH81H[(1]f.fLOLW8IH?tHHHIALHt#H>HHIAHHLf.UHc(H5 IH81HH[(1]f.fUHATSHH~H5Kb(H9tE1?*tc(uLcL[A\]UHATSHH~H5T(H9tE1?*t`c(uLcL[A\]UHATSHH~H5Ci(H9tE1?*t c(uLcL[A\]UH?LOHtIHuUHMu$IA1]HtcHu~H>LBDHb(ILH5UH81 Z(1]DHytHb(IH5TH81Y(Hib(IH58 H81Y(fHywH *HGH9L Z(L9t~LXMt4MPM~"1fITH9tTL9tOHI9u1fDHDHH9t*HuHa(H9tHL9t HuI9uHGH9pUH;5 e(HATSHt"H~H5\(H9tE1?*tWa(uLcL[A\]f.DUH;5d(HATSHt"H~H5\(H9tE1?*ta(uLcL[A\]f.DUH;5d(HATSHt"H~H5Z\(H9tE1?*t`(uLcL[A\]f.DUH;50d(HATSHt"H~H5 \(H9tE1?*tg`(uLcL[A\]f.DH9UHAVIAUIATSLgMt:DLM$$H{HCH9tHCHp&[(0H[(MuI>I^0IvH9t HZ(AoE IMI}0AF H9tqIuIEIIUIvIFIVHtH@(1IHL4IE(IEIE0I}IEIE[A\A]A^]IE0HIF0LVM1HI9tH9|uE1DJTHBtv@tmH9tHXHt,LAM~S1HI9t@H;TuHDHH9tHuH;X^(qf.IM9l1ÐUHSHHNS(oHCHt H=nX(u@HH]@HH]UHSHHd(oHCHt H=X(u@HH]@HH]UHATSHHH;wsHDHH[A\]HM(IHt'HHs](I $uHELU(HEf1f.fUHSHHHGHu&H{(Ht Hs8H)PX(HH]@u'HH9P0uHQL(tH]P(uHCUHSHHH?tHH]D:U(H;uH+U(HH]fUHATIHSHL(HtDHID$LHH@pPH t H[A\]ÐHEHT(HEH[A\]fD1f.ff.{f.kf.[f.Kf.;f.+f.f. f.f.f.UHAUIATIHSHHR(HH`K(HtKHLHLK(H tH[A\A]]fHEHS(HEH[A\A]]@H1[A\A]]HqO(HWHHGHtH t1ÐUHHCS(1]f.DH1O(HWHHGHtH t1ÐUHHS(1]f.DUHSHHHt9HPHHHP HHHtH]1HR(DH]DUHSHHHtHHH]HtH}exH}H@HAN(UHSHHHtHHH]HtH}xH}H@HM(H9t;HGH;\(unHOHtHt"Hy'@1H1DHyHH?H1H)HuGH9fH;](t_UHSH^(HHttH;|I(H; Z(u#H;M(tHaZ(H t4H]fDf1H*f.GE@EHP(E뻸HtHGXHHwXHtHt1H5L(UHHsP(1]f.DUHAUIATISH'T(HHt!E1H5*LHL\(H tHHH[A\A]]HEHP(HEf.Hq*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;V(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VV(f.1f.H1*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;U(f.1f.H9*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VU(f.1f.Hi*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;T(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VT(f.1f.H9*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;S(f.1f.H)*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VS(f.1f.HQ*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;R(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VR(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;Q(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VQ(f.1f.Ha*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;P(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;VP(f.1f.H*HGH9t/HXHt,HqH~K1HH9t8H;TuHH9tHu1H;O(f.1f.UHATL%kD(SHHI$LcHI$Ht Ht:I$H{ HLc I$HtHt [1A\]fDH([1A\]H(UHSHHHHHtHt4HtHtBHtH tH]f.HH]%G(fHuHG(HuDHG(f.UHSHHH*HtHHHtH[C(H{8HHC8HtHtH]1fD*G(H]1fH5褺fUHSHHH]*HtHHHtHB(H{8HHC8HtHtH]1fDF(H]1fH54fUHSHHH*HtHHHtH{B(H{8HHC8HtHtH]1fDJF(H]1fH5ĹfUHSHHH}*HtHHHtH B(H{8HHC8HtHtH]1fDE(H]1fH5TfUHSHHHe*HtHHHtHA(H{8HHC8HtHtH]1fDjE(H]1fH5fUHSHHH*HtHHHtH+A(H{hHHChHtHtH]1fDD(H]1fH5tfUHSHHH*HtHHHtH@(H{hHHChHtHtH]1fDD(H]1fH5fUHSHHH=*HtHHHtHK@(H{xHHCxHtHtH]1fDD(H]1fH5蔷fUHSHHH*HtHHHtH?(H{hHHChHtHtH]1fDC(H]1fH5$fUHSHHHݿ*HtHHHtHk?(H{8HHC8HtHtH]1fD:C(H]1fH5贶fUHSHHHm*HtHHHtH>(HHHHtHtH]1B(H]1fH5DfUHSHHH*HtHHHtH>(H{8HHC8HtHtH]1fDZB(H]1fH5ԵfUHSHHH*HtHHHtH>(H{8HHC8HtHtH]1fDA(H]1fH5dfUH;5=(HtHtHF tAH@1HHHtHt1]f.HwA(1HH(H5MH8yE(]fUfHnfHnflHATSHG`LghH_pG`HOpHtHt-MtI $t2HtH t[A\]H[A\]%@(H@(DL@(DUHHt7HFt*HGHHHwHHtHt1]ÐH@(1H H(H5LH8D(]fUHHt7HFt*HGPHHwPHtHt1]ÐH7@(1HG(H5LH89D(]fUHHt7HFt*HGPHHwPHtHt1]ÐH?(1HIG(H5bLH8C(]fUHHt7HFt*HGHHHwHHtHt1]ÐHw?(1HF(H5KH8yC(]fUHP;(HATISH9tHHuRHHB(H5LH8:(I$HI$HtHt1[A\]>(HFuHLF(H5KH8B(DUH:(HATISH9tHHuRHHB(H5 LH8{9(I$HI$HtHt1[A\]B>(HF uHE(H5KH8(@UHAWAVAUIATIHSHHLL~pdH%(HE1HPLI9}#MH}HI9IMHHPHEHuxM~7HD1HH‰4HHHI9uLH=(I$HEdH+%(uHL[A\A]A^A_]I$=(@UHAVAUIATIHSHH0LvpdH%(HE1HPHI9}(MH}LI9IMHIEPHEH|IHfH)EHHHIIHH9tfoEHH9uHLD(I$HEdH+%(uH0L[A\A]A^]ÐI$<(@UHAVAUIATIHSHH0LvpdH%(HE1HPHI9}(MH}LI9IMHIEPHEH|IHfH)EHHHIIHH9tfoEHH9uHL;(I$HEdH+%(uH0L[A\A]A^]ÐI$;(@UHHtHC+H@ H@(HP]DUHHtH{C+H@ H@(HP]DUHATISH3(HHI\$HHHtHtSHI|$ HI\$ HHtHtFHI|$(HI\$(HHtHt [1A\]f7([1A\] 7(6(UHH*HATSHHtzHHtL%2(H{8I$Lc8HI$Ht Ht8I$H{@HLc@I$HtHt [1A\]@z6([1A\]j6(H5ifUHATSHQL%B2(H{HI$LcHHI$Ht Ht=I$H{PHLcPI$HtHt[1A\]f5([1A\]5(UHFHtOHGHHt_HHt%Ht]f5(7(1]fDH<(H5CH8y9(]f:-(HDUHHtGHH9t-HXHtRHJH~y1 DHH9thH;tu]f.H!B(H5H88(1]DHDHH9tHuH;5;(tfH<(HNH5CHWH81g3(1f.UHSHHHGHuvH%=(H{Ht HCHtOHSHc *Hz 8u% P*H*HH]fDH@HH]3(.(uHSH[H9B0kH*(Zf.@UHSHHHGHuvHU<(H{Ht HCHtOHSHc~*Hz 8u% P~*H~*HH]fDH@HH]3(".(uHSH[H9B0kH)(Zf.@UHSHHHGHuvH;(H{Ht HCHtOHSHcL}*Hz 8u% P7}*HP}*HH]fDH@HH]B2(R-(uHSH[H9B0kH((Zf.@UHSHHHGHuvH:(H{Ht HCHtOHSHc|*Hz 8u% P|*H |*HH]fDH@HH]r1(,(uHSH[H9B0kH((Zf.@UHSHHHGHuvH9(H{Ht HCHtOHSHcz*Hz 8u% Pz*Hz*HH]fDH@HH]0(+(uHSH[H9B0kHH'(Zf.@UHSHHHGHuvH9(H{Ht HCHtOHSHcy*Hz 8u% Py*Hy*HH]fDH@HH]/(*(uHSH[H9B0kHx&(Zf.@UHSHHHGHuvHE8(H{Ht HCHtOHSHc,x*Hz u% Px*H0x*HH]fDH@HH]/(*(uHSH[H9B0kH%(Zf.@UHSHHHGHuvHu7(H{Ht HCHtOHSHc=tO=u\HHC0H'HH[A\A]A^A_]f.t#u"HDH!1H'H5H8}'H t1@Hiu{@H15'tH 9*uS~OUH9*fHHSH8*HHHHC'H'HH]@H01DH P58*u[~WUHE8*fHHSH8*HHHCC C0C@V'He'HH]@H01f.H 86*u[~WUH7*fHHSH6*HHHCC HC0'H'HH]@H01f.H 8:*u[~WUH:*fHHSHp:*HHHCC HC0V'He'HH]@H01f.H 052*uS~OUHE2*fHHSH2*HHHCC 'H'HH]@H01DH 04*uS~OUH4*fHHSH3*HHHCC n'H}'HH]@H01DH 8U4*u[~WUHe4*fHHSH04*HHHCC HC0'H'HH]@H01f.H 01*uS~OUH2*fHHSH1*HHHCC ~'H'HH]@H01DH 0E2*uS~OUHU2*fHHSH 2*HHHCC 'H'HH]@H01DH 82*u[~WUH2*fHHSHp2*HHHCC HC0'H'HH]@H01f.H 00*uS~OUH1*fHHSH0*HHHCC 'H-'HH]@H01DH 0/*uS~OUH/*fHHSH/*HHHCC 'H'HH]@H01DH X1*HuP~LUH 2*HHSH1*HH1 HHH='HL'HH]H01HfH (.*uS~OUH.*fHHSH`.*HHHCHC 'H'HH]H01DH 83*u[~WUH3*fHHSH3*HHHCC HC0V'He'HH]@H01f.H 82*u[~WUH2*fHHSH2*HHHCC HC0'H'HH]@H01f.H 8u3*u[~WUH3*fHHSHP3*HHHCC HC0V'He'HH]@H01f.H 82*u[~WUH2*fHHSHp2*HHHCC HC0'H'HH]@H01f.H 853*u[~WUHE3*fHHSH3*HHHCC HC0V'He'HH]@H01f.H @U2*uS~OUHe2*fHHSH02*HHHCC C0'H'HH]H01DUHHHt@Hb*H@LBM@11ҾHEH=jHEHt1f.Hg'1UHHsHt@H?b*H@LBM@11ҾHEH=wjHEHt1f.H'1UHAWAVAUATISH'o@`HLkpfH~fH~HtHHtHMtIEC`)ELkp'L'H{`foEL{hLspILkpC`Ht HMt IMtItbMt=L\'I $tH[A\A]A^A_]HL[A\A]A^A_]%'H='H[A\A]A^A_]% 'DL'D'kDL'ffUHSHH(dH%(HEHGHHT'HUHuH}'HH;h'HPH{0umH}HUHHu'H{Ht Hs(H)'H{Ht HCHtHVHvMuyHEdH+%(HxH1[A\A]A^A_]AHEdH+%(RHwHxHL[A\A]A^A_].hfDM|$MyI<LEHUH<'IHHULEHt;HBHIM 1I94fDILI HH9uLULLEHU'HULEHLUHPH}IE1L}H}H}MAH}H}IHpHHhHEH}HULELx/f.HEHPL#HUHHKDJIHMHULHu'uHULELuLL}LxHpMLULHAHMLUHH AMLhE1MIM9KC*'HH.C*H@II]HtHH[A\A]A^]'Hu[LA\A]A^]f.UHATSHGHG1Ht1HtoHHtoHt!+'HH [A\]DGWHH ЉH tH'H5}H8'D_[A\]fDGWHH HH u[A\]_ۉ[A\]@IHtHI $[L'MfH['HL^fUHATSHGHG1Ht1HtoHHtoHt!'HH [A\]DGWHH ЉH tH'H5H8d'D_[A\]fDGWHH HH u[A\]_ۉ[A\]@sIHtHI $[L'MfH[`'HL^fUHATSHGHG1Ht1HtoHHtoHt!˻'HH [A\]DGWHH ЉH tH|'H5H84'D_[A\]fDGWHH HH u[A\]_ۉ[A\]@CIHtHI $[L'MfH[0'HL^fUHAVAUAATSHH9H'H9GHIH9F A|$ HCI9D$HSIL$H9@H@t Hs A|$ 8@ +H[H@ mIL$0It$H@HEʃ`g >9uGHHH'1Au'1DL5A'L9u$t 1AH[A\A]A^]f.M9uuHDLh'HHH;='H;'uDL9t?H&'H uEH'E1AH[A\A]A^]f.HK0HH@HE@'B1L'+DIt$HfD >D >f.UHAWAVAUATSHLwM~~HII1fHI9t L9duIDH[A\A]A^A_]DE1 IM9t0JtLtxKDH[A\A]A^A_]@H1[A\A]A^A_]UHAVAUATSHHtHH[A\A]A^]f.IL5ن*'HHtH@IE111HL0H=u*'H IMt6IELLHHHI$IMtgHU'Hٷ'HHHfDHY'HHHHI$HH[A\A]A^]fDH7'ifL''I$D'Hef.UHAUATSH'Hx`Hu1H[A\A]]HH&'H0H9u^LcpHCpfLkhC`Ht!MtIMt&MtI $uL'z'Lg'DK¸jH{`fLkhLcpC`HCpHtfUfHAVIAUHuIHUATISHH dH%(HEHG`HEHGhG`HEHGpHGpH}HE'H{`cHuH}H'DHuIHHH}HEHKxHtxHLJLHHII}M$HH9HHQHHHt HHtH tN1HUdH+%(AH [A\A]A^]@Ht3HII}M$HH9fHǼ'DIIEM$HHhfDIIEM$HHHH=Hj'Hu+HW'*fH}IIEI$HtHt[H}HtHt=H}HtHtH}E1|f'ڻ'ʻ'r'fUHH dH%(HE1S'H'H2H9u|f'HMHUHHuZH}HtHH}HtHtrH}HtHtTHEdH+%(upH6'H5H8%='DHtHEdH+%(u4'ں'ʺ'eo'f.DUHAUATSHHHGH;%'H;'t^L`pLhhMI|$t}Hܱ'IHHHHAT$IMHEL6'HEDHOHHH9HDHH[A\A]]fMtkIEHtbHy HH[A\A]]DHOt'HHyH9s)HSHHH[A\A]]HHrH'IHt_HH'I $uHELP'HEf.IUH_HuHHuHxHIEAD1kHZ'HuH8e'tC'IEHu f.@UHAWAVAUIATISJHXHEHu1HMLEHEdH%(HE1HGHEHELHEHEHEAHt HH}HEHt HHEMHEI;D$HMItH'H}HI1=ID$LLAIHLAHEHLA׾HLuvHHu1H]fDH'Ht1H5UHY*fHɦ'HE1L SH NH5uH:PHpU1'XH]Zf.UIHAWIAVAUATISHHt9L5'HL9t1H['H9Bt&H>'H5H8L5'1MhIH@M9Hu}A@M\MoI@t0M9ALELLȜ' IGLEHFIMMt7Ht'H5H8H[A\A]A^A_]%'fDHE1MtNM9VID$HtA$@d@9I$LELLݫ'LELL'Ht(2'HxpH9tHHXpHt HL@Mt IH[A\A]A^A_]A@LE1{'LEIMtLE1LL'IMLEIMtIO@MMHL[A\A]A^A_]%'fH'H5H8f.E1Hɣ'H5 H8Y'@LE1L9'LEHI@ME10@'DLכ'LE fDLEL1D'LEIH'LH5H81l'Lf.DUH5PHSHH(dH%(HE1'tHEdH+%(H]HUHuH}6'HH5P'HHt.H@8HtHl't6HUHuH}$'fz'Hu2H'f.Z'HtH=n*111H=藴>,'f.fUHHHuRHk*HfDHɡ'HE1L NH IH5uH8R1HO'X1Z@Hyt1H5OH$u1UHHHuRH*HfDHI'HE1L uNH IH5H8R1HFO'X1Z@Hyt1H5&OHx$u1UHHHuRH *HfDHɠ'HE1L MH HH5uH8R1HN'X1Z@Hyt1H5NH#u1UHHHuRH*HfDHI'HE1L uMH HH5H8R1HFN'X1Z@Hyt1H5&NHx#u1UHHHuRHc*HfDHɟ'HE1L LH GH5uH8R1HM'X1Z@Hyt1H5MH"u1UHHHuRH{*HfDHI'HE1L uLH GH5H8R1HaM'X1Z@Hyt1H5AMHx"u1UHHHuRH*HfDHɞ'HE1L KH FH5uH8R1HL'X1Z@Hyt1H5LH!u1UHAWAVIAUIATSH'IąL *MD*LD A9~mHHIA;Yu^M)IELH]*1L'IHX(H'IMPI $YH[A\A]A^A_]AoL$`fLM|$pAD$`LID$p)M'IH#I|$`foUID$hMt$pM|$pAT$`Ht HHt HMt IL {*MD5[*LDn HcA9LcIMA;_,*A9D)ЍPIcHHHHHHHt8HLL'AA_M/D5ڟ*IEzL'I $LH[A\A]A^A_]%֔'fDH}HtH}HttMeHL[A\A]A^A_]UD^*A9Dp@LωUIcH'IHLc}D5'*D5*H*LIIA9DHE'HEODH'JfL'HfM'HUH %H*XH*L(IE1HM<I?M/X IML@UHH'Hca'HtfH.I<H=2IHETHEf.@UH=*HATS=-HHHr*H; 'HCtUL 'HHA'HuXHIHPHHHt L[A\]H'L[A\]H'H"rH5âH81p'H|HH=E1}HHDE1HQHH=jUL[A\]f.UH=*HATS=,HHHr*H; 'HCtUL l'HHA'HuXHIHPHHHt L[A\]H'L[A\]H'H"qH5áH81p'H|GH=E1}HHDE1HQGH=UL[A\]f.UHHʎ'HHP0H'Ht fHFH=MHEHEf.@UHAUATSHLFMHHH*L-q*HCLMH= +'u01HLAHې'H111HLH t/HNFH=TF?H[A\A]]H'D11H=KFSH[A\A]]H'H$1H5FH H1L҇'HHcH=OH[A\A]]H'D11H=[>c H[A\A]]H'H$1H5#>H H1L'HHHHHEHCHpHL5B*HE1f.ID$H9*IN;tuHxJHEHHpHSHHpL=|]*HE1fDID$H9IN;|uHxJHEHXLpI"@HHFoLsHE)UMHH 81MPHxHULEHAZYyqfDLsL%]A*M1HI9tL;duHxHHEHtYIFHSHpDHuJH|'HHUdH+%(He[A\A]A^A_]fDHE 'Hu#DMH=7H7H=*1fDE1IFM9IJtLvtHxJO@E1IGM9IJtL.tHxJ@HCoHpH)]DE1IM9JtLʻtHxJ^HE‰'HAfDHE'HA'UHSHHtoHH)H*HC HCLBHz'HC(HHC8MH9tb'f.@UH5p)1HATIH=)SQHH;YZ'HtTH@LHh'Hu]HIHPHHHtL[A\]H^'L[A\]H Z'Hr=H5nH81\'HH=wE1HHDE1HH=vL[A\]f.H?t DHqY'HDUHATSHHHHCH5*HHHHHtuH;mU'H;e'uHWH=k薺1HpuH=jw1HQsH=jX1H2~H=j91HH=j1T'f.UH;=L'Ht#HG@H@0H8H8Ht*]HL'HwH5`H81O'H0H==j蘹1]@UH;=L'Ht#HG@H@0H8H8Ht*]HaL'HH5k`H81O'H7WH=i(1]@UH;=L'Ht#HG@H@0H8H8/Ht*]HK'HH5_H81N'HH=i踸1]@UH;=K'Ht#HG@H@0H8H8Ht*]HK'H1H5_H818N'HWH=uiH1]@UH;=0K'Ht#HG@H@0H8H8OHt*]HK'HH5_H81M'H=H==iط1]@UH;=J'HtH@HHHt)]HJ'HhH5^H81`M'HH= ip1]f.fUH;=PJ'HtH@HXvHt)]H9J'HH5C^H81L'HH=h1]f.fUH;=I'Ht#HG@H@0H8H8Ht*]HI'HH5]H81xL'HH=h舶1]@UH;=pI'Ht#HG@H@0HxH8Ht)]HQI'HH5[]H81L'H'H=Mh1]@UH;=I'HtH@HH&Ht)]HH'H$H5\H81K'HH=h谵1]f.fUH;=H'HtH HHt)]HyH'HWH5\H810K'HF>H=g@1]f.fUH;= H'HtH HFHt)]H H'HH5\H81J'H(^H=gд1]f.fUHSHHdH%(HE1H;=G't_Hw0H}HP0H}H+}HU'H}HHt HuH)M'HtAHEdH+%(uNHH]fHAG'H)H5K[H81I'H>H=f1N'HfDUHAWAVIAUATISHHdH%(HE1H*HEHEHEHNHHHEHHHLyHEMH]H5)H9sL%VF't L9kM9L9Hs I~0KG'HlB'HHUdH+%(He[A\A]A^A_]HET'Hu#DMH=HH=e課1fDLyH *M~1 @HI9tPH;LuHUHHEHtI@HnHH]fHR'HE1IM9$JtHϺHM膅HMtHEJz1HH-%M9fDHD'Hn$H5XH81xG'HH=d舱1`HD'H_H5XH818G'fDHHP1MPHuHULEHSZYbK'f.@UH;=D'Ht+HG01H@@Ht HHRHP'Ht%]HC'HH5WH81F'H IH=c蠰1]f.fUH;=C'Ht+HG01H@@Ht HHRHJP'Ht%]HYC'HiH5cWH81F'H8H=c 1]f.fUH;=C'Ht+HG01H@@Ht HHRHO'Ht%]HB'HH5VH81E'H QH=Uc蠯1]f.fUH;=B'Ht+HG01H@@Ht HHRHJO'Ht%]HYB'HiH5cVH81E'HH= c 1]f.fU1HATISHH5)H=)}HtxL`8HH1I$H=)H5YH5*L )L *ZYHt@H tHe[A\]HEHsE'HEHe[A\]fDHaA'HHTH=sbNH t 1DH'E'1UH;= A'HtH K'Ht;]ÐHA'HH5UH81C'HH=حrO']HHHfDUH;=@'Ht#HG0HxM<'HcM'Ht']DH@'HH5TH81HC'HlH=aX1]@H;=A@'tHG HtNH@H@DUH0@'HVH5:TH81HB'HC$H=I1]1f.UH;=?'Ht#HG H@Hx L'Ht,]f.H?'HH5SH81hB'HoH=`x1]@UH;=`?'Ht;HO01HQHBH~HR HI HЋ<+'Ht3HO01HQHBH~HR HI HH'H H5RH81XA'HH==`h1]@H;=Q>'t/HG0H@xtH:'HfDHK'HDUH(>'H9H52RH81H@'HVH=`1]H;=='t/HG0H@xtH :'HfDHJ'HDUH='HH5QH81H\@'H$H=_l1]H;=Q='t/HG0H@xtH9'HfDHJ'HDUH(='H8H52QH81H?'HVH=Y_1]H;=<'t/HG0H@xtH 9'HfDHI'HDUH<'HH5PH81H\?'H,H= _l1]H;=Q<'t/HG0H@xtH8'HfDHI'HDUH(<'H8H52PH81H>'HVH=^1]UH5)1HATIH=)S^HHHL*H;;'HCtILHP2J'HuUHIHPHHHtL[A\]HW?'L[A\]HY;'HH5cOH81>'H3H=^E1HHDE1H2H=]L[A\]f.H;=:'t'tH7'HfDHG'HDUH:'HH5NH81Hd='H]H=]t1]UHAUATSHHH;56'L-D:'IH;5G'u@L9t;HzG'Ãu.H'Ht#HH=]]^fDM9tAD$1H[A\A]]H9'H@H5MH81<'HH=\蠦HB'H5H8A'H;=q9't'$tH5'HfDH9F'HDUHP9'HH5ZMH81H<'HH=\1]UHAUATSHHH;5;5'L-8'IH;5E'u@L9t;HF'Ãu.]G'Ht#HH=e\蠥^fDM9tAD$$1H[A\A]]Hy8'HH5LH810;'H)H=\@H@'H5H82@'H;=8't'PtHR4'HfDHD'HDUH7'HWH5KH81H:'HH=[贤1]UHAUATSHHH;53'L-7'IH;5_D'u@L9t;HD'Ãu.E'Ht#H)H=m[@^fDM9tAD$P1H[A\A]]H7'HH5#KH819'HH= [HR?'H5<H8>'H;=6't'QtH2'HfDHyC'HDUH6'HH5JH81HD9'H=H=ZT1]UHAUATSHHH;5{2'L-$6'IH;5B'u@L9t;HZC'Ãu.D'Ht#HH=}Z^fDM9tAD$Q1H[A\A]]H5'H H5IH81p8'HiH=Z耢H='H5H8r='H;=Q5't'RtH1'HfDHB'HDUH05'HH5:IH81H7'H H=Y1]UHAUATSHHH;51'L-4'IH;5A'u@L9t;HA'Ãu.=C'Ht#Hi#H=Y耡^fDM9tAD$R1H[A\A]]HY4'HH5cHH817'H $H=5Y H<'H5|H8<'H;=3't'AtH20'HfDH@'HDUH3'H7H5GH81H6'H}H=X蔠1]UHAUATSHHH;5/'L-d3'IH;5?@'u@L9t;H@'Ãu.A'Ht#H H=X ^fDM9tAD$A1H[A\A]]H2'H`H5GH815'HH=EXH2;'H5H8:'H;=2't'@tH.'HfDHY?'HDUHp2'HH5zFH81H$5'HH=W41]UHAUATSHHH;5[.'L-2'IH;5>'u@L9t;H:?'Ãu.}@'Ht#HH=W^fDM9tAD$@1H[A\A]]H1'HH5EH81P4'HIH=MW`H9'H5H8R9'H;=11'tHw fUH01'HWH5:EH81H3'H%]H=qUHSHHHe6'H9GuHH+'Ht5H tH]f.HEH4'HEH]fH tKHDH= s ?'H]HHHD-'HHr@H'4'DUH;= 0'HtHG0x(<'Ht)]H 0'HH5DH812'HH=UМ1]f.fUHSHH;=/'tNH0HHB,'H5*'17'HHtcHPHHc\<'HH]DHa/'H5rCHBH812'H2H=eU(H]1H5*H=2=*1HHt111HH t ffH2'DUH;=.'Ht#HG0Hx8H+x0HHc;'Ht%]H.'HyH5BH81H1'Hg.H=TX1]@UH@.'HH9t(HW@Hz0H?tHt0]fDH]H.'HH5#BH810'H#H=mT1]f.fUH-'HH9t(HW@Hz0H?tHt0]fDH]H-'H?H5AH81P0'HoJH=T`1]f.fUH@-'HH9t(HW@Hz0H?tHt0]fDH]H-'HH5#AH81/'HyH=S1]f.fUH,'HH9t(HW@Hz0H?tHt0]fDH]H,'HIH5@H81P/'HoH=}S`1]f.fH;=A,'t'HG@xHt H~('HfH 9'HDUH ,'HH5*@H81H.'HH=9S1]UH+'HH9t(HW@Hz0H?t-Ht0]fDH]H+'HOH5?H81`.'H0H=Rp1]f.fUH;=P+'HtHG@HcxH-8'Ht(]fDH9+'HH5C?H81-'HJH=R1]f.fH;=*'tHW0HB8H+B0HH@UH*'HH5>H81H-'HH=yR蔗H]f.UH;=p*'HtHG1PHt%]Ha*'HH5k>H81-'H7H=-R(1]@UHAUATSHHGpLgMl$0AoD$ IHfH~L9I|$H;=)'Ml$L)ErHtBfoEH{ Ml$0AD$ HtHC HCpH[A\A]]fDqHMH=CCpH4H1[A\A]]HI|$4'fHnHt}E1DHE,'HEeDH!4'H8P5'fDH)4'H5PH80'pDp3fpU1HATISHH5A)H=ʭ)}=HtxL`HH1I$H=8)H5 H5X)L )L)-ZYHt@H tHe[A\]HEH+'HEHe[A\]fDH''HHH=P讔H t 1DH+'1H;=''t'HG@xht H#'HfHI4'HDUH`''HH5j;H81H*'H3H=O$1]UH;=''HtHG@xH角Ht"]H''HH5 ;H81)'HH=mOȓ1]@UH;=&'HtHG@xHGHt"]H&'HVH5:H81X)'HwH=5Oh1]@UH;=P&'HtHG@xHHt"]HA&'HH5K:H81('HH=N1]@UH;=%'HtHG@xH臑Ht"]H%'HH59H81('H?H=N訒1]@UH;=%'HtHGHHcxLm2'Ht(]fDHy%'HFH59H810('HOxH=N@1]f.fUH;= %'HtHGHHcxP1'Ht(]fDH %'HH59H81''HH=ENБ1]f.fUH;=$'HtHGHHcxL1'Ht(]fDH$'HvH58H81P''HoH=N`1]f.fUH;=@$'HtHGHHcxP1'Ht(]fDH)$'HH538H81&'HH=M1]f.fH;=#'t'HG0xHt H 'HfH0'HDUH#'H,H57H81Hd&'HH=yMt1]H;=a#'tHG8HUHX#'H@H5b7H81H &'H)H=IM1]UH;=#'HtH0']HDH"'H H56H81%'H7 H=踏H]f.DUHAUATSHHB3'HIHCH5k)HHHIfInfInfl)EM!'HHtMfoEH@ 'HH t H[A\A]]ÐHEH%'HEH[A\A]]@I $IMu L%'HM H=诎I0'HHH[HA\A]]@H uHr%'I $uLa%'B'If.L7%'lfUHAUATSHHGpLgMl$0AoD$ IHfH~L9I|$H;= 'Ml$L)EjHtBfoEH{ Ml$0AD$ HtHC HCpH[A\A]]fDh? HmH=cCpHTH1[A\A]]HI|$,'fHnHt}E1DHE#'HEeDHA+'H8p,'fDHI+'H5GH8''gDg= 3fg> U1HATISHH5a)H=):HtxL`HH1I$H=X)H5 H5x)L )L")MZYHt@H tHe[A\]HEH"'HEHe[A\]fDH'HH= H=΋H t 1DH"'1H;='t'HG x(t H'HfHi+'HDUH'H^H52H81H4!'HJEH=HD1]UHSHH;=('tHGPH,'Hu)HH]H'HdH52H81 'HH !H=5ъHH]UHAUATSHHGpLgMl$0AoD$ IHt|fH~L9I|$H;=_'Ml$HG)ELPHtAfoEH{ Ml$0AD$ HtHC HCpH[A\A]]D3eH5H=FCpH H1[A\A]]HI|$('fHnHE1fHEv 'HE^DH''H8('xHa'H6H5k0H81'kd3fD[d#KdU1HATISHH5)H=¡)0HtxL`HH1I$H=؛)H5 H5)L Ѣ)L)ͯZYHt@H tHe[A\]HEHs'HEHe[A\]fDHa'HHH=NH t 1DH''1UH;= 'Ht#H 9*'H''Ht+]fH'H'H5 /H81'HH==Eȇ1]@H;='tHG8HUH'HH5.H81H\'HyH= El1]H;=Q'tHG@HUHH'HH5R.H81H'HH=D 1]UH;='Ht3H0'tH&'H]fH&'H]@H'HH5-H81x'HH=eD舆1]@UH;=p'Ht#HG0Hx0H+x(HHcB&'Ht%]HQ'HbH5[-H81'HH=%D1]@UH;='Ht#H0!'H%'Ht+]fH'HH5,H81'HH=C訅1]@H;='tHG8HUH'HpH5,H81H<'HY)H=CL1]H;=1'tHG@HUH('HH52,H81H'H*H=YC1]H;='tHGHHUH'H\H5+H81H|'H+H=)C茄1]UH;=p'Ht#HG0Hx0H+x(HHcB$'Ht%]HQ'HaH5[+H81'H(H=B1]@UH;='Ht#H09('H#'Ht+]fH'HH5*H81'H1H=B訃1]@H;='tHG8HUH'HpH5*H81H<'HY6H=yBL1]UH;=0'Ht#HG0Hx0H+x(HHc#'Ht%]H'H!H5*H81'HBH=5B؂1]@UH;='Ht#H0&'H"'Ht+]fH'HH5)H81X'H H=Ah1]@H;=Q'tHG8HUHH'H0H5R)H81H'HAH=A 1]UH;='Ht#HG0Hx0H+x(HHc!'Ht%]H'HH5(H81'H0H=A蘁1]@UH;='Ht#H0%'HX!'Ht+]fHa'HqH5k(H81'H9H=EA(1]@H;='t7HG0H@@Htx tHE'HfH 'HDUH'HH5'H81H'HXH=@褀1]H;='tHG8HUH'HpH5'H81H<'HYLH=@L1]UH;=0'Ht#HG0Hx0H+x(HHc 'Ht%]H'H!H5'H81'HBH=@1]@UHAWAVAUATSHH_H+HHHEHIH}?'IHH$E11IEHMIHHHEI9}}JIHEI$J<('HHtMtIuL'HMH=?IMtBMtIt*IHL[A\A]A^A_]I}t)ILIuLI'L'DL'Dz'HDH=o?~E1DHGH=M?`~H8tM[H7'DUHHH;=)'tOH0H(RHHtZH 'H9GumHHHHtDHE'HEH'HH5$H81'HH=>}1@H}N!'H}Ht HHQzHue'UHHH;=Y'tOH0H(HHtZH; 'H9GumHHHHtDHE'HEH'H!H5$H81'HB5H=%>|1@H}~ 'H}Ht HHQzHu'UHHH;='tOH0H(HHtZHk 'H9GumHHHHtDHE>'HEHA'HQH5K#H81'HrH==|1@H}'H}Ht HHQzHu'UHHH;='tOH0H(HHtZH 'H9GumHHHHtDHEn'HEHq'HH5{"H81('H-H=<8{1@H}'H}Ht HHQzHu'UHHH;= 'tOH0H@HHtZH 'H9GumHHHHtDHE'HEH 'HH5!H81X'HH=E<hz1@H}'H}Ht HHQzHu%'UHHH;= 'tOH0H(BHHtZH 'H9GumHHHHtDHE'HEH 'HH5 H81'HH=;y1@H}>'H}Ht HHQzHuU'UH;=P 'Ht#H0'H('Ht+]fH1 'HAH5; H81'HbH=%;x1]@H;= 'tHG8HUH 'HH5H81H'HWH=:x1]UH;= 'Ht#H0'HX'Ht+]fHa 'HH5kH81'HwH=:(x1]@UH;= 'Ht#H0'H'Ht+]fH 'H$H5H81 'HH=u:w1]@U1HATISHH5)H=j)#HtxL` HH1I$H=x)H5 7H5)L !)LB)mZYHt@H tHe[A\]HEH'HEHe[A\]fDH 'HHFH=9vH t 1DH '1UH;= 'HtHG0Hx0'Ht(]fDH 'H!H5H81` 'HH=9pv1]f.fUH;=P 'Ht#HG0HxH+8HHc#'Ht&]@H1 'HH5;H81 'HPH==9u1]@H;='tHG8HUH'H2H5H81H 'HH=9u1]UHAUATSHHGH5)HHHHtVHO'H9CLcMH tHL[A\A]]DH 'HL[A\A]]H HHqH=ItHL[A\A]]'Hgf.H'IHt!HV'IMItMDIN('HnI3Iu%Lcc@HG 'JfL7 'Iu DcAMcItIt$H'IDcCII IkDcCII UH5І)1HATIH=)S*HH;'HtTH@LHJ'Hu]HIHPHHHtL[A\]Hg 'L[A\]Hi'HH5sH81 'H),H=6E1-sHHDE1H+H=6sL[A\]f.UHATSHHHGH5)HHIMthHCH5)HHHHfInfHnfl)EHts'HtfoE@H[A\]ÐI $trH t]HH=5=rH1[A\]f 'I`f'HxfI $uL'H'DL'f.UH;='HtH0HFHt)]H'HH5H81`'HȾDH=U5pq1]f.fUH;=P'HtHG0Hx-'Ht(]fDH9'HuH5CH81'HXH= 5q1]f.fUH;='Ht#H0'Hc'Ht+]fH'HH5H81x'H H=4p1]@UH;=p'HtHG0HxM'Ht(]fDHY'HH5cH81'Hx H=4 p1]f.fUHAUATSHL-'L9tgH_8IL9tHHH[A\A]]f.HG0HxSHHL9urI|$8HtI\$8붐'H'H5HYH81H' HH=31VoHH[A\A]]H5y)HwH u H ' @UH;='Ht#HG x tYH@<0'Ht&]@H'HH5H81'HADH=~n1]@1f.fUHAUATSHHGH5p)HHHHtVHO'H9CLcMH tHL[A\A]]DH'HL[A\A]]H HH=ImHL[A\A]]&Hgf.H'IHt!HV 'IMItMDIN('HnI3Iu%Lcc@HG'JfL7'Iu DcAMcItIt$H 'IDcCII IkDcCII UHAWAVAUATSHHH&IHHHFH9HG x;=x K H@HID$LH5r)HHIM9HM'I9EMuM.IMH;Lsu$H&u MdH=)H HH{H}HCHHC(HCC$I$Ht_LcID$0I8fHnfIn1HC@flC0H[A\A]A^A_]L'H;LsWv@b'1fH5)H= *1IHtH111%cL݌[fDHa'H5/H8'H[A\A]A^A_]DH&H5H H81'H=/HEjH{HtHHC@r&I%f.1fL&IHtH 'IIt^I@ 'Hu IIMtSNfIuEMcuIf.L'D'/DL'Iu+EuAMcbH=i *111aItIt(L/ 'I)EuAEII IEuAEII f.fUH;=0&HtHG Hx 'Ht(]fDH&H<H5#H81&HyH=-h1]f.fUH;=&HtHG Hx 'Ht(]fDH&HH5H81`&H H=-ph1]f.fH;=Q&t'HG xt H&HfH'HDUH0&HSH5:H81H&H)H=Y-g1]H;=&t'HG x t H&HfH'HDUH&HH5H81Ht&H0H=-g1]UHAWAVAUATISHHGH5)HH!HHHCH5)HHHIHHMHHIEL5&L9IELIEHEH=^)H 'IHQH 8 %&IHH*)HIGAE IE@u tEHڷ)I]MLMo H5ͷ)HH IG(ID$HHuЋMHH|HGL9'HIHHAF @u tE9H~)BI^Mw0H HL;%&IG8kED$`EH~)HHXA|$XIG@H)HS HIGH\HM)HHPAt$TIGPH)H HIGXH)HHPIG`Hz)H HIGhAD$PH)HHPIGp LHy)HHIGx HIpIM.H[A\A]A^A_]L&f.Hg&fH u HR&HH=)JdH1[A\A]A^A_]fH&fHь)H!H)HH)HH)H^H;'MpH}PXH}ȋMIHHMHHu&IHI fHELc&HEH[A\A]A^A_]ÉMG&MZHEL+&HEzf &Hf&IfH;' LPXHIEHHIELHxmDHQH=m(b1&MHIu Lu&HH=%(pb1fH;'H}H5@u)'MH}ItH1&H)H56 H81&sfDcH;'H5t)Lq'HKf.;f.UH;=&HtsWPt,H HHxHP0u3H`'HHQH]@GTtyH@HHxHP0tH&HHQ@H'H0HVH1&H2H5;H81&HH=&`1]@H1&H0HVdf.UH;=&HtoEoM G O0]H&HH5H81`&H ]H=o`f.DUHSHHH;5E&HGt#oF oN0HH]OfH!&HH5+H81&HH=%_HH]f.DUH;=&HtH &Ht$]fH&HH5H81h&HH=%x_1]@UH;=`&HtH(A&Ht$]fHQ&H/H5[H81&HH=m%_1]@H;=&t'0tHB&HfDH&HDUH&HH5H81H&H=H=1%^1]UHATSHH;5&HAH;5[&Du#H;5d&tH&A fAąuH;9&tGDc01[A\]@&HtDHH=$^[A\]fDH&H׬H5H81&HO&H59H8&f.UH;=&HtH8&Ht$]fH&HoH5H81H&HH=5$X]1]@UHAWAVAUIATSH )&H^dH%(HE1H)HEHHfHnH-x)MfHnHHfHn)Mfl)EfHnfl)EHHIH&IH~L}H~)VfDHD݀IHH#MILH0HV&Hu&HtHϩsH=C#6\H*L;-&H`%&o`H&Au opIA}0L;%&t+IEH5ջ)LLHHЅ H;&t+IEH5)HLHHЅ1HUdH+%(He[A\A]A^A_]DML;-@&HUL}LeH]HXH`6&o`HXH;&Ae opAm0t(IEH5)LHHЅL;=&IEH5$)LLHHЅx*DH&H5H`H81@&tHH=]!PZfDHHs1LPE1LE1L>8ZY&tI11ҾH=(5u|sP&E&:&vHz>;&f.UHAWAVAUATISHHGH5)HHHHHCH5)HHHIHHMqHHIEH;&IELIEHH=q)H&IH$H  8&IHH=)AHIFAE IE@u#AtADEH!)I]Mn LH5)HHIF(ID$HH'HHHGH;&HHHH6A @u tEHR)A9H5@)LDBHYIN0HHIF8ID$HHHHHAH;&HIHHAD$ @u tEHҡ)I\$Mf@ѾLHA9HSIFHACHHIVIMH[A\A]A^A_]DL&f.H&fH Hu&H# H=ɧVH1[A\A]A^A_]fHW&MfH;Q&QH}PXH}HHHH$HH I{L&HH==U1HEL&HEH[A\A]A^A_]HM&HMDH&Bf.HEL&HEf.Z&HfJ&I2fH;Q&`LPXHIEH5H8D HH=WU1&HH;+&H}H5g)|&H}H&H.f fDH;&HMHPXHMIMt HHfIH t =HG&D !fD*&Iu L&H H=ST1DHIEYLKH;&H5f)Ln&HH;&HMHH5f)E&HMIH"H=ˤS1HMLh&HMf.Df.f.UHAUATISHHGH5ŵ)HHHHH5H)Hs&AHEHHttEuWL;%&tvI\$ c&HHHH@XH9]&Hc|&HtWH[A\A]]@Hq&HH[A\A]]HO&DHY&H|H5cH81& HH=U RH1[A\A]]HHt( ΐ&HfHIfDH&DH;=&tHGHUH&HH5H81Hl&HH=|Q1]H;=a&tHG HUHX&HH5bH81H &H"H=Q1]H;=&tHG(HUH&HWH5H81H&HO"H=P1]H;=&tHG0HUH&H#H5H81HL&H#H=a\P1]H;=A&tHG8HUH8&HǡH5BH81H&H#H=)O1]H;=&tHG@HUH&HrH5H81H&H/#H=O1]H;=&tHGHHUHx&H<H5H81H,&HϠ$H=&HfH&HDUH&H$H5H81H&H7)H=I1]H;=&tHW0HB8H+B0HH@UH&HhH5H81H4&HSMH=DIH]f.UH;= &HtHG1PHt%]H&HH5H81&HtH=uH1]@UHAUATSHHGpLgMl$0AoD$ IHfH~L9I|$H;=k&Ml$L)E$HtBfoEH{ Ml$0AD$ HtHC HCpH[A\A]]fD;#THH=NGCpHH1[A\A]]HI|$&fHnHt}E1DHE&HEeDH&H8&fDH&H5H8i&"Ds"O3f["SU1HATISHH5Y)H=_)-HtxL`HH1I$H=Y)H5 H5)L )L)mZYHt@H tHe[A\]HEH&HEHe[A\]fDHq&HHmOH=#^FH t 1DH7&1UHSHH(&H9t[H &tt H tLL7&DILL!&Huf.H!&H52HH81&FkfD&HcDH=C=DHAGH=CL&H8;Hw&-fUHATS0HtQHH%)HCHBV)L@MHZW)H9Ct4H[A\]@11H=s^H t^1f.H5)H= )1gIHt111H:I $t/H֏aH=4BDH1&lL&DUHATSHtQHH5)HCHbU)L@MHV)H9Ct4H[A\]@11H=]H t^1f.H5)H=*)1fIHt111H9I $t/HH=|ADH1&lL&DUHHtHS)HPH&HHPP]UH&HH9t0HW z(uH]ÐHcz,N&Hu/f.HQ&H5bH(H81&HH= A1]@UH&HH9t0HW z(uH]ÐHz0&Hu/f.H&H5HH81&HH=} @1]@UH&HH9t0HW z(uH]ÐHcz,N&Hu/f.HQ&H5bH(H81&HH=% @1]@UH&HH9t0HW z(uH]ÐHz0&Hu/f.H&H5HH81&HH= ?1]@UH&HH9t0HW z(uH]ÐHz0N&Hu/f.HQ&H5bH(H81&HH=u ?1]@UH&HH9t0HW z(uH]ÐHz0&Hu1/f.H&H5HH81&0HH=% >1]@UHSHHGH5)HHtpHHt;HM&Ht'H t H]@HEH+&HEH]ÐH t;HH= >H]HfD&HDH&DUHSHHGH5)HHthHHt;HU&Ht(H t H]DHEH{&HEH]ÐH t3HiH=@ c=H]12&HDH/&f.U1HATISHH5P)H=U)lHtxL` HH1I$H=O)H5 H5)L l)L›)cZYHt@H tHe[A\]HEH&HEHe[A\]fDH&HHtAH=k nH='1fDLqL=)M~1@HI9L;|uHMHHEHpI8HfL&Le'f.H u H&fH u?H=Ex&1HW&JfIt{IMuL<&f.HEH#&HEf.E1IM9JtLztHEJLǼ&wf&HcfH'f&Iofz&IfHHz1MPHuHULEHZY*&fDUHAWAVAUATISHHdH%(HE1HΑ)HEHEHEHHHHEHHCHLqHEMLeID$H5v)LHHHHHCH5C)HHHxIHHHMQHID$H5)LHHIIfInfInfl)EMo&HHRfoEH@H=)&HH GHUdH+%(He[A\A]A^A_]DHER&Hu#DMH=i>Hr@H=gr#1fDLqL=)M~1@HI9L;|uHMHHEHpI@HnL&Le7f.H u H&fHiqAH="1H&ZfI $tzIMuL&fHEH&HEf.E1IM9JtLtHEJL'&xf &HsfH'f&Ifڰ&IfHH1MPHuHULEHZY2V&fDUHAWAVAUIATISHHdH%(HE1H˅)HEHEHEHvHHHEHHHLyHEMH}HGGHGHBHHHHJD&HcЉH9fDa&HuBfHEB&Hu#DMH=0s;HmH=wb 1HUdH+%(?He[A\A]A^A_]fDLyH u)M~1@HI9H;LuHUHHEHPIHNH>H}f.1H&I9I}@ &HHIfGWHH HcЉH9tHʶ&H5H8&DGWHH HHcЉH9zwhE1IM9lJtHϺHMHMtPHEJwfDCHHHH EH&uH&HH5H81X&HlH=}hHHq1MPHuHULEH[ZYH&HG&UHATSOHHL%ٰ&H )H0)HC HCI$LBHC(HLc8I$M7L9tRHC0HtLc8H[A\]fLg&D11H=p8H tF1fHQ&HH5[H81&Hpj} H=efDH1&oUH5/)1HATIH=2)SHH;ɯ&HtTH@LHZ&Hu]HIHPHHHtL[A\]Hw&L[A\]Hy&HH5H810&H9jH=E1=HHDE1HjH=L[A\]f.UHAWAVAUIATISHHdH%(HE1H)HEHEHEHfHHHEHHHLyHEM<H]H591)H9sL5f&t L9CU&IM9aL9XHs I} R&L&Hh&HHUdH+%(He[A\A]A^A_]HE&Hu#DMH=n6HCheH=tm1fDLyH )M~1@HI9H;LuHUHHEHpIHfHH]f.H &H1H0hH]&IM9D&HH5H۬&H81&&Lo&H8gtH=il1{E1IM9JtHϺHMHMthHEJHH@m1MPHuHULEHCZYR&f.@UHSHHzHHH;׫&t%H{ HP0H&Ht-H]H&HoH5ÿH81p&H|e;H=1@I11ҾH=Dk31@Hyg1H5#kH,:N1hf.UHATSHTIHulH&I9I|$ HP8HxH˷&H[A\]DHH[A\]DI11ҾH=j21@Hyt1H5jHx9t1@Hy&H5HnH810&EH7dH=@1_fFfUHSHHBHHuZH;&tqHC x HpHxŪ&HH]fDI11ҾH=i21@Hyt1H5iH8u1H&H5HH81H&HcH=WiX1u1^ffUHSHH2HHuJH; &taHC@Hx&HH]@I11ҾH=(11@Hyt1H5H7u1H&H5¼H"|H81h&6H cH=]x1w7fUHATSHHIHH5!()H=R.)1۽HHtsL`HHH=()I$H5$ 58})HI)L ZK)Ls)H}f.z&HH H%HB&H5H8&f.z&HH&H fGWHH HH u1c_XE1IM9tJtLt`HEJ_fD諮IHOHL2HH)d1MPHuHULEH ZY: v&Hb&fUHSH(H&H"H5\HHH@߬&Ht H]@Ho]H=cHE\HEH]fUHAUATSHHHHH")H5)H9pL%)MI$H&H9CH[&fInfHnIfl)EHHX.&HfoE@H[A\A]]fI11ҾH=bc*1@Hy71H5AcHD11H=i)H)H5)IMf.H\GH=m1MH=)IH'&HHI $uL&fI $tJH uH&f.I $tIMpLg&bfLW&DLG&DUHSHR?HtgHHk)HC HCH!)HC(L@MH;&t6HC0HH]D11H=Da+)H tF1fHѠ&H wH5۴H81&HZ4H=` fDH1u&UH5` )1HATIH=#)SHH;I&HtTH@LHڮ&Hu]HIHPHHHtL[A\]H&L[A\]H&HbH5H81&HZH=UE1 HHDE1HZH=* L[A\]f.UHAWAVAUATSHHVHHH#)H )H9HL-)MJIEHCH5h)HHHIMHCH5)HHHDIMHCH5p)HHH(IM&fInfHnHfl)EHfInfInLx(flH؝&HtfoE@He[A\A]A^A_]IMH mH$XH= 1뿐Hy&HE1L VH 5QH5%H8R1H^&X1Z@E1IMu L&I $Mt IMpIgL{&YfDHy1H5Q^HT,1DH=_)H)H5)IMD&IfIMLL&M@H=9_)IL&fH&fL&fz&Ifj&IfIrUHAUATSHHIHH)H5)H9pH)H&HID$H5~)LHHIM&c&fHnfHnIfl)EH=Lh6&HfoE@H[A\A]]ÐI11ҾH=r\#1@Hy'1H5Q\HT*1H=h)H)H5)HHf.HUH=1UH=Ah)H–&If.H uH&H tI $uL&f.H&DH u Hr&IMXL_&Jf.UHAUATSHHIHH)H5)H9pHw)H&HID$H5|)LHHIM&S&fHnfHnIfl)EH=Lh&&HfoE@H[A\A]]ÐI11ҾH=bZ!1@Hy'1H5AZHD(1H=e)H)H5)HHf.HSFH=1UH=e)H&If.H uH&H tI $uL&f.Hw&DH u Hb&IMXLO&Jf.UHAUATSHHIHH)H5p)H9pHW)H&HID$H5y)LHHIM&C&fHnfHnIfl)EH=Lh&HfoE@H[A\A]]ÐI11ҾH=RX1@Hy'1H51XH4&1H=d)Hj)H5k)HHf.HPlH= 1UH=id)H&If.H uH&H tI $uL|&f.Hg&DH u HR&IMXL?&Jf.UHAUATSHHIHH)H5P)H9pH7)H&HID$H5w)LHHIM&3&fHnfHnIfl)EH=Lh&HfoE@H[A\A]]ÐI11ҾH=BV1@Hy'1H5!VH$$1H=a)HJ)H5K)vHHf.HNH=%1UH=a)tH&If.H uH&H tI $uLl&f.HW&DH u HB&IMXL/&Jf.UHAVAUATSH HHH<H)H .)H9HTL%)MI$HCH5-`)HHHIMHCH5q_)HHHIfInfInfl)EM&fInfHnHfl)MH}foEп@&H@foMHHe[A\A]A^]f.Hy&HE1L KH 5FH5%H8R1HS&X1Z@Hy1H5SH!1H=`)H )H5 )IMf.HgLH=X1*H=`)I&Imf.I $uL&&Iif.I $tZH qHȕ&cI $tJIMt$MKIBL&4@L&DLw&DLg&DUHAVAUATSH HHH<H)H ^ )H9HTL%E )MI$HCH5=s)HHHIMHCH5Q^)HHHIfInfInfl)EM#&fInfHnHfl)MH}foEп@&H@foMHHe[A\A]A^]f.H&HE1L HH uCH5eH8R1HQ&X1Z@Hy1H5PH1H=!])H )H5 )6IMf.HI#H=51*H=\)4IR&Imf.I $uLA&"&Iif.I $tZH qH&cI $tJIMt$MKIBLْ&4@Lǒ&DL&DL&DUHAUATSHHIHHW)H5P )H9pH7 )HHL&IH&fHnfHnIfl)EH5Lh&HfoE@H[A\A]]fI11ҾH=N(1@Hy71H5NH1H=j)HZ)H5[)HHf.HgGH=%X1MH=1j)HH uH&H tI $uL&f.H&DH u HҐ&IMhL&Zf.UHAWAVAUATSHHFHHHc )H5L)H9pL-3)M*IEH&IHHCH5[)HHH;IM HCH5Bl)HHHIMn&fInfHnHfl)EHfInfInLx(flH/&Ht"foE@He[A\A]A^A_]f.IMH mHtEwH=Ze1븐Hɖ&HE1L CH >H5uH8R1HL&X1ZxE1IMu L&I $Mt IMpIgLˎ&YfDHy+1H5KH1DH=k)HJ)H5K)IM DIMLLJ&M@H=Ik)IL&fL&fH&fڅ&Ifʅ&IfIrUHAVAUATSH HHH<Hu )H >)H9HTL%%)MI$HCH5j)HHHIMHCH5i)HHHIfInfInfl)EM&fInfHnHfl)MH}foEп@Q&H@foMHHe[A\A]A^]f.H&HE1L EAH ;H5ŅH8R1HeIR&X1Z@Hy1H5AIHD1H=f)H)H5)IMf.HBH=1*H=qf)I&Imf.I $uL&&Iif.I $tZH qHh&cI $tJIMt$MKIBL9&4@L'&DL&DL&DUHAUATSHHIHH)H5p)H9pHW)H&HID$H5B)LHHIM&&fHnfHnIfl)EH=Lhօ&HfoE@H[A\A]]ÐI11ҾH=Gx1@Hy'1H5FH1H=i@)Hj)H5k)FHHf.H?UH=1UH=@)DHb&If.H uHR&H tI $uL<&f.H'&DH u H&IMXL&Jf.UHAVAUATSH HHH<H)H N(H9HTL%5(MI$HCH5X)HHHIMHCH5])HHHIfInfInfl)EM&fInfHnHfl)MH}foEп@&H@foMHHe[A\A]A^]f.HI&HE1L u<H 7H5H8R1HD&X1Z@Hy1H5qDHt1H=D)H(H5(ƿIMf.H7=jH=(1*H=yD)ľI~&Imf.I $uLц&~&Iif.I $tZH qH&cI $tJIMt$MKIBLi&4@LW&DLG&DL7&DUHAVAUATSH HHH<H)H ~(H9HTL%e(MI$HCH5U)HHHIMHCH5[)HHHIfInfInfl)EM&fInfHnHfl)MH}foEп@&H@foMHHe[A\A]A^]f.H&HE1L 9H E4H55~H8R1HAƒ&X1Z@Hy1H5AH1H=B)H (H5 (IMf.Hw:H=h1*H=A)I"|&Imf.I $uL&{&Iif.I $tZH qH؃&cI $tJIMt$MKIBL&4@L&DL&DLw&DUHAVAUATSH HHH<H%(H (H9HTL%(MI$HCH5W)HHHIMHCH5!a)HHHIfInfInfl)EM3~&fInfHnHfl)MH}foEп@~&H@foMHHe[A\A]A^]f.Hɉ&HE1L 6H 1H5u{H8R1H?&X1Z@Hy1H5>H 1H=V)H:(H5;(FIMf.H7H=m1*H=iV)DIby&Imf.I $uLQ&2y&Iif.I $tZH qH&cI $tJIMt$MKIBL&4@L׀&DLǀ&DL&DUfHAWAVAUIATSHHHdH%(HE1HV))EH fHnHEfHnfl)EH1HHEHH5Ht`MH=<rH6H= 1HUdH+%(He[A\A]A^A_]fDL{L%X)M1HI9DL;duH}HHEHIGHSHEL5U)HLE1ID$H9IN;tuHEJHEHL}I6f.HH)HoL{)UM~HH];1MPHuHULEHZYyHHEHCHEH%E1IGM9tgIJtL txTHEJ'E1 IM9tPJtLκtx@HEJHEʈ&HAfDHE&H&f.@UHAWAVAUATSH(HIH H(H(H9X"L%(MI$IGH5[)LHHHHIGH5S)LHHIMIGH5;)LHHIMIGH5L)LHHIfInfInfl)EM9x&fInfHnfl)MHtRfoEfInfHnHEflտP@(w&HUHSfoMHHe[A\A]A^A_]EI $u L2|&H Mt IMMt I Mt IuH2H=%1@HY&HE1L 0H +H5uH8R1H8z&X1ZGHy1H58H1DH==)H(H5(ֳIME@fDL/{&,f.L{&fH{&fLz&fH=<)褲Ir&Hof.I $lLz&EHHH Hz&jr&ICfEE1E1fDBr&IGf.EE1r&IKfE@I $tEdfDH:UHAWAVAUATISHHdH%(HE1HW)HEHEHEH9HHHEHHHLqHEMHUHp&rH5I)H=4(t&HGu&HHUdH+%(He[A\A]A^A_]f.HE&Hu#DMH=r5:H.HH=/1fDLqL=V)M~1 @HI9thL;|uHMHHEHtI@HnHHUf.THR.H=H1E1IM9 JtL蚴tHEJjHo&HUHHHvs&AHAt*HHUHt0ESH=~)111 HHtXSCHw&HUHH41MPHuHULEH胾ZYj@H7w&z&f.DUHATSH?IH=( HHH])H@ H@(HPHr&HHC8H9tNLHh&Hu[HIHPHHHtL[A\]fDHv&L[A\]Hr&HUH5H81@u&HI-H=ݳE1MHHDH5I[)H=R)1HHt111HH t@H,H=E1BH,H=efDHu&DHAw&H9Gu HH@UHHn&HtH+H=5HEtHEf.@H;=Qq&tH0%5&DUHHq&H#H5RH81Hs&H+(H= H]UHATSHHIHH5(H=b(1HHtsL`HHH=(I$H5F5E)Hyc)L )L<)ZYHt9H t He[A\]ÐHEHSt&HEHe[A\]fDHAp&HH=*NH=;.H t1He[A\]fDHs&1I11ҾH=0@1@Hy1H5e0H1UHATSHHIHH5(H=*(1;HHtsL`HHH=y(I$H55D)HAb)L b )LQ)fZYHt9H t He[A\]ÐHEHs&HEHe[A\]fDHo&HH(RH=#H t1He[A\]fDHr&1I11ҾH=<1@Hy1H5<H|1UHATSHHIHH5A(H=(1{HHtsL`HHH=9(I$H5ƞ5XC)H a)L )L9)&ZYHt9H t He[A\]ÐHEHq&HEHe[A\]fDHm&HH'VH= H t1He[A\]fDHq&1I11ҾH= .1@Hy1H5-H<1UfHAWAVAUATISHHHH}dH%(HE1H +))EHfHnHEfHnfl)EHHHEH;HHt\M1H=-H&H=*-1HUdH+%((He[A\A]A^A_]DL{L-e>)M1HI9L;luHMHHEHIML}L%k&1nDHH(H~L>H}L}HH;= h&L%k&H;=x& L9x&ÃH5(I9wt M9yL9eM9HE1҅Iw0Hx0uy&Hg&HfDH~L>H{H}L}AHH,1MPHuHULEHZYFH}L}f.L>L%j&1L},Hw&H"f HL{HEMpHu()HuHYHHHEI9L}zfDE1IM9JtL蚪tHEJ1H%LR^L9ewDHi&HU3H5}H81l&H#H=A*2x&H@HEx&H@w&H;p&L%7i&1UHAWAVAUATSH(HHH:H(H (H9HRL-(MrIEHCH5s8)HHHhIMH;h&HCH5j9)HHHGIMHCH5f7)HHH+HHHUȿg&fInHUfHnHIfl)EtuLpHC8HID$ M|$(IT$0sg&HfoE@He[A\A]A^A_]f.Hg&H$H5{H81j&1E1IMt5IthMtItFHtH tlH!H=.(t1HULSk&IHUu HUL3k&HUDHULk&HUfHk&DHyr&HE1L H 5H5%dH8R1H'i&X1ZHy1H5'H1DH=+)Hz(H5{(IMDH=Y+)I2b&If.b&If b&HIMt$I $Li&xIMnMLMi&MuSf.DUHAVAUATSH HHHHu(H5N(H9p4L%5(MtI$Hp&IHsHCH5X4)HHHmIfInfInfl)EM}d&fInfHnHfl)MHTfoEп@hd&HfoMHHe[A\A]A^]ÐH9p&HE1L eH H5aH8R1H%rg&X1Z@Hy1H5a%Hd1H==)H(H5(趠IMf.H'J H=$13H=Y=)负II $uLg&_&IfI $tZH uHg&f.I $tJIMt$MkIbLyg&T@Lgg&DLWg&DLGg&DUfHAWAVAUIATISHHHdH%(HE1H!))EHfHnHEfHnfl)EHHHEH HrHt]M1H=#H H=#1HUdH+%(He[A\A]A^A_]fDL{H u4)M1HI9H;LuHUHHEHxIM`L}1j@HH/H~L>H}L}HfH;=%^&H;=n&H;=a&o&ÃH5(I;wL;-a&T1҅Iw0I} f&H]&HH~L>H{H}L}YHH>"1MPHuHULEHAZYeH}L}f.L>1L}HfHm&HI.HL{HEMH)HuH虣HHHEIIL}fDE1IM9JtHϺHM֠HMtHEJH9`&H4!H5CtH81b&H H= a1HLHF@f.jn&H @HEJn&H@2n&HCg&1UHATSH?IH=(`HHHq)H@ H@(HPH_&HHC8H9tNLHm&Hu[HIHPHHHtL[A\]fDHb&L[A\]H^&H2BH5rH81a&HoH=E1HHDH5G)H=l)1;HHt111HEH t@H1kH=8E1BHmH=fDHa&DUHAWAVAUATSHH_H+HHHEHIH}ob&IHHE11IFHJHEII9}qIEIJc<hj&HHtMtIuLUa&HMH=HItCMtIt*IHL[A\A]A^A_]@I>t*ILIuLI`&ːL`&DL`&D\&HSDH=wE1DH1GH=UH8tMZHw`&DUH;=p\&HtH(jHt%]Ha\&HH5kpH81_&HH= (1]@UHATSH?IH=m(HHHen)H@ H@(HPH[&HHC8H9tNLHXj&Hu[HIHPHHHtL[A\]fDHw_&L[A\]Hy[&H>H5oH810^&H9XH=]E1=HHDH5)D)H=Bi)1HHt111HH t@HTH=E1BHVH=fDH^&DUHATSH?IH= (HHHl)H@ H@(HPHZZ&HHC8H9tNLHh&Hu[HIHPHHHtL[A\]fDH^&L[A\]H Z&Hr=H5nH81\&HAH=E1HHDH5B)H=g)1{HHt111H腾H t@Hq=H=ŜxE1BHQ?H=XfDH7]&DUHAUATSHH?L% Y&fInIH=s()EHH*Hsk)foEHC I$HCHC(LcHC8L9tXLHZg&HueHIHPHHHtHL[A\A]]fHw\&HL[A\A]]fHqX&H;H5{lH81([&H1*H=E15HHDH5A)H=:f)1HHt111HH tHH'H=]E18H)H=5fDH[&DH;=W&tHG f%"Y&f.UHSHHHHHSW&H9tFHS z(u HH]z)tH{S&H]Hf.Hc&H]HHW&HH5kH81Y&HH=u1@I11ҾH= $1hHyC1H5#H*1<DUHSHHBHHuZH[V&H9tnHS z(u HH]z)&c&HugfI11ҾH=j#h1@Hyt1H5M#Hu1HU&H5jHH81X&HH=1`UHSHHBHHuZHU&H9tnHS z(u HH]Hz)Ub&HufI11ҾH="1@Hyt1H5}"Hu1H!U&H52iHH81W&HH=1`UHSHHBHHuZHT&H9tnHS z(u HH]z*a&HugfI11ҾH=!1@Hyt1H5!HHu1HQT&H5bhH(H81W&HH=՗1`UHSHHBHHuZHS&H9tnHS z(u HH]Hz*`&HufI11ҾH= 1@Hyt1H5 Hxu1HS&H5gHXH818V&HIH=H1`UHSHHBHHuZHS&H9tnHS z(u HH]z,_&HuhI11ҾH=* (1@Hyt1H5 Hu1HR&H5fHH81hU&HyH=Ux1`UHSHHBHHuZHKR&H9tnHS z(u HH]Hcz,_&HugfI11ҾH=ZX1@Hyt1H5=Hu1HQ&H5eH H81T& H H=訾1`UHSHHBHHuZH{Q&H9tnHS z(u HH]Hz06b&HugfI11ҾH=1@Hyt1H5mHu1HQ&H5"eH H81S&H H=Քؽ1`UHSHHBHHuZHP&H9tnHS z(u HH]Hz0v]&Hu(gfI11ҾH=1@Hyt1H5H8u1HAP&H5RdH H81R&'H H=1`UHSHHBHHuZHO&H9tnHS z(u HH]z*f]&Hu5gfI11ҾH=1@Hyt1H5Hhu1HqO&H5cHH H81(R&4H9 H=U81`UHSHHJHHubH O&H9t~HS z(u HH]fZB, R&HuBrDI11ҾH=1@Hyt1H5Hu1HN&H5bHh H81HQ&AHY H=X1PUHSHHBHHuZH+N&H9tnHS z(u HH]B0-Q&HuOfI11ҾH=:81@Hyt1H5Hu1HM&H5aH H81xP&NH H=͑舺1`UHAWAVAUATSHHHH HKM&H9*Ls A~(+H(H =(H9H+L%$(MI$ID$H5 )LHHHI$HI$HHHCL-s2)LMH=$KFZ&g1LHAIP&MH HA(H5j(H9pL-Q(MIEIEH5*)LHHIMIMvS&IHIc~,X&HHH5 )HLJ&xyH H5(LLHHtVI IMHLK&HDI $H upHEH`O&HE]f.1I $dIMMt IdHt H ~HH={1fHHe[A\A]A^A_]fDH= )H(H5(NIMLN&fHN&:fHN&tfLN&|fHU&HE1L %H H5GH8R1H2M&X1ZDHy1H5H$1DH!J&H52^HH81L&fDI $I1LM&f.HM&fLM&fLM&fLwM&8fHELcM&HEf.H=a )Iq@"E&HOf.HLM&f1LH"E&IHuLL&fH= )Hʽ(H5˽(6IMnI $1@D&Iqf.*W&HJHY&H5 GH8zP&.DH=a )II $L1L&fUHAWAVAUATSHHIHHG&I9:Ml$ A}(H(H5(H9p:L5(MIIFH5T)LHHHIHHIHHCL5-)LMH=ET& 1LHAIK&MH H(H (H9HL%Ի(MI$ID$H5;%)LHHHHI $N&IHfI*E0^̷#I&IHH5)HLE&IM=H5>(LHIHtfH *I $0LL\F&HHXI/IMuLI& fDHHeH[A\A]A^A_]E1ItI $AHt H FMt IM'HDH=1菲DH=)Hb(H5c(ށIM.DL?I&f.H'I&fLI&jfHP&HE11L H CH8H50BR1H;G&XZHy1H5H1DHD&HH5XAH81bG&DLgH&fHWH&fR&HuHvU&H5BH8NL&fDH _A}@LH&fHG&fLG&fLG&fLG&fH=)tIQ@?&Hf.E1AItBMf.L1H?&IHB)LGG&~fL7G&DH=A)H (H5 (IM+1AvDH= )~I>&HfA>DHF&fMAfUHSHHBHHuZHB&H9tnH{ (u HH]H0;HuyhI11ҾH=1@Hyt1H5 Hu1H!B&H52VHH81D&xHH=1`UHATISM&HtSHLHdQ&AHEx1HPHHt1A[A\]HwE&DHHt(HfH=-`[A\]fDH7E&DH;=1A&tHW HB8H+B0HfDUH A&HH5*UH81HC&HH=مH]f.UHAUATSHH2IHNH5(H=X(1YTHH]LhH5j(1IEH=5(TIHHXH1HHH=U(H56LhIE5p)L  )L)EIXZMtNI $tH tHeL[A\A]]fLC&H uHC&HeL[A\A]]L%?&I$L-H=҄L責I $LH=لE1葬w@I11ҾH= E1XHy1H5H4f.H!?&L-HrDLB&ZfUHSHHBHHuZH>&H9tnHS z(u HH]HJ&HuPhI11ҾH= 1@Hyt1H5 Hhu1Hq>&H5RHHH81(A&OH9H=81`UHAWAVAUATSH(HHHJH=(H5L)HGHHXIMZHCH5)HHHLIMNHCH53 )HHH`IMHCH5)HHHDIMFHCH5(HHH8HfInfHnfl)EHty<&fInfHnfl)MHtWfoEfInfInHEflտP@(H<&HUHfoMHHe[A\A]A^A_]1E1IMtMI $tgMtIt}MtItcAHtH t3H^DH=Z1fDL7@&DH'@&DL@&DL@&DL?&ufHiG&HE1L H %H59H8R1H>&X1ZHy1H5H1Dj7&IfADJ7&IfAIMtbMI $L(?& 7&If6&If1ff6&HL>&IAxfUHAWAVAUATSH(HIHH5(L%(HFI$IEH,I&H9_1L_=&HHL=D:&L99&HHHLHXHE@M&HUHIH I $H(H5(H9pH(H]HHU39&HUfHnIfHnfl)EHIEHUHID$H)HID$ IM|$(8&HUHIfoELp(@IHuHEL%=&HEH $He[A\A]A^A_]LHHH&HH8&H{`H0p]t111HnfDbG&HL=8&IHx(H Q(H9HL58(MI7&fInfHnHflHIEHU)EHHBH`)HHB I$Lb(7&HUHfoE@I $t/LH HEH;&HEHe[A\A]A^A_]HEL;&HEDI $~H uMA @H!DH=}蒤I $tS1UDH= )H(H5(sIMLHH=|?I $uL ;&1fL;&fH:&fHiB&HE1L H %H54H8R1Ho9&XZ1Hy 1H5KH1nDLg2&HH(=F&HHs6&H{`H0Z-111H@H':&}fA}DAH u H9&M[IRL9&D@H= )H(H5(>rHHM cDLADH= )DqI)@H uHj9&H=q )qHLA0f.HUL#9&HUlHH=zI $@L8&1UHAWIAVAUATSHHL%4&dH%(HE1H)HDž`HfHnH HDžxfHnfHnHH-H flHE)EfHnflHELhLpLe)EMt"L,HH&HcH@HH&HcHfDHY)LLMwvH`H:INHLPILXDžLL}M9H5 (I9wtHT(H(H9XSL-t(MIE2&IHILp ;&HHHXH5)H1&(HPH5)H1& LL5$/&IH5 )LHv1&IH.&H5_(HN1&H5g)LH31&HLL=H|IMI $pH GHUdH+%(He[A\A]A^A_]fDHFH~L6IOHPHpHFHxHXHhL`HH H;= .&H;=>&L9 >&L/L}M9HF oFoIOHE)`)pH~1HH1IPHUL`LLQ|ZYHhHxL`HXHpHP&HFL6IOHXHhL`HGLPDžLM#fDHFL6IOHPHpHFL`HXHhH~H )LLHXHsHXHHHxHHhL`HXHpHPDDL6IOL`HLPLXfDHF HEH~HxHFL6HPHpHFL`HXHhf1@HFL6LPHXHhL`L6LXLPL`HX=&HXHH(LLHXqHHXHEHE1IMI $H Mt IHPH=袛1[L}‰LM91IعH=赶-HH=5M1fDL5;&IXL2&hfH2&JfHi(LLHXpHXHtKHhHu_HXL`LP D1H*L1`HX3<&HXHHy )LLHXWpHXHHpHHhHPL`HXjfDH=(H(H5(viIM;DL0&-fL0&I $HH=(\hIIML}0&IMtJI $LW0&f:&H.L'0&efLE10&HL/&9f.HXH/&HX@HXL/&HXt@HXL/&HXJ@::&H-&fDHX:&HXHѐ9&H2&fUHSHH:HHuRH;+&tqH{0<&H7&HH]I11ҾH=01@Hyt1H5H谹u1H*&H5>HH81h-&HH==px1lfUHAWIAVAUATSHHL-:*&HHUH5\)HML9H9~HGH; 9&HGHL-6&IELH7&IIEMHIE,H H(H (H9H{L5(MI)&IHILxD1&HHH5(LH(&HUH5B )H'&HUH5(H'&HLL׺IHtoI&IM,H I $HL[A\A]A^A_]#f.GL-$&IEIMIHt H hHH=sE1TI $qL1,&c@H9HGH;7&nHGHL%4&I$IM9L+&+fDH+&f.L+&fL+&fH+&fL+&fHw+&fLg+&ILU+&#f.G<6L%#&I$*H=(H(H5(cIMrDIL*&zfH=(bIHIEtGIܾgHfH;6&C7&IHIܾeL_*&DH;6&7&IHtNH6,HUHSHH:HHuRH;%&tqH{0/5&H2&HH]I11ҾH=1@Hyt1H5Hpu1Hq%&H59HH81((&HH=%k81lfUHSHH:HHuRH;$&tqH{0w/&H1&HH]I11ҾH=1@Hyt1H5H萳u1H$&H58HH81H'&*HH=}jX1l+fUHAVAUATSHHIH+&IH ID$H5(LHH;HHH5(HLz"&H ID$H5(LHHHHH5(HL)"&QH ID$H5\(LHHHHH54(HL!&H wID$H5(LHHHHH5(HL!&H &H=ǥ(H5(HGHHIMf"&fInfHnHfl)EHIELh!&HafoEM@IuZHEL#&&HEHe[A\A]A^]fE1AIMu L%&H tsHDH=JhMu1He[A\A]A^]f1ItHe[A\A]A^]@H%&f.H%&{fHw%&DHg%& fH,&HE1L H H5H8R1H%$&XZKHy 1H5HHH=EgIMHRH=g輍&HfH$&ffZ&HfJ&Hf{fDkfD&H?fHH=ufM1@fD&I[f.MMAI $uL#&HQH=f踌L#&H.H=e蘌UHAVAUATSH HHH<H5(H ~(H9HTL%e(MI$HCH5}(HHHIMHCH5 (HHHIfInfInfl)EMC&fInfHnHfl)MH}foEп@&H@foMHHe[A\A]A^]f.H)&HE1L H H5H8R1H%!&X1Z@Hy1H5H1H=1(H (H5 (VZIMf.HHH==d踊1*H=(TYIr&Imf.I $uLa!&B&Iif.I $tZH qH(!&cI $tJIMt$MKIBL &4@L &DL &DL &DUHSHHrHHH;&tHC0Hx()&Ht*H]@H&H H50H81H&HkH=cX1@I11ҾH=舤1@Hyo1H5~HV1kDUHSHH:HHuRH;&tqH{0&H(&HH]I11ҾH=1@Hyt1H5Hpu1Hq&H5/HH81(&HH=b81lfUHAWAVAUIATISHXdH%(HE1H(HEHEHEHHHHEHHudHLyHEMXH]H5=(H9sL%&L9 H\'&HHQ^HE)&Hu#DMH=B芢HzH=a"1HUdH+%(He[A\A]A^A_]fDLyH (M~1@HI9H;LuH}HHEHPIL9$M9H[0Mm0%&HEHUIH#HLHE1#fE%&L &t0H&H!HHH]f.H%&H8HWHf.H&HyH5 -H81&H H=_ȅE1IM9H t1He[A\]fDH&1I11ҾH=dP1@Hy1H5CH̥1UHAWAVAUATISHHdH%(HE1Hn(HEHEHEHHHHEH=HHLqHEMH}HG2HGHtYHHHHAk&HcЉH9YDu$&HunH=#)111zHzH=%趂R@HEB$&Hu#DMH=ʝHyH=bHEdH+%(He1[A\A]A^A_]fDLqL=(M~1@HI9L;|uHMHHEHPIHNH>H}f.GWHH HcH9H&H5I*H8&GWHH HHcЉH9_pE1IM9JtLTtHEJ_fD{IH H'MI $L&DHH1MPHuHULEH^ZYR*H4"&H &UHAWIAVAUATSHHxHxdH%(HE1H(HEHfHnH-X HEHEfHnH&flHEHE)EH!HHpHl:HHHHKHEH[L%&L}M6HHFoHKHE)UHL}LuLeHF(H |(H9HL-f|(MUIE&HHHxH5(H&pH5a(LH&UH5N(LH&:H5(LH&IEL5(LMH=%G&HLLAHx&HxHIMH HxHC&HxLkL%}(Ml1HI9L;duHpHHEH5IMH%~fDHHHM1H=HBH=W}1HUdH+%(He[A\A]A^A_]H(HpHHh;SHhHHEHH(L}LuL%&L%&ML>L}}f.LfLeHFHEIu&Hu H &H5ZH8&fIM6H HH=V|oHK)];E1IM9JtLOtxHpJ&Hh&HhH\H(HpHHhQHt'HhHEHL}}f.LfLeHFHEIu&Hu H&H5ZH8&fIM6H HH=PvoHK)];E1IM9JtLItxHpJ&Hh&HhH\H(HpHHhKHt'HhHEHH}f.GWHH HcH9Hh&H5H8 &GWHH HHcЉH9_pE1IM9JtL*DtHEJ_fD IH Hb1@I11ҾH=>}1@Hyc1H5HdJ1]DUHAWAVAUATSH(H}HHY1%IHuH}H52(HGHHvHHHCH;m%t H;%|HIE1E1HHF1!fIFI;F }nINHHHIFHMIGH5 %I9wI9 IGJIHHtH uHI%IFI;F |HL%tHt H :LIu L %IE1AWDMt IH:DH=϶`ME1IAHeL[A\A]A^A_]DHMLAHMHH28&HMHt!H%H2H9/HM%HMIHt H (H s(H9\(H9PGL% \(MzI$H}H5p(HGHHIfInfInfl)EMHY%fInfHnHfl)MHKfoEI@$%IHBfoMHILu%Hg%fI9K\IHH%HE1L H uH5eH8R1H%XZE19L%fH%fHy1H5H贀f.HWH=^fDH}f%H}fDHML[%HMH%IHt0H@LMt HIH]H%IE1AWH H%H=Q(HY(H5Y(N-IMHXH=]H=(Y,II $uLAX%H}`%IqI $u LY%AX[I $t+AX>HHM!HMHL%L %@UHHSHHxH9}jH]@HyH5 (H=(1要HHt111HTH t{CHH=\H]HfDH5(H=z(1KHHt111HUTH tEfH7%DH'%wfUHAVAUIATISHHGH5H(HHHHH;%tiH%I9EMuM+L%HHtPLHHt?H7Ht-H tYH[A\A]A^]@H%H5H8y%H tTHRH=Q7L[H1[A\A]A^]DHEH%HEH[A\A]A^]f.H%D%HfLG%IHt!H%IMIt=DIX%HFIIuMcu@Lw%Iu EuAMcItIt%L%IEuAEII IzEuAEII cf.@UHSHH:HHuRH;%tqH{0o%H%HH]I11ҾH=Ʈu1@Hyt1H5H{u1H%H5HH81H%HH=5XY1lfUHH%HtH2H=e5HE YHEfDH%H9Gu HH@UHH2%HtHǥH=u-HEXHEf.@H;=%tHG fUHSHQ%HgH5Hr%H817%7%!%Hҥ( H=^7X%H]1UHAWAVAUATSHH_H+HHHEH$IH}%IH;HLE11IEHJHEII9LIHI$HpH8r%HHtMtIuLg%DL%2H=3LUWLMH=3AWIMtKMtIt+IHL[A\A]A^A_]@I}t1ILIuLI%L%DL%D%HCDH=W3VE1|f.HGH=-3VH8tMJH_%f.UHAVAUATSHHB%H9qH0HPHHL-%L9hHIHHH5h(H=n(1IHL`H1HI$H=h(H5U5(L (L (|HXZHIL9kHIHHt]I $HeL[A\A]A^]J%PDH H=28UHeE1[LA\A]A^]DH%I $uL%ef.L%HeL[A\A]A^]H%HH5H81%H H=}1T[HE>%H}HIHtH^HH F%H H=#1>TfL- H=-1HLTH L H=0SE1pDL-: H=0LSIuL%L H=0SfHO%IHMt H@HHtL-ʠc@HO%IUHAVAUATSHH2%H9qH0HPHHL- %L9hHIHHH5e(H=k(1IHL`H1HI$H=e(H55(L (L(yHXZHIL9kHIHHt]I $HeL[A\A]A^]:%PDHLH=u/(RHeE1[LA\A]A^]DH%I $uL%ef.L%HeL[A\A]A^]H%HџH5H81x%HLH=.Q[HE.%H}HIHtH^HH 6%HLH={..QfL-rMH=.HLQH LMH=2.PE1pDL-*MH==.LPIuL%LMH=-PfH?%IHMt H@HHtL-c@H?%IUHAVAUATSHH"%H9qH0HPHHL-%L9hHIHHH5b(H=h(1@IHL`H1HI$H=b(H55׷(L ((L(vHXZHIL9kHIHHt]I $HeL[A\A]A^]*%PDH3H=,OHeE1[LA\A]A^]DH%I $uL%ef.L%HeL[A\A]A^]H%HH5H81h%H3H=-,xN[HE%H}HIHtH^HH &%H3H=+NfL-b4H=+HLMH L4H=+ME1pDL-4H=+LMIuL%L4H=@+MfH/%IHMt H@HHtL-c@H/%IUHAVAUATSHH%H9qH0HPHHL-%L9hHIHHH5_(H=e(1@IHL`H1HI$H=_(H5%5Ǵ(L (L٦(sHXZHIL9kHIHHt]I $HeL[A\A]A^]%PDHrDH=%*LHeE1[LA\A]A^]DH%I $uL%ef.L%HeL[A\A]A^]H%HH5H81X%HҘDH=)hK[HE%H}HIHtH^HH %HxDH=+)KfL-REH=5)HLJH LEH=(JE1pDL- EH=(LJIuL%LEH=({JfH%IHMt H@HHtL-c@H%IUHATSH0dH%(HE1HHHH;%LeHs L%HuH}%HHH}HEH9tHEHp.%HEdH+%(H0H[A\]I11ҾH=1d@Hy\1H5H,kC1H1%HTH5;H81%H"H=61HSHi2H=-%HH}HEH9tHEHpM%U%Hƈf.UHAVAUATSHH;=%HG0IHp HxO%IH ID$0xHuXL-1%IE%HHLpID$8HHHC Lk( %Ht#H tnH[A\A]A^]ÐL-A%IEH t;HH=(Gm%HHH[HA\A]A^]fDH%DHEH%HEH[A\A]A^]fH%HH5H818%pHѕ2H=#@GPItIM>L%0L%f.UHAVAUATSHH0H 7S(dH%(HEHY(H9HL% S(MI$H;% LmHs0L7%HuH}i%HH-H}HEH9tHEHp%%IHHX%HH%Hv%H5G(H%ID$LMH=r%HLLAI?%MtBI $IMH HEdH+%(H0L[A\A]A^]f*%HuH%H5H8~%fDI $H tiMtIMtnHEH=#E13EyfDH%ef.L%DfL%*fH%DL%DH=(H Q(H5 Q(&IMWDH=(DIH%H66#H5H81@%I $LE%Hɒ2H= 8DH}HEH9tHEHp%I $tfI $L%HLL%IHhj%L%^H߃@UHAVAUATSHH0H GR(dH%(HEHSV(H9HL%R(MI$H;V% Hs0Lm1LHP HuH}%HH+H}HEH9tHEHp%}%IHHX%HH#H$%H5(H|%ID$LMH= B%HLLAI%Mt@I $IMH HEdH+%(H0L[A\A]A^]%HuHV%H5H8.%fDI $H tiMtIMtnHAH=VE1A{fDH%gf.L%FfL%,fH%DLw%DH=(HP(H5P(IMWDH=a(IH9%H!H5CH81%I $L%Hy2H==@H}HEH9tHEHp]%I $tfI $L%HLL%IHh%La%^H顀@UHAUATSHHGpLg Ml$0AoD$ IHfH~L9'ID$H;%Ml$()EHx0L%HpH8%foEHt@H{ Ml$0AD$ HtHC HCpH[A\A]]fH 2H=x?PHeH=[?CpHLH1[A\A]]HID$H;%Hx0E1%fHnflfHE%HEBDH1%H8`%xH%H~0#H5H81%3fDN#H%H5H70#H81H%OUHAUATSHHGpLg Ml$0AoD$ IHfH~L9'ID$H;%Ml$()EHx0Lo%HpH8%foEHt@H{ Ml$0AD$ HtHC HCpH[A\A]]fH 2H=x=THeH=[=CpHLH1[A\A]]HID$H;%Hx0E1%fHnflfHE%HEBDH1%H8`%xH%H~.#H5H81%3fDR#H%H5H7.#H81H%SUHAWAVAUATSHH(GpLgmNAo\$ Ml$0)]IHxHEL9ID$L=%Ml$L9Hx0L%HpH8%IHID$L9It$Hx06%HpH8%fInfHnIfl)EH%foEHtN@foeH{ Ml$0Ad$ HtHC HuHE*%HECpH([A\A]A^A_]KIIt}XHH=:CpHH(1[A\A]A^A_]DHID$H;%%Hx0E1%fHnfl)M~fDLw%ufH%H8%vHi%H,#H5sH81 %s1fDH2H=u :K fDH%H+#H5H81%IL%@HI2H= 9fDVfDH%H5H7+#H81H%WYLG%@fUHATSHH H@dH%(HE1{tHtPCtHGH9L(toH;X%tvCtHHUdH+%(H [A\]HEdH+%(H5%H H[A\]fDKfLeH}H5y%L%H}؅t0HEgH%H5ڎH8Y%1OfH%H9GH}H;=%p%H}HCtH{@HEHtHC@H%LH*LeHL%MI $HEL%HEH%H8%oj%jDLe`H%H8%32%X%UHAWAVAUATSHL-%L9HH0H_H%H5%1%HH=HP@Aąt(Ic%HH[A\A]A^A_]HCH5U(HHHHHH5(H9HCH;Y%E1H{AH tJEqH5(H=S(1ZHHt111H.H ];fDH%DH%H5H҅H81%H‚H=e5H1[A\A]A^A_]H5I(H=(1[ZHHt111He-H DH;%H%IHtEH;~%AH; %DM9L]%IAEH u H% fDH H%fH%fz%H fHw%It'E%E1ff.CADEL=%L2%H8H s`%HHDILpIELh ILx(%IHlHxH5(HM%=HpH5פ(L.%ID$LMH=%LHLAI%MH IM{II $^IHEdH+%( HeL[A\A]A^A_]IHXHpH]HHHxHM#HHIH}'H=50E1HhH`%Hh5@o@o IN)e)EH~2HH1MPHpHULEL>ZYGHELuL}HxHEHppHINHEHL=%H%LuLpHx6oIN)]HH%LuL}HpH^%HxfHPo(INHU)mHKHELuL}HxH1%HpDL=%H5%LpHxHE%Hu DM1H=JH |%H=^.$L%fL%wfHw%]fHg%fHW%Hh_A*IMu L3%H Hz{DH=̈́$.I $L%@L%f.LHI9ItHxFHxtHpH{DHx3%HxHH(HpLHxVHxHt/HEHfDH A*H %Hx%HxHAfDHQ(HpLHxHHxHEHH H% H5%HpDHy(H=),WHy*H= `,7HfDLHLa%IHDA(D%HuH6%H5H8%%H%fUHAWAVAUIATISHHdH%(HE1H{(HEHEHEHvHHHEHHHLyHEM,H}HGHGHBH0H~HHt%HcAH9DA%HuAAHEr%Hu#DMH=EHw` H=X*1HUdH+%(He[A\A]A^A_]fDLyH %(M~1@HI9LH;LuHUHHEHPIHNH>H}f.E1H%I9I}0%IcHcHI}0z%H8H bHm HvH=U)f.GWHH HcAH9gH%H5H8]%RGWHH HHcAH9!HH}DDgE1IM9JtHϺHMNHMtHEJ~DgA@ HHHH A{Hb%mDHi%HH5sH81 %l fDHH1MPHuHULEH3ZYZH%H%UHAWAVIAUATISHH(LoIH;d%ILL%IHHH@txHW%HH8HHEEHIT$LH}%H}H HHMtvIuqHEL%HE^HHEHL%EąuH%HtH8HwfHSHEHLIHt H %H9HtIH(1[A\A]A^A_]ÐH;XuLxIH2M>H([A\A]A^A_]H/%HtH8EHM>f.H}^%H}HEąuH}7MufH%IULH5H81%9HEL%HE fHLo%f.H_%HtH8HvHEEfHE6%HE DH%f. % D%^DUHAWAVIAUATISHXdH%(HE1Hc(HEHfHnH%HEfHnflHE)EHJHHEIQIMtbM1H=I{?HqH={?$E1HEdH+%(HeL[A\A]A^A_]LyH m(Md1HI9$H;LuH}HHEH0IGHEH}L}L-%xIVILnLmL>L}KoLy)UM~/HHIz1MPHuHULEHbZYL}LmH5(Hr(H9PL5Y(MIr%HHI$L`ILx %IHH5V(LHj%IFLMH=1%LHLAIܹ%MIH I $L6%L-%@HHEHAHEH}aLkH (M*1fDHI9H;LuH}HHL}HEIX@E1IM9 JtHϺHMHMtHEJH_%f.LG%fE1IM9dJtHϺHMHMt@HEJ.H=aa(HB(H5C(^IMfH nH=w Z%HuH%H5?H8%fDItH f.I $uLa%H=`( Ii@IgL.%YfItbH=LiL=|(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHj1MPHuHULEHY^ @HEr%HZ^%f.@UHAWAVAUATISHHdH%(HE1Hv{(HEHEHEH)HHHEHHtH%HH3iL #AH jXH5ZH8AT1%XZH`H=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5t(H=(11 H`H=LiL=mz(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHg1MPHuHULEHY^ @HER%HZ>%f.@UHAWAVAUATISHHdH%(HE1HVy(HEHEHEH)HHHEHHtH%HHgL #AH JVH5:H8AT1ͥ%XZHl^H=0HEdH+%(DHe1[A\A]A^A_]HLiHEMH5r(H=(11uH]H=mLiL=Mx(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLntxPHEJfDHHe1MPHuHULEHY^ @HE2%HZ%f.@UHAWAVAUATISHHdH%(HE1H6w(HEHEHEH)HHHEHHtHx%HHdL "AH *TH5H8AT1%XZHL\H=H HEdH+%(DHe1[A\A]A^A_]HLiHEMH5p(H=v(11UH[H=M LiL=-v(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLNtxPHEJfDHHc1MPHuHULEHY^ @HE%HZ%f.@UHAWAVAUATISHHdH%(HE1Hu(HEHEHEH)HHHEHHtHX%HHbL |"AH RH5H8AT1%XZH,ZH=X HEdH+%(DHe1[A\A]A^A_]HLiHEMH5n(H=V(115HYH=- LiL= t(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL.txPHEJfDHHa1MPHuHULEHY^ @HE%HZޤ%f.@UHAWAVAUATISHHdH%(HE1Hr(HEHEHEH)HHHEHHtH8%HH`L \"AH OH5ڙH8AT1m%XZH XH=p{ HEdH+%(DHe1[A\A]A^A_]HLiHEMH5}l(H=6(11HWH= LiL=q(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHc_1MPHuHULEHsY^ @HEҩ%HZ%f.@UHAWAVAUATISHHdH%(HE1Hp(HEHEHEH)HHHEHHtH%HH^L <"AH MH5H8AT1M%XZHUH=[HEdH+%(DHe1[A\A]A^A_]HLiHEMH5s(H=(11H~UH=LiL=o(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHC]1MPHuHULEHSY^ @HE%HZ%f.@UHAWAVAUATISHHdH%(HE1Hn(HEHEHEH)HHHEHHtH%HHs\L "AH KH5H8AT1-%XZHSH=;HEdH+%(DHe1[A\A]A^A_]HLiHEMH5=h(H=(11H^SH=2LiL=m(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHH#[1MPHuHULEH3Y^ @HE%HZ~%f.@UHAWAVAUATISHHdH%(HE1Hl(HEHEHEH)HHHEHHtHء%HHSZL "AH IH5zH8AT1 %XZHQH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5o(H=֣(11H>QH=BLiL=k(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHY1MPHuHULEHY^ @HEr%HZ^%f.@UHAWAVAUATISHHdH%(HE1Hvj(HEHEHEH)HHHEHHtH%HH3XL "AH jGH5ZH8AT1%XZHOH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5m(H=(11HOH=RLiL=mi(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHV1MPHuHULEHY^ @HER%HZ>%f.@UHAWAVAUATISHHdH%(HE1HVh(HEHEHEH)HHHEHHtH%HHVL "AH JEH5:H8AT1͔%XZHlMH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5}k(H=(11uHLH=ZmLiL=Mg(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLntxPHEJfDHHT1MPHuHULEHY^ @HE2%HZ%f.@UHAWAVAUATISHHdH%(HE1H6f(HEHEHEH)HHHEHHtHx%HHSL "AH *CH5H8AT1%XZHLKH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5mi(H=v(11UHJH=jMLiL=-e(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLNtxPHEJfDHHR1MPHuHULEHY^ @HE%HZ%f.@UHAWAVAUATISHHdH%(HE1Hd(HEHEHEH)HHHEHHtHX%HHQL |"AH AH5H8AT1%XZH,IH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5](H=V(115HHH=z-LiL= c(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL.txPHEJfDHHP1MPHuHULEHY^ @HE%HZޓ%f.@UHAWAVAUATISHHdH%(HE1Ha(HEHEHEH)HHHEHHtH8%HHOL \"AH >H5ڈH8AT1m%XZH GH={HEdH+%(DHe1[A\A]A^A_]HLiHEMH5}[(H=6(11HFH= LiL=`(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHcN1MPHuHULEHsY^ @HEҘ%HZ%f.@UHAWAVAUATISHHdH%(HE1H_(HEHEHEH)HHHEHHtH%HHML <"AH <H5H8AT1M%XZHDH=[HEdH+%(DHe1[A\A]A^A_]HLiHEMH5]Y(H=(11H~DH=LiL=^(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHCL1MPHuHULEHSY^ @HE%HZ%f.@UHAWAVAUATISHHdH%(HE1H](HEHEHEH)HHHEHHtH%HHsKL "AH :H5H8AT1-%XZHBH= ;HEdH+%(DHe1[A\A]A^A_]HLiHEMH5`(H=(11H^BH=LiL=\(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHH#J1MPHuHULEH3Y^ @HE%HZ~%f.@UHAWAVAUATISHHdH%(HE1H[(HEHEHEH)HHHEHHtHؐ%HHSIL "AH 8H5zH8AT1 %XZH@H=0HEdH+%(DHe1[A\A]A^A_]HLiHEMH5U(H=֒(11H>@H=LiL=Z(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHH1MPHuHULEHY^ @HEr%HZ^%f.@UHAWAVAUATISHHdH%(HE1HvY(HEHEHEH)HHHEHHtH%HH3GL "AH j6H5ZH8AT1%XZH>H=@HEdH+%(DHe1[A\A]A^A_]HLiHEMH5R(H=(11H>H=LiL=mX(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHE1MPHuHULEHY^ @HER%HZ>%f.@UHAWAVAUATISHHdH%(HE1HVW(HEHEHEH)HHHEHHtH%HHEL "AH J4H5:~H8AT1̓%XZHl<H=XHEdH+%(DHe1[A\A]A^A_]HLiHEMH5eZ(H=(11uH;H=mLiL=MV(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLntxPHEJfDHHC1MPHuHULEHY^ @HE2%HZ%f.@UHAWAVAUATISHHdH%(HE1H6U(HEHEHEH)HHHEHHtHx%HHBL "AH *2H5|H8AT1%XZHL:H=hHEdH+%(DHe1[A\A]A^A_]HLiHEMH5N(H=v(11UH9H=MLiL=-T(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLNtxPHEJfDHHA1MPHuHULEHY^ @HE%HZ%f.@UHAWAVAUATISHHdH%(HE1HS(HEHEHEH)HHHEHHtHX%HH@L |"AH 0H5yH8AT1%XZH,8H=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5L(H=V(115H7H=-LiL= R(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL.txPHEJfDHH?1MPHuHULEHY^ @HE%HZނ%f.@UHAWAVAUATISHHdH%(HE1HP(HEHEHEH)HHHEHHtH8%HH>L \"AH -H5wH8AT1m}%XZH 6H={HEdH+%(DHe1[A\A]A^A_]HLiHEMH5}J(H=6(11H5H=2 LiL=O(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHc=1MPHuHULEHsY^ @HE҇%HZ%f.@UHAWAVAUATISHHdH%(HE1HN(HEHEHEH)HHHEHHtH%HH<L <"AH +H5uH8AT1M{%XZH3H=[HEdH+%(DHe1[A\A]A^A_]HLiHEMH5]H(H=(11H~3H=BLiL=M(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHC;1MPHuHULEHSY^ @HE%HZ~%f.@UHAWAVAUATISHHdH%(HE1HL(HEHEHEH)HHHEHHtH%HHs:L "AH )H5sH8AT1-y%XZH1H=;HEdH+%(DHe1[A\A]A^A_]HLiHEMH5O(H=(11H^1H=bLiL=K(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLεtxPHEJfDHH#91MPHuHULEH3Y^ @HE%HZ~|%f.@UHAWAVAUATISHHdH%(HE1HJ(HEHEHEH)HHHEHHtH%HHS8L "AH 'H5zqH8AT1 w%XZH/H=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5D(H=ց(11H>/H=zLiL=I(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL讳txPHEJfDHH71MPHuHULEHY^ @HEr%HZ^z%f.@UHAWAVAUATISHHdH%(HE1HvH(HEHEHEH)HHHEHHtH}%HH36L "AH j%H5ZoH8AT1t%XZH-H=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5A(H=(11H-H=LiL=mG(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL莱txPHEJfDHH41MPHuHULEHY^ @HER%HZ>x%f.@UHAWAVAUATISHHdH%(HE1HVF(HEHEHEH)HHHEHHtH{%HH4L "AH J#H5:mH8AT1r%XZHl+H=HEdH+%(DHe1[A\A]A^A_]HLiHEMH5?(H=}(11uH*H=mLiL=ME(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLntxPHEJfDHH21MPHuHULEHӹY^ @HE2}%HZv%f.@UHAWAVAUATISHHdH%(HE1H6D(HEHEHEH)HHHEHHtHxy%HH1L "AH *!H5kH8AT1p%XZHL)H=(HEdH+%(DHe1[A\A]A^A_]HLiHEMH5=(H=v{(11UH(H=MLiL=-C(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLNtxPHEJfDHH01MPHuHULEH賷Y^ @HE{%HZs%f.@UHAWAVAUATISHHdH%(HE1HB(HEHEHEH)HHHEHHtHXw%HH/L |"AH H5hH8AT1n%XZH,'H=@HEdH+%(DHe1[A\A]A^A_]HLiHEMH5]E(H=Vy(115H&H=ҽ-LiL= A(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL.txPHEJfDHH.1MPHuHULEH蓵Y^ @HEx%HZq%f.@UHAWAVAUATISHHdH%(HE1H?(HEHEHEH)HHHEHHtH8u%HH-L \"AH H5fH8AT1ml%XZH %H=X{HEdH+%(DHe1[A\A]A^A_]HLiHEMH5 C(H=6w(11H$H= LiL=>(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHc,1MPHuHULEHsY^ @HEv%HZo%f.@UHAWAVAUATISHHdH%(HE1H=(HEHEHEH)HHHEHHtHs%HH+L <"AH H5dH8AT1Mj%XZH"H=h[HEdH+%(DHe1[A\A]A^A_]HLiHEMH5@(H=u(11H~"H=LiL=<(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLtxPHEJfDHHC*1MPHuHULEHSY^ @HEt%HZm%f.@UHAWAVAUATISHHdH%(HE1H;(HEHEHEH)HHHEHHtHp%HHs)L "AH H5bH8AT1-h%XZH H=x;HEdH+%(DHe1[A\A]A^A_]HLiHEMH5>(H=r(11H^ H= LiL=:(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtLΤtxPHEJfDHH#(1MPHuHULEH3Y^ @HEr%HZ~k%f.@UHAWAVAUATISHHdH%(HE1H9(HEHEHEH)HHHEHHtHn%HHS'L "AH H5z`H8AT1 f%XZHH=HEdH+%(DHe1[A\A]A^A_]HLiHEMH53(H=p(11H>H=LiL=8(M1HI9t8L;|uHMHHEHIHzE1 IM9t`JtL订txPHEJfDHH&1MPHuHULEHY^ @HErp%HZ^i%f.@UHAWAVAUI͹ATISHhHHHta%fHndH%(HE1HE)HHC((HfHnHX%fHnH fHnH-X"flfHnH )@fHnH`fHnflH-)PfHnH fHnflH)`fHnHfHnflH- )pfHnHfHnflH-)EfHnHfHnfl)EfHnHfl)EfHnfl)EML4HHSJcHHFxH8HFpH0HFhH(HF`H HFXHHFPHHFHHHF@HHF8HHF0HHF(HHF HoFo.M}))IOHRJcHHHRJcHfDHFxH8HFpH0HFhH(HF`H HFXHHFPHHFHHHF@HHF8HHF0HHF(HHF HHFHHFH~L>HHHLHyH;=Z%AH;=Gk%D+ H9" k%AăHH8H;=qZ%AH;=j%DH9Sk%AŃHHH;=(Z%H;=j%H9 k%HHH;=Y%H;=lj%H9xj%HHH;=Y%H;="j%GH9>xj%iHHrH;=JY%H;=i% H9.j%?HHBH;=Y%H;=i%H9i%HHH;=X%H;=Di%H9i% HHH;=lX%H;=h%_H9VPi% HHH;="X%H;=h%%H9i% H HH;=W%H;=fh%H9h%m DH0H8L(HxHH;=tW%H;=h%H9Xh% H5z`%I9vt I9 l%HH< H5.(LHiY% EXL=V%IH5g7(LH;Y%I EL=Jg%IH5=(LHY%\I  L=vV%IH5$(LHX% I  L=:V%IH5(LHX%I M L=U%IH5<8(LHPX%L@ L=[f%IH5;(LHX%mLHcIH3 H5(HHW%2LHcNIHZ H52(HHW%LHcIH- H54(HHdW%LTHcIH H5=2(HH)W%LH5&(LHW%~HcIH H5(HHV%+LH57(HIH aW%IH HHX_%IH HH5(H]V%' HcHH HH5 (L&V%H HHxH57(LU% LLLHH LILLHhHE1HHDžH.HDžH^HDžHHDžHHDžHHDžHHDžHNH DžH~DžfDE1E1AZ1E1H iIpMt IBHt H $HDH=C1MIMHUdH+%(p He[A\A]A^A_]‰YfL=)b%IDž8EE ‰Uf‰f‰f‰f‰=f‰wf‰f‰f‰*fL=P%I HHPX%H{@M}MHHLHHM}HM~H(LLHHIM~H0(LL蹖HHHIuDHLHHFo>M}H)M-H6(LLKHHIMH(LLHdHIMH(LLHHIMHl1(LLHxHIMuH>4(LL蓕HHIMGH (LLeHHIMHb(LL7HHIMHD+(LL HHIMH-(LL۔HHIMH+(LL譔H4H IMaH(LLHH(IM3H1(LLQHH0IMH(LL#HFH8IMHHL1PLMLH@ _AXE1AZH  HT%HLT%H @HwT%fHL`T%H@HL@T%Ht@L=]%I1M1H=iHK H=1qf.LS%hfL=\%IHL81$DLS%Wf.H ZH=x1L=I\%I]%HX3L'S%+f]%H]%H ]%H4j]%H^J]%H*]%Hc ]%HC\%H#\%H0\%HZ\%HL=J%IIo6M})@Hy(LL膐HHItfDHFL>E1HHL@HIE1fDL>HE1LLP%?E1Aa5[%H[%Hn[%HXZ[%HF[%HD2[%H@[%H[%HvZ%HZZ%HFZ%HJ2@Z%H@Z%H~Z%HjZ%HVZ%H&E1AbE1AcE1AdI1LAhE1AfILAhjHhH=ٞI11ILAhRI1LAh$R%ILAi f.DUHAWI1AVAUATSHlO%HvIIGH;G%t H;$H%IE1HE1H}IGHG%I9W~L9IGN$II$Ht H 8ID$H5<*(LHH)HHIEI;E IMHHHIEH tfH}LeHELIHuqX%HtH U%H1H9O%IrHt{H uvHM%kDHwM%DHLJ%qI $IMIHtH tuH< H=E1*HL[A\A]A^A_]HL%fD%HfL94OdII$fDHL%}fLL%IMULL%ILLL%>DL'K%IHtMH@IHHEHIMtcIL0L%LL%IMLL%H1IHI1LK%I1;fDUfHAWAVAUIATSHHhdH%(HE1H ()EH(fHnHPHEfHnHEflHE)EHHHxH"HHHHEHCHpHL5(HE1ID$H9IN;tuHxJHEHHpHSHHpL= (H#E1fDID$H9IN;|uHxJHEHLpI"@H>HFoLsHE)UMH]HULe|LsL%(M1 HI9 L;duHxHHEHIFHSHpHHHVLfH]HULeHCH5,(HHHЅHCH5T (LHHHЅTHME%HHUdH+%(1He[A\A]A^A_]HES%Hu#DMH==JH{cH=ߘ1fDE1IFM93IJtL.tHxJ@dH H=ux1E1IGM9+IJtLƄtHxJO@HCoHpH)]RDesfDE1IM9JtLRtHxJ*C%DC%1DHE"R%HAYfDHH1MPHxHULEH`ZY7HEQ%HAJ%UHAWAVAUATISHHdH%(HE1H(HEHEHEH)HHHEHHCHLqHEM?LmH5'I9ut L;-gB%yA%IHIEH=w'LhNHHHR(HC(HCI $HB%H9EI9YH%Iu IHQH%H{(Lc(Ht&HH H@H9BH%Lc(HCLHH8AT1C%XZHH=!E1HEdH+%(HeL[A\A]A^A_]@LqL= (M\1 HI9L;|uHMHHEH0IH.L.Lmf.I $HJH=kNEfL'D%fE1IM9JtL芀tHEJN1HL(nfLc(DH?%HH8H5S1xB%HH=E1腬HHf.LWC%fHa?%HH8DHHW1MPHuHULEHCY^@HkF%H@UHAWAVAUATISHHdH%(HE1H(HEHEHEHHHHEHHHLqHEMLeH5ܾ'I9t$t L;%V>%H='\HHHO(HC(HCH>%H9I9DqD%It$ IHH:%H{(Lk(Ht HPLk(HCLHoL%HHIHPHHH|HA%qfHE2L%HuADHH%HHL "AH H5:H8AT1&@%XZH0H=t4E1HEdH+%(HeL[A\A]A^A_]@LqL==(M\1HI9tHL;|uHMHHEH4ID@H.L&Le7f.E1IM9JtL|tHEJfD1H/LO%fH$H=h(H!<%HkH8H5(P1>%HH=(E1HH!f.H;%HH8DHf.HH1MPHuHULEH蛆Y^ C%Hf.@UHAWAVAUATISHHdH%(HE1H'HEHEHEHyHHHEHHHLqHEMH}H;=7%H;=G%H;=:% G%ÅgH:%BI(HsDHEI%HuADHE%HHL "AH H5s7H8AT1=%XZHH=Q1HUdH+%(He[A\A]A^A_]LqL='Md1HI9tXL;|uHMHHEH4I@HH>H}f.E1IM9JtLytHEJzG%H@HHq1MPHuHULEHY^~@%f.@UfHAWAVAUATISHHHdH%(HE1H()EHfHnHEfHnfl)EH)HHEHHHt`MH=HrH=E1HEdH+%(NHeL[A\A]A^A_]fLsL='M1HI9tL;|uHMHHEHIFL{HEL-(M41HL9L;luHMHHEHLuI@HL.LvLmLuH='H5i'HGHHHHl6%IHIELh>%IHH5(LHk5%HCLMH=52D%unLLHAI:%Mt+H kI $QIM@L>:%2D%HuH_G%H54H87>%H tI $IDIMuL9%fH0H==آoLs)UMLmLufDHHEHCHEIE1IM9JtLutHEJE1IM9\JtLutHHEJ^L8%fH8%f0%HfH H8%fH tBI $L8%H8%I $Lw8%MvH`8%I $v@HEB%HAfDHH1MPHuHULEH3ZYN@LLH0%IHqHErB%HyL7%P;%UfHAWAVAUATISHHHdH%(HE1H ()EHfHnHEfHnfl)EH)HHEHHHt`MH=bHRH=E1HEdH+%(NHeL[A\A]A^A_]fLsL='M1HI9tL;|uHMHHEHIFL{HEL- (M41HL9L;luHMHHEHLuI@HL.LvLmLuH='H5I'HGHHHHL1%IHIELh9%IHH5 (LHK0%HCLMH=/?%unLLHAI5%Mt+H kI $QIM@L5%2?%HuH?B%H5/H89%H tI $IDIMuL4%fHH=E踝oLs)UMLmLufDHHEHCHEIE1IM9JtLptHEJE1IM9\JtLptHHEJ^L3%fH3%f+%HfH H3%fH tBI $Lx3%Hj3%I $LW3%MvH@3%I $v@HE=%HAfDHH1MPHuHULEHzZYN@LLH*%IHqHER=%HyL2%06%UHAWAVAUATISHxdH%(HE1H(HEHEHEHHHHxH2HHLqHEMLmH٭'HEH 'H9HpH='HHH}HGH5(HHnIH}MH3HEL9%HHHMHL 'I ףp= ףHHHIHH?HHH)HLkdHL)AAD1D)ЍHcAfHuE1 AN<L)Hy AG-HIHE1HɉlLIHcHpMLxLI)*%LxHpHI@  IzHMML` HpIOLx,%LxHpL`HHHclHOփMH1I)HIDAoAHH9uHHHtMIHH)I)HvII)HJLHHAtJ7fAHH9|LUH5%'H}lH}IHHcHE*%HEHHLpH=(%I9E IEHLc+%H}HH3HRHEIMXID$LMkH=\)~8%&1HLAI*/%MI $H }H.%rHE*9%HuADH5%HHL "AH H5'H8AT1-%XZH1H=~E1)HEdH+%(HeL[A\A]A^A_]@LqL=- (M\1HI9L;|uHxHHEH%IDHL.LmfIR0IzH@HEM)@Z-%DA?$%HEHfI $fDH5H=}E1,%DE1IM9TJtLZit@HxJ,%HEIML,%f.Lw,%fH=i(H 'H5 'dHH}H}!*$%IfH=)(cHHHfD+%DHRfH}H1E1Hu+%I $yMtIt7HH }HI $[I]DLo+%D1Hu,@HH1MPHxHULEH`rY^"L7%IHt H]IH}1H&HEMHq5%HuH7%H5V%H8.%DH}E1HI $L}*%1HL"%IH@LG*%"fL7*%y1NL"*%i-%UHH7HurH= 4(111H@H=z1@HI1%HE1L uH H5"H8R1HQ(%X1Z@Hyt1H51Hxnf.@UHAWAVAUATSHHH=4(L%F%%L9HGI1HPXIHL=k!%L9L;51%SM9JLI2%I<H=3(QL9pHG1HP`IHL9L;51%XM9OL1%ADž[I?H=#3(EH3'H 'H9HrL5'MIIFH5,'LHHIIHM]IHLH#%AǃqI $EFHHPHHAE`IEL'%EH=F2(fDL9_HG1HPhHjAEHHtrH[A\A]A^A_]IDL"'%D7Iu L'%HJH=H1[A\A]A^A_]HEH&%HEH[A\A]A^A_]H"%HxH56H81x%%7fH5'H=21( IH!%IHHHX*%HHH/%H5'H %xfHLLIHtPL踰L谰H訰H5)(LyHtLLHE舰HEAEHe@Iu L%%I $tIuLz%%Hg%%DLW%%DHa!%H"H5k5H81$%L%%)f.H!!%HH5+5H81#%8fDL$%fH=I'Hz'H5{'6]IM{::fDz%IvfH='4\I:I $TLP$%F>@UfHAWAVAUATISHHHH}dH%(HE1H')EHfHnHEfHnfl)EHMHHEH[HHt\M1H=HH=虌1HUdH+%(@He[A\A]A^A_]DL{L-u'M1HI9TL;luHMHHEHIML}L% %1H5'I9wM9L9eHEMw0Lh0*%1҅LILS0%Lp%H%H HHH~L>H}L}H6H;=%L%^%H;=<+%L9+%ÃH5'I9w(M9eH*%HHQHpH~L>H{H}L}iHH1MPHuHULEHhZYH}L}-f.L>L%%1L}TIHL{HEMPHe'HuHI`HHHEIYL}fDH1%HmH5;1H81%HP&H=ZE1 IM9thJtLN]txXHEJ1HLf.*+%HJ@HE +%H@*%Hc#%L%/%1UH?HH HAVIAUIATISHH5'HGH9H %H9tHXHLGM~*1fHTH9BH99HI9uH%HwLLH1[A\A]A^]f.HHuyH5Z'HGH9H F%H9HXHuLGM~#1fHTH9H9HI9uH`%H`DHMLLLH[A\A]A^]%t#%@HDHH9t4HuH%%H9t#HH9tHuH9fDHSBLjE1 uLcH='%u~1LAH%HtYH[A\A]A^]HCL5'LMH=n'%u)HLLAH?%Hul(%H1HDHH9t4HuH$%H9t#HH9tHuH9ufDHSB`M6LjE1 uLcH=&%wLHS*%H51H8)!%@LLH[A\A]A^]%%[%H[DHU1HH dH%(HE1HuHuHExHUdH+%(u1 %UH5'HATSHX t 1[A\]@H5'HX$t [A\]H5'HXuH5~'HyXuHL%ؗ'%HHtjHS%H9CH='H)%IHtyHQH=%(LHHtIL2H111d|H@HRH=}lH1I $uL"%H uH%븾ffLH'%HI蒥MqLUf1HHH dH%(HE1Hu)ElHUdH+%(u%%DUHAWAVAUATSH8H`%dH%(HE1H9`HFIHfHfH5w'HFHGIHHHHHPIEH5-'LHHIM]IEH5 'LHHIIEHIEMHI|$0Md$I$H!%I9FM~MMFIIIfInfIn1LflHuLE)EILEIMI $HMIFIMuL%HHHUdH+%(H8[A\A]A^A_]%DLELs%LEHk1DHw$IH$H5HH81@$fDH5'H=:'1nHHt111HAH tfDH$$UHAWAVAUATSH(dH%(HE1HHHHCH5'HHHIMH$I9GMoMMwIEII1HuLHELmIMIMIHIM|HtWI $t`H$H9YST~HHUdH+%(He[A\A]A^A_]ÐL$efL$I $uL$fDHu L$HH=72G1Hu11LHEHE0IL7$fH$HH oH5_L ʒE1H8R1H$Y1^Hy*1H5Hh1DH$IH$H5HSH81p$fDH5'H=j'1CkHHt111HM>H tfDH/$$UHAWAVAUATSH(dH%(HE1HHHHCH5'HHHIMH$I9GMoMMwIEII1HuLHELmNIMIMIHIM|HtWI $t`HB$H9YSP~HHUdH+%(He[A\A]A^A_]ÐL$efL$I $uL$fDHu L$HNH=/D1Hu11LHEHE`ILg$fH$HH H5L E1H8R1H$Y1^Hy*1H5؜Hf1DH$IH$H5HH81$fDH5پ'H='1shHHt111H};H tfDH_$$UHAVAUATSH dH%(HE1HiHH}HCH5'HHHHHH:$H9CLkMLsIEIH ts1HuLHELm脹IMILHHHMtfHtAI $tJHd$HHUdH+%(H [A\A]A^]ÐHG$DH7$I $uL($fDHHH=B1Hu11HHEHE踸IHi$@ $Hz@LLLi$IH|3HE$H7M@$HH"$H5H8$$f.DUHAWAVIAUATISHXdH%(HE1HS'HEHfHnH$HEfHnflHE)EHJHHEI1IMtZM1H=wMHy_H=21HUdH+%(He[A\A]A^A_]LyH ='ML1 HI9H;LuHUHHEH IGHEH}LuL-$xII*LnLmL6LuKoLy)UM~/HH'1MPHuHULEHBZYLuLmID$H5'LHHHHH=$H9CLcMLsI$IH fInfIn1LflHu)E~I $LIMH HQHt;HHrHEHb$HE\fL-Q$@H eH|oH=(/HHEHAHEH}qLkH 'M"1fDHI9,H;LuHMHHL}HEIh@E1IM9 JtHϺHMHMtHEJE1H u HG$IMt I $MIL$L$IL$L$fHEL$HE3f.H$fE1IM9JtHϺHMHMtHEJLO$ f.L7$f$HFf $IffHu1ɺHHELmѣ[@HEL$HE>H$@j$Hz@LLLɻ$IH|3HE"$H7M@ $HH$H5H8Z$$f.DUHAWAVIAUATISHXdH%(HE1H'HEHfHnH$HEfHnflHE)EHJHHEI1IMtZM1H=wFHxqH=,o+1HUdH+%(He[A\A]A^A_]LyH 'ML1 HI9H;LuHUHHEH IGHEH}LuL-$xII*LnLmL6LuKoLy)UM~/HH1MPHuHULEHZYLuLmID$H5t'LHHHHH=>'H5̓'HGHHIMc$IH4ILp$IHH5'LH$IGLMH=%G$LLLAI$MI(I $IH$H9CLcMLsI$IH fInfIn1LflHu)EޟI $LIMH HQHt;HHrHEH¿$HE\fL-$@H eHuH=E(HHEHAHEH}qLkH ~'M"1fDHI9,H;LuHMHHL}HEIh@E1IM9 JtHϺHMfHMtHEJE1H u H$IMt I $MILt$L_$ILM$L7$fHEL#$HE3f.H$fE1IM9JtHϺHMfHMtHEJL$ f.L$fz$HFfj$IffHu1ɺHHELm1[@HEL;$HE>H)$@$Hz@LLL)$IH|3HE$H7M@j$HH$H5KH8$?$f.DUHAWAVIAUATISHXdH%(HE1H{'HEHfHnH@$HEfHnflHE)EHJHHEI1IMtZM1H=}7@H9rDH=$1HUdH+%(He[A\A]A^A_]LyH 'ML1 HI9H;LuHUHHEH IGHEH}LuL-B$xII*LnLmL6LuKoLy)UM~/HH|1MPHuHULEHZYLuLmID$H5}'LHHHHH=7'H5-}'HGHHIMc$IH4ILp$IHH5x'LH$IGLMH=$LLLAIR$MI(I $IH$H9CLcMLsI$IH fInfIn1LflHu)E>I $LIMH HQHt;HHrHEH"$HE\fL-$@H eHRoTH= !HHEHAHEH}qLkH kw'M"1fDHI9,H;LuHMHHL}HEIh@E1IM9 JtHϺHMHMtHEJE1H u H$IMt I $MILԷ$L$IL$L$fHEL$HE3f.Hg$fE1IM9JtHϺHMHMtHEJL$ f.L$fڮ$HFfʮ$IffHu1ɺHHELm葖[@HEL$HE>H$@*$Hz@LLL$IH|3HE$H7M@$HHB$H5H8$$f.DUHAWAVAUIATSHHHL5ʱ$dH%(HE1H'HEHELuHILIEH +$I9MI9tIENf.LxHLỤ$LULx@H$+f.H$fL$MfHw$fAAH}HHEHHE1E1Mt'I $^Mt IM?Mt IQHqYDH=` JfDHu1HHEL}HfLxLHU$LxHU@E1IM9jJtLtVHEJ6HUHELHUHIHU$HUHHt!H$H0H9HUp$HULHU,HUHtH,HEH=<$H9x?HEfInLhfInfl)EMLpIEHIl,foE1HuL)EpLHE,L=,HL,,ADMHMHHEHHHtH t0Mf $HfL$fH$DL$fLנ$fLǠ$fI9O|II-L$;fLADI1H(s$fDHUHL$HUHACfDLE1H'$fHu1ɺLH]HEHfL$H$IHH@HHEHoHIH$E1E1bHHa1MPHuHULEHY^LH}Hu1ɺHELeHL-$H$$HE$HEWACAAЖ$IeACc$HAEJE1E1AARLu1HuHELLe`~LH5)HE1E1E1ACLuE1ACAEE1E1AEHUHU1IAE$f.DUHAWAVAUIATISHdH%(HE1Hr'HEHfHnH-@HEHEfHnH$flHEHE)EHmHNIBMIeHLqHEMH<$LuH`HPAI,HFoLqHE)UMHELuHPHEH`H5('I9vt L;5Ә$1u$HhHIEH5r'LHH HH HCH5Nh'HHH IHHM HHIFLpLH5h'HH LpHH LpLϺHa$LpHI I eH DL;=$L;=$L;=$L$Å I IEH5c'LHHQ HH# H$H9CB LkM5 L{IEIH  LHu1ɺHELmzLI%M IEb$HHf L`H=k'1HILp (IHF H IEH;$t H;$HDž8IEMHDžHIEHHDž@1HDžpL(H8L IGH$I9W HHH9vIGL$HHHI$ID$H9t H;$I|$H# H;̒$Mt$I Ml$ IIEI $kL;5$ L;-$ HtH<$H@HtH($H5Af'L#IHH;$L;%,$9L;%2$,L{$Å I $WuhH5e'L#IHhH5x'HC$HHL#H/AąHd#E{ H5lR'L,#IH L;-$\ $H@HHr IE8HHA$$HH HPH5Zm'H$ H`H5e'H’$ H@HL%H0H L"H@"H|"H0H;$> Hpt HpR"H0HhH&p L@LHp#DLqHp'M 1 HI9H;TuIHEH IMIIIM1H=KY/HM/H=!YE1HEdH+%(HeL[A\A]A^A_]Hk'LHLpoLpHoHEIM|HELuHPHA$H`NDH)$H`HPL6Lu(HFH`HEHFHPHEIML˕$?fDLpH$Lpa@oLq)]7H$f.Lg$fLhE1IHLpIM9KtHtLLpLhKfDLp$LpHHb'LHLpLpHtHEIfL$Lp5$LpHDHHV1MPHULELH|ZYInDH0L($H0y@H$f.1HsVLx"+fH$H`DE1E11A^HDžpMt I $DHIE1H=UHtH tvHpHtHHPHHthMtIMt=HhHHHpHHsH$efL$DH$|fH$DLג$9fHǒ$%fL$YfL$f$HfE1E1A`HDžp@A`E1E1E1HDžp1@MtIt&MI L+$~fDLPL$LPHu11HHEIHEqID$IdfE1E1IE11A`HDžpI $t;E1M?I 6LPLv$LPf.L`LLPI$L`LPfE1E1E1E1HDžpA`vfD$LpHE1.H5!S'H=r='mIHH5f'LRIHH5w\'HHp0LpHILHp.H5?\'LLpHHH$I9D$Ml$fInfInfl)pMMt$IELLPIHu1ɺfopLH])EoLILPLHwM=LfH=ϙ'LrHH>LAaE1E17H111iH1HDžpLE1AfE1@E1E1AeHDžpl@$HfE1E11AeHDžpfH0H8LH0HIHC$fDnHxHPqHPL@HE1E1AeE1E1E1E1HDžpAefDHHH9MdHHI$HH$DH5 'LH0XH0UML@MHAe1MI0H5 'LH0jXH0fHE2$H@MnM6@IE1E11HDžpAeIL@LAiH5aO'H='LIHH5X'LIHH,$I9D$ML$fInfInfl)PMMt$ILLHIL`MHu1ɺfoPL)ENlH`HPLHL HPLLPH=X'LPoHH`LE1AgL`111LLPHPE1L1E1AiH0Ld$H0HHkLH0JHCHLAH0HIoHAH0HIHAԾHH0HH0tH($LH DH5/AiH81։$E1E1BLL@E1Ai*Lz$IH5H@HIEH8HH]HDžH@L1AfE1E1H5 'H0ULH0E1E1AiaHu$HPH5:H81$HPyH0LE1E1AjHp1 LIE1Af*I1LAfME1E1E1E11AcHDžpL(LHP;H5K'H=-'HPHI]҄$HPHIHhHID$$HPHyH`H5d^'LHHP$LPH`LLLLPH`HILL`HP]LUH`IL@HPHu1ɺLLUMLpHEH])hLpIsHDžpE11AcwHPL($HPHHnHQ$H0H9#HP0$HPBHu1ɺLLUML`HEgL`HP Hpt HpH0HhHp L@LHp#DLqH-S'M 1 HI9H;TuIHEH IMIIIM1H={;_HO/: H=v;E1HEdH+%(HeL[A\A]A^A_]HM'LHLp蟷LpHoHEIM|HELuHPHqt$H`NDHYt$H`HPL6Lu(HFH`HEHFHPHEIMLw$?fDLpHw$Lpa@oLq)]7Hw$f.Lw$fLhE1IHLpIM9KtHtLLpLhKfDLpÁ$LpHHD'LHLpLpHtHEIfLv$Lpe$LpHDHH 91MPHULELH謽ZYInDH0LXv$H0y@H?v$f.1H8L/["+fHr$H`DE1E11Ai HDžpMt I $DH,E1H=?8HtH tvHpHtHHPHHthMtIMt=HhHHHpHHsHNu$efL7u$DH'u$|fHu$DLu$9fHt$%fLt$YfLt$fl$HfE1E1Ak HDžp@Ak E1E1E1HDžp1@MtIt&MI L[t$~fDLPL@t$LPHu11HHEIHESIDk$IdfE1E1IE11Ak HDžpI $t;E1M?I 6LPLs$LPf.L`LLPys$L`LPfE1E1E1E1HDžpAk vfD"k$LpHE1.H5Q5'H='IHH5JH'LIHH5>'HHp`LpHILHp^H5o>'L/LpHHHz$I9D$Ml$fInfInfl)pMMt$IELLPIHu1ɺfopLH])EQLILPLHM=LH={'LTHH>LAl E1E1gH111H1OHDžpLE1Aq E1@E1E1Ap HDžpl@*i$HfE1E11Ap HDžpfH0H8LH0HIHsj$fDnHxHPHPL@HE1E1Ap E1E1E1E1HDžpAp fDHHH9MdHHI$HH$DH5&LH0:H0UML@MHAp 1MI0H51&LH0:H0fHEbz$H@MnM6@IE1E11HDžpAp IL@LAt H51'H=&LIHH5:'LIHH\w$I9D$ML$fInfInfl)PMMt$ILLHIL`}Hu1ɺfoPL)E~NH`HPKLHLIOLW^$6fHe$HE1L H H5uWH8R1H-]$X1ZhHy1H5-H1<DL1HV$IHqU$HfU$HfIMKnHj$H5+XH8a$%a$f.DUfHAWAVIAUATISHHhdH%(HE1H<')EHfHnHEfHnfl)EH.HHEHH2HteMH=kHH=E1HEdH+%( HeL[A\A]A^A_]LkH }'M1 HI9H;LuHUHHEHIEHSHxH=2'H!E1@IGH9IJ;|uHEJHEHLxI1DHHFL.HELmHEIFH5'LHHeHHIFH5('LHHIIMHc$H9CLsML{IIH ZfInfIn1LflHu)EfInE)E:IILI $HHMtTHH$HZ$f.oLk)]MHELmHEf.H eH H=Tof.LGZ$cfHHEHCHxHfDE1IEM9 IJtH}腖H}tHEJf.HY$fE1IM9 JtHϺHM&HMtHEJ6HEHu1ɺHLmHELeHE-9I\DL7Y$FfQ$Hf Q$IH Y$@HEc$H*AfDHHt1MPHuHULEHZY@HEBc$H.\$f.@UHAWAVAUATISHHH}dH%(HE1H'HEHEHEHeHHHEHHwHLyHEMLuH&H &H9H7H&HHH `$H9CCHu11HHEHEs7IMgH IGH51'LHHhHIHIHHH_$H9C_LcMRL{I$IH fIn1HuELLu)E6I $@LH HQHHHHEHV$HEtfDHEJa$HuADH ^$HHHL -!AH H5OH8AT1>U$XZH 1H=L1HUdH+%(He[A\A]A^A_]LyL5'M\1HI9L;tuHMHHEH(IH&L6Luf.HELU$HEf.HwU$fLgU$"fHH UH=Of.H'U$fH uHU$E1IM9TJtLzt@HEJHEHu1ɺHHEHELu4@H=$'Hj&H5k&HHDLcMLkI$IEH th1HuLHELe4I $It LfLT$DH=#'ċHy@K$HHS$HH1MPHuHULEHY^P@LS$;$1H(H=էcB$HH=۔1謧L1>$HH=脧f.DUHAWAVAUIATSHHhdH%(HE1H&HEHhfHnfHnfl)E~F$[6$)EHHIHEHHHtbIع11H=H 8H=貦E1HEdH+%([HeL[A\A]A^A_]f.HIL55$L%NF$H>$HHH&HSHHHH=S'IHMHHH5&LDHHH8IHHE$H9CHu1ɺHLmILeHELuwIIEHIEMHwH He<$HHHqL54$L%%E$ILHtRH=yeoHI)]H~/HH1IPHuHULELZYLeLu^L%'1DHH94M9duH}HHHEHHLeL53$@LvLuL&Le@H7;$/fL';$MfH;$4fHHIHEL:${fHxE$HxHHI&HuLHxyHMHxHEHf.L52$4@HHHMk:$BfDE1IL9\KtLHxvHxt2HEJDL{fInfInflM4LkIIEH )EfInfIn1flHuL)EIIL9$ fDHt#HTH=苢fDLg9$D D$Hb@)EH;9$foEP<$f.UHAWAVAUATSH8dH%(HE1HGIH{HĴ&H ݟ&H9HHğ&HHH A$H9CHu11HHEHEoIMH ID$H5&LHHHI$HI$HHH@$H9C8LcM+LsI$IH fInfIn1LflHu)EI $LH3HVHHHHUdH+%(1He[A\A]A^A_]H7$fLw7$9fHt;HCH=c1H?7$3f.L'7$DHEH7$HEQf.H uH6$Hu1ɺHHELm@HEL6$HEfH)>$HE1L UH H5/H8R1Hb5$X1ZHyz1H5tHTa1DH='H*&H5+&nHHZDH='mHLsM$HuADH:$HH8L !AH H5,H8AT1.2$XZHH=[<1HUdH+%(He[A\A]A^A_]LyL5&M\1HI9L;tuHMHHEH(IH&L6Luf.HEL2$HEf.Hg2$fLW2$"fHHH=^?f.H2$fH uH2$E1IM9TJtLjnt@HEJHEHu1ɺHHEHELuy@H='H&H5&iHHDLcMLkI$IEH th1HuLHELeI $It LfL1$DH=q'hHy@($HH0$HH1MPHuHULEHwY^P@L0$;,4$f.fUHHAWAVIAUATISHHxdH%(HU1H]'HEHfHnHH8 fHnfHnH ,$HEfl)EfHnflHMHMHM)EMJ4HpIcAM8IHM|$HEMHhLmIHxRfItHPoM|$HU)]IMHELmHhLuHx@H &H B&H9HL%)&MI$HM7$I9D$R1Hu1LHEHEHI$HI$HHHBHpHH5&HHHpIHHHMgH~)$IHHHXIELh ILp(HxHIG02$HHqHhH5&H($RID$LMH=f(7$(HLLAI3.$MI $IH H-$M|$H=l 'Mc1HI9 I;|uHpHHEH-IM@IFIIM1H=GH7H=ߕE1HEdH+%(HeL[A\A]A^A_]H'HpLkH ($HHEIMLmLuHhHxHxIL(HhLmHpHxHuLpLuHpL+$Hp:@H+$tf.z6$HuH8$H5_&H8/$fDII $MHt H oHH=,f.o M|$)efDE1IM9$KtHxgHxH '$tHpJfDL*$CfL*$*fz5$HH&HpLiHtHEH &$I2=5$HIf.HHe1MPHpHULELxqZYWHELmLuHxHEHhH*$f.H=q&H &H5 &fbIMNDHx@H0L)$"fMD$MM|$III $1ɺHuLLELpHE7 LpHItMqHpL0)$HpH=&`I)@HxH($j $HpIZL($+f.I $/LpL($LpDHq&HpLzgHhHtzHhLmLuHEHEHxL?($f.HLLQ $IHchHE2$H 2$Hu+$f.UHAWAVAUATSHHhH}L%&dH%(HE1Hk&HEHfHnHEfHnLefl)EHHIHxHH;Ht^Iع1H=胫HsH=}1HUdH+%(BHe[A\A]A^A_]LyH E&M1HI9I;LuHxHHEHMwMLu!fHtH2LfLeL6LuH:&H&H9PyHj&HHH.$H9CHu11HHEHEIMH `IGH5]&LHHHIHIHHH-$H9CQLkMDL{IEIH fInfIn1LEHu)EfInfl)E.IMLH;HWHHHHEH%$HEoLy)]M~2HHW1IPHxHULELkZYLuLeWHEL$$HEVf.H$$fHLqHEMMeH 1&MX1@HI9$I;LuHxHH)HEM~ @L$$Xf.HHGH=zf.H#$BfE1IM9KtHϺHM&`HMtHxJH pH^#$bfHEHu1ɺHHEHELuLeE1IM9 KtHϺHM_HMtHxJH=A&H&H5&6[HHtDLkMnHCIEHH HEtmH}1HuHELm?IMItH]KDL?"$DH=&YHq@ $HEH "$,$H@L!$f.HEr,$H{^%$f.@UHAWAVAUATSH8dH%(HE1HIHHD&H &H9HH&H[HL5)$L9sHu11HHEHEIHHMHHID$H5&LHHHI$HI$HH]!$IHYH@IEL(L9sLkML{IEIH SfIn1Hu\&LLe)EIMI/I $LHHMHHHEdH+%(?HeL[A\A]A^A_]fH$fL$fHt#H EH=v賈E1fDL$DH&1HuHߺHEHELe>I $I/LH$!H uH2$rDH$f.H$fL$fHi&$HE1L H %H5H8R1H$XZHy:1H5H蔩!H=&H&H5&VHHDL{MHCIHH HEtpH}1HuHEL}IItH]L$DH=Y&UHq@$HfH$_!$f.DUHAWAVAUATSH8dH%(HE1HIHHD&H &H9HH&H[HL5%$L9sHu11HHEHEIHHMHHID$H5&LHHHI$HI$HH]$IHYH@IEL(L9sLkML{IEIH SfIn1Hut&LLe)EIMI/I $LHHMHHHEdH+%(?HeL[A\A]A^A_]fH$fL$fHt#H H=r賄E1fDL$DH&1HuHߺHEHELe>I $I/LH$!H uH2$rDH$f.H$fL$fHi"$HE1L H %H5H8R1H$XZHy:1H5nH蔥!H=&H&H5&RHHDL{MHCIHH HEtpH}1HuHEL}IItH]L$DH=Y&QHq@$HfH$_$f.DUHAWAVAUIATSHHXL5&dH%(HE1H,&HEHELuHIL&HHHSIHHM/HH#$HHH@IEL(Hr$I9GMoMMGIEIIfInfIn1Ly&HuLE)EfHnfl)EIMLEIMH IHMJIHI $Ls$fDH>HHHH HHHHH?L p!HLHH$HL@H5V1H:SHo$XZHI@H=&m~E1HEdH+%(HeL[A\A]A^A_]fHIHL5\&1HH9M9tuIHHEH6@L6Lu3@LG$6fLEL3$LE3f.H$bfL$fH$fL$fL$IfLcML{I$HIL}l1LHuHELemI $IH]MH]E1A|H HDH=kP}MVE1H$f.H$afLHu1ɺHELLuIfDL$"HH=v1HEdH+%(HeH[A\A]A^A_]MIuHxL8L}fD$HuH~$H5H8V$fDI $IMt IMH^>H=PvGL $fH^H]HHHxHMZ@Hq&LHxKHxHHEI\fDL $of.Lg $MfLW $,fLG $lfo M~)eMHEL}HxL $fHx$HxH!H &LHxJHxHt>HEIDH=!&H:s&H5;s&DIM$wDHx#$HxHHI&LHxJJHxHZHEIH$>f.Hu11HHEHE@I;IM"L=$HH=\5nIHP1L$fHM$HMHfH&LLHMCHHMHEHL$~fH$@H5&H='1[HHt111HeeH HH=[TmBL/$H M#HzffHOAH$H(DH=7[lM@E1IL9KtLHMLE@LEHMtpKHW$fHELC$IHPHEHEH#$HEf.HEL$HEf.#IfIMH Afj $HV$H$kHH=Yk~Hr$Gf.UHAWAVAUATISHHH}dH%(HE1H&HEHEHEHeHHHEHHwHLyHEMLuH}&H i&H9H7Hxi&HHH $H9CCHu11HHEHEcIMgH IGH5&LHHhHIHIHHHy $H9C_LcMRL{I$IH fIn1HuELLu)EI $@LH HQHHHHEH$HEtfDHE: $HuADH$HHL [!AH H5H8AT1.#XZHH=W&LHHHI$HI$HHH$H9C8LcM+LsI$IH fInfIn1LflHu)ERI $LH3HVHHHHUdH+%(1He[A\A]A^A_]H#fL#9fHt;HKnH=O`1H#3f.L#DHEH#HEQf.H uH#Hu1ɺHHELmA@HELK#HEfH#HE1L H uH5eH8R1H#X1ZHyz1H5nHa1DH=A&H^&H5^&6/HHZDH=&T.HLsMIItH]qDL?#DHUL&#G #Ht#f.fUHAWAVAUATSH8dH%(HE1HIH+H#HHH]l&L-&HHCIEI$LLc MH=&#1LHAI#MaH 8IEH5U&LHHHIEHIEHYHHQ#H9CLsML{IIH 71HuLHELuIILHHMHHID$H5&LHH/HH1HL#HIMH HUdH+%(!He[A\A]A^A_]H#fL#f#HH tKH+H=CT1HuL#H#fH#DHw#fIMuLa#|@HEHK#HE fHEL3#HEf.Hu11HHEHEIOL#6fHY#HE1L H H5H8R1H#X1ZhHy1H5^Hu1<DL1H#IHq:#Hf*#HfIMKnHR#H5H8*#%#f.DUHAWAVAUATSH8dH%(HE1HHHHCH50&HHHHHoL5#L9sLcML{I$IH 1HuLHELeI $ILHHMHHIEH5&LHHmIIEHIEM,HM9t$hMl$MZI\$IEHI $1HuHHELmeIMKHtFH HUdH+%(He[A\A]A^A_]H?#f.H u H"#fHgH=e@Q1@H#fL#fHEH#HE]f.L#fHuL#Hu11HHEHE`IJLg#1fHu11LHELHEHEL##HEf.H#HE1L H EH55H8R1HS#X1ZdHy 1H5/Hq18DH#Hr#I$#@UHAWAVAUATSH(dH%(HE1HIH;IEH5&HHPLIM/ID$H5&LHH8HI$HI$HGHIEH5[&LHHIMH#H9CLkML{IEIH fInfIn1LflHu)E+IMILI $tuMt@H HEdH+%(HeL[A\A]A^A_]fL#$fH u H#fH'2H=]=ME1L#}fH#2fH#kfHuL#Hu1ɺHHELeAIfLG#fH#HE1L H uH5eH8R1H#XZ;Hy1H5pHnL#I#Hf.#I<#f.fUHAWAVAUATSH8dH%(HE1HGIH{H$_&H N&H9HHN&HHHi#H9CHu11HHEHEIMH ID$H5f&LHHHI$HI$HHH#H9C8LcM+LsI$IH fInfIn1LflHu)E"I $LH3HVHHHHUdH+%(1He[A\A]A^A_]H#fL#9fHt;HH=x:J1H#3f.L#DHEHs#HEQf.H uHR#Hu1ɺHHELm@HEL#HEfH#HE1L H EH55H8R1Hv#X1ZHyz1H5RHka1DH=&H*L&H5+L&HHZDH=&$HLsM#H9KI9=HCN4IIMt I/IFH5&LHHIMSH#I9GMWMM_IIIHu1ɺLLULUL]HE۴LUL]HI MHI2ID$I;D$ IL$HHHID$H EH}MHEHIH,#HtH#H2H9#H MtL_HEdH+%(HeL[A\A]A^A_]HUHL#HUVIאE1II $H Mt IcMt IMDH GH=#fDItcH tNHUg6H=sE1댐H#|fL׮#fLǮ#fH#DLE1#6H HfH=sMtE1I $ Lh#LEHS#LEf.M&MJLeKL#f.H#^fH#f7GfDH=Q}&H&H5&FHHDL#gfH=}&THL{MLsIHILu%81LHuHEL}&IItH]DL'#D #HfHLBuH#uN@H1t&Hu1HߺHEHEL}Le蒌Iff.LEL#LECf.LLH#IHHH={&H*&H5+&HHHd7H=p2DH={&HL{MLsIHILu61LHuHEL}螋IItH]qDL#DHUL#Gj#Ht#f.fUHAWAVAUATSH8dH%(HE1HIH+#HHHu(&L-&HHCIEI$LLc MH=d#1LHAI2#MaH 8IEH5&LHHHIEHIEHYHH#H9CLsML{IIH 71HuLHELuIILHHMHHID$H5u&LHH/HH1HL#HIMH HUdH+%(!He[A\A]A^A_]Hg#fLW#f#HH tKHaH=m31HuL #H#fH#DHר#fIMuL#|@HEH#HE fHEL#HEf.Hu11HHEHE@IOLG#6fH#HE1L \H uWH5eH8R1Hw#X1ZhHy1H5wH21<DL1H#IHq#Hf#HfIMKnH#H5H8#%#f.DUHAWAVAUATSH8dH%(HE1HIHH"&H =&H9HH$&H[HL59#L9sHu11HHEHE蟆IHHMHHID$H5U`&LHHHI$HI$HH #IHYH@IEL(L9sLkML{IEIH SfIn1Hu &LLe)E̅IMI/I $LHHMHHHEdH+%(?HeL[A\A]A^A_]fH#fLw#fHt#H]+H=cE1fDL?#DHA&1HuHߺHEHELeI $I/L#!H uH#rDHϤ#f.H#fL#fH#HE1L EYH SH5ŝH8R1HgR#XZHy:1H5gHD/!H=s&HJ&H5K&HHDL{MHCIHH HEtpH}1HuHEL}蠃IItH]L#DH= s&LHq@j#HfHg##f.DUHAWAVAUIATSHHXL5m&dH%(HE1Ho&HEHELuHILHHH;OH =OHHHHTH?L HLHHM#HL@H51H:SHU#XZHWH=E1HEdH+%(HeL[A\A]A^A_]fHIHL5 l&1HH9M9tuIHHEH6@L6Lu3@L#6fLEL#LE3f.HǞ#bfL#fH#fL#fL#IfLcML{I$HIL})1LHuHELe~I $IH]MH]E1AH HMVDH=UMVE1Hϝ#f.H#afLHu1ɺHELLun}IfDLw#HH HTH=DL#RfH=k&4H#HHHQ1LPHUILELeY^V}fDHZ&Hu1LHEHEH]Le{If.AD#IfLEL#LEMf.E1IL9$KtLHMLELEHMtK.H=j&Hz&H5{&IMHqS H=w"DL#f.H=aj&IA D#If.MA L#f.fUHAWAVAUATSH8dH%(HE1HIHH4&H &H9HH&H[HL5y#L9sHu11HHEHEyIHHMHHID$H5S&LHHHI$HI$HHM#IHYH@IEL(L9sLkML{IEIH SfIn1Hudv&LLe)E yIMI/I $LHHMHHHEdH+%(?HeL[A\A]A^A_]fHǘ#fL#fHt#HP H=X]E1fDL#DHu&1HuHߺHEHELe.xI $I/L8#!H uH"#rDH#f.H#fL#fHY#HE1L LH GH5H8R1H\#XZHy:1H5^\H"!H=f&H&H5&HHDL{MHCIHH HEtpH}1HuHEL}vIItH]Lߖ#DH=If&Hq@#HfH#O#f.DUHHAWAVAUIATISHHx s#dH%(HU1Hk&HEHfHnHH )MfHnH fHn)Mfl)EfHnfl)EMtWL4H7H HcHfM|$MHӑ#HpIHxDHH {HcHfDoFo.M|$)m)EM~.HHW1IPHULELLZYHEL}H]HxHEHpfH&H5&H9pL5&MIH>#I9FHu11LHEHEtIIHIMHID$H5rN&LHHII$HI$MH#IH>IELhILx #IH HxH5Oi&H֎#HpH5a&L#H5^&HL#LLL!HItI $JIM HUdH+%(He[A\A]A^A_]fDHM|$HEMCHd#L}HpHx%Do&M|$)eM HEH'#L}HxHpHVo6M|$HU)uMHEL}Hێ#HxHEHp@Hil&LLHmHEI%H#HHHpHMHxHxH}L8L}?HY#HpHA#HpHxfI $IMtIMtzHIJH=V1CLב#,fLǑ#]Iع11H=&T HIH=pV1L#xf.Lg#LfH=`&H%H5%IME3Dڛ#HwHIf&LLHHEIfHLސ#fMFMIFIHIH`1HuLEH`LhHE_pLhIItL`@LW#DH=_&I@#HfH ^&LLHtHEID#H?fHZ&LLHHEI?f#I fHL#fHxLp#Hx@HxLP#Hx@HxL0#Hxp@L#fIfH^H]jLhL#Lh+@z#HLf#fDUHAWAVAUATSH8dH%(HE1HHHHCH5i&HHHHHoL5#L9sLcML{I$IH 1HuLHELemI $ILHHMHHIEH5h&LHHmIIEHIEM,HM9t$hMl$MZI\$IEHI $1HuHHELm%mIMKHtFH HUdH+%(He[A\A]A^A_]H#f.H u H#fHD'H=U1@H#fL#fHEH#HE]f.Lw#fHuLb#Hu11HHEHE lIJL'#1fHu11LHELHEkHEL#HEf.HI#HE1L u@H ;H5H8R1Hc#X1ZdHy 1H5cHt18DHG#H2#I#@UHAVAUATSH dH%(HE1HIH-ID$H5h&HHALHHBH H;Ȇ#ID$H5h&LHHCHHHCH5*@&HHHIHHHM(HH#I9D$Mt$MMl$IIEI $1HuLHELuiIH~HtQIMHEdH+%(HeH[A\A]A^]@H#H;#HDIMNHAH=31fLg#tfJ#IfHG#fL7#SfHuH"#L#fH#HE11L =H C8H8H50R1H;R#XZHy1H5RH1DL#HHu11LHEMHEEhHoDB#Hf.L7#܋#f.fUHAWAVAUATSH(dH%(HE1HHHHCH5e&HH0HHHH;#HCH5O=&HHH IM/HϏ#I9D$Mt$MM|$III $1HuLHELugIItjMIHHPHHt>HEdH+%(HeL[A\A]A^A_]@HIHBHHHuH†#L#DHu11LHEMHEmfI[DLw#ZfLg#fIHU>H=4KE1LHHPH#HE1L :H e5H5UH8R1H]O#XZE1Hy1H59OHf.H=H=JfDH}#Hr}#If.Lg#  #f.fUHAVAUATSH dH%(HE1HHHHCH5 c&HHHIMID$H5:&LHHHI$HI$HHH#H9CLcMLsI$IH 1HuLHELe?dI $ILHHPHMHtgHEdH+%(bH L[A\A]A^]@L#RfH'H;H=E1f.Hǃ#DH#4fHu11HHEHEpcI9HuHr#vDL_#f.I11ҾH=LTHy1H5yLH(Hz#Iz#H f.Lǂ#l#f.fUHAUATSH8dH%(HE1H[IHID$H5J&HHLHHH;z#H;1#&H;7~#H#AŅH  EID$H5_&LHHIM>ID$H5r7&LHHHHI $'H#H9CLcMLkI$IEH  1HuLHELe-aI $LH HQHHHuAHEH#HE.DH DH#EH|#HHUdH+%(He[A\A]]fx#HHfDI $>fDH8H=1@Lo#f.HW#fH uHB#HufH#HE1L 4H e/H5UyH8R1H]I~#X1Z Hyf1H59IH M1DLw#HTHu11HHEHEh_CHELs#HE'f.LW#f:w#Ic#f.fUHAVAUATSH0H54&dH%(HE1HGHHIMH2#I9FMfM MnI$IEI1HuLHELew^I $HMIHIHHtxH#H9CHHHHtqHUdH+%(H0[A\A]A^]ÐL'~#lfH H6/H=41L}#zf.HEH}#HEyf.Hu11LHEHE]HL}#fH`Ln}#RfJu#I`fHv#HH HQfH'}# ̀#f.fUHAWAVAUIATSH8H=eN&H56?&dH%(HE1HGHHIMmIEH57&LHHHHaHCH5>H&HHHcIMyH |IEH52&LHH>HH`Hy#H9C/L{M"LKIIH w1LHuLMHEL}[ILMI LMH H#I9D$M|$MMD$III $rfInfIn1LflHuLELm)E5[ILEHMIIMI$E1A*HHtPI$HHEdH+%(H8H[A\A]A^A_]Hz#vfA+I $u Lz#MtItAHtH tGH2DH=1DLMH{z#LMsfLgz#DHWz#DLGz#fL7z#fH'z#kfLz#fLELz#LExf.Hu11HHEHEYILMLy#LMf.Hu1ɺLHELuLm]YH3DLELcy#LEf.A*D*q#IlfE1A+Xf q#Hxfp#Ifp#H|#I $u Lx#H tA+&DHx#f.f.f.UHAWAVAUATSH(dH%(HE1HHHHCH5V&HH0HHHH;0t#HCH5_S&HHH IM/H?#I9D$Mt$MM|$III $1HuLHELuWIItjMIHHPHHt>HEdH+%(HeL[A\A]A^A_]@HIHBHHHuH2w#L'w#DHu11LHEMHEVI[DLv#ZfLv#fIH.H=;E1HHPH~#HE1L E+H %H5oH8R1H?Ru#XZE1Hy1H5?HDf.H..H=+;(fDHm#Hm#If.Lu# |y#f.fUHAVAUATSH dH%(HE1HIHH}HCH5"+&HHHHHH}#H9CLkMLsIEIH 1HuLHELmTIMILHHHMHL;%p#Up#IH1I$L5Nv#L`HsR&HIE IFHH0H=o3~#1LLHt#HIMI$HI$HtcHEdH+%(HeH[A\A]A^]fHt#fHt#L;%p#"I$LHPI$I$HuLs#HH+H=41cHu11HHEHE`SIxLgs#_fLWs#fHz#HE11L 'H "H8H5plR1H{<r#XZHyx1H5Y<H_1DHj#Her}#HuH#H5WmH8v#fDIMu Lr#H*H=1I$Hf.1LLj#HHu#H5r#iUHAWAVAUATSH(dH%(HE1HHHHCH5'&HHHIML-z#M9oMgMMwI$II%1HuLHELe^QI $HMIHIH<HH;Tm#HCH5kL&HHHIMM9l$GMt$M9M|$III $1HuLHELuPIIMI?HHPHHtkHEdH+%(+HeL[A\A]A^A_]Lop#f.LWp#H;Xl#HIHBHHHuH,p#f.HH(H=E1WLo#!fHu11LHEHEOHJLo#1fI'H'H=$E1HHPLgo#fLWo#ffHv#HE1L #H H5uhH8R1H}8n#XZHy 1H5Y8HHf#IHu11LHEMHENIDf#Igf.Lwn#[fLgn# r#f.fUHAWAVAUATSH8dH%(HE1HHH Hj#H9+Ls IVA~(RHH%H@H9%#Ht%HHH5Q2&HAIHHMLÅ L3HS㥛 IN0HHH?HH)HiH)HHH?HH!HiHZH%H5%H9p H%H HH5UG&HIHHp#HH HS㥛 IN0HHH?HHH)HiH)HH?HH!H)ru#IHtAH57&HHg#x*LH5D%HL!HHE)E1ASH u Hl#MtI $MCIM9Lk#+DHHUdH+%(He[A\A]A^A_]H9%H%HHH5E&H)IHH5Oo#HHI~0Qt#IHeH5@&HHe#LH5%HLHHE#LHHEfH9%L-%MIEH50E&L`IHLln#IH I~0s#HH4H55&HL1e#9H!H5R%LL/HHELLHELfH9%L%%MI$H5`D&LHHLm#IHjI~0r#IHH54&HLad#)LQH5%LH_HHEjH*L"HE|fHp#HE1H L H5ubH8R1H}2h#Z1Y4Hy1H5Y2H1DHd#H H5xA:H81g#H DH=i1DHq%H5:%H9pL-!%MIEH57&LHH9L H5&HIHHI~0q#HHc#IH)HXk#HHHd4&H5E&Hb#HLLHLHEaLYHQHELwg#lfADH gIMtE1M?f.L7g#DAFABH=!$&H%H5%vIMAF%H=#&H%H5%>HHABH=5&HJ%H5K%HHAIH=y#&H2%H53%ΞIMCAD}H=A#&IH=)#&̝HI@H=)5&贝Hi@H="&蜝I@E1ABfAFDE1AIwfABDAFI $Lwe#fMAIefE1AD'fAFIMuL;e#fI $Le#AJDADE1Hd#@ABE1pf.AJ[H5'&H=%RHHI~0m#IHHl#H9CiLsfInfInfl)EMJL{IHIfoEL1Hu)EDLILLE1AQMLH=+n&L#GHHLAM111HHH=2&HX%H5Y%$IM,AJH=2&AIH= &H%H5%HHbASH=\ &HE1AJAS$AJ1Hu1ɺHHEILeBIAQ)E1AQf#AM@UHAVAUATSH0dH%(HE1HHHH^#H9H[ {(LcHD%H5%H9pL5%MsIH{0.k#HHr]#IHHXe#HH]A|$Hj#IHH5?&HH\#I $HLLIHwI.IMH ID$H5;&LHH|HH\I $DHi#H9CLkMLcIEI$H !1HuLHELm@IMHI $uHEL`#HE fDHHUdH+%(He[A\A]A^]DL`#f.H`#fLw`#fLg`#fH=&H%H5%ƘIM@E1IDH MtIMu L`#Mt I $HH=ʺ1 fDHIg#HE1L uH H5XH8R1H(^#X1ZHy1H5(Ht1DHq[#H5oHHH81(^#FfDL'_#*fH_#fL_#fH^#fV#H{fL^#fH= &脖Iy@IL^#ItgIML^#Hu11HHEIHE=>`HELC^#HEBa#LE1(^# UHAVAUATSH0dH%(HE1HHHHY#H9H[ {(LcH%H5%H9pL5%MsIHc{,f#HHrY#IHHXIa#HH]A|$HJf#IHH5o:&HHW#I $HLLIHwI.IMH ID$H56&LHH|HH\I $DHd#H9CLkMLcIEI$H !1HuLHELmB\#I9FHu11LHEHE3IIHIMHID$H5r &LHHII$HI$MHN#IH>IELhILx W#IH HxH5O(&HM#HpH5 &LM#H5&HLM#LLLHItI $JIM HUdH+%(He[A\A]A^A_]fDHM|$HEMCHdN#L}HpHx%Do&M|$)eM HEH'N#L}HxHpHVo6M|$HU)uMHEL}HM#HxHEHp@Hi+&LL覐HmHEI%HM#HHHpHMHxHxH}L8L}?HYM#HpHAM#HpHxfI $IMtIMtzH \H=81CLP#,fLP#]Iع11H=& HGH=袹1LP#xf.LgP#LfH=&H%H5%ƈIME3DZ#HwHI%&LLHHEIfHLO#fMFMIFIHIH`1HuLEH`LhHE_/LhIItL`@LWO#DH=&I@Y#HfH &LLHtHEIDY#H?fH&LL΍HHEI?fF#I fHLN#fHxLpN#Hx@HxLPN#Hx@HxL0N#Hxp@LN#fIfH^H]jLhLM#Lh+@zX#HLfQ#fDUHAWAVAUIATISHHdH%(HE1H&HEHEHEHHHHEHHHLyHEMLeH %H "%H9HH %HHHCH5+&HHHIHHHMHwP#HH5H^,&H5$&H^G#IFH5-&LM{HuH=FV#:HuHLAIL#MIH IIEH5*&LHH*HHlHET#H9CLkMLsIEIH jfInfIn1LflHu)E+IM+LH HQHHHHEHoK#HEwfHEV#HuADHR#HHL AH sH5cDH8AT1I#XZHa H=!1HUdH+%( He[A\A]A^A_]LyH &Md1HI9H;LuHMHHEH0ICH.L&Le:f.HWJ#fHGJ#{fL7J# fH'J#fLJ#fk H IteHH=DE1IM9lJtHϺHMFHMtPHEJuLI#uH=(&Hj%H5k%HHUj SS#HuHvV#H5CH8NM#fDj fDA#I(f.H='&贀HHuHH#{DHu1ɺHHELe(@HuHH#f.l fDk tfDHELcH#HEf.HH 1MPHuHULEH[Y^@uH H#u@HL$@#IH?#H|K#f.fUHAWAVAUIATISHHdH%(HE1H&HEHEHEH&HHHEHH@HLyHEMLuH5%I9vt L;54C#IEH5+ &HHLHHL;-B#I}0>#HcO#IHHO#H9CuL{MhLCIIH fInfIn1LflHuLELu)EP&ILEILI $HHM4HHu{H3F#pHEP#HuADHM#HH L AH CH53?H8AT1D#XZH H=7 ԮE1HEdH+%("HeL[A\A]A^A_]@LyH &M\1 HI9H;LuHUHHEH0IH.L6Luf.HAA#H+H5KUH81C#H /H H=b &f.LELD#LEjf.LD#_fLEHD#LEf.E1IM9LJtHϺHMHMt0HEJHu1ɺHHELeLu$ID1H L)6f.L;#HHC#f.HH6 1MPHuHULEHˊY^M6G#fDUHAWAVAUIATISHXdH%(HE1H+&HEHEHEHHHHEHH HLyHEMLeID$HIEH5&LHHHHHJ#H9CLkM LsIEIH fInfIn1LflHu)E0"IMILHHHM~HHB#yf.HEL#HuADHqI#HHL AH #H5;H8AT1@#XZH8 H=1贪E1HEdH+%(HeL[A\A]A^A_]@LyH E&M\1 HI94H;LuHUHHEH0I#H.L&Lef.IEH5&LHHIMdH-I#I9FM~MMNIIIsfInfIn1LflHuLM)Ej ILMHjMIHIHH H5&1HM#IHH;8#L;5I#bL;5#<#ULlI#ADž1IEIEH5&LHHrIMZH H#I9EM}M MuIIIMfHnfInL1flHu)ELLI!IHMIHVH L'?#fE1IM9JtHϺHM{HMtHEJH>#Uf.DHu H>#fH\ H=蘧Hu1ɺHLeHEAIfLG>#fLML3>#LMwf.L>#fHu1ɺLHELeHrfH5 &H=G&1HHtH111赞HmH^ H="襦Hu L=#fHV H=xHH1MPHuHULEHkY^-p@5#Hjf.LML=#LMf.L<#fL<#AfAW Iu L<#HDH=3趥E1XfDHu1ɺLHEMH]VI fDR4#ImW H\H=RH=i%H5z%HHHjD#H9GLofInfInfl)EMLgIEI$foEL1Hu)ELIyMLhH=E&LIHtwLIL111{L3X Z I3#IAZ |Hu1ɺLeHEH}LeIaAX EI $t!HX H=~F@LAX :#~>#f.@UHAWAVAUATSHhH}dH%(HE1HH1:;#HHHEH@H;o3#t H;3#HEHEHEHIE1HEH}HE"ID$H=3#I9|$HMH97ID$L,HHMIEMt I2H%H5T%H9p*L=;%MIIEH5&LHHqIMHA#I9GHu1LHELu;HIGHXIHCH;C HKHHHHCH H}MHELIHC#HtH ?@#H1H9%;#LMtLxH5 %H=j%EIHb4#IH(HHXU<#IHHUH5 &H3#LLLHHELLILHEfDHUL7#HUHE1IMMH p1A Mt I $IMt IM|Mt I^HDH=Ւ蘠1MI $HUdH+%(nHe[A\A]A^A_]ÐHULC7#HUHCH;C HUHH3#HUIE1f.H6#fHi>#HE1L H %H50H8R1Ha5#XZ1>L6#fH=9%H %H5 %oIME1MH H\6#HuH9vMlHHIEHE;.#If.H=%mIy@M_fInfInflMcIGIHIHx1ɺHuL]Hx)EL]HI tLx4@HUL{5#HUDHELc5#HEIIA L75#fL'5#vfL5#TfL5#!fHyk1H5HR1bDH H=ȝ1>LpL)E4#foELpH}63#IHt4H@HHEHt HEIt;A E1zE1E1YH> H=lI,1L 4#HM$x7#f.fUHAWAVAUATSH8dH%(HE1HIH+H%H %H9HCH%HHH;#H9COHu11HHEHE/IHHHMHL;52/#|I~0@#HEH1E1I~0LI4#HpH8/#HHMt IQHL}>#]tIL9euIEMfDI~0L+=#HpH8v/#IHL;-{.#tAHHLj7#xEIuLP2#fDH?2# f.H9#H5ZVH896#I0qHH=eE1H IEHIEHHEdH+%(HeL[A\A]A^A_]HHmH=蟚E1f.Lw1#HL#=#pKfDH2H=vHH?oH=0ME1#L0#"f.H0#fL0#fH0#fH98#HE1L eH H5)H8R1H=r/#XZHy1H5HdH=Y%HJ%H5K%hHH~DL{MLcII$H 1HuLHEL}IItLL/#DH=%tgHy@H+#Hf H5?H81p.#HnH=݊耘T2HH=t`q;fDH7/#3IEMHPIU92#E1Lf.UHAWAVAUIATSHHdH%(HE1H(%HEHEHEHHIHhH<HHLyHEMLuHS%IHEH %HEH9HL%`%MI$H6#I9D$)fInfHu1flºL)E HpI$HEHHpI$HIk1{.#IHOH;)#HEH5%H7#H9CBH9#HHPDžddHEE1E1HEHXHXHUHuHyL}HEHhMt IMHEMt IHpLE:#HEIH5H;%#L;55#L;5(#L5#AŅIHEEdLhMHu8#HPH9HMHUHHu(*#L}HEIL}HHhHEfDHE6#HuADHY3#HHL } AH H5$H8AU1*#XZHZH=!E1虔HEdH+%(HeL[A\A]A^A_]@LyH %M\1HI9DI;LuHhHHEH%IDID|L*#HEEsID$I;D$ }%HhIT$HH HID$GDHhLX'#*LhME1Mt IMMt II $AeH MIH]Ht H DH-E1H= HpHHhHHjH)#\DHL6LuPf.H%#HXH59H81(#I $9AeH]PLw)# fLg)#fLW)#fLG)#nfL7)#HfL')#"fH MtLʳML蹳@E1IM9KtHϺHpSeHptHhJfH(#ifL(#KfHw(#%fLg(#fH=&Hj%H5k%`IMLpAdLLpE1AdMl$fInfInLmfl)pMM|$IELI腲1HufopL)MIMHpt ML'#DH=&4_I)@Ae4LT'#H)HUHHE[hLuHLD LHɱH`HCH; #H;#H%#HI莱LH]HDžPDždH<@HHJL1PHhHUMLEm^_fHW&#;fHQ,#H59H8Y*#L}HDžPDždH$#H:M\L4 LH詰)#f.@UHAVAUATSH dH%(HE1HHHMHCH5z%HHbHHH)H-#H9ChLkM[LsIEIH 1HuLHELmIMICLHHMHHH~#I9D$#I<$U #HHthL`> #HH%fHnfHnHfl@HUdH+%(oHe[A\A]A^]ÐHg$##fII $uLN$#fDHO4H=@1@H$#%f.LH uH##DHu11HHEHEIL##fL+#HHtI $u L##IH +#HE1L 5H H5H8R1HUB"#X1ZHy1H51H41DH#H&#fUHAWAVAUIATSHHHLOdH%(HE1Gpt&1HUdH+%(HHH[A\A]A^A_]fIA0Ma(IA(HEIA8HEH)H}0LMHELIHXH#LMHMaMID$H;&#H#t H9lI$HEH9IL$HE.H95HMID$HEL#f.@UfHAWAVAUATSHHhLndH%(HE1H%)EHXfHnH-HEfHnHEflHE)EHvIIq3MIHFHHE#IH5%%LHVP#HEHIH5%LHV,#HEHIFHRHELeLuHxH=H #H5)%HGHHHHoH#H9GLoMLIEIHmfHnfIn1LflHuLu)EfInx)EIMILHHMHHI $H=&H#HH9GHu11HxHEHE;HxIHHM-HHHCH5%LHHHI$HSI$H1HUdH+%(VHe[A\A]A^A_]DIHF(o^HHE)]#IujHF LfLv(LeHxHELufDH#H5%LIHVI#HEHd#Hu#DMH=]jHH=fsL #fL #(f #D #tD #DofH)e#IfDHu1ɺLeHxHEHxH]HELuHxIgL #Sf.H'HH=erHf#HjfLwM$LoIIEHtS1HuLHELuTIIt L@LW#D#7D:#HHs1MPHULE1L=OZYfDL# f.x#xfj#HAfDJ#HAa0 #UHAVAUATSH0H5:%dH%(HE1HGHHHH HCH5v%HHHIHHHMLHHD#I9D$IMt$M;Ml$IIEI $ty1HuLHELuIH.HIMH:#Ht=H HUdH+%(H0[A\A]A^]@LG#yfH HH=Ec(o1@H#fL#gfHEH#HEff.IMuL#HH#qHu11LHEMHE]HDLg#fJ"Hf:"I,#f.fUHAWAVAUATSHhH b%dH%(HEH%H9HHb%HHH} #IHH} #IHH}  #IHmH}( #IHgH}0} #HEH`H #H9CHEHu1ɺHHELeLmLuL}HEH]I $IMlICIHMHHSHxHHH]HHuH>HWH}HHTHUdH+%(Hh[A\A]A^A_]fLCM,HCIHLxHHE6H]H}1LxHuLeLmLELuL}H]LxIHxL#HxHxH#Hx@HxL#Hx@HxL#Hx@HxL#Hxx@HxL#HxN@HEHc#HEf.H WnfH!H==k1bfDH=%H_%H5_%:HHH=i%9HE1E1nH I $tVMtIMt3MtItYM`IWuL#uCuLt#uuL\#uuLD#uE1o^oQfDpAfDuH#u0@H#n#UHSH8dH%(HE1H;="tgHwHHH}P@foEHEH0foMHD$ $L$CH0HHUdH+%(H]DHY"H5jHH81"HH=e] i1@H5%H=* &1ӍHHt111H`H t뮐fH"_#f.DUHSH8dH%(HE1H;="toHHH}HP0foEHEH0foMHD$ $L$H0HHUdH+%(H]H!"H52H|H81"H̵H=e\g1@H5%H=&1蛌HHt111H_H t ffHw"#f.DUHAWAVAUATASH8H E\%dH%(HEHz%H9HH\%HHI#IHHG#H9CHu1ɺHHELeII $tMHHMtRHHt*HEdH+%(5H8L[A\A]A^A_]DH"DLo"DH H@!H= [E1TffH=%H*[%H5+[%5HHH=%4HL{fInfInflMLsIIH t>1HuL)EIItLL"D)EH"foE@Ho" #@UH;=`"HtcHt%]HQ"HH5[ H81"H>H=Ye1]@UH;="HtSHt%]H"HXH5 H81"HH=Yd1]@UH;="HtH#"Ht%]H"HH5 H81@"H9>H=YPd1]f.fUHAVAUATSH H5%dH%(HE1HGHH'HHH2#H9CLcMLsI$IH tk1HuLHELe|I $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DHG"DH7"DHtkH H=XE1 cfDHu11HHEHEITL"Cf"HfH"_"f.Dkf.UHSHRHHt HH]Ð#HuH#H8#UHAVAUATSH H5%dH%(HE1HGHH'HHHR#H9CLcMLsI$IH tk1HuLHELeI $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DHg"DHW"DHtkHI`H=VE1@afDHu11HHEHEITL"Cf"HfH""f.DUfHAWAVAUATSHH8LndH%(HE1H%)EH fHnHEfHnfl)EHIIcIMMH=u{H)gH=U`HUdH+%(He[A\A]A^A_]IuLnLf LmLeH5Ks%I9uL5"t M9ID$M9t H;#I$L99H{PH|H=s%LcPH52%HGHHfIMH"I9GMgMzMwI$IIVfHnfIn1LflHuLm)EI $HMIHIHKHH 1fo^H)]#HLmLeH#H5%LIHVI"HEH H5%LHV"HEHIFfH"1 L""f"yDHFHHE#If.L"fL"fHu1ɺLH]HELmmHDH7iHyH=TSo]Pf.Ha"H4H5kH81"I $h1HLRf."IfHH#1MPHULE1L:ZYfDHHH"HpH53H81l"Y""HGA!fDLO"$f.L7"f"H"fDUHAWAVAUATSH(dH%(HEHFH]IHHHn%H Xk%H9HH?k%HHH"H9CHu11HHEHEZIHHHMtyHtTL;%e"I$HtJM$1ID$HUdH+%(He[A\A]A^A_]DH"D"HHr,H=PZH"HH5H81"IMuL"H"HE1L -H H5H:PH1:"XZ|Hg"H`1H5{H%|GIH= %Hi%H5i%v)HH?DH=و%(HL{M!LsIIH tD1HuLHEL}mIIt LDLo"DH_"DHO"c"@UHAWAVAUATISH8H N%dH%(HEHk%H9HWHN%HwHL;%"Md$ "HI$LPPy"HpH8"IH@H"H9CO1HuHHELeXI $It?HHMtdHHHEdH+%(H8L[A\A]A^A_]L'"DH1"HTH5;H81"H t;H H=0NE1WfDH"rf.H"DH=ѱ%H*M%H5+M%'HHH=%4&HH2H=3`WKL{fInfInflMLsIIH tF1HuL)EIItI $LDL"D)EH"foEn"f.@UHAVAUATSH H5%dH%(HE1HGHH'HHH"H9CLcMLsI$IH tk1HuLHELeI $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DH"DH"DHtkHH=LE1UfDHu11HHEHEHITLW"Cf:"HfH7""f.Dkf.UHSHRHHt HH]Ð"HuH>"H8m"UHAVAUATSH H5 %dH%(HE1HGHHIMH"I9FHMfM;MnI$IEI1HuLHELeI $HZMIHIHHH;]"H;"udH;"t[HB"HI܅HHtlHEdH+%( H L[A\A]A^]ÐL"kk"HHH5%H胎AƅH EH\%H @%H9H.L5?%MIHT"I9F;Hu1ɺLHELLeLm贿A?HtHHE}jHEP@A=H H"H"HHt)H&"H If.Iu"H'IZ"Hmf.ILcvHg"IHt!H"IItNfDIAx"HusI*IvLckH"BfL"DHH5i Hg"#fA=KDAAD*"HafH5%H=*%1kHHtH111?HhA>fH=Y%H=%H5=%VIMA?M~fInfInfl)EMI^ILHNhfoEHu1ɺHLm)ENLHE"hHEH=©% IrIu$DvAMcIu,DkAMcCItvIt]H"IIt0ItH"I "DkCII ;DkCII IDvFII DvFII I9fUHAVAUIATISH0dH%(HEH?"H9FHGH5R%HHHHHm"H9CLkMvLsIEIH RfInfIn1LflHu)E譻IMILHnHMH HH=;E1DDHYW%H A%H9HHA%H8HH"H9C<Hu1ɺHLeHELmIHlHMaHHt*HEdH+%(sH0L[A\A]A^]H"DL"f."HufH"fHu1ɺHLeHEQIfnfDH=%Hz@%H5{@%HHlH=%HLsfInfInfl)UMLkIHIEdfoU1HuLLe)U薹IItLfDL"DuH"u&"fDUHAVAUATSH H5j%dH%(HE1HGHHIMHr"I9FMfMMnI$IEI1HuLHELe跸I $HMIHIHHH5"H9CH;HEdH+%(H H[A\A]A^]fDL_"df.HHHH=I81:AL"ffHu11LHEHEзH!L"fH"IHMhHPHHu H"L HKL"=j"I@fHg" "f.fUHAWAVAUATSHHXLgdH%(HE1Gp4fMl$(Mt$0M|$8MD$@AD$(IL$HIt$PH$HIUH`"IuH9H9:IE1HHHI|$fInfInID$flHt H HAD$(H{ M|$8MD$@IL$HIt$PHtHC HCpHUdH+%(HX[A\A]A^A_]@LEL"LEHIH@HHHLELHMHuHuHMHLELE`"LEHt'H"H2H9lLE"LEfDIMuLELm"LEMXH"@HWI|$ H5K%HGHHIMHb"I9EM}MMuIIIMMHu1LHEL}襴IMt ItMKIMIVH;"H"t H9DM>ME1INH9L9~SIVE1N,IIEI|$Ml$Ht H)IUH;6"t H9IE1HHi"LHU^HUH:"CpH1f.L"fL"fHEL"HEIV!fDHE"HE0DLEHuHM)Eb"ID$foELEHuHML7"~fH9ID1HHkDHELE"Ml$HELE+AH5DH=:;f.AMtLh]MtL[]fAj"IPfHu1E1fMAHu1jDL"IHH@LMItOMILELAIHtFH"LEfDL9OlE1IIErLEL"LEZ"H"HH2H9HU"HUrL.\LAM\AyA~"HLE LE|AKHHUHU_(A5f.UHAVAUATSH H5*%dH%(HE1HGHH'HHH"H9CLcMLsI$IH tk1HuLHELeI $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DH"DH"DHtkHH=/E18fDHu11HHEHEhITLw"CfZ"HfHW""f.DUHAVAUATISHH0dH%(HEHFH;"}H;="0H;"SLnMHC0LHp(HHHCHPHHUdH+%(xH0[A\A]A^]fDHiJ%H 6%H9HXL-y6%MxIEH"I9E{Hu1ɺLHEH]LeIMHQIUHtH`HEL "HEJH0HBH=L.61"H"HH5H81"2fH"IHt!HF"I $It=DI"HuIqILcnL7"DL'"+fH=%H5%H55%IMDH=Y%IMufHnfInfl)EMfI]ILHgWfoE1HuHLe)EgItIJf.HELc"HEIuDnAMcIt0ItH"I"DnFII ,DnFII If.fUHAVAUATSH0H5%dH%(HE1HGHHHHH"H9C LkMLsIEIH 1HuLHELm8IMILHHHMHtaH2"I9D$ID$H9I $tKHUdH+%(H0[A\A]A^]H"cf.H"DHEL"HEI $HnH= +3HtHu11HHEHE8ILG"fHuH2""HpfL"HHtH"H tQH"H:HHu5IcD$L"f.HEH"HEHu AD$HHt0HtL7"r"AD$AT$HH EAD$AT$HH H=f.UHAWAVAUATISH8H=%H5>%dH%(HE1HGHHHHH&"H9CL{MLsIIH TfInfIn1LflHu)EgIILHHHMfHH=J%H5%HGHH{HH5H"I9D$I$H`"H9CLsMLCIIH fInfIn1LflHuLELe)E虧ILEIiLI $tpHHMHHtiIMtrHEdH+%(H8L[A\A]A^A_]fDHO"f.H7"fL'"DH"IMuL"fDHKHG}qH='E1/TLEH"LEf.Hu1ɺHHELeqIfLw"fHu1ɺHHELmLe-IDLEL3"LEf.L"IHH uH"DHH|rH=&E1.O"Hf"H|\"H"f.@[f.Kf.UHAVAUATSHH0HB%dH%(HE1HFH9t:HXHHqH1fDHH9H;TuHGH5B%HH~IM,H%"I9F;MfM.MnI$IEIfHnfIn1LflHu)EdI $MIHSIHHuIHELR"HE6@HH93HuH;t"!fDH%HHUdH+%(H0[A\A]A^]fDHEL"HE`f.L"Hu L"@HzH=}$,1{Hu1ɺLHEH]QV"Iy"ttH?"HUHAVAUATSIHtxH"I9tMH]"L5"I9AM9Du%dH%(HE1HFH9t:HXHHqH1fDHH9H;TuHGH5%HH~IM,He"I9F;MfM.MnI$IEIfHnfIn1LflHu)E褟I $MIHSIHHuIHEL"HE6@HH93HuH;"!fDH!%HHUdH+%(H0[A\A]A^]fDHEL#"HE`f.L"Hu L"@HRu&H= '1{Hu1ɺLHEH]葞"IyH"ttH"HUHAVAUATSIHtxHQ"I9tMH"L5."I9AM9Du*%H5'j%HGHH{HH5HN"I9D$I$H"H9CLsMLCIIH fInfIn1LflHuLELe)E)ILEIiLI $tpHHMHHtiIMtrHEdH+%(H8L[A\A]A^A_]fDHߦ"f.HǦ"fL"DH"IMuL"fDHKH^H=lE1|TLEHS"LEf.Hu1ɺHHELeIfL"fHu1ɺHHELmLe轅IDLELå"LEf.L"IHH uH"DH]H=kE1}OJ"Hf:"H|"H3"f.@[f.Kf.UHAVAUATSH0H5Z%dH%(HE1HGHHIMH"I9FMfM MnI$IEI1HuLHELeGI $HMIHIHHtxHѥ"H9CHHHHtqHUdH+%(H0[A\A]A^]ÐL"lfH H[|H=tj 1L"zf.HEH"HEyf.Hu11LHEHEPHLW"fH`L>"Rf"I`fH"HH HQfH" "f.fUHAWAVAUIATSH8H=5t%H5e%dH%(HE1HGHHIMmIEH5\%LHHHHaHCH5n%HHHcIMyH |IEH5W%LHH>HH`HI"H9C/L{M"LKIIH w1LHuLMHEL}茁ILMI LMH HЩ"I9D$M|$MMD$III $rfInfIn1LflHuLELm)EILEHMIIMI$E1AwHHtPI$HHEdH+%(H8H[A\A]A^A_]H"vfAxI $u L"MtItAHtH tGHuXDH=!g1o DLMHK"LMsfL7"DH'"DL"fL"fH"kfL"fLELӟ"LExf.Hu11HHEHEILML"LMf.Hu1ɺLHELuLm-H3DLEL3"LEf.AwD"IlfE1AxXfږ"Hxfʖ"If"Hl"I $u L"H tAx&DH"f.f.f.UHAVAUATSH0L%R"dH%(HE1L9H0О"HPHHL9H@H5x%HHHHHdH("H9GLoMLwIEIHm1HuLHELmn}IMILHHHMHHHPHHtyHEdH+%(NH0L[A\A]A^]ÐHA"HH5KH81"HS H=E1HIHBHHHuHҜ"yDL"9f.Hu11H}HEHEo|H}IHt[HR H=E1pHHPq@J"D:"D"H:f"HR H="f.UHAWAVI1AUATSHhdH%(HE1m"H\L;5"HlI~0J"EHEE1E1HEHR%H %HH9HL=%M6II~0D"HHp Hx"IHHj"I9GHu1LLMLMHEzLMHI tqIHHIHMt I $HK HCHHH9H9HKHHHHCAD9mIHULc"IHUHH~I HIPH=|?H 1Mt I $IHEdH+%(hHhL[A\A]A^A_]DHULۙ"HU fHUHHx"HU.IԾ dfDHUL"HUf.H=\%H %H5 %IMDLG"fHP2H=8IDH=9\%IMWfInfInflMIGIHIHEH}1ɺHuLMLU)ExLULMHI tL}DLMLHU"LMHUDLg"MfHW",fHWN H=E1EHA"H5RH~E1H81" LMLLU)p"LULMfopH;t IH;H"HUH"HUA"UHAVAUATSH H5r%dH%(HE1HGHH'HHH"H9CLcMLsI$IH tk1HuLHELevI $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DH"DH"DHtkHL H=O]E1fDHu11HHEHE(vITL7"Cf"HfH""f.DUHAVAUATISH H5V%dH%(HE1HGHH4HHH"H9CLkMLsIEIH tpfInfIn1LflHu)ECuIMILHHPHMtLHt7HEdH+%(H L[A\A]A^]@H"DH"DHtsHJ; H=PE1fDHu1ɺHLeHEtIVfL"LvLuXfLǍ"1qLEL"LEf.L"fL"fHw"f"HuH"H5H8n"fDH wH>CH=4H?H"%fLEH"LEf.HYB%Hu1HߺHELmLuHElIf.1HlRLqfH= k%Hz%H5{%IM6DH=j%I"Hf.1LH2"IHIڃ"Hof!fDLNj"fuH"uu@HHA11PHUMLEHY^  "UHAVAUIATSH H= %H5M%dH%(HE1HGHH-HHHh"H9CLcMLsI$IH tqfInfIn1LflHu)EjI $ILHHPHMtMHt8HEdH+%(H L[A\A]A^]DHw"DHg"DHtkHb@JH=qQE1PfDHu1ɺHLmHEiIUL"Cf"HfH""f.D[f.Kf.UHAWAVAUATISHXH5C%dH%(HE1HGHHHHbHCH5U%HHHIHHM"HHfIEH;"IELIEHH=Y%H9"IHH  Y"HHH^<%AHHCAE IE@u#AtADEIELk LH5>%HEH>%HHC(ID$HHIMI@H;lj"IMIHAG E@u%EtD‰EIGL{0LH5P?%HEH=?%HHC8ID$HHIMH"I9@MxM MPIIIJ1LHuLUHEL}'gILUIMIHMUIHIAH;"HILIHP A@u#AtADEHHHC@LHRQ%H5SQ%HMHHCHID$HHIMHƎ"I9AMaMMQI$II 1LHuLUHELefI $LUIMMI 2I@H;"IMIH?AD$ @u tEHMHEHHD HMHT H@9%IT$LcPHHCXEA9ƉACD9AB9BH¾ [HBH 9IMu[HEL6"HEHL'"!fH"6fH u H"H:PH=1HUdH+%(HX[A\A]A^A_]fDH"fLMLLU"LMLU[fH;"YLELPXLEIIM&H;fDLO"3f.LML3"LMf.LUL"LUf.HEL"HEf.{"HefLELÃ"LEf.{"IafL"fLUL"LU/f.LHu11LEHEHE,cLEI H;A"LPXHIEHHDHEH"HEf.AQf.H8DH=1zLHu11LMHEHE|bLMI|ARf.H uHr"Zz"IfLELLUO"LELU+fARIuL,"H;c"LELH5$"LEIDy"IFf.ASUDASH;"+LMLPXLMHI HQzy"IfATDHATHHI rLf.H;A"LELPXLEIIMtbH'fAQDHIELHASHHxLMHƀ"LMbDATsH;"mH5$L8"H^H;"LMLH5u$"LMH;"LELH5K$"LEIۃ"f.kf.[f.UHAWAVAUATSHXdH%(HE1H;={"HH8H5K%HGHHIMpH5=%LAI$E?HI$"EHC0H@Hx(H ttIHcH=$H5A%HGHHIMBH$H $H9HL=$M0IH6"I9G4Hu11LHEHE^IMI'H"HHv"IH,"HHHiz"H bz"HHCHIELhH"I9D$~ML$MpMT$III $sfInfIn1LflfHnHu)EfInflLMLU)E]LMLUHEI OMIIH H}g I $u L}"IMHEdH+%(lHEHX[A\A]A^A_]fLG}"fHI$Hz5c H=+1HUH=9C%H$H5$vIMI $MtItfH5g H=1HE1L|"*fLULLM|"LMLUofLw|"fLg|"H4g H=,_DL?|"H4c H=7fH|"qfL|"XfL{"?fL{"fHw"H4H5H81z"H4c H=s"IfE1I $ILq{"@HA$H $H9HH$H6HH"H9CHE%Hu1HߺHEIHEZIHLL111LH 3d H=@Hu1ɺLHELuL}H]qZHEjr"IQfLULcz"LULsM!LcIHI$fIn1LI%Hu)EYLIM d fDI $u Ly"H02H=MfDH=?%tI@MOMMwILLMILuALM1LHuHELM>YLMII tL}fL7y"H=$H$H5$蛱HH1H=$辰H|"Lx"bH,1f H=UHAWAVAUATSHXdH%(HE1H;=t"HG0IH@Hx(HmIHH=$H5:%HGHH]HHH$H $H9HGL=$MIH]"I9GcHu11LHEHEWIMINL"HHh*o"IHVSx"IHBHs"Hs"HIEHI$L`H"H9C>LKM1LSIIH fInfIn1LflfInHu)EfInflLMLU)EVLMLUHEI t~LIbIIIM'H}HHHEI $HEdH+%(HEHX[A\A]A^A_]DLwv"fLULcv"LUlf.E1H LIMt IHt.Z H=%I $HEXLu"JDHr"H4AH5 H81t"H.Y H=HELu"f.Lu"fLwu"fHgu"fLUHLMOu"LMLUfL7u"fL'u"fHu1ɺHHELuL}LmTHEl"HfH=9;%Hz$H5{$FIMH Ht"vMOMMwILLMILu9LM1LHuHELM6TLMII t L}aL7t"DH Ht"fH=i:%īI9@Hs"f.HHyw"fUHAWAVAUATSH8Ho"dH%(HE1H9PIH0HHY$H B$H9HXL%)$MxI$HPP"HpH8)p"IHHv{"I9D$S1HuLHELmRIMHtCI$HHtwI$Ht>HEdH+%(H8H[A\A]A^A_]fLr"DLr"DHH*2H=շI $HB)H=1\iHQn"H5bH)H81q"fH=15%H$H5$vIMtI}0DH=5%蔩IM|$fInfInflMMt$III $t[1HuL)EdQIHtIMM|f.LWq"DLGq"f)EL3q"foEt"fDUHAUATSH8dH%(HEHH@(H l"H9 H{%y"HHH='{%HHH tgHUdH+%(H8[A\A]]Hu11H}HEHEGPH}H7HVHHHH uHEH5p"HEH u Hp"H(vH=g1R3HHH@H5T%%HHHHHtpHx"H9G&LgMLoI$IEH1HuLHELeMOI $LHHM'tH=G1H5Q%H=Ry%1HHt111HH H&pH=I14DHQv"H5H8r"H&vH=1fn"FDHEn"HE6Drn"DH_n"^f.HELCn"HEf.H.&sH=}(1he"H>q"f.fUHAWAVAUATSH8dH%(HEHHvHPHHIx(Hi"H9w%v"IHH=w%HrHIEHbHIEmHCH5j9%HHHHHHru"H9GLwM{LIIH@fHnfIn1LflHu)ELIIgLHHHMtfHL;-h"Hh"HLH5|H81hk"Hq#H=xH 1Ht;H<#H=CH 1l"hD l"HIE;H:$3H=H"H=P1l@k"DHIHH@H5 %LHHHHHs"H9GGLwM:LIIH1HuLHELuJIHLHHHHt+HIMLj"f.Huj"H #1H=ZIMLj"DHu1ɺH}HEH]PJH}I@LWj"fH5!O%H=rt%1HHt111H%H H !H=1DH5L%H=t%1HHt111HH H "-H=Ya"H`fH5$LA4tRIELLt"HIEH tYLIMt@HEdH+%(H8H[A\A]A^A_]IMLi"Li"DHh"DHip"H5H8l"H !3H=n@L1h"H 3H=BHH= (H1eh"LWh"FfJh"SD:h"DH'h"fHh"NfHu11H}HEHEGH}HH' 0H=u_"HA\k"H1g"Ff.fUH;=c"Ht;H0H(Ht ]5 HH=%`1]@HYc"H5jwH2H81f"4 뾐UH;= c"Ht;H0H(Ht ] H:H=1]@Hb"H5vH2H81e" 뾐UH;=b"Ht;H0H(Ht ] HH=1]@Hyb"H5vH1H810e" 뾐UH;=@b"Ht;H0H(6Ht ] HZH=]1]@H b"H5vH51H81d" 뾐UH;=a"HtH0HHHt)]Ha"H0H5uH81pd"H H=1]f.fUH;=`a"HtH0HXVHt)]HIa"H|0H5SuH81d"H_ H=1]f.fUH;=`"HtCH0if"HHt ]f.# HH=1]@H`"H5tH/H81hc"" fUHSHHh`"H9t[HH0HCPH9t HH]fe"HBHtmH{PHt HCPfDHEd"HEH`"H5"tH=/H81b"6H"H=H]1þ9fUHSHH_"H9t[HH0HCHH9t HH]fl"HHtmH{HHt HCHfDHENc"HEHQ_"H5bsH}.H81b"?HbH=EH]1þBfUH;=^"Ht;H0H Ht ]8H H=1]@H^"H5rH-H81pa"6뾐UH^"HH9t(H (uH]ÐH0gHuﾭ(HY^"H5jrH0H81a"H!H= 1]f.fUH;=^"Ht;H H@Ht ]HH=1]@H]"H5qHH81`"뾐UH;=]"Ht;H0H Ht ]HH=E`1]@HY]"H5jqH,H81`"뾐UH;= ]"Ht;H0H0Ht ]H:H=1]@H\"H5pH,H81_"뾐UHAWAVAUIATISHHdH%(HE1H[%%HEHEHEHHHHEHH HLyHEMLuIEH5--%LHHZIMLLcm"HI$HHI$L='X"L9H;h"wH;["jHi"Aą&H ZEcH5B%H=~i%1OHHt111HYH fDHEi"HuADHf"HH&L ŹAH SH5CXH8AT1]"XZH<H=)1HUdH+%(He[A\A]A^A_]LyH }#%Md1HI9H;LuHMHHEH0I#H.L6Luf.H DH*^"EH5=%1Lk"HHL9H;f"H;Y"H5g"Aąx^H %E8L;-Y"wLÃIE0HcHH8HfH HH=_vf.DiHI$uL]"@L]"4f.E1IM9JtHϺHMfHMtHEJFT"If.H\"fH\"Hy\"$@HH#1MPHuHULEH{Y^ P@HIX"HAH5SlH81["f"He|_"fDUHAWAVAUATSHhdH%(HE1HHHHW"H9Ls A~(MfHq$H $H9HH$HHHCH5$HHHIHHHMHH$H Q$H9HH8$HHL}Iv0AT$PL]"HuH}W"IHH}HEH9tHEHpY]"Hc"H9CHu1ɺHHELei:II $ MH *Hb"I9EIHu1ɺLHELu:IHIMt2fHUdH+%(pHe[A\A]A^A_]fDHHELY"HEDH=+%H$H5$>HHEfsHH= 1qHuHY"HwY"CfLgY"fHELSY"HEf.H7Y"fH`"HE1L H eH5URH8R1H]"W"X1ZHy*1H59"H1DHT"H5hHH81W"pfDH=I*%DH@bP"I f.H=%Hz$H5{$ƐHH6IMt{fDH=A%ԏHH2H=UH}HEH9tHEHpuZ"IMu LW"H uHW"fDLSfInfInfl)pMLsIHLUILu41LfopL)E67LUII t H]L7W"DIMvLW"hMefInfInfl)MMI]I$LHfoM1LH)M6I $t I{DHELV"HEGZ"LV"YHUHAWAVAUATSHhdH%(HE1HHHHLR"H9Ls A~(MfH$H z$H9HHa$HHHCH5$HHHIHHHMHH$H $H9HH$HHL}Iv0AT$PL^"HuH}`R"IHH}HEH9tHEHpW"H]"H9CHu1ɺHHELe4II $ MH *HC]"I9EIHu1ɺLHELu4IHIMt2fHUdH+%(pHe[A\A]A^A_]fDHHELsT"HEDH=!&%H$H5$ΌHHEf_H9 H=ź81qHuHT"HT"CfLS"fHELS"HEf.HS"fH9["HE1L eH H5LH8R1HrR"X1ZHy*1H5Hd1DHaO"H5rcH8 H81R"\fDH=$%ԊH@J"I f.H=%H*$H5+$VHH6IM`{fDH=%dHH! 2H=萻H}HEH9tHEHpU"IMu LVR"H uHHR"fDLSfInfInfl)pMLsIHLUILu1LfopL)E1LUII t H]LQ"DIMvLQ"hMefInfInfl)MMI]I$LH.foM1LH)M31I $t I{DHEL3Q"HET"LQ"YH<UHAWAVAUATSHHHH=!%H56%dH%(HE1HGHH IMH$H $H9H5L-$MeIEH;L"H}Hs01H}K"HuH}RM"HH>H}HEH9tHEHpR"K"IH\HXS"HHFH_H"H50%%HJ"'IELM3H=\J~Y"HLLAI)P"MHIMIH LHW"I9D$I\$HMl$HIEI $fInHufHn1flL)E/H MIHI $HELN"HExfDHK"H}H5 _H81M"E11I $>IMHt H Mt IHH=萷1HUdH+%(6HH[A\A]A^A_]@HEHKN"HEfH7N"fHEL#N"HEf.LN"mfLM"TfLM"JfHM",fLM"fE"IMfD fDLM"4f.LwM"fH=%HJ$H5K$օIMI $L7M"fH=Q%IH2H=eH}HEE1H9$HEHp~O" fHu1ɺLHEL},@HLLD"IHI $LmL" W"HHY"H5FH8ZP"mO"Hd UHAWAVAUATISHHH e$dH%(HEH$H9HOH8$HgHL;%G"It$@LmLHPPHuH}H"IHCH}HEH9tHEHp N"HS"H9CW1HuHHELe0+I $It?HHMttHHtHHH= %dHH!2H=萰H}HEH9gHEHpJ"T@L{fInfInflMLsIIH t^1LL)E&IItI $L|fDLF"DHF"f.)EHF"foEfJ"Hf.@UHAWAVAUATISHxHhdH%(HE1H%HDžxHEHEH/HHH`HhHHLqHxMLxHhL}H;B"EL}HExH$H $H9HH$H0HHN"HuE1H9C(fInfIn1HflLm)E`%IMt I?HHHMuHTH}LLmtHEHMHUHL9H9oUHuHMUH{HEHuHEH}H9tHEHpG"LmO"HI$HI$HHHhLmLHx0eR"HcM"HH@H}L9HEHp G"qHDžxO"Hu>fHK"HH L AH {H5k=H8AT1B"XZH H=)1 HEdH+%(jHeH[A\A]A^A_]DLqL=u%M_1HI9,L;|uH`HHxH%IZf.H?"H**LmH5SH814B"@HO H=]Lm<1DHL&LxLmHB"fDHuLmHB"@HuLmLB"qfLB"fH9t{o]HM]HUHUH}fDE1IM9JtL~tH`JL'B"fHMHHt>HȃHtMPHuH}HHHMHEDH=A%Hʵ$H5˵$Lm"zHHIH=%Lm@yfDLsMHCIHH H`tH`HuLmH3A"ܐHH1MPH`HUHLx5Y^EHMHE HLHLG1ǃL:L>9rMLLHuH}LfLHuH}'D"Hf.DUHAWAVLuAUATSHhHxdH%(HE1H;=6<"LuHEExH$H J$IH9HH.$HHH3H"HuE1H9CfIn1Hx)EIMt I"HHHMHH}LHEHMHUHL9H9oMHuHMMHHEHuHEH}H9tHEHpA"I"HI$HI$HHtdI}0HuHU ="H}L9tHEHphA"HEdH+%(SHh[A\A]A^A_]@H>" fL>"DLw>"fHMHHt>HzȃtMHuH}HHHMHEDHH+H=DDHuH="H9WoUHMUHUHUHrf.H9"H^H5MH81h<"tH=%H$H5$uHHhFDL{MnLcII$H t;LHuMf.H=%tfL<"fH<"DEHMHEDHLHLG|1ǃL:L>9r_MLLHuH}MLfLHuH}6 @"H@UHAWAVAUATISHxHhdH%(HE1H%HDžxHEHEH/HHH`HhHHLqHxMLxHhL}H;7"EL}HExHq$H $H9HHq$H0HHC"HuE1H9C(fInfIn1HflLm)EIMt I?HHHMuHTH}LLm$HEHMHUHL9H9oUHuHMUH{HEHuHEH}H9tHEHp/="Lm5E"HI$HI$HHHhLmLHx@9"HcfHyA"HHL AH +H53H8AT18"XZHH=11躢HEdH+%(jHeH[A\A]A^A_]DLqL=%%M_1HI9,L;|uH`HHxH%IZf.H15"HLmH57IH817"@HH=eLm1DHL&LxLmH8"fDHuLmH8"@HuLmLn8"qfLW8"fH9t{o]HM]HUHUH}fDE1IM9JtLttH`JL7"fHMHHt>HȃHtMPHuH}HHHMHEDH=%HZ$H5[$LmoHHIH=%LmnfDLsMHCIHH H`tH`HuLmH6"ܐHHg1MPH`HUHLx}Y^EHMHE HLHLG1ǃL:L>9rMLLHuH}LfLHuH}9"Hf.DUHAWAVAUATSHHL-2"dH%(HE1L9HH HGH5%HHIMyID$H5 %LHHII$HI$M1HHCH55$HHHIMlM9ID$H5/ %LHHIMH=%H5@$HGHHdIMNHCLEHH5%HHDLEIMH<"I9@MHMMPIIIfInfIn1LflfInHu)EfInflLMLU)ELMLUHI MIbIHHIHgI.I $Mt!IMuL3"fDHy%HHEdH+%(HHH[A\A]A^A_]fL-)%IEzLw3" fLULLM_3"LMLUf:+"IfL73"NfHA/"HbH5KC1H811"HH=3IHH=}ڛIL2"fLEL2"LEf.L2"fLHu1ɺLEHEL}LuLm5LEH.fHu L22"fHH=1&S)"IPfLUL1"LUf.)"IMfE1HH=hLšItM1DL1"D빐j)"IfZ)"LEIfDB)"I;f.nLL*1"H H="[4"f.f.UHAWAVAUATISHHH $dH%(HEH$H9HWHX$HwHL;%,"I|$Lmp*"L<"HuH}O-"IHKH}HEH9tHEHp2"H8"H9C_1HuHHELeI $It?HHMttHHtMl$fHnfInflMnMt$IEII $t[1HuL)Eo IMHtMVfDLo*"H=$!bIFLP*")EL>*"foE-"UfHAWAVAUIATSHHHdH%(HE1H$)EHfHnHEfHnfl)EH1HHEHHHt`MH=xH#gH=O芒1HEdH+%(HeH[A\A]A^A_]L{L%$M1HI9L;duHMHHEHIGHSHEL5$HTE1ID$H92IN;tuHEJHEHL}IHL&L~LeL}L~0"H,HL5 "IID$H5=$LHHbHHdHCH5$HHHfIHHHMHID$H5$LHH_IMiID$H5E$LHHIMI $H/"I9@IXHMPHIIfInfHn1LflHuLULu)EH LUILMMxI?HX/"I9EM}MMEIIIM=fInfIn1LflHuLELu)EILEHMI $IEHHIEu\Lt&"ILb&"DL5A/"IfHtSHiH=11IfDo&L{)eMLeL}gfDH%"DLUH%"LUfHHEHCHEHfE1IGM9IJtLbtHEJHO%"Tf.E1IM9JtLatHEJLEL$"LEXf.L$"?fL$"fLUL$"LUIf.LEL$"LEf.LHu1ɺLEHEL}Lu9LEI,DHhH=@Hu1ɺLHELeLuH`DLEL#"LEBf"HfHaiH=Ȍ"IfHEB."HAfDb"If.IME1LELF#"MLEtI $t'MIL#"LEL#"LEDHH1MPHuHULEHjZY2@IMy^"IfIM_>HIEIL{"";fDHE-"H%"f.@UHAVAUATSH dH%(HE1HH0H$H5$H9pHH$HHH.*"H9CTHu11HHEHEIHHHMtsHt^111L萂I $t:HtH=g肊HEdH+%(bHe1[A\A]A^]LG!"DH7!"DHuH"!"H("HE1L H UH5EH8R1HI"XZqHy1H5%HīEH=i$H$H5$YHHDLsMLkIIEH t[1HuLHELu$IIt L@L' "DH=$WHy@H"#"UHAWAVAUATSHHHdH%(HE1H$HEHEH"HEHIL4HHHLiHEMHQ$H=$H9xH$HpHH'"H9CHu11HHEHEIHHHMGH~111LI $VHwGH=އfH?H HHH HHHHHH?L 9yHLHH%"HL@H51H:SH;"XZH?H=NIHEdH+%(He1[A\A]A^A_]DLiML=$1HI9tM9|uIHHEIC@HH"fHHE@L"fHw"tfH=)$H~$H5~$UHH_DLsMLkIIEH 1HuLHELuIItLL"DH=$THy@r'"HhHH1LPHUILELcY^5fDHw"Sf1 @HI9tItLHMXHMttIZ"f.UHAWAVAUATSHHHdH%(HE1H$HEHEH"HEHIL4HHHLiHEMH$H=|$H9xH|$HpHH#"H9CHu11HHEHE,IHHHMGH~111L |I $VH=H=;fH?H HHH HHHHxH?L iuHLHH%""HL@H51H:SHk"XZH1H=yHEdH+%(He1[A\A]A^A_]DLiML=4$1HI9tM9|uIHHEIC@HH"fHHE@L"fH"tfH=Y$Hz$H5z$RHH_DLsMLkIIEH 1HuLHELuIItLL"DH=$PHy@#"HhHH.1LPHUILEL_Y^5fDH"Sf1 @HI9tItLHM UHMttIZ "f.UHAVAUATSH dH%(HE1HH0H$H5y$H9pHHiy$HHH> "H9CTHu11HHEHEIHHHMtsHt^111LxI $t:H+H=q蒀HEdH+%(bHe1[A\A]A^]LW"DHG"DHuH2"H"HE1L H eH5UH8R1H8"XZqHy1H5HԡEH=y$Hx$H5x$&OHHDLsMLkIIEH t[1HuLHELu4IIt L@L7"DH=$MHy@H""UfHAWAVAUATISHHdH%(HE1H$$)EH0fHnHEfHnfl)EH.HHpHHHtbMH=u輙H+H=QT~1HEdH+%(c HeH[A\A]A^A_]DLsL=$M 1 HI9L;|uHpHHEH] IFL{HhL-$M1fHL9L;luHpHHEHLhIfDHHH^HpHEH]H51"H9st H;D"Hp)"HH "IH*LS%HEM6P%W==1IF"IHIHH=%1HHXI$L` rIII$HM I$HIEHf "H9t H; "V I}HMeH9AM} I](I$IHIML2IHI $L2IHIH2IHH I0H$H $H9HHi$HmHHCH5B$HHH>IHHMHHL "HH+L "IHL "IHHQ"I9GHu1ɺLLEMLhHEH]LuLhIH IrIMZIMH5'$HpIHH"I9FMnMM~IEII=fInfInL1flHu)ELH͛E1A8HIRI $L"f.oLs)UMHEH]HpWHu1ɺHAI "IHA4E11E1I $>E1Mt IHt H Mt IqHhDH=yM1 HHEHCHhIfDE1IM9JtLBLtHpJLHAILo"f.LW"\fE1IM9JtLKtHpJ.L"fL"fH"(fHuHAICfH"kfL"fL"UfH"SfM)H5$HpMLHIH L"IHL"IHHq"I9GMGfInfInfl)pMMoILLhIE蛘LHu1fopLe)EHhHiLaLYHTLHAHH|.2HH=MPvLhL( "Lhr@LhH "LhI@L "If.4fL "4fH=%H=%5H=%%DLw "fI$L`H%%1LHIHu1ɺLHEMLuLeHk@L "fHE"HAgfDHH1MPHpHULEHRZYEL "fHx`IMt"H4H=ortDLO "D"H9A4E1"H8E1A4E1L "fM|$I\$M$$D"HH H "H=+$H|x$H5}x$CHH7/fHE""HHu1ɺLHEMLe"H-E1E1A7"IH=$AHrE1A7E1L"IHaL虔IFLLALIHAIHLAHHLAվA4HTL/UA7aLHIMA7E1ME11A7M_fHnfInfl)PMMoILL`IELh訓fInHu1LhfoPLfIn)efl)EH`IaLhH8H=1qH!H5H81@"1A;H;ME1E1A;A4E1E1LʒIt;A4E11謒He1踌Yv "HA4E11蓌Vf.@UHAWAVAUATSHhHxy$dH%(HEHT$H9P L=Ky$M6 IH"I9G3 Hϼ$Hu1LHEHEIIAOHMIHIMH˂$Hx$H9PL L%x$M I$ID$H;!t H;A" HEI$MHEI$H1H}L}fDHuH=!HFH9~HMH9HFL,HHMIEHt H I"LxxIH"DIH9t HoMMuE11Hʁ$H=w$H9x7L w$MIIELMLH5t$HHVLMIMRH "I9AVLHu1ɺLULULMHE:LMLUI IHHIHH/Mt IgHt H IMt I+H}L}H}HEIHL}}"HHH "H0H9%"IL"L{HHI"IDHELLM_"LMHEIHHI &H5P%I|$`H9HHFH_HW]@PE@8HXHHJH1@HH9H;tuI|$`fIT$hID$pID$pAL$`Ht HbHt H oHt HwID$xH8HHt HlMIL"HEL"HEHH"H"fL"fH"fL"H=$H\t$H5]t$;IM@HUH9H}HHLlHEIE8!LMIH=}$9IMYfInfInflMIAIHI Hx%1HuLUHxL])EL]LUI tLxhf.LULHE"LUHE/"LMLHHxxLL}AReIPLH`DH=iSj1Ht H 5HUdH+%(Hh[A\A]A^A_]L"HEHU"HEHUHEH"HE{H"{"L"$L}IHw|$H Pr$H9HkL%7r$M>I$ID$H5>$LHHuII$HMBI$HIHz"I9EHu11LHEHEIHIMeI $MH!HLpLL])E!foEL]Lp? M}MmMuILI;1HuLHEL}L}rfM|$I$LI#!IsL!DL!fL!fL!dfL!fL!fL!cfHu11LHEMHEMHEHaLW!RH!H5|H8[!LuH5P%I~`wI~xLLLE1 HE1DžpHhHHELmL}E1IM9JtL:3tHEJHLDžpHhHHEHhpH}d_MI$ME1Hf!IfDžpHIE1LHhE1E1HHEIM&Mt IDMt I MUI LL!>HHHhHHHE^H}HMHUHuJ7{H5$H=Đ$IHH!I9BdMJfHnfInfl)pMBMBILLXIL`Hu1ɺL`fopLLp)EHXItLpM4L\H=$LIHLHpE1/Hp111]UHpHEE1DžpHXLLLHEL`HxxHXL`H!HLmL}t'E1MDžpHEE1E1LUH!LULUL!LULUL!LULUL!LUHH1MPHuHULEH:Y^DžpILM~L}MMnILIELm}1HuLHEL}IHEIjL!\Hu1ɺLHEH]LpLpIDžpHEE1CDžpHE0DžpHEM!@UHAVAUATSH H e$dH%(HEHn$H9HHe$H<HHR!H9CHu11HHEHEIHHHMt?Ht*HEdH+%(H L[A\A]A^]H!DHHH=E1tZfH=9$Hd$H5d$)HH1LsM+LkIIEH ts1HuLHELuIIt L@L!DH=$(Hy@H!+f.H!?!f.DUHAVAUATSH H d$dH%(HEH6l$H9HHc$H<HH!H9CHu11HHEHEIHHHMt?Ht*HEdH+%(H L[A\A]A^]H!DHHH=E1XfH=)$Hc$H5c$'HH1LsM+LkIIEH ts1HuLHELuIIt L@L!DH=$&Hy@H!+f.H!o!f.DUHAWAVAUATISHHdH%(HE1H$HEH0fHnHm!HEfHnflHE)EHLE1*?HHHMDHLsHEMHKL=ɐ$H@1@HH9L;|uH}HHHEM~L!fHw!qfE1IM9tJtLt`HEJL'!fH!fL!fE1E1 IL9tPJtLHMZHMtx0HEJL!fL!Vf:!H@b!If.LCfInfInfl)UML{IHLEI^foU1HuLLu)ULEIIL!DHE!H2!I h!UHAWAVAUATISHHXdH%(HE1Hs$HEHfHnHX!HEfHnflHE)EHHHEHdHHteM1H=jWHH=wE1;HEdH+%(a HeL[A\A]A^A_]f.LsL=M$M<1HI9DL;|uHMHHEHMnMH]L5P!vfDHnHLvLuHH]KoLs)UM~/HH1MPHuHULEH*ZYH]LuHCH5ݖ$HHHIM IG;IGE1HtGHH HQHL!HAH $fD!HI`EL,!IHH=$H;="!H!Iu&H!HI@L5!@L;5!!L;5!L;5!L!HgL$H@A!IH;rH$_H]H$HHH5$HZIHHZH!I9D$9HE$Hu1LHELHEۯIMXHZfL!fHLkHEMQL{H $M@1@HI9H;LuHMHHHEMuKEL\!EZ@E1IM9JtL tHEJrEoH!H9$L!IHH=h$HfIHLdYIE I}L!f.AGAWHH AH H!H5FH8]!AfAGAWHH HH fE1IM9JtHϺHMv HMtpHEJF!If.Af.HDH=6E1fEoA@LIHHTI $AL>!fAI1E1L!HDH= 6MI$HPI$E1H,HH H!@H5$HFKHCHH;N!H߹1!HHH5Տ$LVHHH!H9GLwfHnfInfl)EMLIIVfoEL1H !Hu)EHE˫LIVMLVLMVI<$fL!fADH !H5H8!ADfD!H:JH=͗$HHAH;9C$H$C$HHH5I$HUIHtOHUH:!I9D$HP$Hu1LHELHE薪IHAH H!DADHE!H$-H>$HmHH!H9CH$Hu1HߺHEHEIHHM$HHIM]L!OHHMHHuH uHHHHhzH?L Y HLHH!HL@H5ξ1H:SH[!XZHe{H=&.i.1HUdH+%(pHe[A\A]A^A_]DLqMH@$H@H9Q=$ H<=$H+HHA!H9C/H$Hu1HߺHEHE衤IHHMHHI $txL%!I$L.DH>H}@H $1DHI9I9LuIHHEIc@H'!*fL!zfH!dfHyH=,,H uuH!uf.j!H@HHC1LPHUILEL Y^ fDE1 IM9tKtHϺHMHMtxKH=$HR;$H5S;$HH H=$H*;$H5+;$fHHH=Q$HLkMLsIEHIXMfIn1Hu6$L)EUIMIt L@LW!DL{MLsIHILfIn1Hu$L)EIItLfDL!DH=i$H~!f.@UHAWAVAUATISHHdH%(HE1HN$HEHEHEHHHHEH}HHLqHEMWLmH=$H <$H9H[H|<$H{HHCH5m$HHHrIHHHMHZH#!I9D$XHu1ɺLHELm艠I<$HWI$HHHUdH+%(He[A\A]A^A_]fHE!HuADH!HH L AH soH5cH8AT1!XZHvz-H=()1bDLqL=$M~1 @HI9txL;|uH}HHEHTIT@HNL.LmGf.Hw!fHELc!HEf.E1IM9JtLtHEJZHOHqy/H=''1]HuLҾ!H=aC$H:$H5:$6HHH=1C$THr!If.Mt$fInfInflMI\$IHI $t>1HuH)E ItIvHEL !HE)EL!foE뮐HHτ1MPHuHULEHY^z@H!T!@UHAWAVAUATISHHHdH%(HE1H$HEH8fHnHH!HEfHnflHE)EHHHEHH+HL{HEIFM H]HJ!H9!HSH5bz$HHHOIMW!HH?H$H5|$HH!PIEL5$LMH=! HLLAI!MIMH H!{@HE!HuADHi!HHL AH kH5 H8AT1!XZHa9H=h$E1HEdH+%(aHeL[A\A]A^A_]@L{L5͈$M\1HI9DL;tuHMHHEH(IHHHHR!H9CLkMLsIEIH t{fIn1Hu$L)E蘚IMILHGHMtwHHhHHH]qf.HW!w!HuHx!H5H8P!IMEH HłH=т#dE1IM9JtLjtHEJH$HuHHHHEH;=!H]H;=x!^H;=~!Q!}D@LG!fH$Hu1HߺHEHEIjfL!WfuH!u!H)HH1HPHuHUMLEYH}^ DL!lfz!HpfG^fDZ!IfIMt*E8HLLq!IHL'!ϻ!2UHAWAVAUATISHHdH%(HE1HN$HEHEHEHHHHEHUHHLqHEMwHEHPjHH5f.L!fL!rfLML!LMIf.LxLLE\!LELx@H!HE1H HL MH5mH8R1Hb!X1ZfH!\fHy1H5EbH#q1qDL!HxH}H#HyILHu11LMHEHE4 H=GE1DL}!f.Lg}!fLW}!f!HuHv!H5wH8N!fDIMu L}!ItTI $H3% H=rFE1if.IML|!IuL|!MuH|!*fMIM9JtHxHxtHpJDLG|!8fLx!LxHvH1$HpHLxLxHt/HEIfDHy2$ H=PE6Lxn!LxHfDHF$HpHLx苺HLxHEIdRs!If.IMI|fL^!bfH^!CfHf!HE1L EH H5WH:PH1R]!XZHj!H1H5H=xV!Hf V!I fH H]!fU!IfH~a!f.DUHAWAVAUATSH(dH%(HEHFHIHHH=?#H5$HGHHHHID$H5$LHHIMID$H5($LHHIMTI $"H;e!H9CLsML{IIH AfInfIn1LflHu)E|Z!fR!IfH~]!f.DUHAWAVAUATSH(dH%(HEHFHIHHH=_#H5$HGHHHHID$H5$LHHIMID$H5%$LHHIMTI $"H{a!H9CLsML{IIH AfInfIn1LflHu)E8IILIMHHHMQHH=b$Hu1HHELe`8HI$HttHI$111HYH  H?H=HHEdH+%(Heظ[A\A]A^A_]ÐLX!fHI$uLW!@H I $tY"fLW!fHW!fHW!fHW!BfLW!fLwW!DHu1ɺHHELm17I|fL7W!bfH'W!CfH^!HE1L H UH5EPH:PH@ 1U!XZHb!H1H5 HxN!HfN!I fH H~V!fZN!IfH~Y!f.DUHAWAVAUATSH(dH%(HEHFHIHHH=#H5@$HGHHHHID$H5*$LHHIMID$H5U!$LHHIMTI $"H]!H9CLsML{IIH AfInfIn1LflHu)E4IILIMHHHMQHH=^$Hu1HHELe4HI$HttHI$111H虵H H H=舽HEdH+%(Heظ[A\A]A^A_]ÐLGT!fHI$uL.T!@H I $tYfLT!fHS!fHS!fHS!BfLS!fLS!DHu1ɺHHELmq3I|fLwS!bfHgS!CfHZ!HE1L H H5LH:PH 1R!XZH?_!H1H5S HxJ!HfJ!I fH HR!fJ!IfH~?V!f.DUHH0dH%(HE1~H;=`N!uHHHUdH+%(Hq#HwH9t;HXHtdHqH~{1HH9thH;DuH;=M!tHH}H}ؾyuHH=Ժ1mDHH9tHuH;X!tfDH}1HHuHHET1H{@H#HwH9$NT!UHAWAVAUATI1SHXdH%(HE1Q!HHID$H5 $LHH*IMHE1LuH=Z$HHHEHE0IHIIGH;J!t H;I!HEIMHEIHHEE1H}HELMIfIFHI!I9VHMH9IFL,HHMIEMt I $IGH5$LHHkIMHX!I9D$MT$M}ML$III $ fInfInHu1flLϺLULM)EK/LULMII WMI EID$H5$LHH&IMI $NHCH;C _HSIL HHCI H}MHELIHMiY!HtH V!H2H9YP!LSMtcLFY@MIMH IMt I3Mt I eHH=1;HEdH+%(HXH[A\A]A^A_]fDLM!fLM!IGH;lG!KSLM!ffLMLM!LMHCH;C LMLHIJ!LME1DLMLLUgM!LULMf.LGM!]fL7M!9fHuL1ɺHELm,MIfDLMLL!LMf.LL!fE1IMMMLMLL!H LM!IfI9OlIIEfDLwF!!H IE1DLOF!f.H HME1(F!Mf.=!IMfHE1HHwfLD!IHYH@HHEHtIIHhDMH+E1MME1I!M,UHAWAVAUATSHXdH%(HE1H7IH1E!HHIEH5$LHHIMHE1LuH=N$HHHEHE$IHII@H;=!t H;#>!HEIMHEIHE1HEH}LHEMIFH5=!I9vHMH9IFL$HHMI$Ht H IGH59#LHHNIM{H)L!I9@MHMMPIIIfInfInHu1flL׺LMLU)Eb#LMLUII nMM2IIEH5$LHH IMIMHlK!I9@MhMMPIEII1ɺLLUHuHELm"IMLUHMHIHUHHQ!HUHIH H H}LLHUHELHUHI.IL!HtHI!H2H9yD!LIMHHPH@LULA!LU|f.HELA!LEI@H;:!$,fLA!7fLA!9fLELA!LEHI!I9@\fDHu1L1LEHEHE$!LEHH@A7IMt I#HcDH= HE1HHH8Mt I $HEdH+%(xHeL[A\A]A^A_]@LULLM@!LMLUfHUL@!HUf.Hg@!H HU@!LULC@!LUIf.H'@!fHuL1ɺLEHELeLEIL?!fHULLU?!HULUfL?!fH?!f7!IfH}H9MdHHI$HEQMIA75LELA7C?!LEf.7!IfLEL?!LEf.HyF!HE1L H 5H5%8H8R1H=!XZE1IA7H H>!f.HyZ1H5`HtAf.H5H=eX{*6!I>fH6H=5E1%E1A6fLELMILpL@7!Lp@L'7!MfLpLLU 7!LULp@H6!f.L6!fHwHyl跈IHxL11LMHEHEaLMIDZ.!IMfDLmL1E1ɻlH 7Mt I $HMt I Ht HH\H=<H}t1H]H;HWH}HHtHEMt IMtIMtBH]HtHHEHHt:HEdH+%(oHEHe[A\A]A^A_]@Lo5!DH_5!DHEHK5!HEkfL75!mfHEL#5!HEf.H5!fHEHLM4!HELMfHELLM4!LMHEfH9L>L}"fuLT*!u@E1IM9JtHϺHMfHMtHEJHUL)!HUDHH%1MPHuHULEHpY^wLe(!IHtEH@HHEHtNIpHHUiHUHӾHH=Xg1۾,!UHAWAVAUIATISHHdH%(HE1HK$HEHEHEHHHHEHH(HLyHEMZL}IGL-5"!L9t H;!!mIE1HE1EM9l$IT$ ID$HHH9H9IT$HHHID$HH}IGM9o#L9IGJIHHtH uH'!M9l$yfDH5#LHHHHu'!{HE\2!HuCH/!HHL =AH H5 H8AU1N&!XZH4H=\1HUdH+%(He[A\A]A^A_]LyH ]$MZ1HI9<H;LuHMHHEH&IHL|#!}>IHH=0諏1HFH =HEHz&!HE'HUHELHUHHGHU0!HUHt!H -!H1H98HU{(!HUIH"!HHk@L9~K\IHH>L>L}"fuL%!u@E1IM9JtHϺHMbHMtHEJHULc%!HUDHH1MPHuHULEHelY^wL#!IHtEH@HHEHtNIpHHU HUHӾ=H=H=Mȍg1۾=Q(!UHAWAVAUATSH8dH%(HEHFHEIHHv!HH;H#H5#LHHCID$HHbIMIGH5#LHHvIM8IIFH;l%!^IMIH AW A@u#AtADEIGL{ LH5#HEH#HHC(ID$HHIMIGH5#LHHIMIIFH;$!,IMIHAT$ @u tEH#IT$Lc0H߾HD9HC8HEABHTPIHt\H %H=,$Hu1HHELe-HI$HHI$111H"H u H"!A"fDHXDH=KHEdH+%(Heظ[A\A]A^A_]L!!f.LA!!AW @fDr!I[f.Lg!!UfLW!!hfHG!!fL7!!fH(!HE1L H eH5UH:PHP1!XZH-!Hx1H5#Hͫ_!IfA"H mIL !f.j!IfH;q+!LPXIMtUIHPf*!IfH tA#A#kDH !DA"H u H!IL!DH;*!ulLPXIMt1IHfDHI$L!yDA#H;+!H5t#L ,!IH;+!tH5S#L+!I|"!H+!fDUHAWAVAUATSHxdH%(HE1HIHID$H5#HHLHHH'!H9CmLkM`LsIEIH 1HuLHELmMIMHxHIID$H5#LHHIMIGH5#LHHCHEIHH}IH`1!IH<ID$H5#LHHmIM?H=X'$1HuHHEL}WHHIHAH;_!t H;! HMHHDžpHEHHrLhE1Hp:HUH=!HMHBH9zH9 HBHHHMHHEMt I $/ !HH}HEH@H;!t H;%!_HEHEE1HIE1H}HMLID$H5!I9t$~L9ID$N4IIHt H L=)!L9HCLHHPpHHhHHBHHHRL9HGHuHPpHHhH HBHHxHxIMHH}LL>!I H}LHxHELHxHIHMIHM%!HMHt!H{"!H2H9HM]!HMLHM赥MHMt L褥HMHhHHMHMHyLeH!fL!DfL!fLw!fj!DLW!fHG!fHHu11HEIHEHDH'HykHL!zf.HEL!HMXf.H!fHHyHxkHxIH !HE1L H H5H8R1H$"!XZE1HEdH+%(:HeL[A\A]A^A_]Hy 1H5Hf.L!HEE1fDuHH=O袁Ht-H t@MOIFLm!8M'I@H?!DHMME1H!LhH5Q<HMH:,!HM1IEHEH0HuHH0L}HEH8HH}HH0Mt IHtH tIMtI $tVHtH t$MIMLx!Hg!DHUHS!HUDHUL;!HUDHUHHM!HUHMQfHULHM!HUHM?fHUHHM!HUHMfL9gOtIIEE1E11E11MHMHE1LhEMvaE@:!IfHULHM/!HMHU%fH5!H}ME1LhH>H56:!HUHME1E1L}E1L}1E1 !HEMHMMHLhEL}MHMM1LhIDLEL}1E1E1D* !IfHMH}!HMHIH@HHEHItL}f.E@H}HpHEHLhY!HtH!H2H9!H}BMtL5IH u Hd!H]HHEHHIL;! fDLhE1EE11DH9H}HHHLHEHHMsHHxsHxIDHMH[!HMHHE*H@HHpHHHEH6@HMISLhLhELhH}t E11E1$L}E11E1.HHMHMLhE11d!H9Le1EH}!_f.fUHAWAVAUATISH8H5#dH%(HE1HGHHHH2HCH5#HHHfIHHMHHIEH;!eIELIEHH=#H) !IHH  I !IHEHN#HIFAE IE@u tEH#M}Mn LH5#HIF(ID$HHIMH=$1HuLEHLEHELEHHVIHBH;z!HHHHA @u tEH#9H5#LBHQIN0HIF8ID$M|HHfIMpI@H;!oILIH-A @u tEH#9H5#LBLyIN@HIIFHID$HH:HHHBH;A!3HHHHF @u tEH#9LBL~IvPHH5{#I IFXID$HHIMI@H;!IMIH%AD$ @u tEHo#M|$Mf`Ѿ LH9IWIFhCHIDIMHUdH+%(H8[A\A]A^A_]@LG!QfH7!ffH u H"!HH=w1fDH !fHuH !HuIf.L !fHEL !HE*f.IIu L !@HH={v1@HELS !HEf.HEL3 !HU=f.HMH !HM@f.HML !HMf.!Huf!IfL !I.fH;!LPXHIEHHwDHH=hu1:!IffDH;1!HUHPXHUHHYHH(fD!If.DfDH;!LELPXLEHIHHefDfDr!Hf.H;q!uHUHPXHUHHHHfDfD!If.H;!BLELPXLEIIMHfDHLIE$fDIH H ! H;!H5k#L!HH;!HUHH55#!HUH H;T!KLELH5#!LEH !HUL !HUf.f.UHAWAVAUATSHHHLgdH%(HE1GpID$0M|$ ID$ Mt$(HEHH}EL-n!IGIWL9L9$IGJIHHEI|$ID$Ht HI|$H5#HGHHvIMHH!I9EgMMMrIUIHIMIHufIn1LLMAD$)ELMMt I {HIMHMH{ M|$ Mt$(IL$0HtHC HuHE!HECp2L%!II<$!CpH1HUdH+%(8HH[A\A]A^A_]HI|$H5K#HGHHwIMyH=$Hu1HHELmIHeIMt/IGH; L-]!t L9M7Mt*E1fL!D!DL!fHELs!HEof.HELS!HEbf.HULLM/!HULMf[JAMIu L!HNDH=n]L!IHlH@HHEHnI=MIHELH8!L% !HI4$H9G!IMtIu LH!IMu L9!A0fDL9KDIHHEY IfHuE1Huf.HA IfHAL!fHMAaLd!HIuDLG!HIMu L1!Iu!H OHAM`f.UHAWAVAUATSHHHLgdH%(HE1GpID$0M|$ ID$ Mt$(HEHH}EL- IGIWL9L9$IGJIHHEI|$ID$Ht HI|$H5b#HGHHvIMHHQ !I9EgMMMrIUIHIMIHufIn1LLMAD$)ELMMt I {HIMHMH{ M|$ Mt$(IL$0HtHC HuHEU!HECp2L% !II<$ !CpH'1HUdH+%(8HH[A\A]A^A_]HI|$H5#HGHHwIMyH=r $Hu1HHELmqIHeIMt/IGH;| L- t L9M7Mt*E1fLG!D:!DL'!fHEL!HEof.HEL!HEbf.HULLM!HULMfDA(MIu L!HDH=i]L/ IHlH@HHEHnI=MIHELH !L%!HI4$H9G]!;DMtIu L IMu L A(0fDL9KDIHHEY IfHuE1Huf.CA: If{CA'L' f[CMA'aL :CIuDL CIMu L Iut!HBA'M`f.UHAWAVAUATSHHxH}dH%(HE1HR#HEHPfHnfHnfl)EA )EHIL4HHHt\Iع11H=XTHD%H=if1HUdH+%(He[A\A]A^A_]H LaHpMH;~!18 HH H}H5y#HGHH% IM HE1LeH=$HHHEHhIH I $IGH; t H;  HEIMHEIHsHxE1MH}H 4 I9L$ID$HMH9 ID$HHHMHHEMt IMAH:x#Hb#H9XL=b#MIIGH5#LHHpIMIH}H5#HGHHzIMH!I9GSMwMFMOIIIcfIn1LϺEHuL`)E\IL`H)MHEI IHHX IHHpH5Y#H  LLLڈHH IM4IIHxHBH;B HJHHHHBH LmfHnHHgHt HpLW QfoHI)UH~.HH1IPHULELLKAZYHEHpHEhDL=1#1DHI9M9|uIHHEIMHEHpH HFHEHHpH]DHLaHELg fLW SfLG fL`L0 L`@L fE0HxLE1H}E1Ht HGH ~1Mt I $Mt IHtH tuMtIt{Mt IM|uHH=cdaHH Hk fD1HMHHEM H]HMdH7 }fL' wfL vfHML HM;f.HML HM f.L fL fL fHM HMDH f.HEHh1LHEHE*HfL`L0 L`@H]!HDH#LL5HmHEIL$HELHEHRLmHxa!LUHt!H H2H9LU LULLU;LUMtL*HCr#H{H@H;\#:L5\#MIH5=#LŀIH`Lр IHaHHX IHH#H5#H kLLL蔃HTLHE_LWLOHEHH HHEHi HEH=#H[#H5[#-IME0H}E11HxE1&H t@ IHH¦#H5C#H}HIFsIHH5x#HXIHLdIEH;1 &IELLDC @u tEH#HSI^ LHHIF(}IHNL~H=B#L:HH<L~H111TH1~E'6fH9_HILHHHMHE<@ If.H= #D+I9@E/@H}N IyfDE0E1E11E1RfH} IfDLMIH}E0HxE1DE0E1E11@E1H]L@HI9ItL:/tIH]}KLIE0HxMH}E1xH;n LPXHHE)ME1IQE11`DLE0H}E1HxHxH EIE/H}1HxE1E1fDLw IHH@HHEHIHEH* H2x@HxMH;W#L=W#MrIH5ʰ#L{IHCL{H5_#H}{IHmw IH{LxHx#HIF N IHLp IH<HpH5֯#HM lLLLW~HULHE"{L{L{HEfLE0H}E1Hx.E(1yE)1E1E)1ItdME11E1H1LE1E1uE(1E1E'ME1E11E1HEH HEHML1E1E1E0E1E11E5E11E3PH=Ļ#HU#H5U#'IME5}E5H=#HWU#H5XU#s'IME3?E5E11H=C#~&IE3E1jH=##^&IE3E5HLUeLUE0E1E11E1LZH; H5Cj#L H @UHAWAVAUATISHHdH%(HE1H#HEHEHEHHHHEH5HHLqHEMH}H;= fH H9G HHZL%l#L5]i#ID$LMH= LHLAI MeH uaH VDHE Hu#DMH=e2qHcH=XUE1HEdH+%(HeL[A\A]A^A_]fLqL=#M~1 @HI9txL;|uHMHHEHTI@HNH>H}fH H5XH89 HH=6XUB@E1IM9JtLb(tHEJZb HuH H5GH8 fDH nH~ `fH}Hu1HHHE,HH"LHLY IHHH1MPHuHULEH#2ZYB f.@UHAWAVAUIATISHHdH%(HE1H##HEHEHEH&HHHEHH`HLyHEMH]H5i#H{H9t3HXHHJ1HHH9H;tuID$H5#LHHIMH) I9D$ Ml$MMt$IEII $fHnfIn1LflHu)EeIMH)MI$HHI $H7H=USR1HUdH+%(+He[A\A]A^A_]HE Hu@@H HHL CAH 3H5#H8AU1 XZHH=UQlLyH #M~1@HI9tH;LuH}HHEHQIHNHH]f.H5f#H9t'H 1HH9H9tuID$H5)#LHHIMyH) I9D$Ml$MMt$IEII $fHnfIn1LflHu)EeIMHtHH H9CL{MLsIIH DfInfIn1LflHu)EIILHHM!HHL;%F L;% umL;% tdL+ ÅqI $tTH HHUdH+%(H8[A\A]A^A_]Lw fI $uL^ @HO f.HtKA/DHDH=%*:1jH fH fL DA/H uH f.Hu1ɺHHELm聰IfL ofH=#Hj0#H5k0#HHfDA/I $L7 fH=A#H I9f. H`fH=`#H5*#}ZHHH H9GLwfInfInfl)EMH_IHRZfoEH1Hu)EVLI+ZA1MHZHu1LeH=C#HEH HHtcLA0Y111H0HYHu1ɺLmHEH}踮H]IaA1A0l` UHAWAVAUATISHH#LvdH%(HE1HЙ#HDž@HfHnHpHEHEfHnHM flHHHP)EH&IIsMIHFHH@ IMH L@HHpLuLHDžhH(H`ƅpLuHEEHHHHL;%t  I|$hIHH.I#M|$hH 2-#H9H8 L--#M IEHm HuHDž I9E ~ fIn1Lfl)EH IHtHHHHIEHM+ IEHHELHH gHEHULmHL90L9o]HMHU]H#HEHMHEH}L9tHEHp  HHIHH IHH`H(H9HHUZL9oeHpH`hHJHEHMHEHH5#H\VH =HoIH H`HhID$H HHX0Lm[jH LLH}HL9tHEHp H9 H P1H}L9tHEHpw H`H(H9HpHpO f.IHF(onHHP)@ HmHPL@H;` HHHHH5F#H9qd\H1H"?YIIIM1H=;'NHXH=Ք2HEdH+%(* He؉[A\A]A^A_]fDz DH H5Ș#LIHVI H@HT HLbfH5#HAHHEHH&ExHH5k#H^H H`HhID$H HHX8LmgL 1LLHL H\HSjf.H5#LHV HkHHIMpHH4@H  HL~L@DHF(H; HHPH^ HHH f.H f 0DLw fovH)@ I*LG JfL9o}HU}LmLmLf. H:HHd1MPHUL@1L ZYcr HfH5#LHV, HHPIG L9o}H`hLuLuL@HUHHt>HЃ1tUHMHuHHHUHEDH5#HlAHHEHH.EHH5y#H.HHPH`HhDž8ID$H ƅ<HHX8Lm}dL H8LLHL HAH H,HUHHtAHzЃtU H`HuHHHhHEAfH{DH=z-rHq H՞H5{H81( AH' fAH uH f.H=Ƀ#H##H5##fIHAOH f.H5w#H,ADžHINEH5#H=X# NHHH H9GHGHHLoHIEMH LfHn1LfInfl)EݢHMtLMAHthLMH=#HIH/HwM111L#LaMAfDH fAIMLg fH=)#iIEH H5IMHLHHLLHufDLAfH +fADz HlA5DEHUHEDITHTFl1ƃI|5H<19rNfEHUH`f.ITHTFt1ƃI<6H<19rWAAUATTHMHuUATTH`HuIHuATfTH`HuAkATfTHMHua HHHҀHʀUHAUIATISHHtHHH!#LLHHCH HChHIxHH[A\A]]H t 1H1ſ UHAWAVAUATISHHL-#dH%(HE1H#HEHfHnHEfHnLmfl)EHJHHEII*MteM1H=izCHu H=E(E1HEdH+%(pHeL[A\A]A^A_]f.LyH -#M,1HI9H;LuHUHHEHMwMLe$DItI+LnLmL&LeL HH%HH=U?#Luf1HL)EHH H@H5΋#HHH HHH H9G4LM'LwIIHfInfIn1LflHuLm)E[IILHHPHMH I $HIHH5H+ 'fD DoLy)]M~/HH>1MPHuHULEH ZYLeLmrL CfHLqHEM!LkH #M1@HI9H;LuHUHHHEM~KL? f.* D qDE1IM9dJtHϺHMvHMtHHEJ6HHPr*H=E1$HHYf1ɺLLeH}HELmMH}IE1IM9JtHϺHMHMtxlHEJHq'H=C$Hq)H=##² Hf.j H@ DHE: HLb& fDUHAWAVAUIATSHHdH%(HE1HV#HEH8fHnH3 HEfHnflHE)EHHHHEHH Ht]M1H=0*>HrjH= 1"HEdH+%(HeH[A\A]A^A_]LyL5ŗ#M1 HI9L;tuH}HHEHbMwMGLmH56#IEMEHxI}L9H HHH^H]L.LmKoLy)UM~/HH%1MPHuHULEHZYLmH]IEH55#MEHxI}L9t7IXHHJ1H@HH9<H9tuH;F IH5:#LH"IMpIGH5Mx#LHHIIIHIM)HH I9FMFMMNIIIfInfHn1LflHuLELM)ETLELMIIMMI L;= L;=* L;=0 Ly AąIEEIEH5p#LHHIMH I9FMfMM~I$II7fHnfIn1LflHu)EYI $HMHWIIMH;IHGLIEI}HL& qH! (@H54#L9H~31HH9t H9tuL94#H1#H=##H9x-L=##MWI IH3IELhX IH4H5u#HH ILLL#CHH/III $IEHxfDHLqHEMHKL=#H1@HH9L;|uH}HHHEM~E1IM9JtL tHEJDILG pfL7 fE1IL9CJtLHMHMtHEJ.LMLLE׳ LELM)f.L Hu L @}H=~Hk1IEHx;LDHH9HuH H9H1#I9LHH9HuH9JDII $ILL޲ > HDHu1ɺLHEH]聒IL &LMLp LML^ LP 0LB L4 I}L #}IoL a IHE HfHu1ɺLH]HE葑H@ IL !}H=tf#H#H5#IM6H=:f#II:L& ,~| I0~L  f.UHAWAVAUATISHHdH%(HE1H&~#HEHEHEHYHHHEHHHLqHEMH}H5 H9wt H;=W i  HH5 HHUdH+%(He[A\A]A^A_]HE HuADHa HH?{L AH _H5H8AT1 XZH+{H=1cDLqL=|#M~1 @HI9tHL;|uHMHHEHTI@HNH>H}f.E1IM9 JtLtHEJfDHpzH}ۓH}v1H/zH=1gHHy1MPHuHULEHY^ fDUHH?HuzH5C#H=L#11+HeH=#1H HE1L bH =]H5-H8R1Hi X1Z@Hy{1H5ciH8bfUHH?HuzH5#H=#11{HeH=@s1HѴ HE1L aH \H5}H8R1Hh X1Z@Hy{1H5hH7bfUHH?HuzH5#H=#11 HTdH=1H! HE1L MaH [H5ͥH8R1H'hZ X1Z@Hy{1H5hHL7bfUHH?HuzH5x#H=<#11 HcH=@1Hq HE1L `H -[H5H8R1Hwg X1Z@Hy{1H5SgH6bfUHH?HuzH5{#H=#11k HbH=c1H HE1L _H }ZH5mH8R1Hf X1Z@Hy{1H5fH5bfUHH?HuzH5#w#H=ܴ#11 HDbH=@1H HE1L =_H YH5H8R1HfJ X1Z@Hy{1H5eH<5bfUHH?HuzH53#H=,#11 HaH=1Ha HE1L ^H YH5 H8R1Hge X1Z@Hy{1H5CeH4bfUHH?HuzH5u#H=|#11[ H`H=HS1H HE1L ]H mXH5]H8R1Hd X1Z@Hy{1H5dH3bfUHH?HuzH5u#H=̲#11 H4`H=1H HE1L -]H WH5H8R1Hd: X1Z@Hy{1H5cH,3bfUHH?HuzH5ct#H=#11H_H=P1HQ HE1L }\H WH5H8R1HWc X1Z@Hy{1H53cH|2bfUHH?HuzH5s#H=l#11KH^H=C1H HE1L [H ]VH5MH8R1Hbڥ X1Z@Hy{1H5bH1bfUHH?HuzH5s#H=#11H$^H=P1H HE1L [H UH5H8R1Ha* X1Z@Hy{1H5aH1bfUHH?HuzH5Sr#H= #11Ht]H=1HA HE1L mZH TH5H8R1HGaz X1Z@Hy{1H5#aHl0bfUHH?HuzH5q#H=\#11;H\H=`31H HE1L YH MTH5=H8R1H`ʣ X1Z@Hy{1H5s`H/bfUHH?HuzH5z#H=#11H\H= 1H HE1L YH SH5H8R1H_ X1Z@Hy{1H5_H /bfUHH?HuzH5y#H=#11Hd[H=h 1H1 HE1L ]XH RH5ݜH8R1H7_j X1Z@Hy{1H5_H\.bfUHH?HuzH53y#H=L#11+HZH=# 1H HE1L WH =RH5-H8R1H^ X1Z@Hy{1H5c^H-bfUHH?HuzH5x#H=#11{HZH=hs 1Hѩ HE1L VH QH5}H8R1H] X1Z@Hy{1H5]H,bfUHH?HuzH5w#H=#11HTYH= 1H! HE1L MVH PH5͚H8R1H']Z X1Z@Hy{1H5]HL,bfUHH?HuzH5m#H=<#11HXH=h 1Hq HE1L UH -PH5H8R1Hw\ X1Z@Hy{1H5S\H+bfUHH?HuzH5l#H=#11kHWH=c 1H HE1L TH }OH5mH8R1H[ X1Z@Hy{1H5[H*bfUHH?HuzH5#l#H=ܩ#11HDWH=h1H HE1L =TH NH5H8R1H[J X1Z@Hy{1H5ZH<*bfUHH?HuzH5sk#H=,#11 HVH=1Ha HE1L SH NH5 H8R1HgZ X1Z@Hy{1H5CZH)bfUHH?HuzH5j#H=|#11[HUH=pS1H HE1L RH mMH5]H8R1HY X1Z@Hy{1H5YH(bfUHH?HuzH5j#H=̧#11H4UH=1H HE1L -RH LH5H8R1HY: X1Z@Hy{1H5XH,(bfUHH?HuzH5ci#H=#11HTH=x1HQ HE1L }QH LH5H8R1HWX X1Z@Hy{1H53XH|'bfUHH?HuzH5h#H=l#11KHSH=C1H HE1L PH ]KH5MH8R1HWښ X1Z@Hy{1H5WH&bfUHH?HuzH5h#H=#11H$SH= 1H HE1L PH JH5H8R1HV* X1Z@Hy{1H5VH&bfUHH?HuzH5p#H= #11HtRH= 1HA HE1L mOH IH5H8R1HGVz X1Z@Hy{1H5#VHl%bfUHH?HuzH5f#H=\#11;HQH= 31H HE1L NH MIH5=H8R1HUʘ X1Z@Hy{1H5sUH$bfUHH?HuzH5o#H=#11HQH= 1H HE1L NH HH5H8R1HT X1Z@Hy{1H5TH $bfUHH?HuzH5n#H=#11HdPH= 1H1 HE1L ]MH GH5ݑH8R1H7Tj X1Z@Hy{1H5TH\#bfUHH?HuzH5d#H=L#11+HOH= #1H HE1L LH =GH5-H8R1HS X1Z@Hy{1H5cSH"bfUHH?HuzH5c#H=#11{HOH= s1Hў HE1L KH FH5}H8R1HR X1Z@Hy{1H5RH!bfUHH?HuzH5l#H=#11HTNH= 1H! HE1L MKH EH5͏H8R1H'RZ X1Z@Hy{1H5RHL!bfUHH?HuzH5b#H=<#11HMH= 1Hq HE1L JH -EH5H8R1HwQ X1Z@Hy{1H5SQH bfUHH?HuzH5a#H=#11kHLH= c1H HE1L IH }DH5mH8R1HP X1Z@Hy{1H5PHbfUHH?HuzH5#a#H=ܞ#11HDLH=1H HE1L =IH CH5H8R1HPJ X1Z@Hy{1H5OH<bfUHAWAVAUIATISH8H5Zr#dH%(HE1HGHHHHL5 L9HҞ I9E0M}MH HHmL蕟HHHHIH?L9ID$H5jo#LHHIMIFH5[#LHHIIHIMHkIGH5^#LHHHIHIHH=IEHHpHphHHAHHUHLHUIHHHMVH ID$H5qn#LHH>IMID$H5 Z#LHH!II$HMI$HIGH5]#LHHIMILLfInfHnIfl)EHt]I $ H]foUPH IMtWHUdH+%(H8[A\A]A^A_]DI $tBItKH;I?H=:5H tp1IMuHEL HEDL IuL Lא fLǐ fL fH 1 IPfL fHULs HUf.HW f: HH]fDH6H;H=501fH=#Hu1HLmHEoIH71H11L=HGH=H uH 1*fL IHt)H6 IMIfI IHE1>rfD>efDI{Mc}HGH~<HLHUHUIYHY H5 H8 ?HFH=H pM1)HEH HEf.Z ILfH?: HjfHL' fHuH  If Ifڅ IfL׍ rfLǍ kfHLHUHUI=IuE}AMc&It1ItL1 I  E}AEII E}AEII IH/ Iu L If.@UHAWAVAUATSH8dH%(HE1H;= HW H#IHRH@HzXH9"L%"MI$L5ڔ M9t$Hu11LHEHE?lHI$HHI$H?HCH5d#HHHIHHMHH!~ H-IEHELh HMHH[H$?#H5N#Ht HM8HHLzHMHII $`H GH .H I9G}ILIHH=\#H5M#HGHHIMIEH5pE#LHHIMoIEH5V#LHHIM3IM M9t$Mt$MMD$III $fInfIn1LflHuLEH])E>jILEIMI$MI $:H  HEdH+%(H8L[A\A]A^A_]@H9"H"HHL5! L9sHu11HHEHEiIHHMHHIGH5b#LHHHIHHIH{L9sL{MLCIIH fInfIn1LflHuLE)EhILEILHHM6HHH[ I9D$I$LI$HYL KfDLEH LEYf.H u Hb I $HH H= H;@!H=b5E1DH f.L fH YfHׇ fLLJ wfL pfL fL H?!l%I $uLe uH`?H=_E1HQ H5bH(?H81 H?H=EH (fL fLELӆ LE?f.I $(L A#H u H H>DH=LHu1ɺHHELm1fIdfH7 fH' fL HMfDHu1ɺLHEL}H]eIDLEL˅ LEfLEL LE\f.L GfH=U#H"H5"IMDH=T#HJ"H5K"ƽHHb#H.=$H=U(| IfH=qT#贼Iy@H=YT#蜼HLcML{I$HIL}l1LHuHELemdI $It H]Lo DM|$MI\$ILHH]1HHuHEL}dIHt LeL D{ IfA!KD{ Hlf#I"H;H={ IvfI$A%HI$tCIMtQHu;DH=E1nH++HI$E1A#L1 MtIMuL { I>f.L_| HI$HtH2L?| HHtjIHm@H:!H=~L H:H=XH{:H=z! HS:#H=zM+f.f.UfHHAWAVAUATISHHhH=Cz dH%(HU1H=[#)EHPfHnH}H8fHnH@ fHnHEflHE)EfHnfl)EHJ HxIH qJcHfDHW#HxHLs6HEHIFLsHpL={Z#M1fDHL9tL;|uHxHHEHuHpHSHHpIILnLmHXLxL H]L}LeHCHt0" IHHIVHHHLID$H5Y#LLHHSЅqID$H5H#HLHH7ЅID$H5]#LLHHCЅH{ HH u_HxH HxFC Hu!fDM1H=5H5H=e1HUdH+%(He[A\A]A^A_]ÐHCoHpH)]L=G#HbE1DIFH9IN;|uHxJHEH)LpIMLeL}H]L-w !fDHHEHCHpIlfDoFo&Ls)e)EM[LeL}H]LmDHVo.LsHU)mMmH[#HxHHHEIL-Qv X@E1IEM9+IJtLVtHxJ@H3H=1fE1IM9JtLtHxJ^fH'} fs HHHJ31MPHxHULEHZYscZx DJx DHER H,A fDx DfDHE HA UHAWAVAUIATSHhHudH%(HE1HEHEHEӇ LxxHHw DM'I9t MoMMuHEE1IEH5<#LHHILmMH܃ I9EbMuLuMQMUIIIMLUfIn1L׺EHuLU)E[ILUHEI1MHEMYIMHCxHEHEH8L Ht HKMt IHMHtHHEHHHEdH+%(HhL[A\A]A^A_]@LxLxz Lx@M|$I$LIkx HEf.LEL3z LE'f.fHu1ɺEL)EYHEIDLxLLUy LxLU@LEHy LEfLELy LEf.LEy LEHEH{`H5#HEHEGL5T/L-D LL?HMHUHHuLԂ#H}IHDH}H$H}HH{xHMLLLE0LEp IH}HEH*H!x HEH{xLLHMHpHEHEHEHxZ0HpHtHH}t H}Hx t HxLLE1UH{xHMLL L5-L-C/LEw LELEw LELEw LEO{ f.DttH~ H3UHAVAUATS IHtxHQ~ I9tMHo L5. I9AM9DuHEIDLxLLU ^ LxLU@LEH] LEfLEL] LEf.LE] LEHEH{`H5g#HEHE%~GL5L-u)XLLoHMHUHHu+Lg#H}IHDH}H$H}HH{xHMLLLELET IH}HEH*H!\ HEH{xLLHMHpHEHEHEHxHpHtH6H}t H}&HxYt Hx LLNE1UH{xHMLLXL5sL-(LE\ LELE[ LELE[ LE_ f.DttHb H3UHAVAUATS IHtxHb I9tMHS L5^d I9AM9Du&HA1rDUHAWAVAUIATSH8L5M"H562#dH%(HE1HGIHHIMgLP fInfHnHI$flH3HI$VLb#MFb#)E==VT IHwL%8b#ILpIELh HHX(ID$LMEH=vSb 1LLAIDY MIu)LX HuLAIMH It'HEdH+%(H8L[A\A]A^A_]fLWX DHI${HJH=E18fD)ELX foEfHW ffHu1ɺLAI9zb H'Iu LW H dHW Vf S IHtIELhHHX Hw`#`#u~LLIILAW @"O Iwf.H=0`#H= `#CNH=`# `#D1LLI{fDLV wf.L1LN IGHc H54QH8Z IZ f.D[f.Kf.UHAWAVAUATISHHH}dH%(HE1H#HEHEHEH5HHHEHHt{HT] HHdL xAH H5NH8AT1T XZH H=0 藾1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH} tHuH< H=HE)HELyL-#M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLtxPHEJfDHH1MPHuHULEH{Y^@HE^ HF}W fDUHAWAVAUATISHHH}dH%(HE1H#HEHEHEH5HHHEHHt{H$[ HH4L HAH H5LH8AT1YR XZHv VH=g1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}JxHuH VH=HEHELyL-#M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLtxPHEJfDHH1MPHuHULEHKY^@HE\ HF}U fDUHAWAVAUATISHHH}dH%(HE1H#HEHEHEH5HHHEHHt{HX HHL AH H5JH8AT1)P XZHFH=71HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}yHuHH=HEɹHELyL-}#M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtL趌txPHEJfDHH1MPHuHULEHY^@HEzZ HF}fS fDUHAWAVAUIATISHHdH%(HE1HL5}K HM}H@HG#HEHELuMVMIVIELHEJ[ HH}L9H5"H9wteIH)L9@MQ Hffo BHP0HHHIR @@xHH{L`H@@L`(HǀLHǀ@LHǀ@H@8HH@X@hHCHtHPHEdH+%(HeH[A\A]A^A_]MIMHH HHLHxH?L iMLHH%U HL@H5F1H:AWH jL XZHH==xH 1<LoY IH'L=I}L9H}DH5#LHVR HHEIEfD1HEH}1H}]DjW H/f.HH H5\HH81hK HH==xH1UL 4V HHH11PHUMLELNY^O fUHAWAVAUATSHHdH%(HE1HHH HG H9+H[ {(LsH"I~XHrH(HIH"H9AL-"MIEH{0ST HHF IHHXO HHA~HT IHHEH51(#HHE LEI0H5Q'#LHE HLLHHErLbLZHRHEIHELxJ HEH;5"+L-"MSIEHp"H9BVLW"MIIvXI~PLEYLEHIHKR I9@9LHu1ɺLEHEL})LEIInMI|HQ I9EcHu1ɺLHELe\)II $OE1MsIMH 8"LII H ""DE1IM H Mt I $MtwIHH=Mul1HUdH+%(;He[A\A]A^A_]fHH=#H¸"H5ø"IM-HH=w花1LgH bfLELSH LE7f.LEH3H LEf.LELH LEf.HyO HE1L H 5H5%AH8R1H-F XZHy1H5 HHC H5WHxH81XF HiH=UhmLELCG LE|f.L'G vfLG fH=1 #~IMIM.E1MHH=ͯLF fIMLIF E1HafH=#$~I@IMI $L;F fDH=#HZ"H5["~IMjH"fDH=#}IH= #H"H5"F~IfDMXfInfInfl)EMM`ILL]I$Le>foE1LHu)EB%L]II tLEDL?E DMEfInfInfl)MM~M}ILLEIL}foM1LHu)M$LEIItLmUfDLD DLELD LEf+@H LUHAWAVAUATISHHdH%(HE1H&#HEHEHEHHHHEH%HHLqHEMLeH5|"I9t$H? 2I9H? HH5SH81B HH=蹬f1HUdH+%(He[A\A]A^A_]@HEN HuADHJ HHL AH H5{<H8AT1B XZHH=BgLqL=#M~1 @HI9tpL;|uHMHHEHTI@I9I|$  F HHfH&L&Lewf.E1IM9JtL~tHEJb1HmL/'hrfHHB1MPHuHULEHY^E fDUHAWIAVIAUIATISHXdH%(HE1HEvE HHHs#HEHEMFKHEMIIIOHEH/L}H="H5#HGHHiIMA IHTH@IEL(H2I I9D$MD$MML$III $4fInfIn1LflHuLELM)Eg LELMIIMII$HI$MHIEH5G#LHHIIEHIEMH; IHIHLxJ IHGIFLMH=D:fI LLLAI@ MI7I $@IM.Lj? DMwH=E #M<I Hu=HF HHL ݙAH kH5[8H8AT1= XZH KHKy H=E1HEdH+%(*HeL[A\A]A^A_]bI HuHK H5G9H8B fDI $FIMtIMuLi> fHz H=mE1UH \H3> NfDIM>L}f1@HI9I;|uHMHHEHINLML= LMGf.H= fL= +fL= 8fLMLLEo= LELMfLW= HfLG= I $L4= Hu1ɺLHELuIE1IM9<KtLEH}eyH}LEtwHEJfD4 IfI $6L< (HLl< Hf:4 IfL7< fIL< fHHuHHUHMLELPY^LLL3 IH-(HEWM? f.UHAWIAVAUIATSHHHdH%(HE1HE9? H>IH#HEHEHQKHEMIIL{HEMH}HGH5 #HHHHHC H9C4LsM'L{IIH D1HuLHELueIILHHHMHIEH5`#LHHMHHL&E IHZHCH5"LM_HuH=|4C HuLHAIH: MwH IIMI $L9 L{H =#M&D Hu=H@ HHL AH H52H8AU18 XZI $H YH=_"E1HEdH+%(HeL[A\A]A^A_]f.IfI?H}f.1@HI9\H;LuHUHHEHI}Lw8 HfHg8 fHW8 fB HuHvE H52H8N< fDH I6HjH=KE1 IMFL7 8L7 fH7 fHHiH=E1謠Hu11HHEHEPILW7 fE1IM9 JtHϺHMsHMtHEJn. Hf.. HfL6 H6 @H H6 fLH. IH[HHuLHUHMLEHP}Y^NH?6 f.HE9 f.UHAWAVAUIATISHHdH%(HE1H=L51 HM}H@H#HEHELuMvM IvIELHEA HH}L9H5"H9wt2IHIL9`7 Hvffo HP0HH@ @HHfo H@xH{L`H@@L`(HǀLHǀ@LHǀǀHǀHǀH@8@X@hHCHtHPHEdH+%(HeH[A\A]A^A_]fDMIMHSH UHHLHH?L MLHHe; HL@H5-1H:AWH72 XZH_H=%踜H 1=L? IH'LI}L9H}DH59"LHV8 HHEIEfD1H,H}&H}]D= H`/f.H. H5CHH811 aH H=%踛H12 5:= HHH11PHUMLELyY^5 fUHAWIAVAUATSHHhL-*. dH%(HE1H"HEHELmHcIL4HH\HHIHEHTLmH"Hs"H9PHZ"HEHH1B2 IHIGH5[ #LHH@IM,5 HHH5"LH+ IGL5"LMJH=+: HLLAHEW1 LEMbI9H I@H;E* t H;) HEILE1IHYE1HEH}Hp+HCH ) H9K+I9FHCN4IIMt IIFH5 #LHHXIMJHS8 I9@MPMMXIIIYHp1LLULxL]HELxL]II MMyIID$I;D$ xIL$ILIMmHDH=l~wp6fIM#H"H@E1IM9JtLCtxHEJ6H f.L fHY"Hu1HߺHEHEIfL yfH5"L!HHH H9GxLwMkH_IHfInH17"Hu)ELIڐMcHɐLQÅTL诐H"HHH.1MPHuHULEHLY^X@A<DrHf.A<DHW fA<H H8 L' f DL Hu1ɺH}HL"HEHEH]IH5"L`HHOH H9G LgMH_I$HFfInH1"Hu)ECLIABMHL艚ÅLoH"HHA>zA>Hu1ɺH}H"HEHEH]IA@1A@YA@Hu1ɺH}H"HEHEQH]I ABAB fUHATHHGHLgMt)ItSItuItWIt1H}gH}IHt LLef LLeÐDgGII Lcgf.DgGII IDDgAMc@H@`HtOHHtCHHt9H@H;f uC-H}H}IJf. Ht&I.H5.HHtH@Hx H5׶H8 fDHGtsHGHt"Ht4HtNHt0Ht%@GWHH fHcGGWHH HGHf.UfHAWAVAUIATSHHhdH%(HE1H")EHHfHnHHEfHnHEflHE)EHHHxHWHHHHEHCHpHL="H)E1@ID$H9IN;|uHxJHEHHpLcHHpL5<"M 1HL9L;tuHxHHEHLpIH&HFoL{HE)]MH}Lu}@L{L%"M1 HI9<L;duHxHHEHIGHSHpHL6H~HFLuH}HEZIH]L}L HH\H=u"H AŅH EH=}"H5"HGHHIMH I9EI]HMEHIIMfInfHn1LflHuLx)E9H LxIMMIM L;=1t1IGH;$ZLLHHHI$LI$Hu^LSHEz Hu#DMH=$H+H=tf1HEdH+%(&HeH[A\A]A^A_]LxHPLxHʹ H=QtH}fE1IM9JtHϺHMHMtHEJfDHEHuH@HHQH5H81H9H=-QA1[fHH=1MPHuHULEHY^fDUHAWAVAUIATISHHdH%(HE1H"HEHEHEHNHHHEHHHLyHEMH}HHfHI9vI}@HHHUdH+%(He[A\A]A^A_]@HE HuADHHHL 1AH {H5kH8AT1XZHH=O @1gDLyH "M~1 @HI9t@H;LuHUHHEHTI@HNH>H}fE1IM9JtHϺHMHMtHEJfDHEHuH@H9HH5CH81HH=N?1[fHH1MPHuHULEHY^VfDUHAWAVAUATSHHH}L-"dH%(HE1H"HEHfHnLmH9fHnflHE)EHH HKHHHUHHHUE1HHUIHEHMJHuL(H~c@1@HH9TM;luHMHHt7HMJDMkH}`HEAI^JL(H#HtHH=cMf=HEHEdH+%(HEHe[A\A]A^A_]H L%H}H5m"HGHH IM IFH5P"LHH HIHH IH-IH H5L"LHHH5A"LL-}H5VO"LH3aHEH H  I H=ͣ"H5n"HGHH IM HEH5"HxHGHH IM HI9F> I^H1 M~HII HEfInfHn1flLHuHE)EUHHE)]I $ H} IJ IHn HMH@HL;-zHL;-XL;- KLT VH5M"L`d IEH5ў"H}X\IH2 1HLHH HHEgHU H0\ILL\L\H= "HsIH H[IFH;,t H; ILHDžhHDžpLE1[HhH]LLXLxgHMHPHAH9Qi HpH9 HAL,HHpIEHtH?[H5"H=" [IHH5Y"H}ZIHu HI9D$MD$fInfInfl)@MzI\$ILL`HZHHu1fo@)E趯H`IZMHvZH5_"LGZHHS LSZH5"H}#ZIH HI9GmMGfInfInfl)0MKMWILL@IL`YHu1ɺfo0H`)EH@IYL`MW LYH5C"LkYIHlLwYIHRH>"H5"HN3HH5"L/H5XJ"LL5\H`HLXLXH~I9VdM~fHnfInfl)@MBMfILI$XLHu1fo@H`HE)E襭LIzXHrXH`fXMLUXHxL\LL2XHhH}HhIHLXLxIH]HHtHH0H9gHWH5"H}WHH(1HLMIHHWLcÅLoWH5 "L[E11tLwfE1II9KtLtHEJ~HMH}LHUHt*HHפ1HPE1LEH/ZYLeLmHEE111ҾE1E1E1HEIMHt H Mt IIHt H sHt HMt I HH=kDn4HMHtHHEHHH}uHMtI $tXMILI11ҾH=XO1HMHHEHHtH]LH䉵pLLxHEHUpLxHEHUpHLxHEHUlpLxHEHUpLLxHEHU4pLxHEHUxHLUHExLUHEcuHLUuLUUuLuOHpLhHQE1H= }"LHHHUHHEHHgHOYLAH}"I6HE1E1E1HEE111JLHHEE111E1E1E1侚HEL HEE1E1供HEIEL"HEHu1ɺLHEMLeHE:HEHEE111E1E1E1侚lE11E1IHE1E1E1侞߿I HEE111E11E1E11E1M1E1E1侞E111E1Hu1ɺLHELLmSIH]LxMﾣFHLH]LLxMIMHE1cfDLLHLxH]E1E1IϾ6Hu1ɺLHELm躦MIHH]Lx1LE1ɾxE1E111E1E111E11E1M[E11E1IE1L@IHLIH1H5"HLƯL?H50"LHBHpHH?L?hH9%"L5l%"M}IH5"L)?IHM L5?OIHLRHHH5o"HLH>H50"LLAHpH\L>L>@E1IM9$JtLZtHpJfo&Lc)elE1IM9JtLtHpJ__fDڽHn@蓹IHH_L=LfHS㥛 ILH?HH)HiLH)HH?HH!HiHSHq."H #"H9HL=#"MIH5Ό"LH>H}f.E1H=&"1oIH[H@H5"LHHmHHOHCH5"LMtHuH=˪s1HuHAIvMH IsIEH5a"LHH5HH7IcIHHH9C L{MLCIIH AfInfIn1LflHuLE)E(ILEILIMHHMdHHI $LGWHH HcAH9GH%H5vH8ݣ"GWHH HHcAH9LEL{LE:fLgfHWffLGfH7"fLEH#LEf.DgE1IM9lJtHϺHMfHMtPHEJ; H HVH=kE1Hu1ɺHHELmI~I,DA@ˤHHHwH AH"}DH_V9 H=:k: DfDҕHf.uHĝu@1HݕIH: fDzHf; fDHuHH5H8n@HHj1MPHuHULEHSZYH6H'`U1HAWAVAUIATSHH=s{"HH H@H5O{"HHH IMID$L="LMH=1LLAIɜMt\I $t6It!IEH t8LHH[A\A]A^A_]LDLIuHDHI $t{H+RMH=PE1fDHRLH=-fIf.1LLIH@LWwHH5H8[UfDUHATSHH"H "H9HH~"HHHPHHHtKE1H;tTH"H5A"H9pH("H7HMtI $tyH[A\]HE1H;uH5 {"H=mk"hIHH5Ui"H=>"HnHPH=gE1낐HEL#HEH[A\]fDH=h"Hz"H5{"vHHt~HHH=h"HH=h"H""H5#".HHsOH=&gHEHEH=ih"4fH>OH=fh1HOH=fH1UHATSHH"H "H9HH"HHHPHHHtKE1H;tTH"H5a"H9pHH"H7HMtI $tyH[A\]HE1H;uH5y"H=i"hIHH5g"H=>"HnHNH=eE1낐HEL#HEH[A\]fDH=yg"H"H5"vHHt~HHH=Ig"HH=1g"HB"H5C".HHsMH=7eHEHEH=f"4fH>MH=eh1HMH=dH1UfHAWAVAUATISHHHdH%(HE1Hn")EH`fHnHEfHnfl)EH)HHEH'HHt`MH=XdH#M4H=4d1HUdH+%(He[A\A]A^A_]fDLsL=e"MT1HI9$L;|uH}HHEH IFL{HEL-l"M1HL9TL;luHMHHEHpLuI@HL&LnLeLmH="H5Q"HGHHHH8HH9CLsML{IIH fInfIn1LflHuLm)EtIlLH HQHHHeHEHДHEOoLs)]MLeLmfDHHEHCHEIE1IM9JtLtHEJ~H7fHHJOH=af.E1IM9JtLjtHEJHu1ɺHHELeLm}sHELHE~f.ZHHY-@HEHAfDHH`1MPHuHULEH3ZYNb@HEH)B~f.@UHAWAVAUIATISHXdH%(HE1Hb"HEHEHEHFHHHEHrHHLyHEMH}H5q"HHH;sH;fH;YHPAŅ=H IERH "H !H9HL-l!MIEIEH5/"LHHHIEHIEH`HOHH9CHu1ɺHLeHEqIHKH BH "H !H9HL-!MIEsfInfHnHfl)EHI$L`BHfoE@I $HELHEqHE*HuADHHH1^L AH ?H5H8AU1XZHF}H= ,1HUdH+%(He[A\A]A^A_]LyH 5`"M\1HI9H;LuHMHHEH(ISH DHEL;%ID$ xx HpHxŔIH8f.HH>H}f.HHEH=| f.H׎fH5i"LaIHHI9EYMuMLI]IHIMHHu1ɺHELuDnLIAMH qHDDH=;M1sLf.LfE1IM9TJtHϺHMVHMt8HEJH HH'D~H= ]f~dL]LkfInfInfl)UMLsIEHIfoU1HuL)UlIMILME1fDADHnjfHHZ1MPHuHULEHY^MX@H=\"H"!H5#!IMlHBH=Z1IMuL1H=\"IIMu LAfDH=["H!H5!VIM#DHчH5HlH81fDH=9["DIbHf.LW]1Hu11LHELHEkIHA~H=UHAWAVAUATISHHdH%(HE1H6X"HEHEHEHIHHHEHHuOHLqHEM{H}HHsDHEHuADHHHXL AH k9H5[H8AT1XZH @H=W1HUdH+%( He[A\A]A^A_]LqL=%W"M\1HI9tXL;|uHMHHEH,I@HYHwHH>H}f.E1IM9JtLtHEJzHHV1MPHuHULEH Y^VvfDUHAWAVAUATISHHdH%(HE1HV"HEHEHEHIHHHEHHuOHLqHEM{H}HHsDHEʒHuADHHHVL AH ;7H5+H8AT1XZH= H=U1HUdH+%( He[A\A]A^A_]LqL=T"M\1HI9tXL;|uHMHHEH,I@H)HwHH>H}f.E1IM9JtLztHEJzHHT1MPHuHULEHY^VFfDUHAWAVAUATISHHdH%(HE1HS"HEHEHEHIHHHEHHuOHLqHEM{H}HZ~HsDHEHuADHYHHSL }AH 5H5~H8AT1XZH;H=S1HUdH+%( He[A\A]A^A_]LqL=R"M\1HI9tXL;|uHMHHEH,I@HHwHH>H}f.E1IM9JtLJtHEJzHHR1MPHuHULEHY^VfDUHAWAVAUATISHudH%(HE1HHEHEHEHHxLxx@M7I9t MMMuHEE1H!H!H9P]H=!H|HH}HH9GuLEfInf1flHuLLp)ELcLpIHEMI}HEH!H !H9H*H !HHHp~HpHHEHEYHEIEHpLhHpHHE,HhHT"HH5M"V}HpHhHuHTHpHHPHEH LhHUHHxHHI HEI $HEHEMt IMt IHuHtHHEHHLPI9I$LMt IMI $HEdH+%(HĈH[A\A]A^A_]@M~ILIuHEy@|HEHEHHEIELhHEHeHpH_S"HH5UL"{?HpHuH= "HPHEHH}{ HpHEg LHEW HEf.Lw*LxHbLx'LMMtI tHUHp1 L|LU|LULU|LULUH|LUnLUu|LUmHhLLxU|HhLxHhHLx+|LxHhHhLx|HhLxH5!LFHk2E1HEHIHp@HA2E11HEHIHp!H5AN"LEvHHUI$HhL`HhHI HPHsM"HHhH5bF"uLhHPH=!LHLPHhHhLPHIHLUhH}_LMTH1E1HEHHHpH=3E"HwH=!Lo]IHuE11ɻE11E1E1E1E1E1E11E1siz}f.UHAWIAVIAUATSHHhH}dH%(HE1HEe}HYIHQ"HEHEMeIHEHHIM~HEMLmIEIE5H!H!H9XL5!MIH3I9FIHu11LHEHEXHHItHCH5P"HHHHEH}H LsHHHEHHC$|HEH'H!H !H9H'L=!MOIHUI9GS1Hu1LHEHEWHHIHBHxHH5"HHHxIMH s]{HHHxH5N"LHrHxHHLMHxH5!HLHxHIIH H5D"H}LqIL}HUHLIHtIHxHIH H]HHEHHH}H5P" HH5H~H9GH_fInfHnfl)EHLHIL}foEHu1ɺL)EUHHEHUHH}HUIHUxIIM{I $LuM~H %M"M 6HuILELPRY^E-1E11 DH=i?"H!H5!^IME-11fH=1?"tIMwMIGILHHxB1ɺHuHxHELu?OIHtLxsHpL8oHpLoHf.fHxIfE-LE11@HnUfHn.fH}nE-1HE1E11LuE)Hu1H}HELuCNH}HH}aHE-1OE+1E11/H5kH81dHEH=1pDHH%1MPHuHULEH裬Y^if.@UHAWAVAUATISHHH}dH%(HE1H"7"HEHEHEH]HHHEHHHLyHEMHuHFH;]t H;5`uH}MHHHH`HHUdH+%(He[A\A]A^A_]HE oHuADHkHHD$L AH {H5k]H8AT1bXZHH= 1hDLyL-5"M~1 @HI9tPL;luHMHHEHTI@HNH6HufcDE1IM9JtLtHEJfDHHHjH,H5H81bHH=1pDHH"1MPHuHULEHY^nff.@UHAWAVAUATISHHH}dH%(HE1H4"HEHEHEH]HHHEHHHLyHEMHuHFH;,[t H;5#^uH}PHHHH]HHUdH+%(He[A\A]A^A_]HEjlHuADH)iHH!L MAH H5ZH8AT1^`XZHH=l1hDLyL-E3"M~1 @HI9tPL;luHMHHEHTI@HNH6Huf`DE1IM9JtLJtHEJfDHHHhH)H5+H81d_HH=t1pDHHS 1MPHuHULEHcY^cf.@UfHHAWAVIAUATSHHH8L%[L-("dH%(HU1H<(")@HH fHnH8HEfHnHfHnHx flfHnHH)EfHnH fHnflHWLP)EfHnflLX)EfHnflL`LhHpHx)EMpL,HH oOHcHf.HP8HxHP0HpHP(HhHP H`o@o(M~)@)PHJH(OHcHM~H-3"LLbH@HBIH&"LL=HHH-IMH@LHHPLXH0H`H(HhH HpHHxHHL9jH!H=!H9xL=!MIHeI9GHu11LHEHE>=IIHMIHID$H5,"LHHQII$HI$M H|XIHIELhH8HID$ H0IMt$(HID$0HI\$8`IHH(H5%"HIWH H5+1"L*WHH59"L WHH5"LVtH!H5*"LVULLLIH#II $IMH `DHEdH+%(EHeL[A\A]A^A_]HH5GLHc HfDLo[*f.LW[SfH/"LL>HUHPIMH[#"LLHOHXIMH $"LLHuH`IMHw/"LL贙HHhIMwHI"LL膙HHpIMIH8"LLXHHxIMHH(1IPHUL@LLEZYfDHM~H@Do0M~)@@HPo8M~HP)@AAI $u LYIMtIMtgDHE1H='~H H\YIH L@IA?LYDcHuIع1H=p'JH:H=F'E1 f.H='"H!H5!&IM fDLXfH=q'"4IMOMIGIHIH1HuLMHLHE7LII tL@LWDHLWOIfHHX0HHpHX(H HhHX H(H`LhLXHXHPLpHLHH0H@@HLHL L(HHL L(fHHL L(\HHL )fDHHDHH8HHxfLLpVL#aHfHGVfL7VfL'VTfLV:fLV f`HAfD`H@A?Db`HN`H:`H&`H1~YfUHAWAVAUATSHH(H%!HwdH%(HE1HH9tXHXHHqH@HH9H;TuH1fDHH9$HtH9uH;PHIHPHHHHEdH+%(LH(L[A\A]A^A_]HDHH9HuH;[fDH5 '"HqjHEHu1HH]H=]!3IHH H!It$LH9 HXH[HyHfDH5"H="EIHH[I9D$M|$MMt$III $^fInLHu1C)E$3LIIHIMJHeH=G]"Hu1HHELm2IIEMHIE.111L˳I $$H H=4!E1趻HHHH@HRfHH9HuH;YHfDH=i!$2IHH WIH H I $L;-BL;-RM9 LRÅOIMH>!H ױ!H9HL%!MI$1IHHDIHNHXLHHH5%"LHCHLLHI $IH H8HEHHHEsDHE*SHuADHOHH)L AH H5AH8AT1GXZH{xH=,1HUdH+%(He[A\A]A^A_]LqL=#"M\1HI9L;|uHMHHEH(I@H&L6Lu7f.IMLjGH=!H5 "HGHHuIM\H !H į!H9H]H!HHH5 "LIHH5OH9CHu1ɺHHEIL}&HEIIEHH}KIEHHf!H !H9HTL=!MIH5$"LIHLHNI9GpHu1ɺLLMMLMHE%LMH~EfHnLMfl)EHLXAIHfoUPIHHH5"L:HEIH*H5"HH7@L'HLL9HHEALLHHEA|E1IMMt IHNDH=@LD+f.HD fE1IM9$JtL*tHEJLwD]fLgD[f1H^L_)+IF fDA|&D<Hf.H=a!H!H5!v|IM E1A|H M2H=!!{IE1A|H t*E1I $tMy~LCDHwCI $+APAtDHH1MPHuHULEH[Y^@HEL CHEOfHELBHE.f.HEHBHE f.ADLBfLBfH=!H!H5!zIM1AXH=I! zII $uL1BH&BIH="H!H5!zIMHMHHEHHMA~A}9IH=V"HG!H5H!#zHHE1MA~gH=":yHAH MMLeA~nE1LA~]LSfInfInfl)]MDLkIHLUIEfo]L1Hu)] H}HE%H=s"xIMWfInfInfl)`MnMoILLxIELUFHu1ɺfo`L)eG H}HLxFH}E@\L7@A}MAH@A}Cf.UHAWAVAUATSHdH%(HE1HGpHEHEHEHEHEHEHxdKL-;HXLXxf.MM9t MM[MuHDžPE1HDž`L8L0Hx@^HxHx H HGH5>"HHuILeMH%GI9D$Mt$LuMI\$IHI $H]/1HuHHELu`IHEIHEMH LpLϺH5"KLpHHEHVI H;k6HEH;F6L9-HDGAąWH HEEILxxIfIL9t HMMuHE1HEHDžpHpHEHxHx HHGH5"HH ILMM LhALhHHEI H!LhLH"H@H5s"7LhH5!LLL L HHhHEI I $HEH`HEHtHH@HH HEMt I HEHt H  HpHEHtHH`HH HxHEHxH4HGH5"HHoHH]HHCH9Cb LsLuMQ L{IIH L} fIn1HuhL)E:IHEILHEMH  I $HE HhHEH`L{HHH]IL}8Hp`fHEHhI $ZHEMt I qHhHEHtHH HHUH@AoF`fIN`MFpIFpHMNhEHMH!LEAN`H9P L%š!Mt I$I9 I $I~`IFhAF`MfpMFpHt HHt HMt I $HEHEHEjHL%/LHHh萢HMHULHuL{H}HWH}HEH2H}HEHt HiIFxHEH8HHt HTMt IZHpHHHhHHH8fDH DH8DHpL8LpHEME1H HL8HxHγL0AHEHhL@HLp8L@Lpf.L7sfL7I $HEVLpH7Lp^@MZILLpILh5LhLpHP-DLg7fLW78f@LLh) 37fo @Lhf 7D6DHu11LHELHEHEIQH7HH5H815HELeLMHhHg!H@MHEML LQ6L HEML+6H6H@h6H@hhH5hhL5h55ML5HpHSHL8L0AHxHѰHhH]LeHEHt H HEMt I $H9HXL@LpH0H{`HEULpL@HxHhDѝHHMHUHuvH]LpL@]Hl!HH5!H9pX L%!M I$ID$L@LLpH5!HH} LpL@IM I $HZ<I9G Hu11LL@LpHEHELpL@MIMI H H}Ht!LpLxGLpLxH HEqH}HEHt!LpLxLpLxHXHPLLHEHxxIEH`t#H`HHxHHu H2I $.LHUdH+%(V H[A\A]A^A_]H2HEMIML2HEH@DHd26HV2RLH2fHu1ɺhH)EHEIDHXHPLLHxxHxHhDH`HtHHxHHt1H11L1L@LLp1L@LpSLg1GHY1'L@LLp=1L@LpL8L0HH5H2AL@LpH81/HyLMLpHxH@L@HhM_I H]LeVL@LLp0L@Lp,`(ILMM3HELeHhH*!H@MpDHL8LMAHxHL0Hh=LpHLx/LpLxeLpHLx/LpLxLpLLx/LpLxc'IHb/I HEHhH(!H@LLLeLmHXHPAHxx Ht H Mt I $3MIML.H^L8L0AHxHHhH=!H"!H5#!LL@Hh) fLfo L@HhIMHL8MIHxHL0LHhAIxxHpLHL0L8L@L@L8L0L-H=!LL@Hh) EeHhL@fo LILHLh)@7fo@Lh"H(-&L8L@Lp8HpHEHEIHE衷L@L8IGxL HMM9Mt$Ml$(IMtIEHMHULL@HuLp[nLpL@HEH8HEHpHEH@HL L0IxLLLAH8HpLH@HXLeHEHPH HEHxxLmHEH0L8L0HH5k~H,AL@LpH81U*HhHEHELpH`HL@HxHHhH=i"H!H5!L@LpxcL@LpIMzL@Lp6HEL@HELpIHEHhL8AL0H`H HxHݥHhC"HHhLMAL8L0H`HHxHHhJL@Lp5LHEHEIHE荴LpL@!L@LpIzH="L@LpDaLpL@IMwMaMGILL8IL@LpHu1ɺHpHELuLI̳LpL@L84L8L0MtLLx萳LxMtL|HPHHdH`IEHHHxHHH`m(LxMIL8L0LMLA;HMIE1HxHL8LHhL0AIMtcE1E1E1IIEH`H`8IG`fH@IGhAG`HpIGpIGpH8ML@LLpz'L@Lps+UHAWAVAUIATISHXdH%(HE1H !HEHEHEHFHHHEHH`HLyHEMLeH!Hb!H9P`HI!HHL;-"ID$ID$HXHHTHHAD$AT$HH HcЉH9I}0HpH8#IHMHf.H9C\Hu1ɺHHELeII $HHMHHH%yf.HER0HuADH-HH}L 5AH H5H8AT1F$XZHH=-TE1HEdH+%(aHeL[A\A]A^A_]@LyH !M\1 HI94H;LuHMHHEH0IH.L&Lef.LHcЉH9fG1/H.fDAD$AT$HH HHcЉH9tH$H55H8S(몐1fL$2fH! H2H5+4H81"H HIH=ߌf.E1IM9JtHϺHM&`HMtHEJAt$fDH=!H!H5![HHVDH=Y!ZHAt$@L)IHuH4XI $XEL"uD@HY2H=hȋL{fInfInflMLsIIH 1HuL)EKIIt LqLO"DHH41MPHuHULEHSiY^5@H")EH!foEiH,HFw%UHAWAVAUIATISHXdH%(HE1Hk!HEHEHEHFHHHEHH`HLyHEMLeH!H!H9P`H!HHL;-ID$ID$HXHHTHHAD$AT$HH HcЉH9I}0f!HpH8yIHMH(H9C\Hu1ɺHHELe-II $HHMHHH yf.HE*HuADHq'HHL zAH #H5H8AT1XZH3H=I贈E1HEdH+%(aHeL[A\A]A^A_]@LyH E!M\1 HI94H;LuHMHHEH0IH.L&Lef.LwHcЉH9fG)H.fDAD$AT$HH HHcЉH9tHH5L0H8"몐1fLw2fHHH5.H818H H@H=ԙ?f.E1IM9JtHϺHMZHMtHEJAt$fDH=!H"!H5#!.VHHVDH=!LUHAt$@L#IHuHRI $XEL<uD@H2H=}b(L{fInfInflMLsIIH 1HuL)EIIt LqLDHH1MPHuHULEHcY^5@Hg)EHUfoEiH&HFUHAWAVAUIATISHXdH%(HE1H!HEHEHEHFHHHEHH`HLyHEMLeHy!H~!H9P`Hi~!HHL;-vID$ID$HXHHTHHAD$AT$HH HcЉH9I}0HpH8IHMH&#H9C\Hu1ɺHHELeII $HHMHHH|yf.HE%HuADH!HH=L tAH H5sH8AT1XZH~;H=ѕE1HEdH+%(aHeL[A\A]A^A_]@LyH !M\1 HI94H;LuHMHHEH0IH.L&Lef.LHcЉH9fG#H.fDAD$AT$HH HHcЉH9tH[H5*H8몐1fL2fHHH5(H81H H HH=\蟁f.E1IM9JtHϺHMTHMtHEJAt$fDH=I!H{!H5{!PHHVDH=!OHAt$@LHIHuHLI $XELuD@H2H=\舀L{fInfInflMLsIIH 1HuL)E IIt LqLDHH1MPHuHULEH^Y^5@H)EHfoEiHK!HF7UHAWAVAUIATISHXdH%(HE1H+!HEHEHEHFHHHEHH`HLyHEMLeHّ!Hw!H9P`Hw!HHL;-ID$ID$HXHHTHHAD$AT$HH HcЉH9I}0&HpH89IHMHH9C\Hu1ɺHHELeII $HHMHHHyf.HErHuADH1HHL UoAH H5 H8AT1fXZH"H=Yt}E1HEdH+%(aHeL[A\A]A^A_]@LyH !M\1 HI94H;LuHMHHEH0IH.L&Lef.L7 HcЉH9fGQH.fDAD$AT$HH HHcЉH9tHH5 %H8s몐1fL72fHAHQH5K#H81H Hi/H={f.E1IM9JtHϺHMFOHMtHEJAt$fDH=!Hbt!H5ct!JHHVDH=y! JHAt$@LIHuHTGI $XELuD@Hy2H==WzL{fInfInflMLsIIH 1HuL)EkIIt LqLoDHHT1MPHuHULEHsXY^5@H')EHfoEiHHFUHAWAVAUIATISHXdH%(HE1H!HEHEHEHFHHHEHH`HLyHEMLeH9!Hq!H9P`Hq!HHL;-6 ID$ID$HXHHTHHAD$AT$HH HcЉH9I}0HpH8 IHMHH9C\Hu1ɺHHELeMII $HHMHHH<yf.HEHuADHHHL iAH CH53H8AT1 XZH>H=wE1HEdH+%(aHeL[A\A]A^A_]@LyH e!M\1 HI94H;LuHMHHEH0IH.L&Lef.LHcЉH9fGH.fDAD$AT$HH HHcЉH9tHH5lH8몐1fL 2fH HH5H81X H HH=l_vf.E1IM9JtHϺHMIHMtHEJAt$fDH= !Hbn!H5cn!NEHHVDH=!lDHAt$@LIHuHAI $XEL\ uD@H2H=QHuL{fInfInflMLsIIH 1HuL)EIIt LqL DHH1MPHuHULEHRY^5@H )EHu foEiH HFUHAWAVAUATSHxH}dH%(HE1H=!HEHEHEHHLfDIu L IMI $H@ H=E1`H wHi@HDž`_HuM1H={H ( H=`E1IwLVIMV@L?M@I $6L#(fDMIM9DJtHϺHP{3HPtHXJf.HQ? H=_,I7fLfLwfLgfIaLNSHf.H!HXH5HHEIDHX HXH];@IMLLLLIH4ZHJ FHh2Hqf.@UHAWAVAUATSHH8LgdH%(HE1GpvDMt$ M|$(ID$ HM;~KDI|$IOHMHID$Ht HGHp!H )W!H9HL-W!MIEHHuE1I9EfIn1LAD$)EUMt IHtBIMHMH{ Mt$ IL$(HtHC HCpH[8IJIMHjEH=]CpH1HUdH+%(H8[A\A]A^A_]DHMt$MIE1fDDHELsHEf.HEVHEDHEL;HEfIt{HH8&fDH=1!HZU!H5[U!v+IM?%7ILfDLf.LwfH=!T*IM}MIEILHHE%}LmHuLG:f{6M*I!H@[6fDHAHH5MH81+6UHAWAVAUATSHH8LgdH%(HE1GpvDMt$ M|$(ID$ HM;~KDI|$IOHMHID$Ht HGH0m!H )S!H9HL-S!MIEHtHuE1I9EfIn1LAD$)EMt IHtBIMHMH{ Mt$ IL$(HtHC HCpH4IJIMH4H=5YCpHq1HUdH+%(H8[A\A]A^A_]DHMt$MIE1fD DHELHEf.HEHEDHELHEfIt{HH83&fDH=!HZQ!H5[Q!'IM?3ILSfDL?f.L'wfH=A!&IM}MIEILHHEyLmHuL:f2M*I!H@2fDHH?H5[JH81X2UHAWAVAUATSHH8LgdH%(HE1GpvDMt$ M|$(ID$ HM;~KDI|$IOHMHID$Ht HGHi!H IO!H9HL-0O!MIEHHuE1I9EfIn1LAD$)EUMt IHtBIMHMH{ Mt$ IL$(HtHC HCpH[1IJIMHj H=VCpH1HUdH+%(H8[A\A]A^A_]DHMt$MIE1fDDHELsHEf.HEVHEDHEL;HEfIt{HH8&fDH=1!HzM!H5{M!v$IM?%0ILfDLf.LwfH=!T#IM}MIEILHHE%vLmHuLG:f{/M*I!H@[/fDHAHH5FH81+/UHAWAVAUATSHH8LgdH%(HE1GpvDMt$ M|$(ID$ HM;~KDI|$IOHMHID$Ht HGH0f!H )M!H9HL-M!MIEHtHuE1I9EfIn1LAD$)EMt IHtBIMHMH{ Mt$ IL$(HtHC HCpH-IJIMHMH=5RCpHq1HUdH+%(H8[A\A]A^A_]DHMt$MIE1fD DHELHEf.HEHEDHELHEfIt{HH83&fDH=!HZK!H5[K! IM?,ILSfDL?f.L'wfH=A!IM}MIEILHHErLmHuL:f+M*I!H@+fDHH?H5[CH81X+UHAWAVIAUIATSHHhH}dH%(HE1HEHL=ݜ!IH!HEHEL}HKtMIIEHEHCHdL}Hb!HS!H9XL-R!M)IEHI9ETHu11LHEHEDHHIM~HCH5ý!HHHHEHHH}yHHMHHHEHHCIHHa!H R!H9PL5Q!MIHI9FMHu11LHEHE]IMIpIBLULH5^!HHLUIMI KHEHIHHJg!LxHICIM{ LxHIH@H}LLH5:!lIkL}LLH5u`!LLRrIHvIHUHHxHHH5,!LLIL}LHLqIH/LHEnHnLnH5~!H}unLUHIHI9CI[fInfHnfl)EHmMkHLLUIE>nfoEHu1ɺL)EBHInLUMLLUmLmf.IMvMI $LM>IMH+H -HHLHH?L =MLHH=HL@H51H:AVHֱXZI $H՚H=KE1HEdH+%(HeL[A\A]A^A_]fDLkMzH,!HHu(!HuHHEIE@fDM}L}:LULLUzfLfLtfHfL|fLfHEE1E1EH H}tHMHHxHH1Mt IMIMt I Mt IHMHtHHEHHMt I $Mt I&uH*H=IME1LpHLxLpLxfDLpHLxqLpLxfDLxLILUALxLUL]L#L]f.L]LL]f.L]HL]f.LfLfH=!!HK!H5K!IMEE1E1vH=!$ILmE1E1E1HEE1E1EMuMI]ILHH]i1HHuHELuξIHtLm|DLDEE1AfHEwEE1E1E1HEE1E1DHuHuHHLHUMHLEHPo%Y^pL'fEE1E1E1HEE1E1DEE1E1nfDEE1E1E1HEE1HH=1!HI!H5I!&IMEE1E1E1HEDLo f.HWfM^MIFILLxHHEgH}1HuLxHEL]߼LxII tLumHxLLxH=1!tI @LUIYfDEE1E1E1LoKf.EE1E1fDL}E1E1E1EE1@Hu1ɺLLULUHEL]LmLUILUEE1E1HEZEE1E1E1HE=EE1MLEE1E1HEE1<-f.UHAWAVIAUIATSHHhH}dH%(HE1HEHL=!IH!HEHEL}HKtMIIEHEHCHdL}HV!HA!H9XL-A!M)IEHI9ETHu11LHEHETHHIM~HCH5ӱ!HHHHEHHH}yHHMHHHEHHCIHHU!H@!H9PL5@!MIHI9FMHu11LHEHEmIMIpIBLULH5n!HHLUIMI KHEHIHHZ[!LxHICIM{ LxHIH@H}LLH5J!|IkL}LLH5T!LLbfIHvIHUHHxHHH5IMH;H =HHLHH?L 1MLHHMHL@H51H:AVHXZI $HEH=ƥ?E1HEdH+%(HeL[A\A]A^A_]fDLkMzH@HEHu1ɺHHEHELuLe`HELHEf.HEH@HELHEH=;!H*!H5*!0HHH=!Hr*!H5s*!IL{MLkIHIELm"P1LHuHEL}#IIt H]fL'DLXAeHAؼIKf.UHHAWIAVAUATISHL-Ԃ!HdH%(HU1H!HEHfHnHEfHnHLmflHMH])EMJ4HPI];MIHIL$HEH[HPLeAfDIHPo IL$HU)eH( HELeLmHPIEHDžhHDžpHDžxjH0LxxM7I9t MGMMuHDž@E1H1?!H5!!H9p H=!!H HHpHoH9GA fInfHu1Lpflú)ELL âL HXHhHXHDžxIHDžpHDžhMt IXMt I*H@HtHH0HHH51A!MD$L9t:IXHBHJHb1fDHH9LH;tuI9 I$HDžxH5!Lq H=!H !H9PC H=!H HHpHH9G fHu1ɺLpX)ELGHxIHDžhM9 I^IMHDžp;HDžxMHPH9H5!1H,o> kHxIH7I$H=?!1LL`HPL@HIB NL@HHpIL@KLMHDžx*KHDžpI9t~׻HpHH I$H=)?!1HL`IELh MHxIH H I $HDžpyHDžxMI$LHPI$I$HIHXIMLMt$H=!M 1HI9I;|uHPHHEH INH-I*IIM1H=zWCHuD H=VE1'HEdH+%(3HeL[A\A]A^A_]H)z!HPLHXHXHsHEHHLeLmHPHPL LeDHVHPHULhLmHXHH@HHTHFLfHH9HuH;5fDH9X8HxIH HXH=>!1LL@HIBH͏!HIB IKL@HHpI LHHDžxHDžp7@M~ILI-H@fLFfH5;!LqSu L;ָH8!H!H9PC L!M ILxHI9Ba fInfHu1LxflǺ)ELBHpIHDžhM LFHu1L}H={=!HEHHxIH~ LFHDžpHDžxfDo(IL$)mJfDLfE1IM9#KtL@HXHXL@tHPJLGfH7fL'HpHDžhHtEH0H5!HDžpHDžxHx`yHbq{ H=0#H0HxHpHhxHhHHt?HEHPfH5v!H=/!>HxIHH5n!LH@>L@HHhIHPI9B MrfInfInfl)@MIZILHHx{>HHu1fo@)E|LHpIJ>LB>HDžhMH&>H=w!L臖HxIHLH@E1=H@11HDžp1H@=Hi H=hHDžx$HhHpA| LxHhHpE1A A HhHpA A pH=h!H!H5!ILxMHhHpE1A ;MrfInfInLhfl)@MvMbILI$LxHhHpA E1LxHhHpA E1Lx1Hu1ɺLHEL}L@襐H@HpI%HpLxA @HpE1A HpE1A HhHpE1A Yf.UHHAWIAVAUATISHL-Dn!HdH%(HU1Hk!HEHfHnHEfHnHLmflHMH])EMJ4HPI];MIHIL$HEH[HPLeAfDIHPo IL$HU)eH( HELeLmHPIEHDžhHDžpHDžxںH0LxxM7I9t MGMMuHDž@E1H*!H5 !H9p H= !H HHpH߶H9GA fInfHu1Lpflú)ELL 3L HXHhHXHDžxIHDžpHDžhMt IXMt I*H@HtHH0HHH5,!MD$L9t:IXHBHJHb1fDHH9LH;tuI9 I$HDžxH5k!L H)!HE !H9PC H=, !H HHpHZH9G fHu1ɺLpX)EL跌HxIHDžhM9 I^IMHDžp;HDžxMHPH9H5!1HZ> ۧHxIH7I$H=%+!1LL`HPL@HIB 9L@HHpIL6LMHDžx6HDžpI9t~GHpHH I$H=*!1HL`IELh k9HxIH H I $HDžpyHDžxMI$LHPI$I$HIHXIML Mt$H=!M 1HI9I;|uHPHHEH INH-I*IIM1H=z.H` H=yE1\HEdH+%(3HeL[A\A]A^A_]He!HPLHXHXHsHEHHLeLmHPHPL LeDHVHPHULhLmHXHH@HHTHjFLfHH9HuH;5fDH9XHxIH HXH=/*!1LL@HIBHu!HIB 6L@HHpI Lw3HDžxHDžp7@M~ILIH@fLgFfH5)'!L>u L;FH$!HR!H9PC L9!M ILxHWI9Ba fInfHu1LxflǺ)EL貇HpIHDžhM Ll2Hu1L}H=(!HEHdHxIH~ L)2HDžpHDžxfDo(IL$)mJfDL'fE1IM9#KtL@HXHXL@tHPJLfHfLHpHDžhHt61H0H5!HDžpHDžxHx`H\ H=u9H0HxHpHhHhHHt0HpHDžhHt0HxHDžpHtt0H0H@LLHDžxHxx]HXHX#HXHH\!HPLHXFHtHXHEHدHHHt1MPHPHULEL ZYzL@LѤL@kH@HL@:H=z}!H!H5!HHpHHDžhHPLWLIL_fInfInLxfl) MLGILILpLX.1HuLXfo LL )U誃LL HXI HhgLL RH=a|!DHHhHpA LxHt HHtH t7MtI tTHYDH=r9 MI$1HLPHD@LPD@D@LۢD@LPHD@LPD@UE111A H LLE1H0H@LDPHxxfZDPHDžXH LH5J!LblKTH5\!LBlj4H5\!L"lm^HxIHI$H="!1LL`H8!L@HIB u/L@HHpIA HhH!HpLlHhHpA E1LxHhHpA LxLxA HhHuH=_!H H5 NHHpHLxA HhA H:CH=^!EHHpLxA LOfInLhX)0M@LwIL@ILp*LHu1fo0)uH@HxI*HEHPfH5b!H=n!Z*HxIHH5Z!LH@1*L@HHhIHI9B MrfInfInfl)@MIZILHHx)HHu1fo@)E~LHpI)L)HDžhMH)H=!LHxIHLH@E1b)H@11HDžp1H@9)H U H=mHDžxf$HhHpA LxHhHpE1A A HhHpA A pH=~S!H H5 KILxMHhHpE1A ;MrfInfInLhfl)@MvMbILI$Lx(LHu1fo@)}}LHpI'[H=R!IGHpLxE1A HpE1A HhHpA E1LxZHhHpE1A >HhHpA E1LxHhHpA E1Lx1Hu1ɺLHEL}L@|H@HpI%HpLxA HpE1A HpE1A HhHpE1A Yf.UHSHHGtzHGHxQt/HtHu3GWH]HH ËGH]H]1H]%mDHH5H8IHH@`HtJHHt>HHt4HH9CuKHI $HEH !H5!H9pDL5!MILuH5P!LHEIHIHI9EfHu1ɺELM)ElIHEM6 I $LLHEIHL LL}LuH DH2EHEE1HEIHHL !H=!1LHIFHI^ IHIIGH5b!LHHHH]HIHؓH9CNL{MALKIIH LM*1LHuLMHEL}kILMI'LM+H LLNHEIHIHEI $Mt IMHMHtHHEHHHEdH+%(HeL[A\A]A^A_]Lf.LgfHULSHUaf.L7fLMH#LMf.LfLfLfL׉fHljfE1E1E1Q HEE1H tBMtI tPMtItVMtIt\H?H=3YE1|LMHSLMDL?DL/DLDLBf.HyHE1L =H 58H5%H8R1HXXZE1Hy1H5~XHf.HwHQ HEE1HW@fLGUfL7]fHu11HHEHEgILMLLMf.LׇNfH=a!HZ H5[ 6ILuMMHLmE1E1ɻT @HL}MHPH/Lf.LMV ML}V MHMLMLUIR E1xLMMV E1MchR {fD~If.H=`!Hj H5k 6ILuMHLmE1E1ɻS LMMM*V fDHyLMHBH5H81,LME1E1E1R HED ~HmfMHLmL}E1S NDH=9_!褽I)@}If.HLHULMHELmLMLLLMhLHE4HULMH#HBH;~H;S~ HLMHUH}ILMLHUIHDžtHHDžx@LML}V L&Ht HMt IMbHEHtHHsH]H0H$Ht H Ht H Mt IHUuL#1"HE}HEHEL|HEHEH|HE8ML9}xHuHs@H13E111EL54HEu|Wj|?_|'LQ|*HC|HUH1|HUHUHHM|HUHMHULHM{HUHMHUHM{HUHMV{f.ttHHSUHAVAUATS@IHtxHI9tMHsL5^I9AM9Du!M1HI9DL;|uHhHHpHIEL{H`ofDIIt`M1H=bO H H=j1HEdH+%(/HeH[A\A]A^A_]fDH~HhH}HXL(HxLpH9dHhL5G!IHGHN H=1HKL-C!H1HH9L;luHhHHHEMo@E1IM9JtLtHhJL7gfoL{)p@HnH5BH8!kHHH=O@fDE1IM9LJtL:t;HhJfHu1ɺH^hH]LuHEHF!LmHEHh)E(FLhHfL'ffE1 IL9t`JtLH`臢H`tx:HhJ8f.]HfLefZpH@HDžx7pHAHDžppHhf.UHAWAVAUIATSHHXdH%(HE1H/!HEHEHaHEHsILH;=k^H}AH H H H9BH HH1bIH]IHLpeIH;IEH5=!LHHMIMH5=!LL\\I $LLH\IHtgH IILPa1H=ZJF1DHa{f.E1H I+AMt IMtI $tLH,DH=\L`fXHfAL`D*kHHH`01LPHUILEL}Y^fDH=)!HHA9DH=!ėHHADL_f.L_fE1IM9,KtLEH}%H}LEtKf.H;kHlIHtHLAEHAHH@E1HE1 _M#L^`f.H H^fL^PfH^7fH^KfH=)!H H5 HmfDH=Y!H H5 ΖHfDH E1Af VIfL{fInfInfl)MMLsIHIfoM1HuL)M=IIt LL]DH tYIrL]d@Hw]nfZUIfE1ff.CADEA`UfHHAWIAVAUATSHdH%(HU1H$;!)`HfHnHxHDžpfHnH fHnHflfHnHE)EfHnH flHE)EfHnHXflHEHx)EHRIJ HPIH WMJcHf.HV(HUHP HUo@o Ml$)`)pIH#MJcHf.Ml$H$!HPLŚH`HIH9!HPL蜚HhHIL$IL5!H$ 1 HH9dM;tuHPHHpH IMH`jAă.H}LhLpLxH7HP H}HDGH5 I9ut L;-VI9vt L;5VI9wt L;=VH= H5!HGHHHHH0 H H9HoL MIHubI9@LHu11LPHEHE9LPHXHX.IIcbHPH>cZIH?H@IHcHL8IELhILpqbIHHcDXbIHHaH9CLKMLkIIEH HX1HuLLMHEHPLHHELeLuL}8LHII oHXHHHHHHPHHXHHEI $IILMIMSHEdH+%(aHeL[A\A]A^A_]fDDžHH}HFDžDKfItT@ItI0HPHxHPo(H8Hp)`IHV(HUHP HU@HQ$!HPL蒖H^HxIM@H'$!HPL`H@HEIMH$!HPL1H%HEIMHH'1MPHPHULL`ZYaH1MH=& HH=西E1*Do6Ml$)`LHVo>Ml$Hp)`HMl$H`@LHH(VLH@LV;f.1@HH9ItLHXcHXt}HPH`fLXLULX@LXLULX@LXL`ULX@LXH@ULX@LHH ULHb@LXLULX@_H /1H$L9H5 1HxIL9H5X A H HXHHHHHtxHPtHPHHXHHteMtI $tjMtItpHg DH=7pD1H$L8`MfHSzfHSDLSDLSDA uDzKH"f*^H HXHu1HߺHEIHEHPLeHELuL}3IIL]HuA1H="? -DH=!H H5 6IM~H LXHE1E1RLXA LPfHgRfHHLPRLHu@H=1!Iy@MHMI@ILLXHHP1HuLXHPHELML81L8HXI tLPLQDB\HQ E1E1A E1A fHDžp[HWA4[H8[H$[HTM@LPA E1E1zUfHHAWIAVAUATSHdH%(HU1H.!)`HfHnHxHDžpfHnH fHnHflfHnHE)EfHnH flHE)EfHnHLflHEHx)EHRIJ HPIH AJcHf.HV(HUHP HUo@o Ml$)`)pIH@JcHf.Ml$H!HPLUH`HIHc-!HPL,HhHIL$IL5 !H$ 1 HH9dM;tuHPHHpH IMH`Aă.H}LhLpLxHǃHP H}H覃DGH5 I9ut L;-KJI9vt L;58JI9wt L;=%JH=x H5!HGHHHHH H 9 H9HoL MIHVI9@LHu11LPHEHEd-LPHXHX.IIcNVHPH>MIH?H@IHcHL8IELhILpVIHHcDUIHH-UH9CLKMLkIIEH HX1HuLLMHEHPLHHELeLuL}R,LHII oHXHHHHHHPHHXHHEI $IILMIMSHEdH+%(aHeL[A\A]A^A_]fDDžHH}HFDžDKfItT@ItI0HPHxHPo(H8Hp)`IHV(HUHP HU@H!HPL"H^HxIM@H!HPLH@HEIMH`!HPLH%HEIMHH1MPHPHULL`譑ZYUH1MH=K HH=5E1*Do6Ml$)`LHVo>Ml$Hp)`HMl$H`@LHHILH@LI;f.1@HH9ItLHXHXt}HPH`fLXL0ILX@LXLILX@LXLHLX@LXHHLX@LHHHLHb@LXLHLX@*SH /1HmL_-H5 1H=L7-H5 A H HXHHHHHtxHPtHPHHXHHteMtI $tjMtItpHDH=誰pD1HL,`MfHgGzfHWGDLGGDL7GDA uD ?H"fQH HXHu1HߺHEIHEHPLeHELuL}&IILBQHuA1H=} -DH=$!H H5 ~IM~H LXHE1E1FLXA LPfHEfHHLELHu@H=#!}Iy@MHMI@ILLXHHPK1HuLXHPHELML8:%L8HXI tLPL/EDOHQ E1E1A E1A fHDžpOHWA4eOH8QOH$=OH)HM@LPA E1E1zUHSHHGtzHGHxQt/HtHu3GWH]HH ËGH]H]1H]% NDH1DH5RH8GHH@`HtJHHt>HHt4HNH9CuKHflHE)EfHnflLxHPHMH})EHJ4HHIMH 3JcHDHP(HUHP HUo@o Lc)`)pIHt3JcHLcH!HHH趀H`HIH !HHH荀HhH L{IH M 1fHL9H;LuHHHHpH IM^HhL`HpLxH0HELeHPH5u I9vt L;5<HI$L;%$9L;%IL;%< LJI$MI$HIFH5 !LHHIMLHHHH{ 7Hl HHHLMLHHIy I $GI L;?8L;HL;;LHLILHAykAIwDHE1H=3莨H IM{HEdH+%(BHeL[A\A]A^A_]fDDIE H!GH HH;;y 11HEIH HL;%: H5 !LdHHHVHFLHHH  6LHH LHLϺHH@KLHL@HI I IL~H LH H5;!HL$y 6H5?!H=ؾ sH L;-9 11LHHLHHI 1ҾLL@тL@HHH H=EI9xMxMIpILHH@H FHuHHfInH@fInǺHMH0flHEHPHM1LeHE)ELILHH{M- H@f#1E=IHqH8gI$HI$HLZ<DIfTI:IPL%18LxLxHHHXL0HpH0HhL`fIL`(LeHX HPH]DHy!HHHzHyHxIMpH !HHHzHHEIMAH HHHQzHS HEIMHH6 1MPHHHUHL`=ZYHA^A>HWH5!H=>>!1IHtH111L詾AGfDAE1ULPL3LPE16U>H5AAH5 H= HHPH5W!LLPHHHH/;LPLHHHLHLPx*LPLHHIH/;I9@(MPfInfInfl)0MMpILLHILPSHu1ɺfo0LL})EPHPI!LHLL MLH=bM|HIEL{H M1HI9H;LuHpHHHEMn"@E1IM9JtLBetHpJ~oLq)]|E1IM9JtLdtHpJFH'(]fHpL(Hp.@E1IM9JtHϺHhcdHhtxvHpJLpL'Lp#HH=gLp苐LpMZI LY'@1H@L/'f.ILձIL'tfDHE1H8H~H=LxLxMtkI LHpHu1HߺLhIHEHxHEHE]LhI:oIE1HH5H=CLxGLxMJI AL3LMx|I $t3DLH3H=WfUfHHAWAVAUATISHHH=dH%(HU1H )EHfHnHEfHnHH}flHM)EHuJ HpI0MIWHHEHCHhIL- M1fDHL9L;luHpHHEHLhIMHLmLeHprfIHVoLkHU)UM~2HH1MPHpHULEH^ZYHELmLeHpI$HPI$IT$H1I$ IHHLu$HHyIH;AH;H;H ADžH jEI $1IHHL#HHIH;H;<AH;B4HAƅ@H $H H@E-H;| sL5g M7IH,I9F7HpHu1LHEMLmHEHIAHH-IH!I $LnfLkL=u M1HI9L;|uHpHHEHMIEL{HhIIt`M1H=5HH=1}HEdH+%(HeH[A\A]A^A_]fDH~HpH}L`L(LeLmDH iHpH5 LP f1HHI $IqH DHHÏ H@EH;? L5* MIHI9FiHpHu1LHEMLmHEXHIAHHIu LRHtH u H?HFDH=f17|fHKL-E HL1HH9L;luHpHHHEMo*@H5 LDOeHHuAE@E1IM9JtLNtHpJFDoL{)]<HHHL`IHH L#ÅIL聜I $H= H5 IHH{I9FMFMM~IIIfInfIn1LflHuLx)ELxIIrMIHMIHI $HCH5w HHpHH ЅHlH@MuL% M1 HI9dM;duHpHHEHIFMuHhHHt`Iع1H=(5H?GH=p1HUdH+%(He[A\A]A^A_]fH^HpH]LhHLmH]mDHiHpHuHvH5H8N fDI?HHH=,pEfIuLLrfMMH ML1HI9tI;LuHpHHHEMf:@LHf.E1IM9KtLBtHpJ~o&Mu)eLpf.LNfLxLLx@E1IM9KtL:BtxHpJnIlfDHu1ɺLHELmAIfLGfE1IM9KtHϺL`HhAHhL`txxHpJFLf1LLIH=HH5H8u@zI5f*H$@DHEHlAIfDHEH)?f.@UfHAWAVIAUATSHHhdH%(HU1H$ )EHXfHn)EH°fHnHfHnHEfl)EfHnfl)EHtvN,IH JcHLcL= MLHI9L;|uIDHEHvHKIt$DIdHHFHH]H^HHH]H^HEHH]HDž81HDž@HDžHHDžPHDžXHDž`HDžhHDžpHDž(HHHH;BTHH5v L@L誘L;t L;&HHDžHDžHHHDž1IHH HGH5HH9_H90HGHHHHH8Mt I $HDž8' HLpx@M>Mt L;=9sMvMuHDžE1HH;UHCH5 HHHIL8MjHLaH@IHI $HHDž8HtHHHHHDž@Mt IMt IHHtHHHH[LL-{ IEH=c H9xL-c MOIEL(jH8IHHHIFH@IHHCH5 HHHOILpMH5~ LL"H"ILLL#HpIHIMIHDž(I $HDž8]HLHDž@RIHDžpIfDMwILIHfLfHHsHEHH HrE1DID$H9IJ;TuKDHEH>L{HN@oHK)]IHj M1DHL9H;TuIDHEHL{HH M 1HL9H;TuIDHEHHAHHEHHEHHEHHEHDHVo.HKHU)mI`@oFo&HC)e)E느E1LHMMI@IM9-JtLb9tMHLKDHEOHuMH=܀H̲PH=teHDžHEdH+%('HHe[A\A]A^A_]E1LHMMI@IM9JtL8tMHLKDDLif.LBfLfLpfLwLfE1LLMMHIIfDIFM9CIJtL7tLHMLIKDLf.1IHHDž0HH9CHH5 HH9CrHHHDžHE11HDž8H(H@HH8HH0HDLHHHHHL8HtH蕄HDž8L@MtLvLLHDž@谈Hpu H=y] H9xH=`] HHH8HH9GHu1ɺLuL8HELH@HHdLӃHHHDž8 ]HML袃HDž@fHfLefLGfHfH(LhML`HHDžE1E1HDžHDžHDžHE1DžyHHHHtHHHHHMt IMt I HHtHHHHMt ICMt I Mt IMt I HHtHHHHHHtHHHHUHH,`Ht'E1HHHHHLMt IMMt I $HHtHHHHtoHtH tuHHtHHHHt7HHHHHHHXHGDH7DH'DLHL@LfLfDLLL@LLL @L fHwfLLLLNLLLL>DLLLLLLLLLfLLLLLLLLHLLLLLLLLILLfDLM@IM9gJtL0tLLIIDHH8HHYLkHHHt'HH0H9HHHH2~H5õ H}HHHHHHHpIHHHIGHHH@IHHHHH5( LHHLHLH5 GLH`HLLHL1LHHH8IHE1E1LHH|L|HHDžp|HHDž@HDž8LHDžHDžH$H5H8H(L@Ht<|HDž(HDž8MtL|LH5k HDž@I}`HmHHHzHHZH(H8LH@2HXHPHxxHHHHH; Hxl HqT L-jl H9P8L%QT M( I$LhH5 HzHpIH(HI9D$ Hu1ɺLUHhLHEHHLH`ILzHDžpM} HzHLPHHhHLVzHHDž`HtH7zHHHDžhHtzHPHDžHHtyHXHDžPHtyH@HDžXHtyH8HDž@HtyH(HDž8HtyHHLLHDž(Hxx触HHwH90 HHHH\HHH8H(L@H1I\H= HQ H5Q &IL(MH\E1HDžL8HE1HGLpHDžLhHL`HDžL@HDžDžyE1E1E1E1HDžHDžDžcHE1E1E1H1E1HHDžHDžHDžHDžHH(H`wLHDž(wgH(LpLhL`HL@8H=Z ]$IyIE1H(HDžLpLhHDžHL`HDžHDž%IH5 H=7 1yH8HHt!H111LHvHDž8E1E1E1E1Dž}HDžHDžWHEYH$ALMvHDž8$HHL1PHUMLEH2A\A]H= HO H5O #ILhMMMH`HtuHhLpHDž`HtuHDžhMtLuLH5 HDžpI`G HHpSHpH`LHhJ,H5Ӳ HtHHMHH H9HLpMLxIHILtLHu1ɺHELuLHtHHyt1iLpLhL`IHH5a HLLHtLLHHLH@H;t H;7HHDžHHHDž0HE1LLLpsLLLLLLLHLpHMIMH0H=I9}H H9kIMHHHH0HtHsHLxuHLwtHMLHLLLLHHLLLrLDžsLLHDžHDžHHXLHPHHLHxxL`H(L8L@LHLLHHLLLE1E1HxxLLLLLLLDžpE1E1E1HDžE1HDžHDžHDžH؜E1E1E1HHҶLhDžyL`HDžHDžHDžHDžHGHEHAaHEHeA?HHHN H@HHH HDžHMMH5 H8WLpLhDžqL`H= I HoH5y HoHH(HV{H@IH HLHIBIEMj LHH8I HH5 HLL LLLLHrLHH| LLI1nHHDž(nLHDž@nLLHDž8HDžHDžL@ME1IDžfE1LE1HE11HDžHHHDžHDžHDžHDžHELHhMMHDž`HimHDžhTM\$fInfInfl)pMID$ILLHLHHhmHu1ɺfopH)uHH`IRmLLE1E1E1DžgE1E1H= H;F H5LhLDžtLLHDžLE1HDžxH(LhMDžyL`HH9E1E1E1HH3HDžHDžHDžHDžH}LE1E1E1DžhL@E1H{HH5H812DžfE1E1E1HDžHDžHiLHxHDžxL"HxHHu1LuHHE蟻HHpfHXLIFH;dH;WL,LH)fHHDžHDžH8HH9#I\HHH0H(L8HDž{LhL`HL8H%IE1E1H1E1HLE1HDžHDžDžiHDžHDžHDžHOLHu11HELHELHNH(MME1DžnL8HLpHDžLhL`L@HDžHDžHDž}HMME1HE1HLpHDžE1LhDžmL`HDžHDžHDžHDžHDžHDžHDžDžsXHDžE1LpLhL`HDžHDžDžrHUH/E1E1E1E1DžkHDž E1E1E1E1HDžDžkH(L8LpHLhL`L@HDžE1HDžHKHE1E1HHEHDžHDžDž{HHDžHDžDžsH(L8HIHDžHDžHDžDžsHDžLpE1LhL`HDžDžrkMLHILLLLH(L8LhL`HHDžL8ILL@DžiH(LhE1HDžHDžL`HAHHkHH`gHjH(E11E1LLpE1E1HH)LhHH%L`LE1L@HHHDžkHhHDž0HLLHHLoHH@HHHDžs1HmL1Hu1HEHLLLLHE觴LLHL1LLHDžt*LLLPLLLGL>LE1Džw1LE1HDžvbE1LE1LLDžw8Džs1H f.UfHAWAVAUATISHHhdH%(HU1H )EH`fHn)EHfHnHfHnHEfl)EfHnfl)EHJ HxI#H JcHLsL=e MLHI9L;|uHxHHEHIFL{HpIH>LnHVHFH}LmHUHEH;xtvH5_ H= 19`HHtH111C3H\mH}H=pP3;1HEdH+%(e HeH[A\A]A^A_]@H9uHGH5 HH8IMHH(H5 LHeH5M HLk_IHI $H lH=N MEH5 P L9IXH3HJ1HbI9DH5! H=2 1^HH'1H111H[ryHH9H;|uI9t;IXHHJH1HH9H;tuIH5 LHHHH5 H9HCH;RHHSHHHxH<f.H HuHIELHPIUIEHLfDI $H u HHpH=M8HHEHCHpIL5 MS1HL9L;tuHxHHEHHpL{HHpfDHCoHpI)]L5{ M1fDHL9<L;tuHxHHEHHpL{HHpL- M@1@HL9LL;luHxHHEH LpIMH}LmHUHEDHCHVo.HUIHp)mmDoFo&Ls)e)EfHfLmfLH fH;HIHtHcLAJXEH u HstHH=K1i6IEHTDE1IM9JtL tHxJHEHuMH=]7QHkH= K5f.E1IM9JtL txHxJE1IM9,JtLtHxJMIM9JtLtHxJ.I9ZH1f.HH9H;tu ppfDIfI $L}HEHA\fDHH1MPHxHULEHPZY:HEH"AE1{AHHeEH5 L_UHHHH9GLLgM?LwI$IEUfInL1 Hu)EBLHUH;LULIT.fDHEHBALHH9+HuH4H9I9MLHH9tHuH9LHH9HuH;5H HwHiNHH Hu1ɺHEHEHx LxHuIukLHLuH=:G1IEHrhE15f.CADEHHSHuH=F1fUfHHAWAVIAUATSHdH%(HU1H )EHXfHn)EH°fHnHǰfHnHEfl)EfHnfl)EHt~N,IHH JcHDLcL=} MLHI9,L;|uIDHEHIL$LcSDIvHHxHvH@H0H]HXHuHpH}H(HE1HHH HpH; HpHQD HxH9t;HXHHqH+1HH9H;TuHH5 HpHHxHxHH@H;t H;HxHDž8HDž@HhHHxHHHDžxE1H8vHhH=HAH9yH@H9HAL$HH@I$Mt IM1uIH HXH53H@H9t H;O HXE1HDž`HHE1H`HPHCHPH9{%I9HCNHhBH5y H0AHhIH+IHHHHIFHHHpH5F HH(H5ρ H~}LHLDIHcHhPALHAH@AHHHHpHHMHH1MPHULELHdZYCDHEH'AfDH@H9HhHHLdH@I$fDHEbHAHxIHhHHhH@HH8HHxHDž@HH`HLL@IL`LhHE1MuTMALL@ILhHHiL`E1LpH5 H8LpLLpAl?LpMmI9K\IHOLLIIH8HLH8LLhIIL@L`HE1E1AHDžx)MAE1HDžx HDžhL@HMAE1Hu11LLxMHEHE聓LxHLxHںLxHIME1E1AHDžhHDžxHH5oH8NLxMAE1HDžx'L`E1MAHDžxE1AE1E1LxE1ME1ME1HDžxALxE1E1HDžxALxE1E1HDžxAsME1AgHHBL@ILLhLpHMELpME1AE1ME1E1AMMAE1LpMME1LpAMME1LpAMMAf.fUHAWAVAUATSH8dH%(HEHH~H@HHx(uOH(+ H H9HH HgHHUdH+%(H8[A\A]A^A_]fIHH=O H;=HQAŅI $EHHx=IHH@H5z LHHIMUHI9FMNMM~IIIfInfInL1flHuLM)EH}I8IHIMHFL;%qHH[H5H81HdH=&,IM1DI $jHELHETH=G H H5 H)H>d H=+E1 fDHu LH dH=+IMuL1@H5ё H= 1:HHt111H H DHcH=+1qDH5q H= 1[:HHt111He H LHMcH=*T1DL/nf.HH5bH8)I $ E1LHbH=V*M1fH=E tYLf.LMLLMMt IHt H I $XHUdH+%(nHh[A\A]A^A_]fDLpLxLxLpfM}IELLmIL}̌HEHxHxHxH#HEH5 Hx` H}111MHELHLHxx=FH H5 H9pHf H HHLAŃH EGHMHUHxxHuI)H= LpHHLgH5M L8IHHՕI9D$Mt$fHnfInfl)EM`M|$ILIfoEHu1ɺL)E mLHEHEHMLuLeH3LHxHMI}xHMLLIDHxHELÌHEf.Hu1ɺLeHxHEmlHxHpLLxiHpLxfDHEHCHEf.HEL#HEf.HEHEH I}`H5 zLEL}Lu111LLEdJI}xHMLLCHb H`HEHLLHxxwCH~AH=/ r1)UHH8BH=# Hm H5n HHH=# HHu1ɺLHEMH]jLIܻ!I}xLLLB0QttHHUHAVAUATSIHtxHaI9tMHL5>I9AM9Du L6 HEIH I}`= Mu`H52& L HEIH,I}X MuXH54 L HEHH H5@ Hp HpHI H5< L HHEHpL L I}p HpIEpA{HEIH< IUXIHHPIUPHHP IU`HHP(IUpHHP01 MH5 4 L HEIH H5@ H HpH L H5m Hp HEIH Hp I}x MuxH53 Ly HEHH H5[ HpV HpHI ^ H5E L/ HHEHp] L3 I' HpH53 LIHEIH H5Z HHHpS LI}8HEHpH52 LIE8IH[ H5-Z HmHHEHp, LqH5 Hp>HpHI HCI}hHE2HMuhAEADžHHI $uJLC}?E>f.uH7H=*Mt I $H}t1HMHHpHHtHEdH+%(V HEHx[A\A]A^A_]H|DL|D|DHpLp|Hp@LW|GfHpC|HpHpL |Hp@L|`E2HhH5% LMHEHx`xLMuHT6H=LMHhHMHUHuzLuL]LM:I9d`AELm:LLML]HH}HELHEHhHULHpHEHxx 3Mt I $H]E3E;E;ME1@I t3H}HtHt5M|IsLzeDLzDzzE;H}E1HyvH5H5H810yE= L3z4EXEJItE1M??fLpLyLpE?MWfInfInfl)pM5MwILLhILULuXHu1ɺfopL)MYYH}HEHp%LhHpE?LuML]IGHEH3MwHLHEILu~EL1Hu`)EXH}HpHE EBwEBL]EBE1EMEQL]wIE(Hu1ɺLHEHEL]@XLEHpHEEE4HhHpLLL]E1Hxx!0ML]HEEPL]MwLuMMGILILELEH}fIn1+HuLp)EzWHpIKLEH59 H=H HEHHHH9CKLKLMM:LCIHLMILELpLMHu1HpfInAE()EVH}HEILpHEMLH= LYHEIHLHpUHp111HE{Hp/HEEE+H=T HH5HH}H?EJEPmH*rL`HFH5-L]H81tE5L]L`H$EPLpDEJL]H=9T THNIGHEHMwHLHEI%~EL1Hu)E"UH}HpHEL`L]zHULLH3HEL`HEHEE7HhLLE1HpE1Hxx,1HEfERL]EJL]xEML]hMwLuMMGILILELEH}fIn1HuLp)E THpILEp~pHEQ?EU~HERLpDEREVpEWdEYEYIEY=E[1E]E]E_ E]Lp~E`E_fE_I1fHu1ɺAE(H)ERIHEIEGE`EELuEGL]EaIEaEaEvuH,H=Ni?@UHAWAVAUATISHHdH%(HE1H- HEHEHEHYHHHEHHHLqHEMH}H5dsH9wje|H,m HiHHUdH+%(He[A\A]A^A_]HE|HuADHxHHBL AH H5sjH8AT1pXZH'A H=)1kDLqL=m, M~1 @HI9txL;|uHMHHEHTI@HNH>H}f.HyyHH'P H=x1E1IM9JtLʬtHEJZH<H}UH}=1fHH@1MPHuHULEHY^fsfDUHAWAVAUATISHHdH%(HE1H+ HEHEHEHYHHHEHHHLqHEMH}H5pH9w=hyH,s HMgHHUdH+%(He[A\A]A^A_]HEryHuADH1vHH?L UAH H5gH8AT1fmXZH %S H=t1kDLqL=) M~1 @HI9txL;|uHMHHEHTI@HNH>H}f.HvHHq$_ H= 1E1IM9JtL*tHEJZH9H}{RH}=1fHH>1MPHuHULEH[Y^pfDUHHAWAVAUIATSHXdH%(HU1H: HEHfHnHEfHnH=hflH})EHJ4HHuI9I7MtbM1H=l=Hn"H=H=_1HEdH+%(HeH[A\A]A^A_]LyL5! M 1HI94L;tuHMHHEH MwML-dL9-gLeHgIvI H^L-cL L9H]H;ugLe H;Mt@ rDoLy)UM~/HH4<1MPHuHULEHJZYH]L-]cLeL9H;s H;f H2tHCH5'. HHH HHH5GK 1HHMaHMHHH HHL5KsL9L9H;HfHs H.HHHH5H9p*H HHHAHMHH5m1 HHUHMIHHHMjH1HqI9GHu11LHEHEPIHHD IL5DrL9L9PH;AeCHrH'HHH5. LHHFqIHHH8vHH IL9L9H; dHMHqHMAH EQqIHmHHuHEHLH}nADžH}EApIH HHIuHHELH} HMA"HdE!pHHEHHtHMHIHLEjLHEtILB@L&L-n_HcL9-cLe/fHHHH5KC HFID$H=~qH9u1LeIHM9M9L;=bLoI?HWIHH)HRH;H=H HHfnH9G1HuH}HELeH]EH}HIILLMLMM9M9 L; aLMLnLMDIH LI LbeDHLqHEMyHKL=)3 H1@HH9L;|uHMHHHEM~HH=5tE1IM9JtL*tHEJOHgdfLWdfH;H=HHHylH9G01HuH}HELeH]CH}HIAIHjf.IHQeHxIEL@Hc0fDELtcE@E1IL9JtLHMΟHMtHEJHcfAE1H MtItDE1HDH=3E1MIt*Mr1Ff.LbDLMLbLMLH E1AH vH^bhflHL}A&A=fHELbHMWf.HafLMHaLMcf.H=0 HH56HHHH=2>DHa f.ZYHMIfDH=0 HA1H>HE1#aHH!DH=1MwM[I_ILHH]1HHuHELu@IHtHtL}dH_HHIEH5 3 LHHIMH52 L$IHHLgHHIMWI $[H;uKH8Z@fDLDHH9 HuH;TafDHc HHH[A\A]A^A_]@H(HuHYH t}HH=1fLYjLqYSHHHcHIIu L?YH/YuH!Y]H ^uHYuJLXLXPIUPItIzLXlPII $tL뾃iLwX%LiXttHO_HUHAVAUATSIHtxH!_I9tMHmPL5`I9AM9DuwH H@H= E1MDHE-fI $tHu11HHEHEh%ILwEfLgEH HUEuHDEuL@"=H f.H5i( H=BO 1HHt111HH tPufD<IfI $t:wLDfLDfHDDLDDHLL<IHX Hf.f.f.UfHAWAVAUIATSHHhdH%(HE1H )EHfHnH-HEfHnHEflHE)EHHHxHH HYHHEHCHpHL5 HE1ID$H9IN;tuHxJHEHHHpHSHHpL=$HE1fDID$H9RIN;|uHxJHEHLpI"@H~HFoLsHE)]MiH}}LsL%] M41HI9L;duHxHHEHIFHSHpHHFoH>HE)UHGrHGHtYHHEHH;9HcЉH9Du]LH@H=K 111莢HH=;膪HEdH+%(He1[A\A]A^A_]fHEKHu#DMH=f$zHjH=늋GWHH HcH9GH8AH5RH8DGWHH HHcЉH9E1IFM93IJtL}tHxJ@E1IGM9SIJtL|t<HxJ@HCo&HpH)eD_0E1IM9|JtLJ|thHxJN_fD FIHHtI $Lb?DHEIH'AfDHHl"1MPHxHULEH8ZYefHEIHAHnIHZBf.UfHHAWAVAUATSHHHHdH%(HU1H )`HfHn)pHfHnH HHEflfHnH)EfHnfHnfl)EfHnfl)E9)EHHHXHIHH5 H5HX\IGLMHXH=5f.Hg6fHXLP6HX@L76fH12@H= HJH5KnIM^DMOMIGIHIH81ɺHuLMH8L@HEL@HI tL8@H@Lp5H@H=Q mII@2-H@IMLM?H>AL@L4L@f4DH4fL4fL4gfHLL,HXHI&fDADHDžx?HA`HDžp>H[A8>HXHuH:AH5.H88HXn>HfH0H3"fZ>H@B>H.7f.@UHAWAVAUIATSHhHudH%(HE1HEHEHE?L`xHH%/DM<$I9t MvMd$MuHEE1IEH5.LHHILmMH ;I9E`MuLuMOMUIIIMLUfIn1L׺EHuLU)ECILUHEI/MHEMIMHCxHEHEH8L8Ht HIMt I $HMHtHHEHHHEdH+%(`HhL[A\A]A^A_]fLxL1Lx @MgILI$/HELELc1LE)f.fHu1ɺEL)EHEIDLxLLU 1LxLU@LEH0LEfLEL0LEf.LE0LEHEH5: Lk`HEHEL5: L9t M9|L5|L-NLLgHMHUHHu#rx_L: H}IHH}HH}HH{xHMLLLELE'IHEH{xLLHMHpHEHEHEHxHpHtH[H}t H}KHxOt Hx0LLsE1}H}HEHH7/LE(/LELE/LELE/LEIEtZA@tPHt>LLUH{xHMLLNL5L- /LL$L)= LL=2DttHO5HUHAVAUATSIHtxH!5I9tMHm&L56I9AM9Du~DnH;(stoCu-Et(HCHDH`HHTHH(HH(HUdH+%( H[A\A]A^A_]DLL5IWLHHH+8IHH@HHHHDLLIHsLHM~HHHL7HH_H@LMLLAHXHHH@H;3LOL`MLwIIHLXLHu1ɺHELMLH HHHPIԵHDž`MIGIHDžX#HDžP6HxHpHxxHhIKFHCHDHPHHhH@HpH8HxHHHmCI~xHN&HHHDžPH8H@HH&1LH5 Hx߷LIHp譴MgL蜴V):DI$H%H"H59AfL=nH81k(LLDL~1HhHtHpHDžhHtHxHDžpHt׳H5 1LLIHx贳MnL裳HDžxHHX6L(fL(fLH(LH7LXHu11HELHE9HPI6f.LPApLL=MI LXH`t9MtI $tNHyH pLHH'LHTLHL'LHLHL'LHH#LH8%AgLL=I@Hq#HHL=+H8z%LLAgLXH`f.LL=L2HMHUHDžPHxxIHuHMHULHuhHEH(HEH0HEH IxHMCHUHunH(H0LH L=/LLjLL0HEHEHEHEHEHE H`HXLHPgL0}HXLPL(H`HHL1L0H L0L(H)1HLLLH(3LH0HEH(H0LLHHLL(PH0A豯EHEL(LMtLHHDžPHtH`H HDžXHtHAI~xHHHDž`H8H@XLPI~xHHL(AgH8H@L0L0L(IG`fH IGhAG`H0IGpIGpH(FL(L0(H HH0HO1I~xHHHPH@HXH`H8jL(+'UHSHdH%(HE1H;=9t_GHt%CHUdH+%(H]fHiHuHElHHtTHu"HH52H;H81!{H6H=訋1o|3&UfHAWAVAUATISHHXH}dH%(HE1H)EHfHnHEfHnfl)EH-HHEHHiHt\MH={>HiH=֊1HUdH+%(EHe[A\A]A^A_]fLkL=M 1HI9L;|uHMHHEHIEHSHEL=HDE1IFH9IN;|uHEJHEH LmIlHHLfH]LeHEH;6H}1;HH?H6ID$4HCLHHHHHtPHue ID$HCLHHHHj3@r"LH=RIHH@HCLHHHI $HQI$HHHELHEDoLk)UMH]LefDjDHHEHCHEHfE1IEM9+IJtL[tHEJHH|H=+fE1IM94JtL:[t HEJ.HH3H5.H81XufDH%H5,H8i"NLH#LHLDLf.HE(H AfDHUf.HHM1MPHuHULEHdZY@HE*(Hu!fDUHAWAVIAUIATSH8dH%(HE1u%HGH HRHAL;-tL1zHH~HMIFH5LHHIMIEH56LHH IMID$H5)LHHHHI $HBHUHH5HHHUHHH HCH5&HHHIMH IEH5aLHHHH{HCH5UHHHIMH IEH5LHHHHIMHCH5HHHoIMQH xLLÅIMI $I}H5LnHH"fHEdH+%(H8H[A\A]A^A_]DHUL{HUfHgIfH5QLHHuAfDLLIM(LI $LILDIf.HL%B)H C)HHXH9uIH LHHH@I9mIEHHHXfDH5ALIHH5HvIHt!I9GHu1ɺLHEMLuHAHL螣L薣HfH/fLUfHzfHH3H5(H81XAf.HDH=Ϙ1XLAIu L)Mt @I $tHtH uHDLDI fAuDAIH t 1MuPHD1A\zIfMffZHfLAE1f:HUHfDIAf.AKLHHi1rfH!fI=fHbfIf1IfA5IEHHH@LH%莠H9%H%utL@AMofInfInfl)EM MgIEI$֠foEL1Hu)ELH诠H_%H\%q1KrL@UHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHH.L oAH vH5fH8AT1XZHH=|~1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}JHuH0H=HE}HELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLPtxPHEJfDHH1MPHuHULEHZY^@HEJHF}6fDUHAVAUIATSHH0dH%(HE1u%HGH HHsL54L9H1HHHHL%H;=G L9L9/GHHHH9GdH_HWLgHI$HfInfHn1LflHu)E>H tyLHHQHHHu(HE4HEfD^I$LHUdH+%((H0[A\A]A^]fDDHEHHEqf.HL%!H !HLpL9uIHHHHH@I9;HCHHLpfDH5AHIHgH5^HfI$HcI9D$Hu1ɺLHELLmHHHE葛L艛HEHH=H=Ly1DfH HH5 H818fHu1ɺH}HELmH}Mf>HHHY1bI $t#H t@HDLDE1HHCHHtHH@HHR蕙L95FH7u L%HH1Mt$fInfInfl)UMI\$IH̙foUHu1ɺH)ULHE褙HEDUHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHHyL iAH H5H8AT1 XZHH=Ď'w1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}JHuHPH=ZHEvHELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLItxPHEJfDHH1MPHuHULEH TY^@HEjHF}VfDUHAVAUATISHH0dH%(HE1u%HGH HHH;TH1HHHHLHHAHAHHtIEu4HHHUdH+%(H0[A\A]A^]f.HH@H Et nDHL-H HLpL9uIH0:HHH@H@I9HCHHJLpfDH5H豕IH H5H6IEH3I9EPHu1ɺLHELLeHHHEbLZHEHH3H5H81X HH==hs1gHHu H? MfH HH1IMtjH t fH DE1HCHHH@HHѓL95HuwLYAL MufInfInfl)EMI]IHfoEHu1ɺH)ELHEHEsHHn1H fUHAWAVAUATISHHH}dH%(HE1HBHEHEHEH5HHHEHHt{HHHL 8cAH ƷH5H8AT1IXZHH=,Wq1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}ZHuHH=ˆHEpHELyL-5M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLCtxPHEJfDHHH1MPHuHULEH;NY^@HEHF} fDUHSHHHHH;taH1HHtoHt2uHqHH]HH]HZtfDHYHH5cH81HH=% o1@I11ҾH=YP1kfHy#1H50HĐ 17DUHSHHHHH;taH1HHtoHt2uHQHH]HH]H:tfDH9HH5CH81HH=-n1@I11ҾH=.01kfHy#1H5H褏 17DUHSHHHHH;staH1HHtoHt2uH1 HH]HH]HtfDHHH5#H81HsH=5l1@I11ҾH=1kfHy#1H5޾H脎 17DUHAWAVIAUATSHxHpdH%(HE1HEHEHEu%HGH HNH=HI9L1kHEHH[HHEHELhx@MeI9t MfMmMuHDžxE1I~8H5HGHH ILuMH I9FM~L}MMVIIILU=fIn1L׺pHuLp)EILpHE_MHEH&IHMHEHEHQxH:L"Ht H~Mt IMOHxHtH1HVHuHHHUdH+%(Hx[A\A]A^A_]f.Ml$I$LIEBHxfD;DHpLHp"@LhLLh@HHH HL`L9uCH ZLHH`H@H9qIFHHL`H5LHEHHH5HpyHHnH]H9CHpHu1HߺHEIHEHEHLHE莊H膊HEQDfHu1ɺpL)EvHEDHhLLpqHhLpwfDHEHKHEfHEL3HEf.HEHEoDHHH5#AL=xL5gH81H]E1Ht)H MtNI H]Ht H LDLg1f.AL=L5H]PIHEHEH5% HEHEHx`:L=L5LLgH}HMHUHu?xmH}H衈H}蘈H}菈HELLHxHxx赵HAH}HEHHS>H"HELULLH]HxAHxxLpQHLpy}@L'HH1LUHLUGHYL=HEHxLLAL=^L5M~Hxx輴KE1QIFHHH@LH 訆L9% H H,HEZAL=L5}LUAL=ǶL5}aLkfInp)EMLcIEI$Le跆foEHu1ɺL)ELHEHE苆HEH H 31 Jf.UHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHHL UAH VH5FH8AT1XZHzH=d|c1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuHH={HEycHELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLf6txPHEJfDHH1MPHuHULEH@Y^@HE*HF}fDUHAWAVAUATSHHHxdH%(HE1HEHEHEu%HGH HHL-L9RH1XHH\HHELpxDM&M9t MgMvMuHDžpE1H{8H55HGHHjIMUHEHH0HxHhHHAHhHHEIH`HHHhH5'LhH`LHLL`Hh藅HhL`HHEIIMH I HEHpLLHxxcHEdH+%(~HĈL[A\A]A^A_]Mt$I$LI;Hp BDIMu%L`LHhHhL`H HEMt I L}H5 HEHEI`2HHHhHxHH`n_HMHULHu*8QH{0H5wHGHHILmMIGH5LHHHHIH%H9CyLCMlL[IIH fInLHu1xLxLX)EZHXI+LxMvI KIM3H}HH}H(HL-2H 3H/LpL9uCHHHH!H@I9HCHHLpH5gHGIHH5$H̙IEHLmI9EfHu1ɺxLL)E&HEIHEMH~L~fIMSHECfLefLxHLx>@LhLHxLhHxfDHH3H5H81XHDžxHhHuH`HhxE1H`>\LhHLhALIIDžxLm1E1HEHpLLHxxĪM4IMLuLetUMtIt@MtI $tKMtIt H;H 2Hs$LeLZLOLDL9XM9pfDL LLPHLXLXLP$HHH1LDIMt=LuM HE11DžxHhHsH`LTHxHu1HߺHEHE IIDžx12IDžxHDžxLE1HCHHH@HHzL95HLH{HDžxHhHrH`MufInLux)EMI]IHH]zfoEH1Hu)ELHEIzH HI1|HEHpL1LE1Hxx譧H8DžxHhH>qH`LuLef.UHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHHL IAH 6H5&H8AT1XZHZH=lpW1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH} HuHH=pHEYWHELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLF*txPHEJfDHHT1MPHuHULEH4Y^@HE HF}fDUHAWAVAUIATSHHH@IH^HL;- ^L1tHHhHLIHMmHIELMBH=3ULLHAIMtcItNH t)I $t2HL[A\A]A^A_]ÐZvDHGI $uL8fDL'DHIxHH=mE1TjIH5LvtLIHE15HHCH5H81hwsfDLLHIH?LG:HsH5H8KfDUHAWAVAUIATSHHH@IH^HL;-^L1THHhHLIHMmXIELMBH=5LLHAIMtcItNH t)I $t2HL[A\A]A^A_]Ð:vDH'I $uLfDLDHItHnH=kE1RjIH5LttLIHE15HH#H5H81HssfDLLHqIH?L':HSH5H8+fDUHSHH;=t>1HdHHtLHtHCxH]Hf.HHSH5H81xHH=jQH]1UHSHH;=ht>1HԺHHtLHtHChH]Hf.*H1HùH5;H81HH=MjPH]1UHSHH;=t>1HDHHtLHtHC`H]Hf.HH3H5H81XHH=ihPH]1UHSHH;=Ht>1H费HHtLHtHC(H]Hf. HHH5H81HkH=iOH]1UHSHH;=t>1H$HHtLHtHC H]Hf.zHHH5H818H۠H==iHOH]1UHSHH;=(t>1H蔸HHtLHtHC0H]Hf.HHH5H81HKH=hNH]1UHSHH;=t>1HHHtLHtHC@H]Hf.ZHaHH5kH81HH=}h(NH]1UHSHdH%(HE1H;=GHtA[H}HHHUdH+%(H]@H RHuHE HHtHuyH|H=mdHeMHu9H3H~HIHH5SHH81HھH=g M19fDH{UHAVAUATISHH@dH%(HE1u%HGH HHH;6CtDk%HLEt4H[PLHHAHAHHEusHHHQHHUdH+%(dH@[A\A]A^]fHiPHHuHEiHH-HUJfHH@HL-2H 3HLpL9uIHpzHHHH@I9HCHHLpfDH5QHlIHLH5~HVIEHSI9EHu1ɺLHELLeHHHElLzlHEH;|H=aHJ?HSfDHH=1e|J1CDHWfHaH H5kHƛH81HMtfHHHt1}IMtVH tH_MHDHHu HH%fDLDE10HCHHH@HHijL95HulLjMufInfInfl)EMI]IHjfoEHu1ɺH)E¿LHEjHEHRHOy1SUDUHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHH L 9AH fH5VH8AT1XZHH=bG1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuH H=BbHEGHELyL-իM1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLvtxPHEJfDHH1MPHuHULEH$Y^@HE:HF}&fDUHAVAUATISHH@dH%(HE1u%HGH HHH;$6CtDkHLEt4H[HLHHAHAHHEusHHHQHHUdH+%(dH@[A\A]A^]fHIHHuHEHH-HU^JfHH@HL-H HLpL9uIHpzHHHH@I9HCHHLpfDH5HafIHLH5~HIEHI9EHu1ɺLHELLeGHHHEfL fHEH˕|H==[H5DHSfDHH=^ D1CDHfHHH5HVH81H;MtfHHHt1}IMtVH tHMHGDHHu H/HfDLDE10HCHHH@HHcL95H{ulLdMufInfInfl)EMI]IHNdfoEHu1ɺH)ERLHE&dHEHHy1SDUHAWAVAUATISHHH}dH%(HE1HrHEHEHEH5HHHEHHt{HDHHL h3AH H5H8AT1yXZHH=d\A1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuHH=[HEAHELyL-eM1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLtxPHEJfDHH61MPHuHULEHkY^@HEHF}fDUHAVAUATISHH@dH%(HE1u%HGH HHH;6CtDkEHLEt4H[XLHHAHAHHEusH<HHQHHUdH+%(dH@[A\A]A^]fHCHHuHE艨HH-HUJfH1H@HL-bH cHLpL9uIHpzHHHH@I9HCHHLpfDH5H_IHLH5~HvzIEHsI9EHu1ɺLHELLe״HHHE_L_HEH[|H=TH=_HSfDHH=X=1CDHwfHH-H5HH811HˎMtfHHHt1}IMtVH tHMHDHHu HHEfDLDE10HCHHH@HH]L95 HulL^MufInfInfl)EMI]IH]foEHu1ɺH)ELHE]HEHHy1SuDUHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HHHL ,AH H5vH8AT1 XZHH=$V;1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuH@H=UHE:HELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtL txPHEJfDHH1MPHuHULEHY^@HEZHF}FfDUHAVAUATISHH@dH%(HE1u%HGH HHH;D6CtDkHLEt4H[`LHHAHAHHEusHHHQHHUdH+%(dH@[A\A]A^]fH=HHuHEHH-HU~JfHH@HL-BH CHLpL9uIHpzHHHH@I9HCHHLpfDH5HYIHLH5~HtIEHI9EHu1ɺLHELLegHHHE2YL*YHEH|H=]NHU7HSfDHH=aR,71CDHfHHH5HvH81H[MtfHHHt1}IMtVH tHMHgDHHu HOHՇfDL/DE10HCHHH@HHWL95HulLWMufInfInfl)EMI]IHnWfoEHu1ɺH)ErLHEFWHEHbH_y1SDUHAWAVAUATISHHH}dH%(HE1HHEHEHEH5HHHEHHt{HdHHȝL &AH {H5H8AT1XZH:H=O41HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuHЅH=rOHE94HELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtL&txPHEJfDHHd1MPHuHULEHY^@HEHF}fDUHAVAUATISHH@dH%(HE1u%HGH HHH;6CtDkeHLEt4H[hLHH4AHAHHEusH\HHQHHUdH+%(dH@[A\A]A^]fH6HHuHE詛HH-HUJfHQH@HL-H HLpL9uIHpzHHHH@I9HCHHLpfDH5iHSIHLH5~HmIEHI9EHu1ɺLHELLeHHHERLRHEH{|H=GH0HSfDHH=!L01CDHfHHMH5HH81QHMtfH/HHt1}IMtVH tHMHDHHu HHefDLDE10HCHHH@HHvPL95jH[ulL1QMufInfInfl)EMI]IHPfoEHu1ɺH)ELHEPHEHHy1SDUHAWAVAUATISHHH}dH%(HE1H"HEHEHEH5HHHEHHt{HHHgL  AH tH5H8AT1)XZHH=I7.1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}HuH`H=2IHE-HELyL-M1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLtxPHEJfDHH1MPHuHULEH Y^@HEzHF}ffDUHAVAUATISHH@dH%(HE1u%HGH H HH;dCDkHEID$H5cLHHIM2HCH5HHHHHHCH5jHHHIM H LL5I$HI$IMu{HTHHQHHUdH+%(H@[A\A]A^]f.H/HHuHE虔HHHfHAH@H1fLSfL9fHHmH5H81xH|HH=F*1fDHL5H H LhL9uIHHHHH@I9HCHHLhfDH5HKIHH5HfIH I9FHu1ɺLHEMLepHLHE;KL3KHEf.Hz|H=]@HU)HM*fHHH1@IfڷHfI $u LѿHVz;If.I $u LH uH뱐HI$=IMHyuLQufE1@HCHHH@HH1IL9-HLIHvy[I^I^fInfHnfl)EHMnHIE]IfoEHu1ɺL)EaHHE5IHEH!HT1*L;`L-UHAWAVAUATISHHH}dH%(HE1HbHEHEHEH5HHHEHHt{H4HHL XAH lH5ֶH8AT1iXZH xH= Bw&1HUdH+%(VHe[A\A]A^A_]HLyHEMHuH}jHuHwH=AHE &HELyL-UM1HI9t@L;luHMHHEHIo@HH6ffDE1 IM9t`JtLtxPHEJfDHHQ1MPHuHULEH[Y^@HEHF}fDUHAUIATSH(H *dH%(HEH7H9HH*HKHHCH5HHHIHHHM;HLLI$HI$u~HL%8L9C[HQH=L9HG1LP(HH8u!HEHʺHE@HHHUdH+%(H([A\A]]LADHof.HI$to5HrH=ZO#1HuH*H'HHuHE艌HHt)HDL߹DHct|H=9"jHHHfH=H(H5(HHDZIfH=مHHYHH5cH817fDH)H-H53H81UHAWAVAUATSHHH?jHC H4H $H9HyL-j$MIEIEH5rLHHwIIEHMZIEHID$H5zLHHvII$HI$M=HdH;WH5}HIHH9CdOIMnXIHTL`IH>H5vHHH9C3OIMH5qLL/InH5_HHH9C3>OIMt^H5/LLxGILLLDIHLALALAHHU@IMI $Mt I)Mt IHnH=ƈE1HHHHtVHL[A\A]A^A_]DbH;HOHC }HIHPHHHuH fDuL uE@uLu#@H=IM;#fDL]fLfuLu@uL|u@MIMMI $uL?uH=YH H5 I5fD뙐IfLfHLδfIfH)H5E1H8$@If.E1»IfuLE1M>MuxDLf.rI%UHSHH;=t^WPGTt$tHaH]HHYH]HÅtlG\uHFH]HDHgH]HHH5HmH81HHiH=8XH]1H5aH=b1 AHHt111HH tHf.@UHHAWAVAUATISHHH5ԮdH%(HU1H~HDž`HfHnHHEfHnfHnHHhflHHu)EfHnflH}HpHx)EHt)J HPIH EJcH@IH ?JcHfDLsL=nMrLHI9L;|uHPHH`HCIfo@oLs)`)pMH`LhLpHPHxH@HSH0H.ID$I$IH9t3HXHHqH~'1HH9tH;TuL;5HPH@H L;% H5sH=^/9;HH0P L0HH HPHHC7L0HHPI H5LHL0H@H5{LĪL0LLHL@=L@HImL:H}:HPq:I $&I HEdH+%(HeL[A\A]A^A_]HH9gHuH;U]fHP o@o0LsHU)`)pM~6HHhH1PHPHUML`_AXH`LhLpHPHxH@HEH0dHLsH`M HZzHPHsHL HhIMHHPHAHHpIMH&yHPHHdHxIro Ls)`|@HPo(LsHp)`@HH0IIH@HHPH`CHH0H@LpLpL`LhHQH0IH@fDH1H0HXH@HxfDHX H0H]H(HH9XLMaIH5sLL O7L HIIIHܴI9G~HPHu1LHEMHE9HHL7HBAŅH6EL;%"H5H=619HHtH111 H6E11۾E1HDž@HDžPfH5nH=b*=6HHIHiHPLPHI@?LPHI H5wLHLPH5LLץLPH@H5vLLPLLH8LPHIHLPr5HPf5L^5fLfLwHDž`HuM1H=|.H`[H=}|E16I$L4LI4LMIMIM9rJtLbtVHPJLLHRH!vHPHzH HEIHDH5^HP}s L;%=%H5H=Q16HHtH111 H3E11۾E1HDž@HDžPfH _H={Mt1IIH@HtHH0HHHt H HPtHPHH@HHsMIML7L9@H5jH=&2HH IH HPHIEIH'HPH5!}LHuLPLH0H5-tOLPLLHR5LPHIHLP2L2HP1H5p\HPTqI  L;% H5H=(14HHtH111H1E11۾E1HDž@HDžPH HDž@1E1HDžPE1Mt IMt IME1MI w0L80]DH0LH0_@uDHpf.HץfL LE1퉵0L 0LD@HLP@LPL Llj0ZL 0H=_tH@ H5A IMKE11E1HDž@HDžPH5QH= 12HHtH111Hu/E11۾E1HDž@HDžPE1E1HDž@HDžPH=sI:HDž@1۾E1E11E1HDž@HDžPAE1dMGfInP)MbMoILL IE.LHu1fo)}苃H H\.AE1E1HDž@HDžPE1E1HDž@HDžPE1E1IE11۾HDž@ȭHL9@M9WH55XL-IHH"I9GI_HMGHLIL@b-Hu1ɺH@HEH]_HI4-L@ML-H5WHP,HHnHH9CLkfInfInfl)PMLCIEHIL@,Hu1ɺL@foPLLP)E裁LH@t,LP1H@WLP,H5fH= ,HHHH9C1LkfIn@)0MLCIEHILP+Hu1ɺfo0HP)ELH+LPHL+H5sHl+IHDH I9EMMMMEILL0ILPB+Hu1ɺL0HPHELML 1H H0*LPH0L*L;5H5huH0*HH LHKL HHPpL|*HP6LPALV*E&H5^YH0*IHHI9E?MEfInfInfl) MMMILLILP)Hu1ɺLPfo LL )E~HHP)L HP(L~)H0r)HPE1HHIM6<H^=(HW)f.LPIݾ1HDžPE1HDž@E1E1HDž@HDžP;E1^L9@HHH9XL=MIHI9GJHPHu1LHEHELuk}MIML4(H5_H=&(HHPLPHHaH@IEL(HqI9AmMAfHnfInfl)0MKMyILL@ILP'LHu1fo0)E|H@Ig'H_'M1HPJ';E11۾E1HDž@HDžPHu1ɺHHEL}|IH@H@HHu1ɺHEHE{IH Hu11LHEHE{MH0HDž@E1HDžPLHu11HEHEl{MIHDžPE1HDž@1۾HDž@E11۾HDžP]H5~H=71(HHtH111H%E11۾E1HDž@HDžPE11۾E1HDž@HDžPHu1ɺLHEH]LPdzIHDžPH0HPH0E1HPH0E1HPNHu1ɺLHELuyMHPH0E1E1HP^1E1H@HP\HDž@1E1HDžPHDž@1E1HDžPI_fHnP)@HMOHLILP#Hu1ɺfo@HPLu)ExHI#LPoH=elIM1E11۾H@HPH=.lHH5#IH0E1HP*H0HP11LP1HPH@UHAWAVAUATISH8dH%(HE1HTIHHIHH5fj"K|H8IHH=ev1IHBH@H5?vLHHDHHFHϟH9CeLCMXLKIIH fIn1LϺHuLELM)E wLELMII6LHHMUHHIIFH5OrLHHIMCLHH)IGH5LMHuH= -HuHLAIזMtzI1H IMfDI $tBHEdH+%(\HeL[A\A]A^A_]DIH*E1뿐LDHuHH5oH8ޙfDIH qHKH=E1IMILx;HgItLUfLMHLE?LELMfH'fLfHHu1HߺHEHEtIfLMLӔLMf.H9HE1E1L bIH CH8H5ߍR1HgoXZNIt{HJH=ME1e:HffDuH$u{@ICf.LwfL3fHLIHn mf.UHAWAVAUATISH8dH%(HE1HTIHHIHH5e^K|HIHH=q1IHBH@H5qLHHDHHFH?H9CeLCMXLKIIH fIn1LϺHuLELM)EzrLELMII6LHHMUHHIIFH5mLHHIMCL%HH)IGH5LMHuH={HuHLAIGMtzI1H IMfDI $tBHEdH+%(\HeL[A\A]A^A_]DRIH*E1뿐LWDHuHvH5ߋH8NfDIH qHXIH=dcE1 IMIL;HאItLŐfLMHLELELMfHfLfHIHu1HߺHEHE:pIfLMLCLMf.HHE1E1L DH b?H8H5OR1HpbߎXZNIt{H'HH=.bE1HffDuHu{@rICf.LgwfLW3fHLtIHn ݒf.UHAWAVAUIATSHHdH%(HE1HIHH%IHH5_aHK<HHH H H9HL5MIH˖I9FHu11LHEHE1nIIHM IHIGH51LHHIIHIMHHIHIFH5 LM%HuH=BdHuLLAIM=IIHT H H9HL5MIHI9FHu11LHEHElIIHMmIHAIGH5FLHHSIIHIMHpIH H@I$L HI9FMFMMNIIIfInfIn1LeHuLE)EfInflLM)ElLELMII_MIIHMdIHIMH tyHEdH+%(jHeL[A\A]A^A_]@HHE1fL_fLMLLELELMfHgyfLW]fHuHvH5߅H8NfDIIH]CH=}]E1fDLgfL׊NfLNJfLfLfIHBH=\MxE1vfDHHE1E1L ?H 9H8H5R1H\XZ?LfL fLE1Of.H=QYHJH5KFIM8DH=!YdIMnMM~IELIL}41LHuHELm5iIMIt LuL7DHLf IfLfH!bHu1LHEHEL}LmhIf.LMLLMf.LLĀIH[H=WHH5IM H@H=ZRDH=WIMNMM~ILLMIL}LM1LHuHELMgLMII t LuLDHML?fzIfuLtufDUHAWAVAUATISHhHuLodH%(HE1GpHEHEHEIE(fI]IE(Mu AEHEHHtH u H̆@MtIuLDHMHtHHxHHu H@HEHXxHfL3I9t MoH[HuHEE1I}H58YHGHH|IL}M]HcI9GYM_L]MHMWIIILU1LHuL]LpLxHEeLpLxHEI YHEHI HMI|$ I]HEHEMu IM(HtID$ H+AD$pHUdH+%(Hh[A\A]A^A_]HBL=;I^ILHHELHELmH5AHEHEI}`@HEHMHLL=k;Hxxz<LH=~qAD$pL`n1fHpL0HpLxDHEHEDHELHEkf.LpLLxɃLpLxfDHu11LHEHEpcMHE@L=a:H=LuHMHULHuLm-HEL]LHLUHMHxxLmLpLx;OLpLxMt I MtI t~MkIMauLłuMHEzI{H}Y H}P H}G HEHMLHHxxp: LbtLULPLUTUHAVAUATSH0dH%(HEH%~H9lHW@HHzXHH H9HjL%MI$HrXHzP~HHHI9D$RHu1ɺLHEH]kaH tfI $HQHI$HtdHUdH+%(H0[A\A]A^]@HH=aDIMt^HS@MHEHHEDHELHEDHy82H==I $H6H=1CH|H5ʐH_9H81p븐H=CH*H5+I-fDMt$fHnfInflMMl$IIEI $tZ1HuL)E_ItM{fDHELHEDL f.)ELfoEffDUfHAWAVAUATISHdH%(HE1H3)EH`!fHnHEfHnfl)EHHJ)EYHPILXMPLiLX>LKX= IEL8H8HEHhHtHHELBHHH8HHxHtHH8HxgfDEHCH LcH9 Lk I$IEHHHtHuHPHtHaL=tHXIHtHCLpoLi)UM{ HELuH`'L7xfHLyHEMDE1LL}IMH}IIM9 JtLjtLL}MH}I J]fDwHEMML(HH HH9G LoM HGIEHHXfInHu1HX;)EWLHELMMF HXH}B AŅH}HEEIWHBpHx H@Hk H5YLHEIH LKHEH5N'H}E1IM9 JtLҲtH} JLvf. vD;1E1E1IMt IMt I sH}Ht HHt H;H+H=H}tE1HUHHEHHLuHt H Mt I $Mt IMHpHtHHEHHHhHtHHEHHt5HxH HHEHHHt@HtDHtUf.LtLfLtKfHtZfHwtfuHdtu@`LHEEt`HEkfD`HE t`HEXXLH`LMsXH`LMXLH`LMsXH`LMH@LHHALe>~LUHt!HzH2H9 LUuLULLUH1H5 LUH9p H=HP HH}Hn{H9GQ H}Hu11LUHEHERLUIM H}LUH57LHE[LUHHEIm L_H`H5LH)LUHI H5:H LUHI- mLUHI LpHELpHLMMy(IA uLMLpHIH5OHLMLpHInH5wOHLlLMLpo LpHuH}LLMLpHI H}L`9H}HE(L HXLPLHH`HpH=$L8HEHH5$H}IHH}HEH8LmHEIHL~HxL}HtHfHELxlHEIHI$HxL`H}IELh HpHIG(HIw0LT H}H8HpLHHEIHhHXLP_HX2E1E1LPLHHp1fE1E1E1E1HDžx1۾/1HDžhHDžpDLwoLMJfDHuH9RI\HHHHE@IUHBpHH@HH5+RLH8HH8E1E1@H`fHHxHHX3E1E1LPLHHp1fH!3Hu1ɺHEHEHXFNLXHEIHuH5HRE1H81m@1E14@HExHAfDM=1E1E1HHe@1HPHUMLEHZYWDH92LHu1ɺHEHEbMMIiHxH=HX6E1E1LPLHHp1?Ml$ID$M$$3DH`dH[LkIH5H@HH@HHEfDHEZwHp@1E1ɾ9H1Hu1ɺHEHEHX?LHEIC1E1ɾ@THsH5}HRME1H81 k>1E1HE1;HjH8H L8IELLAIH(L8LAIH\H8AHEH3H8A׾Hڶ^H8&1@e@1E1VMl$M$$g1E1ɾ<9H8AE1E1Hh1MHBbH5~H81iH8E1ɾEHh1M1=LXHZiHEIHH@LHIIHH}AIHJH}A׾H謵BH}HEeH8EE1E1Hh1H8DE1E1Hh1H/aH5|H81h H8DHh1E1E1E1E1HDžx1۾01HDžhHDžpE1E1HXLPLӾHLHHp1^HDžx0E1E1HDžhE1HDžXHDžPHDžHHXLPLLHHp1M1E1E1>E1E1HXLPLHHp6H8E1E1HD HXMM6LPLHHp1YH?H=07HH5LU衠LUHH}HHXLE1E1LPLHGHp1H=6LU茟LUHH_HHGHLUHHE_H}Hu1ɺHEH]_GHI4LULCH8AH8AE1ɾG-E1ɾH GAH}HEt^HXM3E1LPLHHp1E1HXLP1E1LHHp3LpHXLPLӾGLHHp1|HLUGLLUHX1E1E1LPLHLHp0*iH8MMCHXMM3LPLHHp1H5=mUHAWIAVAUATISHHdH%(HE1HHEH8!fHnHzaHEfHnflHE)EHH IIALpLH5VHH LpIIHMsIHLpdLpHI" L`H5:HHHp[LpL`H5LLLPwL`LPHHpILL`'H`H5t8LIHJ HhI9A( MyM MqILAIHu1ɺLHEL}?LH`L`MM:LL`E1wH`kH`H5U2Hh1IHw HgI9F5 MFfInx)PM I^ILL@HHHu1foP)E>H@IM} HMtLL7 *H5z%HpfIH HgI9F I^H M~HLIJLHu1ɺHEH]K>HHPHP LHPHAƅE HEl HEL@HPI|$ H5-!IHv H5fI9Fq MnMd M~IELI{L1ɺHPHELmy=LHNH L=HAƅ H"E H2HH9X L5Mg IH5g4LLHH(HTeH9Cv H<Hu1HߺHEIHEH}f.H lH=1HPHH HEHGHEE1IM9JtH}H}tHEJ1fDHqHuHUHHHEH}11HM$HH(HML5u?L9H; PH; CHOP1҅UQUHm)@HBH5VH H81EnH H=]蘯1HBHy H5VH81HEHg nH=X1CPHHH:1MPHuHULEH=ZYHMhI@UfHAWAVAUATISHHXdH%(HE1HG)EHx'fHnHEfHnfl)EHHHEHHuHt`M1H=H\ H=M1HUdH+%(He[A\A]A^A_]fLsL=!M1HI9L;|uH}HHEHpMnMOLu1iHH,H~L6H}LuHFH;=<H;=cMH;=i@MÃ1HULE]"HH0 H=HEHE@H~L6H{H}LuYHH1MPHuHULEHZYbH}Luf.L61LfD>HLkHEML{H M1 @HI9txH;LuH}HMeH |I}(H5 HHpHEH9CLSMLsIHLUILUHu1LLULUHEfInLUfHnHfl)pMtLHI:;HHMfop`IM8HMMe0IM@HMHy HtHA HE+HEHMApHI} H5HGHHHHH;=;I}H5IHI} H5IH1AHIE1Mt I $Ht H Mt IH DH= 胧HE@pHp(1HUdH+%(Hh[A\A]A^A_]@H5HHHMI $H5 HIH(HH9FHEI9GI_HIGHLHHExHufInH}fHn1fl)ErIHtHBL:M9H}(I}H5]IH\I} H5QHHhH5HIHEHH5LHH"LHEI9D$HMT$MrID$ILLUHHErLUHufInfHnH}1flLU)EdfInLUfHnIfl)UMtLHMH}7HHfouH=E1HpIHHID$H;4L5|5L9L:HHH@HHEH8LIeHEDHELIHsL5 5Lg;,fE1AIMAIfDI$LHEeH;sf.L:ifH:vfL:-fH:fL:fL- BLaI}/CfL:ofHu1DL9HEH LH]HCHLAIHKHH]AH}HH6A׾HgH}L9+f~AI ~AH}AA'H9HEHEM|HHEI j1HOf}AEH}AH1"1Hf}AHG }Iu L8AHf|AIIt1L18fIHu1E1]IHu1Hf.L}Hu1@s|HMHHEHHAH}82DL}HuGfD+|E11xo|E1H.H5JH816{GLeHuE1{1Le{E1AHLeHu${u{H QH#7CY{L}EAL-c>HQIuH9u^C9=H}wu1譻{E1M1AHY:zMAHE1H{uzMuzMAHI16UHAWAVAUATSHHL-@2dH%(HE1L9H~IHL~IM9I$HFH{M$ Ls II|$`H.H{Mt$`&Ls(II|$@HH{Mt$@ALs0II|$HHH{Mt$H,Ls8II|$hHwH{Mt$hLs@II|$pHbH{Mt$pLsHII|$xH]H{Mt$xL{PIL;=G-L;==zM9qL(>AƃI`H{Et$L{XII|$PHH{ M|$PL{`II$HH{ M$L{hIL;=,L;=+=pM9gL~=Aƃ2IH{ E$.L{pIL7AƃSL蓾H{ E$PL{xILAƃuL]H{ E$rLII$3H{M$LII|$ Mt$ HIH&I|$0Mt$0HIHHNAƃL誽E$HIHI|$XMt$XH]IHHAƃmLEEt$H#IH_I|$8Mt$8HIH7I|$(Mt$(HIHH_Aƃ%L軼HCE$HHIEL@HUdH+%(fHH[A\A]A^A_]fD1D1D1Dr1H{Mt$H IDLJ1D(IHt%HH8II}M@ H H=R1fD0~D0DD0D0DH 8H5TH84l@j0D1'IHDHH7II<M#fDL0 fH!,H H5+@H81.IL/f='IHHH7IIM&IHqHH6IIqMK&IH1HH6IIAM H5aL1H5AL聹HHH5. HEeH}HIpHSIHH6I9D$MD$fInfInfl)EMI\$ILLEHfoE1HuH)EH}IL޸MtaHѸLɸ)@L-QfL-fL-fHu-LH uHfD%IHHH4IIM0c$IHIHH4IIM#$IH HHm4IIM7H@=$HHEHH4HUIH MfD #HlHEHH3HUIH ZM>fD #H$HEHH3HUIH  MfDL+f.6H@ 5#IHHH 3IIM26H@ "IHQHH2IIM+5H|,@ "IHHH]2II;M`E"IHHH2II MJL*uf.Lw*f5Hol@LO*f.L7*9f &fD4H@H)^H)H)4HL)#L)mL)L)1LHuHELuS LI(ML>-UHAWAVAUATISHHH}dH%(HE1H"HEHEHEH]HHHEHHHLyHEMHuHFH;!t H;5$uH}\HHHH$HHUdH+%(He[A\A]A^A_]HE 3HuADH/HHD L AH { H5k!H8AT1&XZH H= 1hDLyL-M~1 @HI9tPL;luHMHHEHTI@HNH6Huf'DE1IM9JtLctHEJfDHHH.H H5 H81&H H=1pDHH 1MPHuHULEHnY^n*f.@UfHAWAVAUIATSHHhdH%(HE1Ho)EHHfHnHHEfHnHEflHE)EHHHxHWHHHHEHCHpHL=H)E1@ID$H9IN;|uHxJHEHHpLcHHpL5M 1HL9L;tuHxHHEHLpIH&HFoL{HE)]MH}Lu}@L{L%M1 HI9<L;duHxHHEHIGHSHpHL6H~HFLuH}HE"IH]L}Lx-HH\H= H0AŅH EH=xH5YHGHHIMH`,I9EI]HMEHIIMfInfHn1LflHuLx)EH LxIMMIM L;=t1IGH;ZLLHHHI$LI$Hu^L=#SHE-Hu#DMH= bH H=1HEdH+%(&HeH[A\A]A^A_]LxH"LxH- H=Y蜋I$H1Hw"E'#HHHHSHHHH=IHH H5LcHH5H8IML*IHtFH=CHz(IIEMHIEc11LH覂I $<H% H=Q蔊H Hr!DE1IM9JtL]tHxJE1IFM9;IJtL]t$HxJ@HCo&HpI)eDE1IM9lJtL"]tXHxJLg fLxLP Lx_@H7 f*H@H H=ͥHu1ɺLHELuI.AH t%HN DH=|迈f.HDHE2*HwAQfDHHI 1MPHxHULEHpfZY /H'f IfH H=Ť LfHfHEr)HAfDAIM1LH DH=D臇HfLWLIH H=<=ARHHH%Hz 1H5 H81H H=I$HA%HIEAY!fUfHAWAVAUATISHHdH%(HE1H)EH fHnHEfHnfl)EH.HH`HyHHtZMH=z \H *H=1HUdH+%(He[A\A]A^A_]LsL= M 1HI9DL;|uH`HHEHIFL{HXL-M1fHL9L;luH`HHEHLXI"fDHHLfH]LeHЗHEHEH ّHDžpHDžxHEHEH9HQL=MILpL;=L;=n$cL;=tVL$AŅRIFHDžpEOHH H9HL=M%ILpIGH5LHHILxMIH51L薨HpIHIMIHDžxHpIHHH=ř1HHXI$L` 'HxHILH`LH`0fIDLHDžpE%HMHUHxxHuHPHH H9HL5}MILpH5;L#HEIHIH!I9@fInfHu1flúLL`)EL`HxIHDžpMIH}HEHDžxHt蟣H}HEHt艣H}HEHtsH5LHEHȃtMHuH}HHHMHEH>HH5H8_ I $tYH 0H'fLfHfLofLUfLfH=HZuH5[u6@IHeDH=T?fH 2H=LpI $xLejHHq 1MPH`HUHLxeNY^2fDI $L EHMHEHLHLGY1ǃL:L>9rHW I9D$Hu1ɺLHEMH]IH菎ML~LUHEIH)L]H5~L.HH5L:TIHHHiH5HAH5:LHHxIHH܍LԍL;=0Hx11GHHtH51HHE LE{L胍H~HkH9PL%kMI$H5L IHH I9D$Hu1ɺLHELL} ILMHH}H &kH9HL% kM|I$H5L艌HH0L蕌H5~LfHEHJH H9CWL}Hx1HHuHEL}HEZLI/M& H]H}H2}HKjH9PL2jMIH5LLE軋LEHHLËIHIHELpLEHILEHHHEH5lL]LELLHL]LEfLEL]HIHL]LE"H}H}H5QH=~݊IH7H |HiH9PLhM0IIT$HBpHH@HLUH5tLLEfInLELUfHnHIfl)PL`HELELMHL`I<foPIELEHEp;L]LEHL`HHxHPHBpHH@HL`H5L]HxLELEL]HL`HH@H; H5wAHBHH HcBHpHxLPL`LELEL`HELPHpL}MHLPL`LEH5LHLEL`LP LL]LEʈL]LEHLLL`LEɋLEL`HHELPLL`L]wH}nHfH5H=|3L`HIIT$HBpH7H@H*L]H5LL`L]L`HIH5\HHELPL`貇LEL`HLPILH`L]衇L`L]HLPI9AMyMIYILL`HXHu1ɺHHEL}YLHE-LPL`LEIMHLPL`LEL`LEHsLPI9CM{fInfInfl)`MsI[ILH螆LEHu1fo`HLEL`)ELHEdLPL`LMLL`LM?LME1IL`MHL`LML`HLMI9BvMzfInE)`MYIZILHDžLMHu1fo`HLML`)ELHE荅L`LEH}LEL`nH`bLEMeHLEIHxLEHHEHHM:HEHuADHHHW L TAH s H5c H8AT1XZHS H=)E1cHEdH+%(HeL[A\A]A^A_]@LqL=-M\1 HI9L;|uHMHHEH0IsH.L6Lujf.LWfLGfH7}fLEL#LEf.H2fL5fE1IM9|JtLZ5thHEJEE1E1E1HEE1E1fMt I HMHtHHxHHMt I uH H=t~OaHt H Mt E1IMtCMMtIt&MILfDLDLELLEDLxLLx*@LxHLx)@L%f.Hg1fJHufEE1E1E1E1HEE1E1E1HDžxH tcHxE1Mt I $MtItxM_I VL`LLxL`Lx,@LpHLPL`LpLPL`eLPLL`LxbLPL`LxPLpLLPL`LxLpLPL`Lxf.IMH5H=1豃HHtH111VHsEDf.H5IH=1kHHtH111uVH-EHEE1E11E1E1E1H=Hj^H5k^-IMHEE1E11EE1E1E1eLMD$fHnfInfl)UMM|$ILLEIpfoUL1Hu)UtH}IHH=,7,IMEE1E1E1HEHxE1E1EE1E1E1HEE1E1DIf.HEE1E11EE1E1E1E1H=I+I@HH H1PHuHUMLE:^_x@jLEHQfDEE1E1E1HEE1E1E1@fHEE1E11EE1ME1E1MxfHnfInfl)MMM`ILI$}foML1Hu)MLIw}fH; LIHtHՈL;}EL@H=پHz[H5{[*IMHEE1E11EE1E1E1DH=)IHEE1E11EE1E1E1E1EE1E1E1E1E1E1E1HEMHEE11EE1E1E1E1E1kE1E1E1E1LEE1E1E1HDžx-LeE1E1E1EE1E1E1HEE1HEE1E1E1DEE1E1E1HEE1E1E1HDžxHPH5H8EHxE1E1E1E1HEE11ffA.EEEE1E1E1HEHxE1E1EE1E1E1HxE1E1E1HE!H5H=E1}HHtH111PHzEHx+H=HXH5XC(IGEE1E1E1HxE1E1E1HE}MD$fInfInfl)eM I\$ILLEHzfoeH1Hu)eH}IyIEE1E1HxE1E1E1HEE1H=sHWH5W`'IMEE1E1E1HEHxE1EE1E1E1HxE1E1E1HEoH=4&IEE1E1E1E1E1E11L{fIn})`MLcIHI$Lex1LHufo`Hx)}HELIxH}xMtH]E1E1E1HEE1E1EH=(HVH5V&IM[EE1E1E1HEHxMEE1E1E1HEHxE1M6H=$IMEE1E1HEE1E1MEE1E1HEE1H]HRLUML`H5$ H81LeE1E1EHxE1HEL`yMEME1HEE1E1GHL]H5 L`HRLEH81#L`L]LEMEME1HEMEME1HEHxE1MEIH=zLU#LUIM1E1E1HEMEME1HxH=2HkTH5lTLU#LUIE1E1MEME1Hx71E1H]1MEMH}LE11Hu11LLPL`HEHELMH]L`LPI~LEHxLETMEMIHpHLPL`LEH;LpLPL`^HUHALpLPL`HUHEHHRLUML`H5w H81LeE1E1EHxE1L`MEMMHxE1pHE1HuHML1LML`HELUpH]L`I1Hu1LHULPLEL`LuL]*H]L`LPI1MEIHME1E1E1HxBHU\V!HUHEL`LPLpa1MEMHuE1E1E1L}HuBHE1'Ht5HtDHHUHLpH@`LPL`P_BJHH HBJHH {fUHAWAVAUIATISHXdH%(HE1HHEHEHEHHHHEH"HHLyHEM4H]H5eH;sL;-bHTHEHUI}0Hs0HE4UfEHHHUdH+%(He[A\A]A^A_]fDHEHuADHyHH L AAH + H5 H8AT1XZH H= O1eDLyH M~1@HI9H;LuH}HHEHPIHFHH]f.HHH1Hd H5; H81HG H=! N1E1IM9JtHϺHMF"HMtHEJ.1H H18HHx 1MPHuHULEH{,Y^xfDUHAWAVAUIATISHHdH%(HE1H۩HEHEHEH^HHHEHHHLyHEMH}L;-{HHI}07HpH8jHHUdH+%(He[A\A]A^A_]HEHuADHHHC L >AH [ H5K H8AT1XZH 6H=IjL1dDLyH M~1 @HI9t@H;LuHUHHEHTI@HNH>H}fE1IM9JtHϺHMHMtHEJfDH9H=H5C H81H @H=]iL1xfHEHuH8HI 2H= (KfDHH 1MPHuHULEH)Y^xfDUHAWAVAUIATISHHdH%(HE1H HEHEHEH^HHHEHHHLyHEMH}L;-{HHI}0OHpH8HHUdH+%(He[A\A]A^A_]HEHuADHHHF L ;AH H5{ H8AT1XZH+ BH=gJ1dDLyH ͥM~1 @HI9t@H;LuHUHHEHTI@HNH>H}fE1IM9JtHϺHMHMtHEJfDHiH;H5s H81 H? LH=f0I1xfHEHuH8Hy 2H==%HfDHH 1MPHuHULEH&Y^xFfDUHAWAVAUATI1SHXdH%(HE1HL;%]HI|$0HX.IHH3I9EIEMIEH I<$I|$HEE1E1HELOtIHt HHZH ABHH9HL=%BMIHI9G`Hu1LHELuQHH}IHCH;C HKHHHHCH  IM9l$=I $Lh{H=1HzAH5{AvIMI1IoH I $<Ht H ~Mt I`H H=Ud1FHEdH+%(HXH[A\A]A^A_]ÐHULcHUHCH;C HUHHHUE1EDHf.LfLI<$f.DHUHI $HU#H IM1E1MHULHUHHULcH HU@LGfH7tfMWfInfInflMIGIHIHEH}1ɺHuLU)E»LUHI tL}_DHULHUDH=ўdI@HH H5 H81`H HfLIHIEH%LUL)E'LUfoEI $yLkLHIM0;DUHAWAVAUIATISHHxdH%(HE1HHEHPfHnfHnfl)E)EMHH`HHHtVIع11H= ^Hu H=h &C1HUdH+%(He[A\A]A^A_]fL5IMLhHL;5HEHDžpHDžxHEHEHEID$H5~LHH HH ?HEH H`H4HH5L`H5ULHfL`HHEIH sIZL;5;HEHE]HhH; HTH DH9He L%DM I$LeH5:LcHH L&cHEIH IELhHEIH HhH5_H LLHeHEH HHhbLbLbIEHPHhHHH]L5LhbHpHMHxxHxHsID$H5XLHH ILpLuL`MHEIHHhH5iH8H5RLLdHEIHLaLHE~aHEHEMtLaaHxHDžpHtEaHDžxH}Ht,aHERHEH vBH9HH]BH HH]H5&H`HEIHs H`HEIH IELhHEHH HhH5H~&HH5H_HLLicHLHh1`L)`H!`IEHPHhPDoIM)UH~2HH 1IPH`HULEL?ZYHELuHhL5)1DHH9M9tuH`HHHEHHHEL5HhfLvLuHHhHEDL;%qI|$8H5W^HEIHH: IHL^HE =H5L^HEIH+H5H9/H@H;31ILx^HEHOH ?H9HL%?M I$LeH5iL ^HEHHL^HEIHFIELhIH,HhH5NHHNH5ߎLLLH`HEHsHHht]Ll]Ld]IEHPHhLfHwL`xE1E1AE1IMt I"Mt IHt H Hm DH=b ;MIEHP1IUHHhLHh@HIMHELx\Lp\H}Htb\L{`LshfLCpHkMC`L}H =LuLEHCpH9HrL-=MIELhM9L[LLHHhяHEHEHEHP H=C :HMHUHHuUH5&H=1^IHtH1111Lb[LELuAL}H{xHME11HxH`LhiLhM@LGfL7fH'fLfHhHhHHًH`LHhHHhHEH{L5@LL腵LAJZLLHHh%HEHEHEEKH{xHME1AHxH`0E1IL9KtLHh HhtH`JLuL}AwH H= 7H$fIA1'LELuAL}fLYADE1AfIfH@IUHBIEL@H=RH :H5 :LhOLhIMnA!H=YRLh]LhIHH H5 H81YH H= i6MH=͙H^9H5_9HH]HHq H=d "6H=H9H59sILeMkH* H= 5fDH H= 5L}1AMQcH==H>H=)IqL}AM#1AH=H&8H5'8ILeML}1AMIA1AH=NIL}AMt}HdVHEH;tOLIHtHaL&VA1ILA1ffA.GEcf.UHHAWAVIAUATSHLdH%(HU1HHEHfHnHLUfHnHfHnLUflHE)EfHnflLU)EHIJ HpI[H JcHI\$L=܆H!LfDHH9 M;|uHpHHEHL{LMHƕHpLLH HEII& IvIMMHXH]L8L}HFHCHI$H9t/HXHpHqH~1 HH9tH;TuM9IGHw DL9;H=GH5ՋHGHHjIMjPHpHjILxH`IH~H5LHE5 H5~LL* HpLL0VIH LRHpRH`RH HI $LHH9HuH;fILvLuL`Le H5H=ZF-RHXHE H`IH ILx2IH HpH5HHLp LH5sLLps LH5LLpQ HXLLTLpHI, HXLpaQH`UQHpIQfH _I $hHEdH+%(HeL[A\A]A^A_]HM|$LHEM3HqCHCMHL}IH9MoM|$)]MH]@oFo&I\$)e)EHHH# 1MPHpHULEL ZYL}H]LeLuLxHVo.M|$HU)mMjL}H]MLefDMMLHEHu"M1H=g MIH={ H=C E1-`DH5zL LM9%H5yL)OIHc HI9F MnM IFIELHH`OHu1ɺH`HELmLINML H`NH53yLNIH> H0I9E M}fInfInfl)pM IEILHH`[NHu1ɺfopH`)EXLHp)NLpM H`LpNLpH@IAH9t H; IyH{ MyH9 Mi ILIEMH5%H=AMHpH H=H9xV LXfInfInfl)`M4 L@IHLXILp>MHu1ɺLpfo`LmLL`)E-HXHpLL`LpM0 LLpLLLpM9 H5VLLLpH HXLH6LpHH` HXcLH`WLpXH H`6LXLpb H52{LLpKLpHHB HH9G LGfInfInfl)pM HGILPHL`HXKHu1ɺfopHX)E螠H`HpkKLPHp HXL`CKH`7KHpHHDHIHPIHIH ILMIL9KtLL tHpJHH3HpLLŻHHEIUHy;H=#H9xTH#HXHHH5OHXIIHhHXJHI9EHu1ɺLHEL}L`HpHpH`ILpL,UAŅLIEH5H=z=MIIHbH`IHILxVIHHpH5LHLpLLLLLpHIsLLpHH`HHpHtHDHHpLL˹HHEI_DL}H]LeLu MHwLx-L_L`IHDLpMFE1HDžpE1E1DH`HHXHHMt IMt I H7s H=B %Mt1IIMt IMt IMHpHHH`HHxI$MeE1E1E1E1@HDžpHXHHPHHE1H`H5H="1IIHtH111LFE1E1XE1HDžpH`LH`@LfLfLHLPjLLP*LHLPLX3LXLPLf.`L`fDXLL`ںXL`H51H=1HIHtH111LUEE1E1DE1HDžpaIE1E1FHDžpgME1E1E1FHE1E1E1@HDžp31E1E1FLpL`LpMIE1E1GE1HDžpyHHu1ɺLHELu|L`I+D@E1E1E1HDžpHu1ɺL}HpHELm#LpIH=]HNH5OHXHXE1E1E1THDžpE1E1E1THDžpHDž`sH=rHXHDžpE1LLHu11HEHEQL`IKE1E1E1LHDžp$E1E1E1E1E1ɾT MufInfInfl)pMIEILHH`BHu1ɺfopH`)}譗LHp~B1E1E1THDž`E1HxLpbL9{8{Ls(M9'H&H5@#H9pL-'#MIE?fInfHnHfl)EHHCHUHHBHHHB I$Lb(HUHIt|foE@I $LAH=E|H"H5"IMTL1E1AbLAE1QH={ILAI1-f.@HGHtUHH]f.DUHAUIATSHHHt[I8M$$HA$IT$H{HCHCIt$HnHAoD$(C(HC0Ht H=ʪu@I]H[A\A]]fD@IH߾8LUHAWAVAUATISHHHdH%(HE1Hx=IH9VhHHEdH+%(FHHL[A\A]A^A_]@LuL[Lm-H5LzHLfH5X LYH]LHLHھLGH}HEH9UHEHpBLuLéL}#H5CLHLΩH5FLIuhLH5aW LH]LH'L֡HھLAIHHLLHHGHGHHG(fGHG0HG HGHfG0HGPHG@fGPf.DUHSHHHHH9tHHp HHH9tHHpHHH9tHHpبH{xHH9tHHpH{XHChH9tHChHpH{8HCHH9tHCHHpH{HC H9tHs H]H%bfH]f.UHSHHhu>H{ HC0H9tHC0Hp#H;HCH9t?HsH]H%DGhHHHCXH9tHCXHpH]fUHAWAVLLAUATSH(dH%(HE1HPLHDžHƅLHDžƅƅHHWHHPH`HL9H9oXHHHHPH`HDžXLeHHpHL9EL9oxHHH5HpHMHDžxEH f}\H(H8ƅHH9t"H8Hp1EuDHpL9tHEHpHPH9tH`HpH"H H9HL-MIEHHHHH9XHH'HHHHHHH9CHDžHX~1H)PPHIHtHHHH HHHHH>M^H H5yHLŜ%I $CHHH9XH|HKHHH腱HHHDžHHXH9C5~1H)P=HIHtHHHHHHHHHM H H5:_HLI $HHH5LH.IH Ls+Hk+HDfDH(H8ƅHH9 H8Hp  @}HUH8HEHuH(H9SH(HUH8H0HEHEEƅHH}EH9rHEHp_DžL1IMu LHHHHHMt I $Ht H sHf H=r zE1䀽HHL9tHHpݡHL9tHHpHEdH+%(H(L[A\A]A^A_]HfL9oxHLpLeLfDH9oXHHPH`H"HgFfHuH(<E2fH7fH'fH fHEHxHHtDHЃEtUHHxHHHHpfDHXHHtGHЃt`HHXHHHHPHrfHfLafH=`HH5VHHE1DžLLfH=?HZH5[IH]DžKIMlDžLH=?f.H=Q_HH5HHBE1DžM,HyS 2H== IMDžLE1@H=^lHCHHLcHHI$9&HLDHWfHTHTF1ƃH<3H<19rITHTF?1ƃI<4H<19r"H~@8@T8fT9|DLf.H=]4Y`HXH@HCHHLcHHI$$HLDEHxHBANLNLE1EAN N A9rDžK1HXUATTHHx`TTHHX 8T8T9TfTHHXATfTHHxELLۘHXf.@UHSHHHPH=Nu,C PS tH]HHH]H@C f.fHHWH9t-H=uGPWtE@HOUHHHHGH}PH}HH@+f.UHAWAAVLAUATSH8dH%(HE1HH=<L5HL9HAŅH MAL95:LIHHH=֡H6/IHH 1M90HEdH+%(*H8L[A\A]A^A_]HAvH5H=W1HH<L9CH@LmDLHHEhHELHH}Ht(IH L95/LNIH1H=۠HHIEHIE~HIHPHHHHHfHK { H=hi E1H9H5 H8əA{ H tHK DH=(i fDHwDHHt H5 E1H815HTK  H=h EIHf.HH5J H8)HK | H=h $@H5iL_I $u L”fHJ | H=Hh LtfA| DHJ ~ H=h x HbJ H=g E1UHH)f.HH5Z H89 fHIEu LHJ H=g HHHyf.U'HHHt HI H=Cg HEHEf.@U(HH~Ht HWI H= g HEDHEf.@U"HH.Ht HI |H=f HEHEf.@U#HHHt HH ^H=f HEHEf.@U HHHt HgH H=ef HETHEf.@U HH>Ht HH H=(f HEHEf.@U HHHt HG kH=e HEHEf.@U HHHt HwG PH=e HEdHEf.@UHHNHt H'G 2H=te HEHEf.@UHHHt HF H=7e HEHEf.@U%HHHt HF H=}HEtHEf.@U HH^Ht H7F H=d HE$HEf.@UHHHt HE H=ld HEHEf.@UHHHt HE H=/d HEHEf.@UHHnHt HGE H=c HE4HEf.@UHHHt HD {H=c HEHEf.@UHHHt HD `H=vc HEHEf.@UHH~Ht HWD EH=9c HEDHEf.@UHH.Ht HD *H=b HEHEf.@UHHHt HC H=b HEHEf.@U1HHHt f.HgC H=~b HETHEf.@UHAWAVAUIATISHXdH%(HE1HQHEHEHEHHHHEH"HHLyHEM\H}HGHGHRHH~HtH ԃHcAH9uDA HuAAHEҖHu#DMH=Sa ZHJB FH=)a E1HEdH+%(>HeL[A\A]A^A_]f.LyH }PMt1HI9H;LuHMHHEH@IH>H>H}f.E1ID$H5AXLHHfHHPHH9C_LsMH yIcLޖIHL5҆M9It$0H]HHPH H}HHt4HL9ID$H5_HHLIMM9IcLIHI $IEH5ULHHIIEHIEMtSHL9H{8HHLc8IHPHHHHfHH? VH=^ E1HH@HgyfGWHH HcAH9HH5 H8ML(f 1DGWHH HHcAH9Lψ3f.H? SH=] DoGL\fE1IM9HeL[A\A]A^A_]f.LyH HMt1HI9H;LuHMHHEH@IH>H>H}f.E1ID$H5aPLHHfHHPH1H9C_LsMH yIcLIHL5~M9It$0H]HHPHH}HHtTHL9ID$H5WHHLIMM9IcLIHI $IEH5MLHHIIEHIEMtSHL9H{@HHLc@IHPHHHH΁fHH8 $ H=E1HH@HyfGWHH HcAH9HH5 H8mL7(f*1DGWHH HHcAH9L3f.H07 " H=DoGL\fE1IM9Hu1ɺLLmHEZII$HI$M@HM9L9H{hHLkhM9;HCxL0}IHChoH )MHEHtH=|@HuLj}LeHE|IHMeIEHR|LmHIEI$H@M|IWHRQHuHAH}IHtvH}HthMI $MCPHEdH+%(HeH[A\A]A^A_]fLH5FLIHV~IUHEHHu@@HHH5 L AH k( H5[r H8AW1wXZH/ [H=IH  1.@IuMmLmD@XLxfxDH\H/ H= DL_xf.LIH=yAE IHt'H=y9G PW u HPDMoH=-HH5^IMFDM|$fInfInflMID$IHI $Hx1Hu)EHxUWIIt Lx}LWwDH=,Ia@H5LAcIML wHsH4X H5 H81ufDH1v)`Lvfo`LIMGT@AE FfDLovf.HqrH5 HW H81(u^fD]fDHH2 11PHUMLEL-Y^fDG yHfHfHff.UHAWAVAUATISHLndH%(HE1H@HEH(fHnHlqHEfHnflHE)EHHIIM"M H [$ AL ) H~|HHb1 H54n H8AU1sXZHn+ SH=J AHEdH+%(>HeD[A\A]A^A_]f.IIZL~ L}LvLu-f.oVH)UvHLuL}L;%>pHDž0HDž8HDž@HDžHHDžPHDžXHDž`HDžhHDžpHDžxHEHEB I|$hIHqL;=oMt$hwIMIEH5KLHH[ HHPHV H{H9CuLCLHMaLKIIH LPfIn1ɺLQHuLL )ERLL HXHI-LHDžHH. H uH;jHDžPH;y{H;nH H{H 0H HDžXHPHHL;=$nHBtjH9O1L=:MILPLLm L HDžPHHH9B HH HHPH HLmAǃ" H H "HDžPE H5CH躭"ID$H@HzsLH IH$mL LuHEFsIHH LuIFHjIvHIHH@HLHQH R HHuLH}HPIHtMYLHDžPAD$PfIMFH^H UH pGfDH|H5p4HIHVImuHEHMlLuL=kzL=k0@H MHoHDžXDH5GL HPHHF HwH9C LCLHMq LKIHLILPL Hu1ɺLL fInLL3AL)ENHH HXqLH HDžH LIH HDžPÅ H HDžX H@Hf.*nL;=+jMt$hyH@H8HxxH0H! H5B9LIHf H0HtH8HDž0HtiH@HDž8HtMHDž@DHFHHEyIMH58HHVrHHEIFH H mH o@LHL lL LfDLlffHu1JKH)ELHXHHLL lHL fDH@8HnLH IH|wL LuHEnIHlH LuIFH^wIvHIHH@HLHQH* RHHuLH}HPIHtGM LVHDžPAD$TMD"vHHH' 1HPHUMLE1v^_ZH'kfDž eH! E11E1HHy HHXHt HHxHt HH}Ht HH}HtHtXMtI $t]MtIt3H AHM;@fDLWjDJjL7jD*jHDjMD jODH5Q<H| H5<LoHXHHx HrH9G LGLHM LOILILXL 3Hu1ɺLH HELE)IHHPIL HDžHMR LLHDžXM  L HDžPHH5MH=r1HPIHt!H111LMHDžPDž r5fDHdH) 1E1H5x H813gDž TH E1E1HH H=f._HfDž eH HHE1E1HHT HHHPHIt%MILgDgDž eLPH 1E1E1HH HfqHTH i AL H5FLHXHHH7oH9GOLGLHM;LOILILXL eHu1ɺLH HELE[FHHPI%L HDžHMVLLHDžX  L HDžPHMH5RJH=p1HPIHt!H111LHDžPDž udH HHMtH=0gkAF HyHt5H=gAG PW uHH PH DLqH HHMtDH=fAF HyHt5H=fG PW uHH PH DLqfHu1ɺ7H)E~DIH HXH=HH5HHPHDž yN@Dž yH E1E1HH HHPHHIH=@DH=I褛Hy@H=HH5>ILPMDž n@H5GH=m1cHPIH+H111bLHDžPDž zJH=aIq@Dž nfDž gDž |Dž gLPkf.Dž gHH1%DHH1DH=H5$-HPHHHjH9GLgfInfInLHfl) MLwI$ILPLHu1fo )EALHXHHDžHHLH=lHDHPIHHr111HDžXLLQHDžPDž j14DH{`H5EkH XHHH HHHu1ɺL LHELLLML#@HH HELH wLH HEKH HEwL-T>IEH`HtoHhHDž`HtSHpHDžhHt7HHHDžpHtHPHDžHHtHXHDžPHtH{xH@HDžXH8H0lDž }Dž Dž pHu11HEHEH >L HPIbHu11HEHEH J>L HPIL-0IEDž q?Dž t0G AF G AF H E1Dž tHHR HHW E1Dž qHH$ HfInfHu1flĺH )EP=L HXHbDž qH ME1E1HH HzfDH}HELH5fHEI`}HH]HMHULHx虞..XIHHH5LHH ID$SIHqH]I9GH 8AG @mEH+IWM|$ LHHJID$(K2IHLH=fL?IHLE1L111購LE1hDž _HHpHhH`HxxyH{xH@1H8H0Y@Dž tDž z@Hu11HEHEH:LH HEDž jLPvDž ]E1E12Ha E11Dž jHH, HH}1HMH~HtDž ^E1E1^:Dž YE1E1H E1E1Dž XHH HLfIItJMtME1Dž aJDž _E18Dž _)LYH!KH4KHCKHRKHuKHKfUHAUIATISH(HtHHHqdLLHHCHMUHChHxHH[A\A]]H t 1H1YUHAWAVAUATSHHL5TIHILvL9HG0Hx HPHH=TZGz HBH-I}8L9L8IM90IG H5-HC(HGHH}IMH58H9cI9@IPHHIcHHHyHLEHIH?H=HHH)H?H!H)`LEIIHMIH$LwHHI $HSI}0HCHw\HCIU0H{HCHB0H+B(HC$IEHHBMe@Lk@LC I$]HHC0I $MeHI$L|]HHC8I $HC@L9st71IH[A\A]A^A_]z HBHfLXHCfDLVf.HULcVHUf.JVIU0L7VffL'VfGz y1vLUf.HAZH5 H8YHQH5e H H81TH=D H 蠾H{HtHu|U1HS@HQH5e H* H818TfPIHH:1I}8LEL%HI@nNLEHIHVI9EAE @u tEIUMh LǾH!LEH'HI@(*LEHIL4H=}^L7IH'LL111CLAAf.H= H{ DH{HtHt1HCfDfDSHCKIzfAIuLSf.LELULEIHE&^HUHAIMDA(I $ L7SAHunAHHc fDA)HgA/A$LLz_LIMLEtMHt#Ht4H@`LELLEIAHA@HH HjAHA@HH VfUfHHAWAVIAUATSHHdH%(HU1H )EHfHnHEfHnHHMH Zfl)EHMHJH5Z H8:HIEu L6H H=& 耟HHI $u LV6H] H= NA;LHBHIMtL9H(f.fUHAWAVAUATISHXdH%(HE1HHEHEHEHHHHEHH#HLqHEMH]H5| Hq'H5HqAA#L>HHH=?L-1L9Hn2Aą H ZA`L9-I?L=HH^H='?HIHcH u}H4rHE"?HuADH;HH& L AH H5- H8AT13XZH3 nH= $E1HEdH+%(HeL[A\A]A^A_]@LqL= M\1 HI9TL;|uHMHHEH0I E1E1rDHHH]f.H5aH=B1苉HH*HXBHCL93HLeDLHEt>HELHH}Ht誚=IH7L9-q=SL;IHUH=O=HH-IE=HIE HIHPHHHH2rHw2fE1IM9JtLntHEJ~H7 H= (H H= Hi9H52E H85AH tH DH= ȚH1DL1fHHU 1MPHuHULEHxY^0@H_ H=  P'H8H5bU H8A5H( H= @HL%(HHHu2H9CH= H ?IHHoH=:LHHOLL111H~H6H} H=' nEfHW H= HARH, H= H,H H5#@ E1H81.H H= ݘIEH$H H=r E1趘HHH7H5S H83HIEu Lh/Ho H= `HHI $u L6/H= H= .A;LH;HI跹MtL2H!f.fUHAWAVAUATISHHdH%(HE1H HDžPHfHnH*HEfHnflHX)EHHH8HHHtaM1H= ~H% 3H= E1HEdH+%( HeL[A\A]A^A_]fDLsL= M 1 HI9 L;|uH8HHPH] MnMdHPL-)fHH#LnLXH>HP\Do&Ls)PM~5HH 1MPH8HUHLP1tZYHPLXHpƅpH`H0HEHDžhHEHEEIEH8(B7IHH5wH=P1衂IHHv;L;-_(ID$IEHHHHH9 ןHŸHrHHG4E1HuH9CfInfIn1Hfl)E H Mt IXHHH HHPIM6L HXH !H9HOHHHH3HuHDž H9C~ fIn1Hfl)E H IHtHHHHHHM HHH}LHEH8HUH9HHMH9lo]HuHM]HHEHuHEH}H9tHEHp,5HIHH IHH`HuHH ZL;%&H ID$L@(LH!LLHHHtoy4HtI$MHPI$I$HTIM:H}H8H9tHEHp,H`H0H9bHpHp+LL-I%@HLkHPML{H ~M1fHI9H;LuH8HHYHXMufL;%$AL@H(Lb0LLHHHt!+3HM(H(HT1HH:H=2H;=J$5H%AHHE1HH5H2A4H;$H(0HHH=_2HϿIHH{I$MH@E1IM9TJtLdtCH8JLW'fLG'fH7'bfL''fE1IM9JtHϺH0cH0tbH8JfH9ouHMuHUHUH0f.H [H=d 蘏E1H ]H=D xfDHW&gfLG&fH7&fHMHHt>H9ȃAtMHuH}HHHMHE[DH;!(H(.IH&H=+0LHW @LGf*0HENH}!H H55 H814$Dž(if(HA H= 7I$HjH=HH5~]HHDž(hfDH$fDž(hH uH$sHCH HmL{HHIJLHuJfH=$\aLO$f.HDžP.HDž(hIL$H=xHH5e\HHDž(fL{MHCIHHH dH HuH=C[Dž(fHH H53 H81:"HY `H= JH;MHHtDž(`Dž(aH*H5I6 H8'Dž(aEHMHEHLHLG1ɉσL:L>9rH*H5F H8&Dž(c5H)H5F H8~&Dž(bDž(c*Dž(bkMLLHuH}+LfLHuH}%HHHfUfH 4HAWAVAUATSHHHH@H8dH%(HE1H&HEHEHÖfoEfHnHO)E)`fHnH ؀fl)pH9H3L%M{I$ID$H5 LHHpII$HM{I$HH7)Hu1I9EBfHn1L@)EIHt H MFIMTHmHH9PlL5MIIFH5FLHHHIHHIH Ht(HuE1H9CfIn1H8)EIMt I $MH HH"H9PL% MI$IGH5LHHHHHH'H9CHCH@HLsHIH LHuH@HHEHM1H@IHtHH8HH"M9H IEH5=LHH2HH4H&H9CSHCH8HHSHHH H@H@HuH81HHEHE(H8H@HtHH0HHH@H HQ&HuHDž8I9D$~8H@fIn1LflHE)EH8HHtHH0HHIZH@HH8HH-H4I $HxfHE)EHEHpHtfo`HEf)p)eLeHPHL#HHfoPf)PHxHtvHXHteHEHt LLH IIMHpHtH`HHEdH+%(HHHĨ[A\A]A^A_]f.L7fL'4fLfH2fHKfLfH]fHfH=H {H5 {&TIHH+ H=W 蒄tDLof.HWrfHGfH7fL'fH fH=HzzH5{zvSIHH{ H= :DHILML0f.HDž@1E1E1Dž8I $teMtItkHtH tqH@HtHH0HHtc8H H= "M\ISELDLDHDHDH=ylQDI]HMeHLI$0MHuHDž@M1E1Dž8fDHDž@E1Dž8H=HjxH5kxvQIHH{ H= E1f.lDHDž@E1Dž8;H=QDPfLwfHgCfLcMELkI$HIELHu fDDž8IE11HDž@fDH_fH=O9IDHDž@Hu1~fHDž@Dž83fDzDHDž@Dž8fDHu1!DHDž8Hu1f.H@1Dž8ID$H8HI\$HLH訡IHuHu1`oH H f.DUfHAWAVAUIATSHHXdH%(HE1H)EHfHnHEfHnfl)EH1HHEHoHHt`MH= bHI :H= ~1HEdH+%(xHeH[A\A]A^A_]L{L%Mt1HI9TL;duHMHHEH@IGHSHEL5HDE1ID$H9IN;tuHEJHEH L}IHL.LvLmLuH5I9uL=t M9H5;I9vtMM9$HH H5$ H81H MH= }M9tIEH5LHHHHI~8H!IHHM3HHL;%p L;%M9LQÅI $eM9JI I @1IHLLI\$ID$H8HHI$W fHnfInH=fl)E7AD$H}iH}HHtzHL9/HCH5HH6HIHHSI9E"MuM4M}IIIMhMHu1LHELuIMt IIEHMIEHIH;uL zI $vLJhDoL{)]MkLmLu'fDE11AMI $HJ DH= zMHtH ALMRfHHEHCHEH=fE1IGM9kIJtLNtTHEJ/HO+f.E1IM9JtLMtHEJ~IH1H Lf.L3fLf1H L fLwofLgfH MH=E XyY*HfAD$DHEHAfDAMH tH4 DH=ԗ x1HDHH 1MPHuHULEHVZYf@Lf.H=٣H5HHHH9GLMH_IHHufIn1HAF8Lm)EIMtL诙AOMH蘙H=LHHALu111HoH_H NH= wH H H5 H81J H QH=G Zw[DHEH "Hk RH= wH5hHARHHu1E1MMASHu1H OH= vHHuE1aE1ANaHHuBH SH=< OvUHHHHHHHfUfHAWAVAUATSHHL5L~dH%(HE1H<HHXfHnHhfHnHpfHnflHHE)EfHnHfHnflHxL@)EfHnflHE)E~*;)PHM+IH6IHLuH~)#J@IIIMKLH0HVKHu`HtƾfDH H= tMDžHPH`LXHHH;=AH;=&DH;=+wAăHhHQH;=LH;=H;=,HpHH;=H;=)H;=H;pbHH{8ffoCHC(HC C$H HC8Hqo(8H CPk@Ht|qNHHIHCH5.LHHH(IMHɅIMHBHCH5HHHHЅHCH5`LHHHЅHCL;=%Ht$H5LHH<хHCEH8HIHH5lLHHuIMHɅ/IMHyHHIHCH5 LHHH+IMHɅIMH5HHIHCH5'LHHH<I]HK@IMH1HUdH+%(zHe[A\A]A^A_]fDHhAHHpDžHDžHiHIfD‰f.o(8s@CPZfDH HIDfDHHIfDM'HHL@HiH;=1H;=uPH;=tG=TH.HyHITfD‰f.EL‰fL7fL'8fLsfLfHH5" Hq H816fDHH 1LPE1L@1LL^_@BHDDDDDHoH<OM11H=* 7-WLIMLվľ뽾DUHAWAVAUIATISHXdH%(HE1HCHEHEHEHHHHEHHHLyHEMH]H5!H;s'HI9?H9vLc(Ls MH=5AD$,HEHu#DMH=R 蚇H8 ~H=% 2l1HEdH+%(HeH[A\A]A^A_]LyH M~1@HI9H;LuHUHHEHPIAD$T HHoI} HULHEHENpfEtHHHM5Li(HHH]Zf.H H@ aDH H= j1@E1IM9\JtHϺHMF>HMt@HEJ1H HXf.HH_ H5 H818HN H=; Hj H9H H5C H81H 7H= i E1E1#HHȽ 1MPHuHULEHGZYR6idZcUPKFf.@UHHAWIAVAUIATISHxH5L5-dH%(HU1H7HEHhfHnHufHnLufl)EMJ HpIIMtYM11H= H ZH= 1hHEdH+%(HeH[A\A]A^A_]fIL$LxHHf1H)EH IHH;wAH;DM9LVADž I $EHLHpHCH*IH.1HH IHIL;%L;%bukM9tfLADžyZH u HHI $AIDH DH=Q *g1I $LDI $BEKHIHHwI<$H L9xM9uLHHHxIHIu0H}LHHP foEH}f)M)EHtdH}HtdH}XHHI $H}HdI $EL@IIM LxHl@oID$)]H~2HH 1MPHpHULELCZYHEH]H5Hx H1DHH9LI9\uHpHHYHEHHiLxH]H5fHXHxH]HH]DHIL$HEfL/fLEHp IHHMIAf.HWI<$LCH wH1L9xnHxI9L,HH/Iu0H}HHHNP zDAI $LfHxcHxHHHpLHx9HjHxHEHAfDLWfLxc@IAfH5qH=*1IHt111H[L轄IAE1IL9KtHHhLxD6LxHhtHpJj@2HfA5D HA DHIH H5S H81fDHc[H HI H5 H81AzHiH tImL_HHf.UHAWAVAUATSHHL5pdH%(HE1L9HHLnIHG H(I9t%MtH=cAEHt_Lk(I$HC0oH)MHEHtH=)@H}H}IHtl_MsM9H{8HeHCLk8HH5HHIMHCH5"HHHIMHI9D$/ML$M!MT$III $fInfIn1LflHuLMLU)ELMLUII MII$HM/I$HIEM9t H;,H{@HHCLk@HH5HHIMHCH5HHHIMH|I9D$M|$MML$III $fInfIn1LflHuLELM)EILMLEIMII$HMAI$HIEM9t H;>H{HH)LkHHEdH+%(HHH[A\A]A^A_]BDL/[f.LULLMLMLUfLELLMLELM%fL f6D@LfLf.I $HEdH+%(\HHHʪ [H= A\A]A^A_][]BDLELLM'LMLEf.Hu1ɺLHEL}IfLULLUf.AEH{(HfLfHH +H5 H81SfD/fD-fDHu1ɺLLELEHELEIWH5oL?-@IMeLWI 0H= 8W1HDLfH ,H=Y V1yHiH r_H9HL5Y_MIHuH]1HHP HuH}eIHMHHuE1I9FfInfIn1Lfl)E'HMtLwLwHLwH=?HVIHHwH=LIHLw111LMLwH -H= U1:fH 1H= U1jD1It&Hd H= aUMfL7DHuE1HuH=+H]H5]p$IH/H .H== T1]D.KH=׮j#MfM8M~I$LIHvMH޺E1.A.-MA1ItIcfUH0HH9t(H (uH]ÐH0Hu(H H5 H H81HѢ H= s S1]f.fUH5HAVAUATISHGHHHH}HCH5nHHHIHHHMH(IH"HLHHIMItpH;IL-H;uyL9ttH+AƅH tdEtmM9ID$ x(IEL[A\A]A^]fDLDLoIn@H DuHNEuM9I|$ (tH@,Hu)HfIu LIMtdH H=aq Q[1A\A]A^]fH uHHcxXHHx@HLDrHf.HkDJIfHaH5r H8 H81/fDH1H5B H H81fDUH;=Ht;H H@Ht ]H H=Up P1]@HH5 H H81p뾐UH;=Ht;H H0fHt ]HQ H=p PP1]@HIH5Z H H81뾐UfHAWAVAUIATISH(H5ƜdH%(HE1ID$LHHTHH^HH9CLsML{IIH }LHu1HHELuCIMt IHHMHHWI>ID$H5LHHHH!HJH9C0LsMcL{IIH 0LHu1HHELuIMt IHHMHHIL;%zID$0I\$8I}IEH9t$HtH=CHtLI]HEdH+%(H(L[A\A]A^A_]@Huf.LfHfLIfH0fCI}dHfL0fLwfZHHfDAH DH=Am LMAH uHf.Hu1E1fHu1|DDAAHu1E1fHH& H5 H81xA3DHu1HUHAWAVAUIATSHHGHRILMu I]I9u%lAD$PAT$t=HI9tELcMtID$IT$L9H=ytuLHII9uI]HtIu(HH)7I}8HtIE8HIUHc+Hz XuSNP+H+L,H[A\A]A^A_]ID$I$LPI$LP fDH@HL[A\A]A^A_]fDpDIUHoH9B0Lz]f.DUHSHH_Ht/HSHKHH9t'H=uECPStGH]fHHHCPHHH]H@fDDHH]KHf.UHSHHH_HHHt/HSHKHH9t!H=iu?CPStAH]HHHCPHHH]H@fDDHH]GUHSHHxH_ HHHt/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]GUHHHATSLgHHMt5IT$IL$HH9t6H=&uLAD$PAT$tJH߾ [A\]%ID$I$LPI$LPǐDLXFfDUH0HHATSLg HHMt5IT$IL$HH9t6H=vuLAD$PAT$tJH߾0[A\]%HID$I$LPI$LPǐDLEfDUfHSHoH_Ht/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]EUfHSHoH_Ht/HSHKHH9t!H=)u?CPStAH]HHHCPHHH]H@fDDHH]cDUfHSHoH_Ht/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]CUfHSHoH_Ht/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]#CUfHSHoH_Ht/HSHKHH9t!H=Iu?CPStAH]HHHCPHHH]H@fDDHH]BUHHAWAVIAUIATSHHxH5L=dH%(HU1H'HEHhfHnHufHnL}fl)EHmJ HpII~MtYM11H=ڰ ^Hؒ oH= 1CHEdH+%(HeH[A\A]A^A_]fHKLxIHf1L)EI$HHVH;gAH;DxL9oHFAŅsH _EI$LHpHHpHH1HLIHH oL;-L;-NuwM9trLÅyfLAH u H,Mt IM-Mf.HW DH= B1IMRLDIMI$MI$HI} I $)HxL9H5+Hx1HHH;H;OtL9kHAąWH &EM9L4HHHxIHLeIv0LHLH]LHH}HtZ?HiHHIMH}H*?DH EHjDIIMLxI@oHC)UH~2HHX 1MPHpHULEH'ZY\HELeH5~HxfI9'LHHLeIv0HLfL%1DHH9 L9duHpHH HEHHLxLeH5׵fHHHxHML LeDHHKHEHfLHpJII$HMLAE1mLI}LI $LpAE1 f.HxHxHHQHpHHxHRHxHEHAfDLfDLx@MAfH5H=1{bHHt111H5H=_MA_E1IL9JtLHhLxLxHhtHpJ@Hf.ADMAE1ADbH@HH H5 H81hAeDH5H=1;aHHt111HE4H]A"fHoH)H\ H53 H81ADHzHiI $tH tAHLHfUfHSHoH_Ht/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]#9UfHSHoH_Ht/HSHKHH9t!H=Iu?CPStAH]HHHCPHHH]H@fDDHH]8UfHSHoH_Ht/HSHKHH9t!H=u?CPStAH]HHHCPHHH]H@fDDHH]7UHxHHATSLg HHMt5IT$IL$HH9tfH=u|AD$PAT$tzH{HtH=u'G PW t [A\]@H[A\]H@G @ID$I$LPI$LP뗐DL7yUHHHATSLg HHMt5IT$IL$HH9tfH=&u|AD$PAT$tzH{HtH=u'G PW t [A\]@H[A\]H@G @ID$I$LPI$LP뗐DL(6yUHHHATSLg HHMt=IT$IL$HH9tvH=FAD$PAT$H{HtH=u/G PW tH߾0[A\]%HP߸G @ID$I$LPI$LP돐wfL85qUHHHATSLg HHMt=IT$IL$HH9tvH=VAD$PAT$H{HtH=)u/G PW tH߾0[A\]%HP߸G @ID$I$LPI$LP돐wfLH4qUHSHH;=foH_ GHt3HSHKHH9t;H=[u!CPSH]fDHHHCPHHH]H@fDHiH H5s H81 H]H H=ʢ +5HH]K3f.UHATSHGHHLc(Mt9IT$IL$HH9t2H=buXAD$PAT$H[A\]v^fDID$I$LPI$LPH[A\]G^D@u7H@H9P0RHA[A\]LX2q"HCf.fUHATSHGHHLc Mt9IT$IL$HH9t2H=RuXAD$PAT$H[A\]f]fDID$I$LPI$LPH[A\]7]D@u7H@H9P0RHA[A\]LH1qHCf.fUHATSHGHHLc(Mt9IT$IL$HH9t2H=BuXAD$PAT$H[A\]V\fDID$I$LPI$LPH[A\]'\D@u7H@H9P0RHݿA[A\]L80qHCf.fUHATSHGHHLc(Mt9IT$IL$HH9t2H=2uXAD$PAT$H[A\]F[fDID$I$LPI$LPH[A\][D@u7H@H9P0RH;A[A\]L(/qHCf.fUHATSHGHHLc(Mt9IT$IL$HH9t2H="uXAD$PAT$H[A\]6ZfDID$I$LPI$LPH[A\]ZD@u7H@H9P0RHA[A\]L.qHCf.fUHATSHGHHLc(Mt9IT$IL$HH9t2H=uXAD$PAT$H[A\]&YfDID$I$LPI$LPH[A\]XD@u7H@H9P0RHA[A\]L-qHCf.fUHATSHGHHLc8Mt9IT$IL$HH9t2H=uXAD$PAT$H[A\]XfDID$I$LPI$LPH[A\]WD@u7H@H9P0RHA[A\]L+q¿HCf.fUHATSHGHHLc(Mt9IT$IL$HH9t2H=uXAD$PAT$H[A\]WfDID$I$LPI$LPH[A\]VD@u7H@H9P0RHA[A\]L*qHCf.fUHATSHGHHLc Mt9IT$IL$HH9t2H=uXAD$PAT$H[A\]UfDID$I$LPI$LPH[A\]UD@u7H@H9P0RH}A[A\]L)qHCf.fUHATSHGHHLc Mt5IT$IL$HH9t2H=uHAD$PAT$tvHCH[A\]H@fID$I$LPI$LPːD@u7HPH9P0bH}Q[A\]L(fD.HCf.fUHATSLgHMtAIT$IL$HH9H=AD$PAT$H[Ht3HSHKHH9t$H=ujCPS[A\]HHHCPHH[A\]H@ID$I$LPI$LPrfDDEf.L'7H[A\]'@UHATSLgHMtAIT$IL$HH9H=AD$PAT$H[Ht3HSHKHH9t$H=lujCPS[A\]HHHCPHH[A\]H@ID$I$LPI$LPrfDDEf.Lh&7H[A\]T&@UHATSHGHHHLc(Mt9IT$IL$HH9taH=iuAD$PAT$H{8Ht HC8HtH[A\]gQzH[A\]NQfDID$I$LPI$LPfDB.HSHH9B0HԴ[A\]L(%:UHATSHGHHHLc(Mt9IT$IL$HH9taH=9uAD$PAT$H{8Ht HC8HtH[A\]7PJH[A\]PfDID$I$LPI$LPfD.HSHH9B0H[A\]L#:UHATSHGHHHcLc(Mt9IT$IL$HH9taH= uAD$PAT$H{8Ht HC8HtH[A\]OH[A\]NfDID$I$LPI$LPfD.HSHH9B0Ht[A\]L":UHATSHGHHH3Lc(Mt9IT$IL$HH9taH=ټuAD$PAT$H{8Ht HC8HtH[A\]MH[A\]MfDID$I$LPI$LPfD.HSHH9B0HD[A\]L!:UHATSHGHHHLc(Mt9IT$IL$HH9taH=uAD$PAT$H{8Ht HC8HtH[A\]LH[A\]LfDID$I$LPI$LPfD.HSHH9B0H[A\]Lh :UHATSHGHHHLc(Mt9IT$IL$HH9taH=yuAD$PAT$H{8Ht HC8HtH[A\]wKH[A\]^KfDID$I$LPI$LPfDR.HSHH9B0H[A\]L8:UHATSHGHHHLc(Mt9IT$IL$HH9taH=IuAD$PAT$H{8Ht HC8HtH[A\]GJZH[A\].JfDID$I$LPI$LPfD".HSHH9B0H[A\]L:UHAUATHL/I9tOIHt H=DuZFMt1IUIMHH9tKH=u!AEPAUtaI4$HA\A]]DFL/MufIEHuLIEPIELPHuf.LHu4Huf.@UHAWAVAUIATSHHdH%(HE1H({HDžxHEHEH HIH@HHOHLyHxMLxfLeEH;FLeHE)PHC0HHIEHLIHHC0LH@HpHH+p@HHcHHHaHHpHXHPHPFHHH}L9tHEHpHXH~wHDžx߾HuFf.HHHy L AH Kc H5; H8AU1βXZH+l H= 1HEdH+%(hHeH[A\A]A^A_]DLyH yMW1HI9tI;LuH@HHxHIf.H/HRH9XH9HXHH^HuE1H9CfInfIn1Hfl)E輒IMtIuLƲfDHHHMnHUH}LMHEHMHUHL9H9Ho]HuHM]HHEHuHEH}H9tHEHpԴ޼HIHIH H`HHH`HUfo`HXf)`)PfH~HthHhHtWHPH H=LȔHHt111HH:<AHFL.LxHGfHHyi H= /1HuLE1IM9KtHϺHHcHHtH@JPfLfHH{ H5 H81hDfDH9t;oeHMeHUHUHfDH?f.HMHHt>HkȃstMHuH}HHHMHEDfDHHPu 1MPH@HULLxY^3fDH=HH5HHEDH5)H=Z1=HHt111H%H9H(fDL{MLsIHI9LHuH=AtIEHMHEHLHLG1ɉσL:L>9ruMLLHuH}cLfLHuH}FH驢f.UfHAWAVAUATSHHXdH%(HE1HEHDž`HDžhHDžpHDžxHE)EĪH})H (H9HLL5(MILuHFsHXHHHHpH8HEHHHxIHJHXHEHHHHIH5H)HLL:IHLw7LHEg7HDžxHT7HHEHXHHH(HHpH}HUH]HhH(H t'H9H L-['MJIEHLuHLPLPHuH}»HHEL:Hy VHxIH# HXHEHHqHH5ÀHJRHLLT9HEIH' L6L6HDžxH6H=9LyHEHH L5HLEHEIHeH5LHE5HEMHXHHt[<<x<<<  < <b < <  < HI&H $H9HH$H.HH]LuHXLPLHuH}IHxL^MzHEIHL`HxIHMHH5~Hn9 LLHx7IH HD4LHE44LHE$4HDžxL4H*%H #H9H?H=#HHHxHhH9GP1HPLxfHn1fInHflLH)E貈IHtH3HEM:Li3HDžxMfDbHxxHpHHhH`DHX\H-H`Ht2HhHDž`Ht2HpHDžhHt2HDžpP@H|H5H=٥H HFHFHFH@HH@HH5pHH=HfDHA@H=ajHz"H5{"HEIHDžXgMfXHda H=} MuBH}E1HtqHEdH+%(AHĈL[A\A]A^A_]fE1ItH}fLDDžXgLxE1E1Mu>@H}HtHtRMIIM?L61DžXkI $t0H]HtH uH LDH=iDžXgE1MfDDžXgE1E1uHq!H H9H H H HHxHEH H9H LxHu1fHnfIn1Lfl)EHEIHtH/HEM L/HDžxHE=f.H=gIHDžXjLxM~ME1mHi H H9H HyHy HHxHEH H9H LxHu1fHnfIn1Lfl)EHEIHtH.HEMDžXzHH H9HL-MIELmHDžxHI9EKH]HuE1fInfIn1Hfl)EHHEIMtL.HDžxMH-HEHEH{`H5DžXatAHl] aH=y HMHUHHxDžXb_H{xHpHhH`ZE1DžXjMfDH=eHH5DžXjLxf.LXLLAąHcHEHHc vHxIH HXH=Ы1HILx /HEIHDžX{zHyH H9HHyHHHxHEH H9HLxHu1fHnfIn1Lfl)EHEIHtH+HEMDžXHH H9HH=H HHxHEHH9G1LxfHnfIn1flHtL)EQHEIHtH+HEMRDžXDžXjHH H9HoH=HHHxHEH=H9G|1LxfHnfIn1flHtL)EHEIHtH[*HEMDžXDžXjH}E1 LmHxIE *HDžxL)H}HE)LHE)H{xHpHhH`V%HH H9HH=H*HHxHEHH9G1LxfHnfIn1flHtL)EW~HEIHtH#)HEMXDžXH=9LaHEIH:DžX}LxHcHEIH IcHEHHVHxIHLhH=1LILx HI\$0ID$(HHEc+HEIHyDžXy>@H9H H9HH=HHHxHEHoH9G1LxfHnfIn1flHtL)E|HEIHtH'HEMDžXDH=V!HH5HEIHDžXnLxDžXE15DžXnE1-MeLxMI]I$LHH]&HuH= lH= H H5 eHxHoDžXH]qHXH]HL`HHI$LxW&HuNH=J =DžXHXgH6H@H;.L%MI$LxHcHEIHVH R11I9L$LxfHnÍPH)fInHcLuflHt͠L1)EzHEIHtHe%L]%HEMDžXDDžXH]H=H_H5`HxHDžXH]HXH]HL`HHI$Lx$HuH=H=HH5GHxHIDžXH]SH=\HH5 HEHH9DžXLxE1H=*HXH]HL`HHI$Lx#HuH=;\DžXH=HH5cHxHHDžXH]lH=qH_H]HHGHHHxH#1H;H=HHHx1HHUH9Gtn1H]LxPH)fInHcfHnHt͠L1fl)EwHEIHtH"1HEMDžXkHGHEHtHWHHHxq"gH=0HqH5r HxHH~DžXH]H_H]HsHGHHHx"1]H=H=HH5HxHH5DžXH]DžXy)H_H]HHGHHHxp!1H=EXDžX{LxE1H=NH'H5(HxHH"DžXH]DžXyLxE1DžX{E1MTDžXyE1M?H=HdH5e`HxHHDžXH_H]HHGHHHxW 1H=?'DžXMH=6H_H]H.HGHHHx1H]HuLxHuLxHuI\$HID$HLHHx}DžXLxHuLH=CHxHHDžXH] H=HH5H=HxIHDžXH]H=HH5uH鎈H骈H鋈f.UHATSHHA̐H߄t-QDH[A\]D[HHt111HeH t@DG1[HM H=:j G[A\]DHDXf.H?u 1 f.UHATS1H?u[A\]f.Ãu~HM H= ADj[A\]UHHCHt@HL H= HELHEfDUHAWAVAUIATISHhdH%(HE1HSjHEHEHEHHHHpHoHmHLyHEMH]H5H{f)EH9t8HXHHJ1HDHH9tH;tuL%^L9HC0H}H@HHpHUqM9IE@H}1HpH$DHmHCH9t7HXHHqH/1HH9H;TuL9 LeHS Iu LL"H}HHt.HEIHMtwLmHEHu@@HїHHMA L AH ? H5s H8AT1XZH#F H= 1HEdH+%(HeH[A\A]A^A_]DHH9HuH;fDL9LeHS Iu LJLH}HHtLeHbfLyH gM1HI9H;LuHpHHEHISDHHH]EfH5 H9t'H[1HH9DH9tuL%L9HC0H}HPHp HU^E1IM9JtHϺHxHxtHpJf.HAHie H5K H81Le HD H= 1f@HDHH9kHuHH9VH\ H9HH9HuH9fH5GHIHH@H;IEMLH=L}IHLH=2LBpHHL111HHHB H=e EIHH"!HID$IEH;/IELB A@u#AtADEHLHJIT$ H5dHHy)ID$(HxLIH H@H;IEMLAG A@u#AtADEHOE9H5gHEBLxMwM|$0HIID$80HHrH@H;hHIH%AG @u tEIWM|$@LLA9ACgaIHLH=L$nHHL111HHLe ILe HH; 1MPHpHULEHY^NH{HU H5 H812HQ@ H= B)H>Ho H5H H81H@ H= H;=c!HHA H5 H81LeHHA H5˙ H81xLeH;dLPXHH I $u LOLeFfDH;ALPXIHIMu LH; 1HPXIHI $7H tLeHH;LPXIHQIEHPHEIUHLwHEHu> H= fM 맻H;rH5-LĔH;JH5LH;(2H5Lz#H;H5HXLHk|H|Hi|DUHAWAVAUIATSHH`dH%(HE1HHEHEHE HhHXxL#Mt L;%H[HuHDžpE1HH5H9puHHHHEHH9PL}HuHDžx~xfIn1Lfl)E]fHxHEIHtHHXHHHEM.IHEHEIHILpHxHEHHdH5@QHHxH=LHXHEHIHxHEHHhHHIMHEHEHtH u HRfMtI $u L<@HpHtHHxHH\LXH5IEH9t:HXHGHJHb1fDHH9LH;tuL=ƀM9HƀIEH[ H5̔ HEH81qDž`1LHDžxHDžXfDL}E1@I\$I$LHSHp;Lf.Dž`-HDžxHDžXHhH@xH8L Ht HQHt H NHpHtHHhHHH]H4H L}E11Mt IH}Ht HRHXHtHHpHH@HxHtHHpHH.`H9 H=!Z HMt IIMMtLwHEdH+%(HĈH[A\A]A^A_]HH9HuH;5ԉfDH5DH=b4 HXHHEHJHEHH9GkLgLeMZLI$IL} HufIn1LAE)EaHEHMtL HEHL H=ۋHdHXIHEH;H\ 111LHEL> HE1E1Dž`3DfHWfLG^fL7DfE1H tL$HHEMtL H}HEHt LhH5HEI~`kHT7 'H=W HMHULHuw7I}H5dH}Ht) H}HEHt H}HEHt HhHpLHHEHxx8E1 DHfHfHGfLNfLEfH#fLHpzHlH=8XHH5ͷHEH{HE3Dž`'E1HDžxHDžXH=W迶HpHxHuHLxHxHHIL} HuH~H5]LH zHXIHYIELh@HxIH&H\H5IHxH=GLLLxM IHLHxLMkLH@IEIEH}LHEP(H]LeZHu!H`LHHxLIWDž`1L}LHDžxHDžXDž`(E1HDžxHDžX@L}HuDž`+E1HDžxH]Dž`4HVLeMLE1gLXHuE1Dž`)E1HDžxHDžXDž`0H]HDžxHDžXHDžxDž`3HDžxE1HDžXDž`4Dž`+E1%HDžxE1Dž`+Dž`+HpHpUHATSHGHHLc(Mt9IT$IL$HH9tZH=2}uxAD$PAT$H{8Ht HC8Ht H[A\]0J{H[A\]fDID$I$LPI$LPfD@u7H H9P02Hq![A\]LQuHCf.fUHSHHHGHu>H{PHt HCPHtHH]ZzHH]}D@u'HH9P0uHptH]*uuHCUHSH8dH%(HE1H;=utH]Hw0HwH H]Ht7HKHsHH9H=B{uxSJKHtAHUdH+%(H]HquHD H5{ H81(xH1 H= 81@DHHEHHCRHHRHEcfHHEHEEu|Hmf.UHSH8dH%(HE1H;=ttH]HwH^zHmH]Ht7HKHsHH9H=yuxSJKHtAHUdH+%(H]H!tHB\ H5+ H81vH. :H= 1@DHHEHHCRHHRHEcfHHEHEE%{Hlf.UHSH8dH%(HE1H;=IstH]Hw0HFH6 H]Ht7HKHsHH9H=xuxSJKHtAHUdH+%(H]HrHB H5ۆ H81uH. Q H= 1@DHHEHHCRHHRHEcfHHEtHEEyHlkf.UHSH8dH%(HE1H;=qtH]Hw0HHH]Ht7HKHsHH9H=RwuxSJKHtAHUdH+%(H]HqH@ H5 H818tH- H=u H1@DHHEHHCRHHRHEcfHHE$HEExH3jf.UHSH8dH%(HE1H;=ptH]Hw0HuHH]Ht7HKHsHH9H=vuxSJKHtAHUdH+%(H]H1pHd? H5; H81rHG, H=U 1@DHHEHHCRHHRHEcfHHEHEE5wHhf.UHSH8dH%(HE1H;=YotH]Hw0HpHFH]Ht7HKHsHH9H=tuxSJKHtAHUdH+%(H]HnH> H5 H81qH* H=5 1@DHHEHHCRHHRHEcfHHEHEEuHgf.UHSH8dH%(HE1H;= ntH]Hw0HsHH]Ht7HKHsHH9H=bsuxSJKHtAHUdH+%(H]HmH< H5 H81HpH) H= X1@DHHEHHCRHHRHEcfHHE4HEEtHff.UHATSHGHHHyLc(MtAIT$IL$HH9H=%rAD$PAT$H{8Ht HC8HtCH{@Ht HC@HtH[A\] fpH[A\]fDpID$I$LPI$LP}fD`f.jHSHH9B0Hdf[A\]LUHSHHHGHufH%xH{HHt HCHHt7H{PHt HCPHtH}HH];onjuHSHkH9B0{HejH]f.UH}HHATSLg8HHMtAIT$IL$HH9H=pAD$PAT$H'gLc HHMt=IT$IL$HH9tnH=oAD$PAT$H{HtH=ou'G PW t [A\]@H[A\]H@G @ID$I$LPI$LP뗐ID$I$LPI$LP fDWf.fLxLh)UHSH8dH%(HE1H;= iHGHo@h)EHEHtH=n@H} H]Ht;HKHsHH9H=GnSJKHtBHUdH+%(H]fHqhHC H5{| H81(kHG" H=% 81@@CrfHHEHHCRHHRHERfHHEHE4eoHoaf.UHzHHATSLg8HHMtAIT$IL$HH9H=m$AD$PAT$HdLc HHMt=IT$IL$HH9t~H=lAD$PAT$H{HtH=lu7G PW tH߾@[A\]%WmHPG @ID$I$LPI$LP뇐ID$I$LPI$LPfDGf.fLXLHUHATSHGHHHrLc(MtAIT$IL$HH9H=UkAD$PAT$H{8Ht HC8HtSH{@Ht HC@HtEH{HHt HCHHtH[A\]#:iH[A\]fD"iiID$I$LPI$LP]fD@f.cHSHH9B0Ht_[A\]LUHSH(dH%(HE1H;=idHGoH)EHEHtH=i@HuH]Ht;HSHKHH9H=iCPSrHu=HEdH+%(H]@HcH:G H5w H81fHEdH+%(unH]H H= @@5afHHHCPHHPIHh;jH\DUHAWAVIAUATSHHHHt IvXH)iI^0Ht;HSHKHH9H=bh$CPS MnI^I9tyI$fAD$PAT$t9HI9tELcMtID$IT$L9t]H=gtøuLHiI9u@I^HtOIv HHH)[A\A]A^A_]%hfID$I$LPI$LP`fDH[A\A]A^A_]fHHHCPHHPHDUHAUATSHHIHHOaI9Ml$ fAD$Mt5IUIMHH9tcH=fu)AEPAUHHH[A\A]]@DI11ҾH= < 1@IELIEPIELPfHy71H5; Ht1vDHq`H; H5{t H81(cHG 5H= 812LXUfHAWAVIAUATSHHdH%(HE1Hb[)Eu HkIF0I^(1IHEI)IidIHHEE1H9ui@H}XHH+IT$ ID$HHH9H9IT$HHHID$H IM9LH]HIF(HH@HUH9t^HtH=d>@Ht;HSHKHH9[H={dSJKHEHUHH^H HbIM9S13cHH!IVHIF@HH)HHMH9E1LHH<-IHHS HCHHH9JH9AHSIEL,HHCIMHEII9IF@LHH<fDHLt^nEsHEH HEE1E1E1HaMt IcHuHtHHEHHWH]Mt I%uH H= ?I $5HtHEH H]Mt IMH}HtHEdH+%(6HHH[A\A]A^A_]f@H]HHEHEfHHEHHCRHHRHEfEsE1L7`6fH'`fL`fL`fH_fL_fL_fLH|\H]LEyH pH= 1H]E1/@EyH=-i111RH mH= J17I~XHIHSLMHEHI~gIHI~gHEHI~ gIH:ZHEHHuH}Lx Lp0HpHx(I$L`8HHX@IELhHI $TLc^FL-_ZIE9H vH= FE1Hu H= &HEE1EHuH]HmH][DEE1EEH  |H=? vOaHSUHAUATSHHIH.HGH5pH9pFHWHfHL;%DYtrID$ HxIHt|XfHnfHnIfl)EHiLhtXHfoE@He[A\A]]HXH H5l H81[H UH H=3 1HcHE1L % H H5U H8R1HE 2[X1ZgHy1H5! H$1;DH=0H H5 vHHTFH=Q0蔓HH tI $uL[fH[DH[H u H[IMLq[f.fUHAUATSHHL/MtgMe0Mt5IT$IL$HH9tnH=\uTAD$PAT$tzI}IEH9tIEHp]8L]HH[A\A]]DID$I$LPI$LPfLyUHAWAVAUIATSHHxL5jVdH%(HE1H.HEHELuHSILHHxHH4L;CHHnSIL9esEHELmIV0HLHpHP H}L:GI$LfDMHLiHEMTHKH=H1 @HH9txH;|uHxHHuHEM}tE1IM9 JtHϺHMVHMtHxJE1IL9JtHpH}H}HptHxJGDA_Hct@XHR! H:FH8H5HZ 1HOLEH H=$ wfWH. HEH8DA_AGHH HfA_AGHH @2TH@A_!DLFHSH{=1H L/.QIL9eHY1HJ L-tE1HErSH1LܴH}u!LE,E1LH>fUHAWIAVAUATSHHhH}dH%(HE1HHEHhfHnHQHEfHnflHE)EHHHEH HHtaM1H=U &HW H= 辰1HUdH+%(He[A\A]A^A_]f.LkL%M1HI9L;duHMHHEHPIMHKHHuHHxH8HxHEHDHtHHFHEH>H}DIH?H}H;= ?H;=Ou{H;=BtrOE1AŅNH~BIH9]HELeDLLHp HPH}LBHHAoHK)UH~/HH 1MPHuHULEHZYH}HHKHEE1IM9JtL*tHEJ>*PH@PH@f*SH& H5lU HKAH81DKL@H H=l Lf.HEOH@zOHLH}u aLE7EH}f.E1IM9 JtHϺHMHMtHEJfDHy>H H5R H810AH H= @1mfHELHUH+HH 1MPHuHULEH Y^@LH}ucLE@EEEH8f.UfHAWAVAUIATSHHhdH%(HE1H)EH8fHnHEfHnfl)EH)JIHEIIbMt]M1H= BHs .H=w E1שHEdH+%(HeL[A\A]A^A_]fLyH }M$1HI9I;LuHUHHEHMwMIL$H=H1fHH9I;|uHUHHHEM~kItIHFHEH>H}=IHH}E1HttAă2H;;HEHHEHHCPL}Lu*JH sGHEA-AkE$MH=EH5{HGHHHH@IcHIHHEGH9CLkMLcIEI$H )LHufInfIn1Hfl)EIMtLQI`M:H H=HHu1HHELe.HHI $111H*H }DH}9SH H= E15fDILeLLPHH}HEHpKx`HxICM8HpIIGH}IwLPH}\H}/9HCH5$HHH9HH;HEH9CZLkMLcIEI$H LHu1HHELmIMt IMtHHHMHMLHoLy)UM~/HHC 1MPHuHULEL蚃Y^~H}~HG<:fHLqHEILeLHpLHP(H}~HEHpIxFxIAMAH}7H` LH=d ǤfE1IM9,KtHϺHMxHMtHEJH FH= `MtLE1tH/;qf.E1IL9KtHxH}wH}HxtHEJzEH@H6H; H5J H819H) FH=- 萣8"EH@LW:~<2*DHJ@L:f.1DH WH= H9\fE1Hu1fHuH9WH9fH9afLw9:fHEDH\r@Hu1I},?H}4H t TH9H}H}4L[8H}tLD1HpfDL/8H}tL1Hpgf.H}4OCE1Hu~Hup}>H}3I $L8H}xx;H.H.H.H.H.H.H.f.UHAVAUATSH dH%(HE1HIHID$H5HHLHHIH;/L-:3H;@L9Hk@AƅH EuH>HM9AD$PuwIt$@LeLHHpHPH}Hr2IELHUdH+%(He[A\A]A^]H D|Hj6nDIt$ LeLHHpHPH}tLhH}[quH1uH H H= 15HI=HE1H L n H5. H8R1H{ 4Z1YHy1H5W Ht1DLG-HH?5Cf. CH5SE H< AH*1H813D:LH}u&)LEgELETEY8H+H+fUHAUATSHHdH%(HE1HHHH;`0LeHMHHs LMLLE>H}H]H;teLeI<$t}LmI}/HfHnfInLh(fl@HUdH+%(He[A\A]]H3H;uH3LeI<$uL3I<$oLx3LmI}f?H t{I $teIMH H= C1S@L3I}L 3.HDL2DH2wfHI:HE1L u H  H5+ H8R1H 1X1ZHy&1H5 Ht 1DHq.H3 H5{B H81(1fDL'2fLHH}u tLEE5HD)fUHAWAVAUATSHHdH%(HE1HWIHX2HHHHQHHMHHH=HMHIH GH5L HEHH8AI $'L%(-M9H]HMLIu LMHLE:H}H]H;LuI>"L}I?>L9HCH5vHHPpHHhH*HBHIM+H L`IHELx ILp(+HMHIHH4HHIEHMLH5HHHMIMHMHH5L*HMpIMH}HLHM落HMHI@I $H HuIEHHEHHH ILtDMt IHt H HEdH+%(NHeH[A\A]A^A_]H/fL.fH.I $H.H;H.LuI>L.I>L.L}I?L{.I?Lh.E1H EI $Mt IMuH H= .H}tHuHHEHHHu#MIL-E1H tPLL-pf.H-ffL-^fL-?fHw-DHg-bfH4HE11L  H H8H5& R1H  ,XZHyj1H5 HQ1DHR H= 1EE1E11H LeE1E1H,MOTEE1E11E1,fHoHydW~IHY(H H5c< H81+E1E11۾Hz H= L+HM+fDH+f.H+5fL+HMfDH!3H5O H8/o^fDHg+fE@ELeHhH}uF7E1"HMIfDÊIcH߉EE.EI4"UHAUATSHHdH%(HE1HHHH;&LeHMHHs LMLLE3H}H]H;teLeI<$t}LmI}%HfHnfInLh(fl@HUdH+%(He[A\A]]H)H;uH)LeI<$uL)I<$oL)LmI}f?H t{I $teIMH H=p s1S@LO)I}L;)$HDL)DH)wfHy0HE1L H 5 H5%" H8R1H 'X1ZHy&1H5 H褳 1DH$Hc H58 H81X'fDLW(fLxH}u tLEE+HfUHAWAVAUATSHHdH%(HE1HWIH(HHH%HQHHMHHH=5@HMHIH GH5LHEHH8AI $'L%X#M9H]HMLIu LMHLE7)H}H]H;LuI>"L}I?>L9HCH5 HHPpHHhH*HBHIM,"H L`IHELx ILp("HMHIHH@*HHIEHMLH5HHHMIMHMHH5L HMpIMH}HLHMHMHI@I $H HuIEHHEHHH ILtDMt IHt H HEdH+%(NHeH[A\A]A^A_]H7%fL'%fH%I $H$H;H$LuI>L$I>L$L}I?L$I?L$E1H EI $Mt IMuH H= ^H}tHuHHEHHHu#MIL$E1H tPLL#pf.H#ffL#^fL#?fH#DH#bfH +HE11L 3 H H8H5 R1HP @"XZHyj1H5. H4Q1DH H== 1EE1E11H LeE1E1H"MOTEE1E11E1,fHoHydtIHHK H52 H81@!E1E11۾H H=j EL'"HM+fDH"f.H!5fL!HMfDHQ)H5F H8%o^fDH!fE@ELeH蘍H}uF7E1*HMIfDIcH߉EE$EIUHAUATSHHdH%(HE1HHHH;LeHMHHs LMLLE"H}H]H;teLeI<$t}LmI}HfHnfInLh(fl@HUdH+%(He[A\A]]H H;uH LeI<$uLI<$oLLmI}f?H t{I $teIMH H= 裈1S@LI}LkHDLGDH7wfH&HE1L H e H5U H8R1H X1ZHy&1H5 Hԩ 1DHH H5. H81fDLfL訊H}u tLEE"HfUHAWAVAUATSH8dH%(HE1H?IHsIHHIT$LHHH=sHI$HkHI$H5OHIHkH8H L;-H]HMLIu LEH7'H}LuI>L}I?!HHL;5'IFH5LHPpHHhHHBHHHwHUH5lHHUH IGH5 LHHHH"HUH5gH6HUZH IEH5LHHHHHUH5HHUH H5HLکIHH IEI $INIUDL=fLfH fHH @LoI>L\L}I?LEI?L2DHdf.H gA H HY DH=f MtLI $MMt I+E1HEdH+%(?HeL[A\A]A^A_]E1MtItsMt IMtIMuLaHI$H H= CvfDA H@LDLMM-E1_DHQ!HE1L } H H5 H8R1H XZHy1H5 H|iH 7H H=̦ WI $E1DE1E1AHHyjHHH H5 ) H81E1E1H' H=7 DH9f.LfLwfLg%LE1E1E1PAND qfDHH5Z< A H83fDHf.HfLJfHH}uN_HfHfzHDfKwHH߉EE I(DUHAWAVAUATSHxH}dH%(HE1HH HH#HHSHHHH=xIH?H ~H5LHHH8yI $_L=L9}HULeHMLELHr I H}LeI<$?LmI}\M9IEH5LHPpHHhH0HBH#IMH5LwHEHM9H5LvHHp >HpfHnIfHnfl)pHILpHEH`HLhI@ LhH`HIfop@HLxHpLpHIH5H}Hh莟LpLhHHL`LH5sLpHhoLpHhL`H QLLHLhLpMLpLhHILLpHpH5H}ȞHHHEcLEpL蹞pILDH GHMt I $%Mt IMFMt IHMHtHHEHHMt IHEdH+%(HeH[A\A]A^A_]fHGtfL7fH'I $~LI<$LLmI}LI}Lf.L fHfLfHUHHUf.LgfLWfHHE11L H H8H5p R1H XZHy1H5 H1gDE1E1E1E1HEH" H=b zH1DHHEE11E1E1E1E1H t*MtItPMtI uuLRuDLhH׉pLE.LhpLELpLljuLpufHHybIME1E1E1HEE1E12@H Hs H5 H81hE1E1E1HEE1fH5LIHiHI9@!MPMMHILLpILMʚLMHu1LpHELLpLULhHhHE肚LpH}CLcH}ZDME1HH53 E1E1H8HEHEE1{LhHLp!LhLpfDME1E1E1fE1lHYH5 3 E1H8@E1LzH} !E1E1E1ɾE1nI5E1fL¾E1E1Hu11LHEHELpLpHE!LEgE*iHUHATSH0dH%(HE1HHHQH; tLeHMHHs LEL0H}H]H;tSLefHnfInI<$fl)EtVHtffoUPHUdH+%(sHe[A\]ÐH H;uH fDL I<$uL DH I $H H=b u1qfDHHE1L  H H5 H8R1Hg * X1Z,fHy1H5; H1DHH H5 H81 >fDL %fH  fLwH}u tJLE,E1H|fUHATSH0dH%(HE1HHHH;BLeHMHHs L H}H]H;t'HEdH+%(H0H[A\]H H;uH fDI11ҾH= 1@HyT1H5 H脕;1nDHH H5 H818 H pH=I 1Fs*LXvH}u tLE謮E HfUHAVAUATISH@L5dH%(HE1HHEHELuHHHAIHHtHL;5HtII9It$0LeLHPXH};LHHHUdH+%(VHe[A\A]A^]@H=9HqH9<E*HHH 1HPHUE1LELOZH}YH;=HL9H9mII9It$0LeLHPPH}LdtH};LRuHI11ҾH=' HH8 =H= p1fI}HH}f.MH H= p1MZH H5 H{H81@@ LOH H5\ H;H81 L0sH}uLE胫ELEpEu JHHf.UHAWAVIAUATSHxHxL-dH%(HE1HHEHPfHnHEfHnLmfl)EHJHHhIIMt^M1H=; SHC H= n1HUdH+%(He[A\A]A^A_]LyH %M,1HI9H;LuHhHHEHMgM,H}!fItI2LnLmH>H}1jIHHoLHuEHEjIH HIH9xHxL}LLLHp H}RLmHHf.oLy)UM~2HH 1MPHhHULEHKZYZH}LmHLaHEMLkH M1@HI9H;LuHhHHYHEM|$WE1IM9LJtHϺHp;@Hpt*HhJf.E1IM9JtHϺHp?HptHhJ@f H hf. H Hf.H3 H5  HH81L H H= k1: H@HE HSiLnH}u LxԦxHf.@UHAVAUATISH@L5 dH%(HE1HHEHELuHHHAIHHtHL;5H II9It$0LeLHPpH};L"HHHUdH+%(VHe[A\A]A^]@H=YHqH9<=* HHH* 1HPHUE1LELHZH}YH;=HL9H9 II9It$0LeLHPhH}LlH};L uHI11ҾH=G hHX H= i1fI}HH}f. H H=Ս h1Mz H H5 HH81``L/ : H H5| H[H81  LPkH}uLE裣ELE萣EJHPH`f.UHAWAVAUATISHHL5dH%(HE1HHEHELuHILIM9ZAoL$ ID$(H )MHtH=@LuHuLDH}H}Hts`LIHI $Mt IMHH {HmL>L}>@H=1DHH9$I9|uIHHEH@LL(L@@DL?f.LfI$aHfL(fHeffInfL1flHuL)EIH(I`f.LGHfHHH2 L1PLEHUIL=>_AXH5uHH薁LS`LshfL{pHrC`LPH }]L0L(HCpH9HVHN]HHI9LL"LLHHHDžPHDž0HDž(H| SHHH_ HH_HPH0HH(7L(L0DžTLPH{xHH1LH@H8E1蒭MLt IMt IMt I HE1HU^Mx}H=yH[H5[L-LHHHS DžTHH4 HLLLE@LAfLLL@L(L0MH E1Dž]HH HE1IL9KtHLH0HLHtKH۫ H5 AHH81DE1LLSL(Hԩ Dž`HH L0MHLf*IfAOf.Hp DE11H=Z \FAQH uHf.AOAQIfH E1E1E1HH DžQH@L0}H$}H}}HLHL*H|LLHHðHDžPHDž0HDž(H{xHH1E1H@H8H DžSHH H5fDMt{Hۧ E1Dž]HH HDL]H}tLەH}HtXLP~A@H` ]H=M Z/L0IfIH[Hs|H5LHIGU{HHH@H;6sHIM{AD$ @u tEIT$HMg H"HIG(IEH;bIELG @u tE9HݣCHWI0HHIG8IFH;lILp @@u!@tEHP9IG@BLHIHLM zH=LIH/Ly111L/PLyDžUII|$xHMHxHpHhH`LHX0tLyI|$xHMHxHp详HhH`LHXBHEL(HDžXLPHDž`HDžhHDžpHDžxHxYLxHx@HIxDžUH=rL%LHH;HILPXHHHliILFxDžVcIDžVIKID$`f1HXID$hAD$`H`ID$pIL$pHhRH;&uHPXHIMmHHIwPH;HLPXHH MH;tHH5h$HIeH;LH5^hHHH;tjH5/hLH[HHf.fUHAVAUATISH@L5dH%(HE1HHEHELuHHHAIHHtHL;5HtII9LuIt$ LFH};LHHHUdH+%(VHe[A\A]A^]@H=9HqH9<'*HHH 1HPHUE1LEL1ZH}YH;=HL9H9mII9LuIt$ LH}LdVH};LuHI11ҾH=' HnH8 H=Ux R1fI}HH}f.H H= x R1MZH H5 H{H81@@LHK H5\ H;H81L0UH}uLE胍ELEpEuJHHf.UHAWAVIAUATSHxHxL-dH%(HE1HHEHPfHnHEfHnLmfl)EHJHHhIIMt^M1H=; SlH: }H=v P1HUdH+%(He[A\A]A^A_]LyH %M,1HI9H;LuHhHHEHMgM,H}!fItI2LnLmH>H}1LIHHoLHuEHELIHHIH9xHxL}LLLHp AH}RLmHHf.oLy)UM~2HH 1MPHhHULEH-ZYZH}LmHLaHEMLkH M1@HI9H;LuHhHHYHEM|$WE1IM9LJtHϺHp;"Hpt*HhJf.E1IM9JtHϺHp!HptHhJ@fHhf.H Hf.H( H5 HH81LH H=Ls M1:H@HEHSiLPH}u LxԈxHf.@UHAVAUATISH@L5dH%(HE1HHEHELuHHHAIHHtHL;5HII9LuIt$0LH};L"HHHUdH+%(VHe[A\A]A^]@H=YHqH9<*HHH* 1HPHUE1LEL*ZH}YH;=HL9H9II9LuIt$0LWH}LNH};LduHI11ҾH=G hfHO OH=N K1fI}HH}f._H H= J1MzH H5 HH81``L/a:H H5| H[H81  LPMH}uLE装ELE萅EJHHf.UHAVAUATSH dH%(HE1H}IHHII9Lu1It$0LH}LHHHUdH+%(H [A\A]A^]f.I11ҾH= hd1@Hyd1H5` HjK1H H5 HH81L_Hޗ H=Ln H1.LKH}u tLEEHfUHAVAUATISH@L5dH%(HE1H1HEHELuHHHAIHHtHL;5HII9LuIt$ L.H};LBHHHUdH+%(VHe[A\A]A^]@H=yHqH9<*HHHJ 1HPHUE1LEL'%ZH}YH;=:HL9H9II9LuIt$ LH}LIH};LtuHI11ҾH=g aH& _H= F1fI}HH}f.oHٔ H=N E1MH H5 HH81LOqZHp H5 H{H81@@LpHH}uLEÀELE谀EJHVHff.UHAVAUATSH0dH%(HE1HIHH}HHL;%ILuIt$0HULLH})LIHHHt*HEdH+%((H0L[A\A]A^]HDI11ҾH= X_E1Hy81H5y Hef.Hǐ  H=i E1CVzH[ H5 AHH81_D^LE1*Hi " H=7i ZCHHL`FH}u tLE~EHUHAWAVAUIATISHHdH%(HE1HSHEHEHEHFHHHEHHHLyHEMH}HI9h1>HHLeIu0L,H}HHHUdH+%(He[A\A]A^A_]fHEHu#DMH=" :]H H=g A1fDLyH -M~1 @HI9tHH;LuHMHHEHtI@HnH>H}f.E1IM9,JtHϺHMHMtHEJfDHH¾ H5# H81 H H=f @1fHEfHUHL fHHد 1MPHuHULEHZY@LCH}ucLE{EHf.UHAVAUATSH0dH%(HE1HIHH}DHHL;%ILuIt$0HULH})LI]HHHt*HEdH+%((H0L[A\A]A^]HWDI11ҾH=ڮ ZE1Hy81H5 Haf.H H=e E1>VHn H5 AHH81DLE1jH H=d >HHLAH}u tLEyEHUHAWAVAUIATISHHdH%(HE1HHEHEHEHFHHHEHHHLyHEMH}HI9h1):HHLeIu0LH}HHHUdH+%(He[A\A]A^A_]fHEHu#DMH=b zXH! H=_c =1fDLyH mM~1 @HI9tHH;LuHMHHEHtI@HnH>H}f.E1IM9,JtHϺHMHMtHEJfDHYH՘ H5c H81 H* H=mb <1fHEHUHL fHH 1MPHuHULEHZY@L>H}ucLE wE%HFf.UHAVAUATSH0dH%(HE1HIHH}HHL;%ILuIt$0HULH})LIHHHt*HEdH+%((H0L[A\A]A^]HDI11ҾH= UE1Hy81H5 HT\f.HG H=` E15:VH H5< AHH81DLE1H H=G` 9HHLH}f.E1IM9,JtHϺHM6 HMtHEJfDHH H5 H81PHj H=] `71fHEHUHLfHHX 1MPHuHULEH+ZY@L:H}ucLE`rEeHf.UHATSH dH%(HE1H}HHH;r{0u&HHHUdH+%(H [A\]@LeHsLH}tLH9H}tx룐I11ҾH=Q HQ1@Hyd1H50 HWK1]DHH5 H H81xH H=E\ 51LEqEQHqUfHAWIAVAUATSHHdH%(HE1HԪ)pHfHn)H-fHnfHnH HDžfl)fHnfl)HHY HKHHE1HH HXHHHPL@IHPJL0My 1DHL9, L;tuHHHHI HXJpIId HXV L{H E1HHPHHt7H;= H;=H;=|ƅHHE1fo8D-98HH HHHHLHH)HDžƅDžnullHDžƅD"L HhHHhL"HHXDHE1HH@HHHHX HL艝DDžDžDžnullƅHDžHHDžnullƅHDžƅ!L HhHHh!HHDDžDžnullHHHHHHDžH8HH9HHH9( foHH)HHHH8L8HDžH(H0H9HH(L9~ fo0H8H()0HrH(H8HXLXHDž0HHH(H9HHHL9A foPHXHH)PHHHHXHxLxHDžPHhH H9HHh$L9Z fopHxHh)pHHhHxHDžpHHHHuH9HHMH9fo}HH)HtHEH}HEHHuHHH}IH9H9fo]LH)HHELEHEH}H9tHEHHp6HH}H9tHEHpHhL9tHxHpHHL9tHXHpH(L9tH8HpHH9tHHpHHXH9tHHpHHH9tHHp\HL;=PHhHIw0LHXHLBHh H@H(@H a,H9HML-H,MIEHHHHDIHHXHHH'H5HHLLQIIEHPMIUHI $H DHHH9tHHpHHH9tHHpHhH H9tHxHpHHH(H9tHXHpH(H0H9tH8Hp\HH8H9tHHp8HHH9tHHpHHH9tHHpHEdH+%(s HeL[A\A]A^A_]DžPX@E1 IM9t(JtLtxHHJHtHx H=Q E1*a HXL@HHHPHt)HH@ 1HPE1LpHZYxHpH AăHxH/iÃ6H'9f.IEHIEH tMMtI $t7HH#w H=P HXL)E18LHHL{LmL_kIо11H= DtlH9#foH)HHHH9+fomH)HuHuHH9/foeH)HMHMHL96fopHh)pLhLxLL9nfoPHH)PLHLXL5L9fo0H()0L(L8LHHHtGHrЃQtHHHHHHHuIHtCH$v@ tu@7HLELHH0HEH0HHtGHЃDt8H(H0HHH0H(IH}IHtCH @t}A8LLMLLH8HEHpHHtGH7ȃtx HhHpHHHpHhmHPHHtGHЃltX~HHHPHHHPHHHLHXWHH H5 H8HHX1/HHX!HH@LHXҷ+HƅHpH= H#H5#PIHHHs 2H=  $IMLH=~CHHEHuH8H0H(MEAH}HxHpHhXHPHH;IMLѺvHZHcH&HhAwACITHTF1҉փI|5H<19r~HTHTFY1҉փH<3H<19r>HtHtA@1LL9rsITHTF1҉փI<4H<19rH|I|AA1L M 9rILHLG+1ɉʃI<H<9rud8ATTH(H0 H]| XATTHHHP"}A8|A|LLM,xALLHhHprTTHHu7ttHLEHxE1ATfTHHHPrALfLHhHpATfTH(H0|fA|LLMDtTfTHHtftHLE7I龱Iȱf.UHHAWAVAUIATSHHHxdH%(HU1HHDžH8fHnHHfHnH`fl)MH)M L4H^HH HMeHMHLHhHHpIfHx HVoMeH)MCHH 1IPLLLLLFZY6 HLHhHHp3MeM[ H$LHpIHhH1foC#HH( HHH`HF#)HHHHDžƅDžnullHDžƅHع HhHHh HعLHHN" HDžHHHI"DžnullfHHDžƅƅHHm Hع HhHHhHH HHXƅHH9HHHDž& H9 foHH)H HHH8L8HDžH(HPH9HH(t L9 fo0H8H()0Hd H(H8HXLXHDž0HHHHH9HHHL9 foPHXHH)PHHHHXHxHxHDžPHhH@H9HHhH9fopHxHh)pHHhHxHDžpHHMHH8H9HHulH9 fouHH)H\HEH}HEHHH0H}H9IHuH93 fo}LH)HHELEHEH}H9t*HEHH HpHH H}H9tHEH HpH HhH9tHxHpHHL9tHXHpH(L9tH8HpHH9tHHpiHL9tHHpLHhHDžH; H9AH; zHhLLAŅ L;5DI9AL;5"LLL]AŅ HpH; 9DH9AfH; ǫYHpLLAŅ DLL}HHxH;l HxLLLLLHp0LHq HH*H"H9X L5"M IHHɫHH JIHHXHH HH5ƒHIyHLLS9rHTHTF1ƃH<3H<19rITHTF:1ƃI|5H<19rf.ʬH6LxFxYXATTHHHP8ATTH(H0xLLHhHpTTHHu7ttHLE!}A8|A|LLM|fA|LLMtftHLETfTHHATfTH(H0)LfLHhHp,ATfTHHHP~(HٚHUfHAWAVAUATSHHLfdH%(HE1H`)PHfHnHEfHnfl)EHHIiIgMMH= V $HU dH=/ HEdH+%( He؉[A\A]A^A_]IuLvLf LPLXL;5UH5I9vtHfLmELpLmH UL`HDžhƅpHE)0H9HHHHH HuHDžH9C+~fIn1Hfl)E\~HIHtHHHH_HHHMuHHELHHV9HEHUH]HL9H9%oeHMHUeHHEHMHEH}H9tHEHpakHI$HI$HH;H`HUHL9\L9ouHpH`hHLHEHMHEHu L;5,HH`H]HhHH@HHHЃHtUnHMHuHHHUHEDHUHHtAHЃtU%H`HuHHHhHE1H^ L~Zf.HHjx H5 H81ufDH=AtHH5&HHDHf@HCHHHKHHHH#HHufDH=sqH!HS H5+ H81ؖs$fDHH#N 1MPHULP1HZYVrfDBHAfDLH(L;Hc|@EHUHEDHTHTF\1ƃH<3H<19r?EHUH`f.ITHTFd1ƃI|5H<19rFH=S111xqUTTHMHuUATTH`HuTfTHMHuATfTH`HuH鋐H鳐H鍐HԐf.@UHAWAVAUATSHdH%(HE110HHH}L-HCHC L@IEHC(LkML}LLhM*H}H5m LHELHxIHʘLyHEL HpH%H}HEH9tHEHpjL9qHCC0HH5ZHHIHHڜI9D$Mt$MAM|$III $|HxM1LHELutIMt IfI$HMFI$HIHH bH9HL%IMI$L;%%L;%0M9'LAI$HPEHI$EHUH H9HL% MI$H H9HoL= MIIGH5WLHHIHIH@HuHDž`I9FB~`1L^r)ErH`IHtHHXHHMIHǚHuHDž`I9D$~`fIn1Lfl)ErIH`HtHI9I$HM_I$HIsH=fHu1H)EqIH+H{HLcHCH9C HxHELeHvM=I|$0HtI|$ID$H9tID$Hp8LHEHUHEHUHHp|H}7HEdH+%(,HĈH[A\A]A^A_]fLאvfI$HcAH'K DH= H}H 1{@I$DHI$LjDLWRfLG9fHEHEHCL9KH@LhHuL LHH{LAH}HtH)H}C0Hpn4fLfDHH5 18Hf11H=K fDLWfHaH?D H5k H81AhDLfAHDXDLdfL׎fAI $E1LMIL@Hu1E15fLwfLgffH= SHH5IHfrDHu1DH=RfHp2cH1H׍BfH=gH H5 6IH~AADH=DHH5IHtRH?  f.H=YDfH=fyI $u LItALfLDڄDHHZl H5 H81ADI$AHI$@IFH`HM~HLI*HxMfDID$H`HMt$HLIHxM@L9t+HxHpϏH}uHEHxt0|H͆HӆfUHAWAVAUATLeSHLHhdH%(HE1pH}H5=d LmLLLLeŅL L|H}HEH9tHEHp#H;IHs0LvHEH]HHH{0HtvH{HCH9tHCHp[8HMHEHUHEHUH8L}HAIWH{HCHCIwH)AoG(C(HC0HtH=@H]L׉H}tL.1@HEHEH]H]AIDݏMLHʒHH}u2HUdH+%(Hh[A\A]A^A_]@H][LHx!.HxJHyH\ H5 H8100E LH? H= -1MfDF fH9t#LLnjH}uHEBLp-fDG kH*4:Hf.fUHAWAVAUATLeSHLHhdH%(HE1PH}H5a rLmLLLLeL L\H}HEH9tHEHpH;IHs0LބHEH]HHH{0HtVH{HCH9tHCHp;8H-HEHUHEHUH8L}HAIWH{HCHCIwHp&AoG(C(HC0HtH=͈@H]LH}tL+1@HEHEH]H]”AIDMLHHH}u2HUdH+%(Hh[A\A]A^A_]@H][LHx+Hx*HYHX H5c H81LځHb< H= 1MfDfH9t#LLH}uHEBLP*fDKH=_ioH5f.fUHAWAVAUATLeSHLHhdH%(HE10H}H5] RLmLLڈLLeL L<H}HEH9tHEHpH;܀IHs0LNHEH]HHH{0Ht6H{HCH9tHCHp8H HEHUHEHUH8L}HAIWH{HCHCIwHP#AoG(C(HC0HtH=@H]LH}tL(1@HEHEH]H]AiIDMLcHHH}u2HUdH+%(Hh[A\A]A^A_]@H][LHx'Hx H9Hh H5C H81L~HB9 H= 1MfDfH9t#LLH}uHEBL0'fD+Hr~~~~Hj~f.fUHAWAVAUATLeSHLHhdH%(HE1H}H5Z 2LmLLLLee|L LH}HEH9tHEHpaÉH;}IHs0LHEH]HHH{0HtH{HCH9tHCHp8HHEHUHEHUH8L}HAIWH{HCHCIwH0 AoG(C(HC0HtH=@H]LwH}tL`%1@HEHEH]H]AIID}MLC|HjHH}u2HUdH+%(Hh[A\A]A^A_]@H][LHx$HxH|HLK H5# H81~ЅL{H8 H= 1MfDfH9t#LLgH}uHEBL$fD H{{{{H{f.fUHATSH dH%(HE1HHHH;{LeHs LsH}H]~HH;tHEdH+%(H H[A\]H~H;uH~fDI11ҾH=G 1@HyT1H5G HD ;1vDHAzH5R H6 H81|iH 6 H= 12L}H}tLx"1jsHdzf.UHAVAUATSHPdH%(HE1HHH=LeL`H}H5-V LmLL LLewL Ll~H}HEH9tHEHpH;yHs L7}HEH]H6HH{0HtoH{HCH9tHCHpT8HFHEHUHEHUH8~LuHAIVH{HCHCIvHAoF(C(HC0HtH=}@H]L{H}tL 1DHEHEH]H]H1H;t3H}HEdH+%(OHeH[A\A]A^]DH{H;uH{fD@H]ZHHE11L +0 H * H8H5t R1H:? 8zXZifHy1H5? H$15DH!wHG3 H5+ H81y5H23 H= 1Lh7fH9t#LLw~H}uHEL (~4w>wHAwHHw[wfUHAWAVAUIATISHHdH%(HE1HU1HEHH!fHnH vHEfHnflHE)EHHL}JoLs)UM~.HHQ H1PHUMLEHӿY^L}LmH51H=HDž`HDžhHDžpHDžxHEHE0IH IEL;%1ts ID$H}LP HEH8HEH@H L;-@pL;-΀cL;-sVLHHHH9P H fHPHb HHXHHHIEHIEHH*HH9P HH HHoE1HuH9C fIn1HV)EVHXMt IM HX H HXH5tILIF(@ rHH/IH=1HLxHUHHC :HXIH)HM~ HL15lhL H=dL_GH 5HXZH HqI^L1AFHH=ZdH5!hL L GH4ǃHXAZA[H H5IMt IM`HHHM&HmL;=.ZIGH55HHLHHH \IH7H &L;=YIIG x xHXeHEI$HLHpLLeHLPH}ZLmPkAhHDKcH}HYL7fIH9IMt|LrHEgHuADHydHH5 L AH + H5V H8AT1[XZHU H=9 E1HEdH+%(HeL[A\A]A^A_]@LyH M\1HI9H;LuHUHHEH(I3H&L>L}*f.Ht;H (H= E1L[f.H[DH[fH[fL[hfE1IM9dJtHϺHMHMtHHEJHYWH> H5ck H81ZH &H= _1TH &H=u M3Lw&fH=9+H H5 6HHDLkMHCIEHHHE]H}MASM9~HxLNPLeI<$.ML蝾HHMHH H  HHHHx H?L iHLHH%^HL@H5O 1H:SH/ kUZYH |H=e/ E1vHEdH+%(HeL[A\A]A^A_]ÐLyML;54REFXEH5LIH1VHEIHrIFH5(LHHHHH]H9CLCMLkIIEH LHufInfIn1HflLE)E85LEIMt IMH HMHtHHEHHL;-fML;-]AL;-P4LC^[GIW IGHHH9H9IWIEL,HIGLmH>H}c@H5+LHHL-\L9hLxM'L`II$H#LHu1HHEL}3IMtLMXH H5-LHHtL9hLxMLhIHIEjHu1LHEL}k3HMtL;HbL*HLFbIHsL HLZQIH{LH 1DHI9I9LuIDHtsHEILRI<$LRfDHRqfLEHRLEf.*]HHHt+ I1PHULELL}AYAZ@HEE11ADH DH=+ Mt E1I $tTMMtItWH}tHMHHEHHtHHdHQDLQDLQDLQ7f.HgQMfE1IM9KtHϺLEHMHMLEtKDfDH!ME1E1E1H4 H5"a A1H81OHEfLL|MALmE11fDLE1E1AHEE1aAHgPsf [HOHEE1E11AE1HEE11AHPfKHEE1E1ALOH}tLhE1WGHfLmAHKE1E1E1H, H5_ A1H81WNHEBfAf.H t+M IML/Ozf.HODHuE1Hurf.HEE1E11ADE1E1E1AHE@HEE1E1A7Hu1E1KfE11A@I$E1HI$LE1E1A7NHEHHu1DI $t1E1E1AH}IHu1E1>I $tHEE1E1ApIHu1\XHv1AE1E1HuJH=L\LLmIHt 1E1dE11A6Q1E1E1AHE1IKIKI$KHJUHAWAVAUATISHHdH%(HE1HfHEHEHEHaHHHEHHHLqHEMH}H5$NH9wE%WHLeLGH}]VHHc/UHHUdH+%(He[A\A]A^A_]@HEVHu#DMH=$ 2Hc  H=g ʴ1fDLqL=%M~1 @HI9t`L;|uHMHHEHtI@HnH>H}f H H= H1E1IM9JtL蚇tHEJrHU H}/H}M1 kfDLOJH}tL81BHH# 1MPHuHULEH蛑ZYNHpHf.@UHAWAVAUATISHHdH%(HE1HHEHEHEHaHHHEHHHLqHEMH}H5TKH9wBUTHLeLLH}])THHc_RHHUdH+%(He[A\A]A^A_]@HESHu#DMH=4" bH q H= 1fDLqL=UM~1 @HI9t`L;|uHMHHEHtI@HnH>H}f} H H=E x1E1IM9JtLʄtHEJrH H}-H}M1| kfDLGH}tLh1BHH 1MPHuHULEHˎZY6KHEf.@UHAWAVAUATISHHdH%(HE1HHEHEHEHaHHHEHHHLqHEMH}H5HH9w?QHLeLUH}]YQHHcOHHUdH+%(He[A\A]A^A_]@HE QHu#DMH=~ H b H=' *1fDLqL=M~1 @HI9t`L;|uHMHHEHtI@HnH>H}fo H< H= 訮1E1IM9JtLtHEJrH H}K*H}M1n kfDLDH}tL1BHH, 1MPHuHULEHZYfHHCf.@UHAWAVAUIATSHHXL5@dH%(HE1HHEHELuHILH H5{R H81(AHH H= 81aLHHHy 1LPHUILELY^fDHwAH}tH`1fHA6fJ[fDE1IL9\KtHMLEH}}H}LEHMt+KyDI?@UHATSH0dH%(HE1HHH1H;=TLeHs L@H}h]De{KHBHcIHH}IcIfHnfHnIfl)EH><Ht&foE@HUdH+%()He[A\]ÐH u HB@I $u L3@\Hr H=U (1@HGHE1L H E H559 H8R1H^ >X1ZdHy1H5: H18DH;H H5O H81h>[KfDL?H}tL1E1}fDH H6?BH=UHAWAVAULPATSHLHdH%(HE1@HXH5BL}LL0LzBL)9HHL H8H?H}L}L9tHEHpAHH8@LHIAIVI|$A$ID$ID$IvH]AoN(AL$(ID$0HtH=?u @HH @HHL#HtDH8HEdH+%(3HĨH[A\A]A^A_]fHLuH8L61L}LHDžP%H0LJHPfoHstatus: HEHUfo@HHHPHUHELEHUHMIL9lHuH}H9sLeL9HuH9H?L)H9BHLpH0`6L`HHHH9H`HPHpHPHLeH@Hh@H`4;H`L9tHpHp>H}L9tHEHp>H}L9tHEHp>HH2!fD11LpL\EL`HHHH9H`HPHpHPHH@Hh@3@fDHPMHzHʃsO1@t 2A0@tD 2fE 0H2A0f@A1AփL1MD5D9rMDHHPMHrHʃs<1@t :A8@tD :fE 8H:A8A1A׃L9MD=D9rMDHE>H=c6HH*H0?HF8L<3L4HFUHAWAVAUATISHXdH%(HE1HvHEHEHEHiHHHEH HHLqHEM7H]H5H9sL-5t L9vAIL9LmHs0HUL>H}L35H}YBHHUdH+%(He[A\A]A^A_]fDHECHu#DMH= ZH H=G 1fDLqL=-M~1 @HI9tHL;|uHMHHEHtI@HnHH]f.E1IM9,JtLttHEJfD1H H/5@IufEHo H5H H3H816=L3H H= 迠1PfHH 1MPHuHULEH~ZYb@L耣H}ukLEE:H5f.UHAWAVAUATISHXdH%(HE1HHEHEHEHiHHHEH HHLqHEM7H]H5H9sL-2t L9vx>IL9LmHs0HULBH}L2H})?HHUdH+%(He[A\A]A^A_]fDHE@Hu#DMH= *H H=] ž1fDLqL=uM~1 @HI9tHL;|uHMHHEHtI@HnHH]f.E1IM9,JtLqtHEJfD1H H5<IufBH H5D H0H813:L_0Hs H=* 菝1PfHH 1MPHuHULEHk{ZYb@LPH}ukLEE7H2f.UfHHAWAVIAUATSHHdH%(HU1H )EH0fHnHEfHnHPHMH /fl)EHMHUJ HXIMI7HHEHCHPHL=# HE1DID$H9IN;|uHXJHEHHLPIMuH}H]L5.l@IHVoLkHU)UM~2HHP 1MPHXHULEHyZYvH}H]LuHGf)pLHGHHH'<IIH 0HH%LhLHLLpLMr8HhL&HþHHxHfLkL%M1HI9L;duHXHHEHIEHSHPIIt`M1H= EHv H= 1ۙHEdH+%(HeH[A\A]A^A_]fDLvLuHXH8H]H}<@L5,E1vDgeHHXH:oHHEIDE1IEM9sIJtLlt\HXJLHhHA H=d 識1fDoLk)]DgGII sDE1IM9JtLktHXJHq/H5z H8)39IH9f[5IHtHLI耹b9HHE:9HAy 9H}fHE9HI_LXXe1H,H,DUHAWAVAUATISHHdH%(HE1HHEHEHEHHHHEH-HHLqHEMH}HGbHGHHH1HgH-GWHH HcЉH9fYH]H-H}xH)HT@HE7Hu#DMH= *H[ OH=? •1HUdH+%(iHe[A\A]A^A_]fDLqL=}M~1@HI9L;|uHMHHEHPIHNH>H}f.#HcЉH96Ht))GWHH HHcH9ttH5H=51蚹HHt111H褌H bH' H= 蓔fDwdE1IM9\JtLgtHHEJwfD1HH HG`H EH*uH*?f.HH 1MPHuHULEHqZY @HH}uEpcHu5H^H*H5< H8i.AH߉EE-I)fDUHAWAVAUATSHXdH%(HE1HEHEHE5HXxIL#Mt L;%%H[HuE1E11L}HEL&H}H}P%IExL=!H8IL HtHtjHtH tPMtItfLHUdH+%(HX[A\A]A^A_]ÐI\$I$LH3'I_H)Mu(HuL(L H}D7H}$H-I}`H0LIH L=. 8LHHE蒑HMHULHuNjxH(H8HXLXHDž0HHHH9HHHL9r foPHXHH)PHHHHXHxLxHDžPHhH(H9HHhL9fopHxHh)pHHhHxHDžpHHH HuH9HHMTH9fo}HH)HDHEH}HEHHuHHH}IH9H9Lfo]LH)HHELEHEH}H9tHEHHp #HH}H9tHEHp"HhL9tHxHp"HHL9tHXHp"H(L9tH8Hp"HH9tHHpy"HHHH9tHHpU"HHH9tHHp1"8L;=@HXH`Iw0LHHHL"HX H0HH H9HNL-MiIEHhH`HHSUIHHX"HHHH5HTHLL^IIEHPMIUHI $H f.HHH9tHHp HH H9tHHp HhH(H9tHxHpt HHHH9tHXHpP H(HH9tH8Hp, HHH9tHHp HHH9tHHpH`HH9tHpHpHEdH+%(^ HeL[A\A]A^A_]A =E1 IM9t0JtL^YtxH8JZ'HH H= E1薅aHHL0H8H@Ht)HH 1HPE1LHecZYxHHZ ZQ@HH6QÃ&H6IEHIEH tMMtI $t7H`H H= HHL萄E18LoHdHYLKL=L/kIо11H= rlH9fomH)HuHuHHH9foeH)HMHMHL9fopHh)pLhLxLL9foPHH)PLHLXLeL9tafo0H()0L(L8LH99foH)HHH H0HHtGHrЃXt8H(H0HHH0H(H}IHtCH$!@ot}A8LLMLLH8HE|HpHHtGHȃUStxZHhHpHHHpHhHЃotUCHMHuHHHUHEDHUHHtAHЃtUH`HuHHHhHEH_ f.H=HJH5KEHHwH1 ?H= "vfHDž(H=DfLsMMLcIHI$萗LHu)A?H tH DH=D uDH D?I $tH| H= puKLO DH=H2~H53~DHHH) BH= uDHCH HLcHHI$虖HLDH=YtCfABDH5!LQB H`HhLuHHHH 腪ID$(AoT$ )PHtH= @HPH8H HHL@HHH8Dž HXHtmrH}L9tHEHpV  fo@HH)PHt H=. u\@HHAH8HXHtqHSHH(@@@fCH" H= sEHUHEDEHUH`OfHuH8 HLITHTF1ƃI<6H<19rwHuH8AtHHXHtpFAfDITHTFt1ƃI|5H<19rVUATTHMHuUATTH`Hu(ATfTHMHuATfTH`Hu( HHIIHUHAWAVAUATISHxdH%(HE1HHDžxHEHEHFHHHhHHHLqHxMHxEHGLuLeLuH5tHHEHTHHHH9CEL{MPLkIIEH LHufIn1LeH )E IMt I)HHHMH^L}LLeLHEHMHUHL9H9*oMHuHMMHHEHuHEH}H9tHEHp& Le,HIEHIEHHLeLLt H}AH]HFH;H}L9HEHp}HDžxHuFf.HY HH L }`AH H5 H8AT1XZH H= 1nHEdH+%(OHeH[A\A]A^A_]DLqL=MW1HI9TL;|uHhHHxHI3f.HuLeHfDH /H= Lem1DHH>HxLeHfDLeH{"fDLgfHoLeLJ]DH7H; H$H9tsoUHMUHUHUHNfDLfE1IM9JtLJ@tHhJ~HMHHt>HȃtM%HuH}HHHMHED*DHuE1Huf.LH}L脧EHMHEv@HH 1MPHhHUHLxIY^HLHLG1ǃL:L>9rMLLHuH}LfLHuH}HH fUHAWAVAUATISHdH%(HE1H3HDžXHEHEHHHHHHHHLqHXM+LXH]}LuLpHDžhL`H wƅpLuHEEH9H HvHHHn HuHDžHH9C ~HfIn1HL`fl)EHHIHtHH@HHHHHM#HH}LL`跛HEHMHUHL9H9oUHuHMUHHEHuHEH}H9tHEHpL` HI$HI$HNHH`HUHL9~L9Uo]HpH`hHnHEHMHPL`HELHHPoHxHH}L9tHEHpH`L9HpHpyHDžX HuFf.HHHA L YAH ; H5+ H8AT1XZH۴ H= 1gHEdH+%(HeH[A\A]A^A_]DLqL=MW1HI9DL;|uHHHHXHIf.HL&LXL`HaLf.H9oeHMeHUHUHvf.HH H=d L`f1HuL`HkfL9omH`hLuLuL@H'mfE1IM9JtL9tHHJHMHHt>HQȃptMHuH}HHHMHEADHUHHtAHЃ*tUMH`HuHHHhHEuH=HqH5qL`w4HH?fDHCHHHHSHHH H@t+H@HuH=!L`M3L`H{EHMHEfDHH 1MPHHHUHLXeBY^RfDL`LdH0gHPr=HLHLG#1ǃL:L>9rEHUH`aITHTF81ƃI<6H<19rH߉HHTMLLHuH}UATTH`HuLfLHuH}[ATfTH`HuIIfDUHAWAVAUATISHhdH%(HE1HFHEHEHEHHHHxHHHLqHEM4H}HfHU1HEHH^H/vH@H9t9HXHHqH1DHH9H;TuL-L95oC HC()EHtH=SE@LeHuLaH}H}Ht`HQtH=:nH9x L%!nMI$HI9D$aIT$ ID$HHH9!H9IT$HHHID$I $IELH HxHHxoHEsHuBfDH1HH L URAH H5 H8AT1fXZH rH=a t`1HUdH+%(He[A\A]A^A_]HH9[HuH;lIfDH5!H="1IHt111HWI $fLqL=uM1HI9thL;|uHxHHEHI4HH>H}*f.@LGHfE1IM9[JtL2tGHxJjHLI $tFH H=ˈ ^1Hǫ H= ^1?LDHH H5 H81XHw H=U h^1^LH`H=HjH5j-IM/DHHJ 1MPHxHULEHH}f.1H]HH}H7H?GWHH HcЉH9tHZH5 H8DGWHH HHcЉH9jwXE1IM9dJtL.tPHEJH]H}HH H=a LZMwfDHHHG&H EHuHH2 1MPHuHULEH7ZYHLH7fDH߉EEIUHATSH0H=ldH%(HE1ZHHHLeHuLHCH}H;HCHuHHHIHPHHHt)HEdH+%(H0L[A\]f.HDH H=ł E1XHH H5 H818HD H= E1EXHHYf.H fL8[H}uH LEEHBf.fUHATSH0H=.kdH%(HE1躈HHHLeHuLHC"H}H;hHCHuHHHIHPHHHt)HEdH+%(H0L[A\]f.HDH H=M E1VHHJ H5 H81H H= E1VHHYf.Ht fLYH}uHL LEߑEHf.fUHAWAVAUATISHdH%(HE1H[HDž(HEHEHCHHHH HHLqH(ML(L=fL@HEL0HDž8ƅ@)p)EM9 H&hH dH9H}HdHHHkHuE1H9C}fInfIn1Hfl)EIMt IMgHHHMH\HELHHֆH0HUH]HL9[H9"oUH@H08HKHEHMHEH}H9tHEHpHIHIHjHH0H8L`HPLPHHPH]HXL9HEH`HE}HULPHDžXƅ`EAHEHMHpH9HpHEHEHxLpHEEHDž(Hu>fHHH L DAH [ H5K H8AT1XZH4 PH=y} 1RHEdH+%(HeH[A\A]A^A_]DLqL= M_1HI9L;|uHHH(H%If.HLpLz}0HPL9tH`HpH LH`H ILH0L9tH@Hpo}HpHEEH9HEHp?f.HVL6L(LgfHWfLG1fLp!@H9o]H08H]H]H@H}EH9HEHp@H]H H=T{ P1HuLE1IM9DJtL $t3HJHUHHtAHCЃNtUH0HuHHH8HEHJHL1 07@H=9HB_H5C_HHpDLkMvHCIEHHH!qHHuG@H=f.D0fD7HLD070f.A1AƃI|5H<3D9rH<LHrHH؃ 1@t @tDfDH-!HH 1MPHHUHL(,Y^ZfDH?kf.HXQH _=EHUH0HTHTF1ƃH<3H<19rA1AǃL;L9D9rH<HH߉'bUTTH0HuFTfTH0Hu,IIf.DUfHAWAVAUATISHHH8dH%(HE1H])EHHfHnHHEfHnHEflHE)EHHH0HMHH<HHEHCH(HL=أH E1fIEH9IN;|uH0JHEHI H(HSHH(L=eH E1IFH9IN;|uH0JHEH L(I#DHfHFoLkHE)]Mq H]~LkL=ݢM1HI9,L;|uH0HHEHIEHSH(HHFoH^HE)UHL%HDžHHDžPHDžXHDž`HDžhHDžpL98H8x0pL9vHh]H A[H9HL-([MIEL`LHAƃIMHDž`EHCH5HHHIL`MUL;-DL;-M9L%AƅIM HDž`EHu\H >ZH9HL-%ZMQIEL`H5LjHhIH5LjH5ȬLHDž`AŅ LjH5HHDžhEjHhIHyHAŃ LLxtjDLHDžh-HxtL+fdI$H t*HUdH+%( HeL[A\A]A^A_]DH/DL9HHHXHxxHPH0{H8HpH9LxHLFHx HHHtgiHPHDžHHtKiHXHDžPHt/iHDžXHEHu#DMH= bH H=q E1GfD=E1IEM9IJtLVtH0J@L`f.E1IFM9IJtLtH0J@HCo&H(H)e!DE1IM9JtLtH0JHHϑ H5 AL5~p H81L  DLLE1E/fDLfH=`H*VH5+VIL`MLhAL L5o 3fDL`LhAL L5o MtIMtcMtIt9HpH=H4L8L8L8LL8L8LL8H=_4I @fHhIHHAŃLeH WHDžhH TH9HGL5TMIL`H5LeHHpH8uLeL8H I9@Hu11LHEHEL8jL8HhIHDž`MLeH5fLdHpIHLdIFHDžhIFHHHLIILLxdLDLHDžpHx) L`LhAL L5f HVUHAWAVAUATISHHdH%(HE1HFHEHEHEHHHHEH-HHLqHEMH}HGbHGHHH1HgH-GWHH HcЉH9fYH]H+H}xHqHT@HEHu#DMH=ë UH ?H= ":1HUdH+%(iHe[A\A]A^A_]fDLqL=݌M~1@HI9L;|uHMHHEHPIHNH>H}f.HcЉH9 Ht))GWHH HHcH9ttH5H=Q1]HHt111H1H NHą H=p 8fDwdE1IM9\JtL: tHHEJwfDHH HH EHPuH??f.HH 1MPHuHULEH;ZY @H ;H}uEpOHuzH^HH5b H8AH߉E9sE>IfDUHSH8dH%(HEHkH9HW@HR0HHR8HR0oB)EfH~HtH=BfH~HtnH}oH]Ht;HKHsHH9H=SJKHtTHUdH+%(H]H]HHuf.HHE H5 H81XHw H=Ea h61@BHU(`fHHEHHCRHHRHE@fHHE44HE"Hf.UHSH8dH%(HEHH9HW@HR0HHR8HR0o)EfH~HtH="BfH~HtoH}mH]Ht;HKHsHH9H=SJKHtUHUdH+%(H]@H]HHuf.HH H5 H81Hǁ H=_ 41@BHU'_fHHEHHCRHHRHE?fHHE2HE!H\f.UHAUATSHH;=HLnHLg(HG M9tYMtH=uhAEMt=IT$IL$HH9H=Zu(AD$PAT$Lk(H[A\A]]DAELg(MuHqHO H5{ H81(HH: [H=ʤ A\A]].3fDID$I$LPI$LP\fDL(1IUHAUATSHH;=HLnHLg(HG0HG M9tYMtH=FulAEMt=IT$IL$HH9H=u,AD$PAT$Lk(H[A\A]]DAELg(MuH1Hگ H5; H81HH F [H= ] A\A]]1fDID$I$LPI$LP`fDL/MUHAVAUSHH;=HLvHLo(HG M9tUMtH= upAFMt9IUIMHH9H=u2AEPAULs(HHC0H[A]A^]DAFLo(MuHH H5 H81HH ~ 7[H=k A]A^]0fDIEHuLIEPIELPHuQLHu.Hu5UHAVAUSHH;=DHLvHLo(HG M9tUMtH=upAFMt9IUIMHH9H=u2AEPAULs(HHC0H[A]A^]DAFLo(MuHH~ H5 H81XHHs| [H=Z A]A^]^/fDIEHuLIEPIELPHuQLHuL-Hu5UHAVAUSHH;=HLvHLo(HG M9tUMtH=jupAFMt9IUIMHH9H=<u2AEPAULs(HHC0H[A]A^]DAFLo(MuHQH H5[ H81HHl{ [H= A]A^].fDIEHuLIEPIELPHuQLHu+Hu5UHAVAUSHH;=HLvHLo(HG M9tUMtH=upAFMt9IUIMHH9H=u2AEPAULs(HHC0H[A]A^]DAFLo(MuHH{ H5 H81HHz D[H= A]A^],fDIEHuLIEPIELPHuQLHu*Hu5UHXHHATSLg@HHMtAIT$IL$HH9H=TAD$PAT$nLc0MtAIT$IL$HH9H=xAD$PAT$HHHH[Ht7HSHKHH9tvH=&CPS[A\]ÐID$I$LPI$LP@fDID$I$LPI$LPbfDHHHCPHH[A\]H@f.SffH[A\](@L(L(UHAVAUATSHvH9HLvILoHHG@M9tVMtH=uoAFMt9IUIMHH9H=u1AEPAUMt$HHH[A\A]A^]fDAFLoHMuHѼHI H5 H81H1w H= U )[1A\A]A^]DIELIEPIELPZL'FUH8HHATSLg@HHMtAIT$IL$HH9H=tAD$PAT$Lc0MtAIT$IL$HH9H=XAD$PAT$4HLcHHMtAIT$IL$HH9H=AD$PAT$H߾H[A\]%f.ID$I$LPI$LP fDID$I$LPI$LPBfDID$I$LPI$LPrfDf.=ffL%'L%L%eUHAVAUATSHGHHH{tLc fE1CMIT$IL$HH9H=vAD$PAT$zEt MtL MLc MtAIT$IL$HH9H=AD$PAT$H[A\A]A^]&PfD'4Lc fAICM\fID$I$LPI$LP,fDkf.fID$I$LPI$LPH[A\A]A^]cO@uOH0H9P0FH-5[A\A]A^]@L#Lp#y:HC@UHATSHGHHLc0Mt9IT$IL$HH9tBH=ruXAD$PAT$H{HHt HsXH)<H[A\]PID$I$LPI$LP뿐D@u7H@H9P0RH A[A\]Lh"q2HCf.fUHAVAUATSHGHHH{t{Lc fE1CMIT$IL$HH9H=6AD$PAT$zEt MtLILc MtAIT$IL$HH9H=ڻAD$PAT$H[A\A]A^]LfDr'Lc fAICM\fID$I$LPI$LP,fDkf.fID$I$LPI$LPH[A\A]A^]#L@uOH0H9P0FH5[A\A]A^]@L@ L0 yHC@UHATSHH dH%(HEHGHHUHuH}HUHuH}ϲLcHMt9IT$IL$HH9tMH=ucAD$PAT$HEdH+%(H H[A\]f.ID$I$LPI$LP봐D@uOHH9P0#HHEdH+%(u2H [A\]DLNHCf.@UHHHATSLgHHHMtAIT$IL$HH9H=¸AD$PAT$Lc8MtAIT$IL$HH9H=xZAD$PAT$tHLc HHMtAIT$IL$HH9H= AD$PAT$ H{HtH=u)G PW t [A\]fDH[A\]H@G @ID$I$LPI$LPfDID$I$LPI$LPfDID$I$LPI$LPBfD%f.kffLULLxUHгHHATSLgHHHMtAIT$IL$HH9"H=AD$PAT$Lc8MtAIT$IL$HH9H=HjAD$PAT$H]Lc HHMtAIT$IL$HH9H=AD$PAT$H{HtH=õu9G PW tH߾P[A\]%fHPG @ID$I$LPI$LPfDID$I$LPI$LPfDID$I$LPI$LP2fDf.[ffLXELHL8oUHAUATSHHdH%(HE1H{HHf)EH;IHsLmH]HLHPH}L=H}HrHH]Ht;HKHsHH9H=гSJKPHUdH+%(He[A\A]]fʿHH[ H5 H81LHh H=TF 1DifHHEHHCRHHRHEIfHɸHE1L e H ` H5u H8R1H5 X1ZHyF1H5 H;-1DH=a111@HHEHELH}u LE UE%H鳱H麱DUfHAUATSHHH;5MtxHF Lf(HMtH=бuAD$LcHH[A\A]]AD$LoMtIMIEHH9uLAHЫHg H5ڿ H81DpHg 7H= AD\HH[A\A]]fDIELIEPIELPI雰f.DUfHAUATSHHH;5 txHF Lf(HMtH=uAD$LcHH[A\A]]AD$LoMtIMIEHH9uLbAHHnf H5 H81GDF0HWd H=C AEDHH[A\A]]fDIELIEPIELPIrf.DUHAVAUATSH@dH%(HE1HyHHf)EH;ILuLeHsLL_H}|L+Lk?HH]Ht;HKHsHH9H=ɮSJK)HUdH+%(+H@[A\A]A^]fHHd H5 H81LoHc H=A 1;`fHHEHHCRHHRHE@fI11ҾH=R 01Hyh1H51 H7O1DLPwHHEHEuHWf.UHAVAUATSH@dH%(HE1HyHHf)EwH;pILuLeHsLL/H}|LL;=KHH]Ht;HKHsHH9H=SJK)HUdH+%(+H@[A\A]A^]fHHa H5ú H81ppL?JHa H=? o1;`fHHEHHCRHHRHE@fI11ҾH=" X.1Hyh1H5 H4O1DLXNwHHEHEEH>f.UHAUATSHCHfH@HK%L@MH;GIHHhLk fInfHnflL`HPHHHCMt9IUIMHH9thH=hu.AEPAULcLc(HH[A\A]]@D11H=d ,H tn1fIELIEPIELPLcfDHIHV H5S H81H^ H=]= fDH1FL)I铩H霩H骩f.UHAWAVAUIATISHhdH%(HE1HvHEHEHEH.HHHpHHEHLyHEM H}1 IHjHII9AoE)EHEHtH=@LmHuLLAH}LeE1M^IT$IL$HH9H=1cAD$PAT$A7LH6] H=; P1HUdH+%(xHe[A\A]A^A_]@HEHuADHyHH1~ L AH +U H5 H8AT1XZH\ 'H=1; gLyH tM~1@HI9|H;LuHpHHEHMIDLHH@H.H>H}ff@*E1IM9JtHϺHxcHxtHpJHfZH5BID$I$LPI$LPfDBH܇ H5 HcH81(߻8#fDHH`| 1MPHpHULEH0Y^-LH}AtLvHLeMW@L F]HۥHf.UHSH8dH%(HE1H;=y+HG@H@0HH@8H@0HXHPHH HCH3CoJ8)MHUHtBHH0H9H$CPSH}!GH]Ht;HKHsHH9 H=]SJK=HtxHUdH+%(JH]oB8HB@)EHxH=u@f@]HQHZ H5[ H81H'X H=7  1iCoR8Hr@)UHFD fHHEHHCRHHRHEfHHHPHHPxfHHEHEHQH鉣f.UHSH8dH%(HE1H;= #HG@H@0HH@8H@0HXHHH yHCH4CoJ8)MHUHtBHH0H9H%CPSH}DH]Ht;HKHsHH9H=SJK>HtqHUdH+%(KH]oB8HB@)EHtH=u @q@@dHHX H5 H81HU H=}5 1pfCoR8Hr@)UHFD fHHEHHCRHHRHEfHHHPHHPwfHHE$HEHPuH0f.UHAWAVIAUATSHHhL-+~dH%(HE1HmHEHPfHnHEfHnLmfl)EHJHHXII Mt[M1H=9y `"HGV H=3 1HUdH+%([He[A\A]A^A_]@LyH 5lM1HI9\H;LuHXHHEHMgMH}!fItI5LnLmH>H}1IHHLHuEHEIHHH`H9hfHUMLHhLx)ELHp HxLeE1Mt|IT$IL$HH9H=AD$PAT$Au5H` HT H=32 >1AH`ۗHH"oLy)UM~2HHw 1MPHXHULEHZYH}Lmo>fHLaHEM$LkH jM1@HI9H;LuHXHHaHEM|$GE1IM9TJtHϺH`H`t2HXJhf.E1IM9JtHϺH`CH`tHXJ@f:HS 4ID$I$LPI$LPfDH5 H`R H5D H#H81߻fDH@HErHLHxAtL7>LeM*L"HHfUHAWAVIAUATSHHhL-xdH%(HE1HgHEHPfHnHEfHnLmfl)EHJHHXII Mt[M1H=s HO \H=. 1HUdH+%([He[A\A]A^A_]@LyH fM1HI9\H;LuHXHHEHMgMH}!fItI5LnLmH>H}1jIHH/LHuEHE@IHHH`H9hfHUMLHhLx)ELHp HxLeE1Mt|IT$IL$HH9H=ϘAD$PAT$Au5sH`HCM H= - 1AH`HH"oLy)UM~2HHq 1MPHXHULEHZYH}Lmo>fHLaHEM$LkH dM1@HI9H;LuHXHHaHEM|$GE1IM9TJtHϺH`KH`t2HXJhf.E1IM9JtHϺH`H`tHXJ@fHSo4ID$I$LPI$LPfDH5pHm H5 HӐH81߻tfDBH@HE"HLHxAtL8LeM*LmҗH͖HזfUHAWAVAUATSHHHdH%(HEHGHMHUHuH}HH;ɏHPHtS`!HUHuHH}Lc(MtAIT$IL$HH9H=pAD$PAT$zLc8MtAIT$IL$HH9TH=Ĕ6AD$PAT$ LcHMtAIT$IL$HH92H=zAD$PAT$HEdH+%( HHH[A\A]A^A_]m%DHCH5LHHHIM<HI9D$*Mt$MMl$IIEI $1HuLHELuqIIMIM0IHH+@ID$I$LPI$LPfDID$I$LPI$LPfDID$I$LPI$LPfDLOOf.HQHIK H5[ H81H=q' $0;f.ffLǐHH@Lf.Llf@HlH9P0H9HEdH+%(HH[A\A]A^A_]IML%Hu11LHEMHEoIDL(-LLyIMfŠHCCUHSHHHGHuNHEH{hHt HChHtHH]:f.:HH]D:uHSHH9B0uHԅtH]f.UHSHHHGHuNHH{hHt HChHtHH]f.HH]}DuHSHH9B0uH4tH]f.UHSHHHGHuNHH{hHt HChHtHH]f.HH]DuHSHH9B0uHtH]f.UHATSHGHHLcpMt9IT$IL$HH9t2H=uXAD$PAT$H[A\]6fDID$I$LPI$LPH[A\]D@u7H@H9P0RHA[A\]Lq҇HCf.fUHATSHGHHHSLcpMt9IT$IL$HH9taH=uAD$PAT$H{xHt HCxHtH[A\] H[A\]fDID$I$LPI$LPfD҆.HSHH9B0Hd[A\]L:UHATSH@LfMtAIT$IL$HH9H=̌AD$PAT$LfMt9IT$IL$HH9t1H=quAD$PAT$f[A\]ÐID$I$LPI$LPf[A\]ID$I$LPI$LPFfDDf.LX LHRUHAWAVAUIATSHHݔHHHHt;HSHKHH9H=WCPS8IHt;HSHKHH9HH=CPSHMI]xHIEI9tuI DAD$PAT$t=HI9tELcMtID$IT$L9H=tuLHI9uI]xHtIHH)DI] Ht;HSHKHH9H= CPSH[A\A]A^A_]ÐID$I$LPI$LP fDHHHCPHHPfHHHCPHHH@H[A\A]A^A_]DHHHCPHHPIf)fHH[A\A]A^A_]HHfUHAWAVAUIATSHH-HHHHt;HSHKHH9H=CPS/IHt;HSHKHH9XH=`CPSHMI]xHIEI9tuI DAD$PAT$t=HI9tELcMtID$IT$L9H=هtuLHUI9uI]xHtIHH)I] Ht;HSHKHH9H=pCPSHL[A\A]A^A_]%4@ID$I$LPI$LPfDHHHCPHHPfHHHCPHHPqHHHCPHHPIf)fH HHf.UHAUATSH8dH%(HE1HHH/qH;jIIHs@LeLHHpHPH}LoCh)EHEHt H=uy@L&yH]Ht7HKHsHH92H=uHSJKVH=HUdH+%(He[A\A]]@fDHHE1L %8 H 2 H5| H8R1H^ 2X1Z@Hy1H5t^ H$1XDHR H5, H H81ЁЈL~+Hc9 H=D 1HHEHHCRHHRHEf,fHHEHELH}u`LE&EՅHH DUHAWAVAUATSHL-~L9/HL~HLw(HG M9tYMtH=sAGMt9IVINHH9H=AugAFPAV3L{(H>H{0H8%IHtdL9uBH{8HtLc8H[A\A]A^A_]fD:DH5HKuI $HH7 H==\ [A\A]A^A_]DAGLw(M >H|H5 H>V H81fIHuLIFPILPHufLWOfLHuHuf.UHAVAUATL%'|SL95HLvHLo(HG M9tYMtH=AFMt9IUIMHH9/H=gu}AEPAUILs(Lk8fC0Mt5IUIMHH9t}H=uCAEPAUI$LCX[A\A]A^]DDAFLo(M,`fIELIEPIELPfHzHPN H5 H81}HQ5 H=U [1A\A]A^]DIELIEPIELPLLUHAWAVAUATSHL-@zL9/HL~HLw(HG M9t]MtH=AGMt9IVINHH9H=ugAFPAV3L{(HC HHxHS0!IHt`L9u>H{8HtLc8H[A\A]A^A_]fz}DH5HHuI $HH5 H=X [A\A]A^A_]-DAGLw(M >HyH5" H05 H81{fIHuLIFPILPHufL|OfLHuHuf.UHAWAVAUATSHHXdH%(HEHsL%?xL9HL~Ls(HC M9t]MtH=}AGMt=IVINHH9H=}AFPAVL{(L>HEHEL{0HEpHELpxM.Mt M9_MvMuHEE1L9%fA(LHEIHcH=݅H5IHEIM,HILULH=81HEHELU1IBH;t M9SIMHEH@xH8L(Ht H%Mt IH}tHUHHEHHMt I H{8HtbL{8HEdH+%(IHX[A\A]A^A_]@LUL+zLU fMuIELI$xHEyHqH5" H8~L}H5HEHEI`]H/ ^HHEHk HHEHMHULHu[H}I$HH}HEHH}HEHHEME1dDLH=,XuLUHHEIHpH;5t L9HEH . E1E11HMH  ^E1HMHMHIxH9L)Ht HMt IHUHtH HMHH jHt HFMt IMt I $HUH}LU1LUMt I HEdH+%(HXH- H=dS [A\A]A^A_]fDLwWfLUH}wLU0fLULwLUf.LUvwLUDAGLs(MXH>H=1114 f.H1sH5B H2 H81uHI LvIHuLIFPILPHuH , E1E11HMH k bHMHEL}_E1LeHHLUE1E1H}H2o fH5* H81)uH H, LU1HMH HM{LHuTHuJLUHuLUHEHLUuHELUxHELLUuHELU>HELUuHELULULuLUTLUL{uLU/LiuU^uSuHuH=n LULUAH .+ E11dHMH HM]xf.fUHwHu ]f.H* $H=VP ]UHATISH.HHuH;pt2I$HC@[A\]D[Hy* H=\ A\]l@HipH5z HK H81 s뾐UHATISHn~HuH;pt2I$HCH[A\]D[H) H= A\]@HoH5 HK H81r뾐UHATISHH dH%(HE15~HuPH;otwI4$LeHLHsHxH}HEdH+%(H [A\]xHEdH+%(uzH H) H=R [A\]HoH5" HN H81qyfLH}u{t^LEGELvHu@UHATISH(}HuH;tnt2I$HC@[A\]D[HY( H= A\]L@HInH5Z H * H81q뾐UHATISH|HuH;mt2I$HC@[A\]DP[H' H=T A\]@HmH5ځ HdM H81pQ뾐UHATISHn(|HuH;tmt2I$HCH[A\]D[HY' H= A\]L@HImH5Z H* H81p뾐UHATISH{HuH;lt2I$HCH[A\]Df[H& H= A\]@HlH5ڀ H) H81og뾐UHATISH({HuH;tlt2I$HC@[A\]D0[HY& H=L A\]L@HIlH5Z H) H81o1뾐UHATISHzHuH;kt2I$HC@[A\]D[H% H=K A\]@HkH5 Hw( H81n뾐UHATISH(zHuH;tkt2I$HC@[A\]D[HY% H=VK A\]L@HIkH5Z H' H81n뾐UHATISHyHuH;jt2I$HC@[A\]D[H$ H= A\]@HjH5~ Ho' H81m뾐UHATISH(yHuH;tjt2I$HC@[A\]D[HY$ H=\ A\]L@HIjH5Z~ H& H81m뾐UHATISHxHuH;it2I$HC@[A\]Do[H# H=I A\]@HiH5} HE5 H81lp뾐UHATISH(xHuH;tit2I$HC@[A\]D[HY# H=| A\]L@HIiH5Z} H$ H81l 뾐UHATISHwHuH;ht2I$HC@[A\]D[H" H=I A\]@HhH5| Hf% H81k뾐UHATISH(wHuH;tht2I$HC@[A\]D[HY" H= A\]L@HIhH5Z| H# H81k뾐UHATISHvHuH;gt2I$HC@[A\]Dh[H! H='H A\]@HgH5{ Hr# H81ji뾐UHATISH(vHuH;tgt2I$HC@[A\]DB[HY! H= A\]L@HIgH5Z{ H" H81jC뾐UHATISHuHuH;ft2I$HC@[A\]D[H H=EG A\]@HfH5z Hh" H81i뾐UHAWAVAUIATSHHpLHHHI9~IAD$PAT$t=HI9tMLcMtID$IT$L9H=ktuLH-I9uIHtIHH)alIHt;HSHKHH9H=:kCPSHoMI]xHIEI9tI"AD$PAT$t=HI9tMLcMtID$IT$L9H=jtuLH-I9uI]xHtIHH)dkI] Ht;HSHKHH9H=@jCPSH[A\A]A^A_]ÐID$I$LPI$LPfDID$I$LPI$LPfDHHHCPHHH@H[A\A]A^A_]DHHHCPHHPW9f/H+HH[A\A]A^A_]f.DUHAVAUATSH,HIHIrID$HEL@ML;%Ac8iH@ HH@(@0H:oHPH@HRHHlHCHPHCHHC@iIfHnHfHnI]flIEH!eHIEHH@LtIVHttRtmI\$HAD$@Ht;HSHKHH9H=gCPS2AD$THL[A\A]A^]fHIHgHAE I~Ht-HG PW uH)EPfoEMn:gf11H=" I $E1PfHHHCPHHPHaaH4 H5ku H81dH 2H= (fDG &H8LdafAE Hgf.@UHAWAVAUIATSHHkLHHHI9~IAD$PAT$t=HI9tMLcMtID$IT$L9H=etuLH]I9uIHtIHH)fIHt;HSHKHH9H=jeCPSHiMI]xHIEI9tI"AD$PAT$t=HI9tMLcMtID$IT$L9H=dtuLH]I9uI]xHtIHH)eI] Ht;HSHKHH9H=pdCPSHL[A\A]A^A_]%4e@ID$I$LPI$LPfDID$I$LPI$LPfDHHHCPHHPqHHHCPHHPW9f/H+H UfHATSH@H]dH%(HE1)EH9vHEHwHkfoELef)M)EfH~MIT$IL$HH9H=bAD$PAT$LeMtAIT$IL$HH9{H=b%AD$PAT$HEHtvH}UFHH]Ht7HKHsHH9tOH=/bSJKHUdH+%(AH@[A\]HHHHEHHCRHHRHE@H)\HJD H53p H81^BH H= 1*fJfffID$I$LPI$LPefDID$I$LPI$LPHEfHHEHELp\L`bHWbf.UHAVAUATSHGHHhHgH{trmBLc fE1CMIT$IL$HH9H=-`AD$PAT$QEt MtLLc MtAIT$IL$HH9H=_SAD$PAT$Lc0MtAIT$IL$HH9H=_AD$PAT$H{HHt HsXH)M`HHtHǃHtQH[A\A]A^]afteLc fAICMf:]H[A\A]A^] f.ID$I$LPI$LP;fDID$I$LPI$LPmfDff0fID$I$LPI$LPqfDWHSH_H9B0uHSd[A\A]A^]LpL`XLPf.UHATSH dH%(HE1HFfH@HL@M H;WtH}YfoMLc f)EKMIT$IL$HH9H=]AD$PAT$sLeMt=IT$IL$HH9H=\u;AD$PAT$HEdH+%('H H[A\]fDDnf.11H= H 1fDID$I$LPI$LP#fDID$I$LPI$LPAfDHIVH1 H5Sj H81YH 2H= `L0H1YLm]f.UfHATSHH@dH%(HE1)EaH;UIMHsH}HPfoEH]f)M)EHtHSHKHH9H=ZtCPSH]Ht;HSHKHH9nH=Z CPSLTH}\HH]Ht7HKHsHH9t>H=.ZSJKHUdH+%(0H@[A\]fHHEHHCRHHRHE@fH1TH; H5;h H81V]LSH H= 1"KfffHHHCPHHPoHHHCPHHPHHE蔾HEH耾mHpZH~Zf.UfHATSH0HSdH%(HE1)EH9HG o@0fH~HtH=hX @)EfH~fDHtsH}K9HH]Ht7HKHsHH9tLH= XSJKHUdH+%(+H0[A\]HHHHEHHCRHHRHE@H RH,7 H5f H81T4Hd H= о1-f@Le)EMIL$ID$HH9t"teHE@ fID$I$LPI$LPHEf.HHEdHELPXHxXUHAVAUIATISH@dH%(HE1藃H+HH_HEHEMuHCH HEM`MOIIELHE`HHHLmH5-I9uL%ZPt M9gL9M98VIu IHUVIfHnHXfInMeflIEHNHIEI$H@MtIVH.R#LcHC@MtAIT$IL$HH9qH=1UAD$PAT$=CTHEdH+%(zHeH[A\A]A^]L__H5` LIHVXIUHEH]Hu@@HyZHH] L AH + H5L H8AV1QXZHU H=1 輻H c16@LIH3TH2AE I~Ht-HG PW uH)EPfoEMnI.MmLmID$I$LPI$LPfDtf.1H3 L6L9fDHMHA! H5a H81PH) H= 萺HMH2 H5a H81@PfDAE fDH15QLhHH 11PHUMLEL-Y^*fDG vTHZTHiTUHAVAUATSH@L-LdH%(HE1L9rHG0o@P)EHEHtH=R@H}JLeHMtAIT$IL$HH99H=QAD$PAT$eHlL9#HCH5*HHH HHH XH9G&LoMLwIEIH1HuLHELmf/IMILHHHMH+HHPHHHEdH+%(H@L[A\A]A^]DH1KH H5;_ H81MH  H= E1HIHBHHHHNqfLN)fHu11H}HEHE_.H}I@HH^  H= E1LHHP]*NDNpDfID$I$LPI$LPfDEHf.M^DLH  H== 蘶-QHCQDUfHAWIAVAUATISH8dH%(HE1HD)Eu HAIW0IG(HH)HHMH9L5I1E1LHHHH@HUH9tZHtH=N@Ht;HSHKHH9H=QNSJKHEM9H}tmH}AHHIT$ ID$HHH9H9IT$HHHID$H t-HEII9IG(H]ILHKDHHHc H5 \ H81JaH H= ȴ1H]Ht;HKHsHH9H=2MSJKrHUdH+%(H8[A\A]A^A_]HLG H bH)KT@@H]BmHHE2HEXfHHEHHCRHHRHE(fHHEHHCRHHRHEffIWHIG@II)IH9tA1DHL9s0IG@HLHH<HHtaHuJ@HFHSfHHE4HEyH=S111٪]ehMHMUHATSHIH1DJHHL;%Et_ID$ HHxHHtfHtHIHHtL[A\]:IH'IL[A\]H)EHO H53Y H81GH? zH=% E1HHDI11ҾH=j% E1eHy1H5I% Hf.H yH=% xfDUHAWAVAUATISHXDwpLodH%(HE1EA`AEHEuPA]LEAHD9HEu0H |H9HL=cM"IIcHIEHHp HxDIHHOI9G6HuE1fInfIn1LflLULM)EF'LUfnEfnLMMfbt I I HI(I|$ EuPfAEHHtID$ HAD$p7+H H=> AD$pL01HUdH+%(HX[A\A]A^A_]fDHIE8H;B~H@ IMHPIE@HB0Hr8HZ0IUH9M}IE(HH)L)H9IE HL)IH9HKIILIXfDFHt5LWL_M9'H=G DWEZD_AHrHHIH3HzH2HsH9tHtH=6GtFHzfIMIM IUHZ8H+Z0H]YHELfENEHELM~E@HE.EHE#DHELfEEHE~EHELfEDHE~EHɺHOIL9HI#@SrsIL9I_HtHSHsL9'H=Ftf.IIHIP@@Ht3HwLWL9H=EYwDVDWIGHIIt4HIIHCH9tHtH=|Et@IIE M}Hr8HZ0IM)LHH)HH9u@BHHH9o HSHtH= EtB@HHMHHEHCRHHRHEHMIyHqJH8K0H=!HzH5{f{IMIcE0AEA2H=dzIH! 2H= 萫軆ILiBy@MWMWIGILLMHLUHEL}LUHuLMS떐H7HUHELEHMHGH}VH}H7VHMLEIHEHUDLHuLEHMHUHEHGH}ARH}LARHEHUIHMLEHuHH9gHuHHMCHMHuIHH9u?BHHH9t(oHSHtH=BtBIU I]IH9u$[GpwHH9t5H{HtHGHwL9tOH=?BtŸf.I]HtIu(HMHH)CHMM}IM(fHHMHUHGH}PH}HPHUHMIWDKH<H5"P H H81>HHMHEHMHEIfHMHUӦHMHUI@HUHELEHM裦HUHEILEHM?@HuLEHMHUHEgHuLEIHMHUHEALHud?BfUfHAWAVAUATISHHdH%(HE1H)EHfHnHEfHnfl)EH.HHHHyH'HtbMH= HC H= E11HEdH+%(hHeL[A\A]A^A_]@LsL=M1 HI9DL;|uHHHHEHIFL{H@L-M>1fHL9L;luHHHHEH L@I*fDHH>LnH}LmHLXf1L)`HXHHL1LIHxL-8L9HC0@(tt M9}Ao\$ ID$()]HtH=@>@oc HC()eHtH=>_@HpHUHuFfopHhf)p)`HṭHxHt軣H}Ht譣H}Ht蟣H`3IžHt{H u H;I $~HhHHSHKHH9sH=C=UCPSbH転UfDHr H= hH ME1pfDo.Ls)mMH}LmfD@@hHHEHCH@I0fDE1IM9\JtL:wtHHHJE1IM9JtLvtHHJLG:tffHHHCPHHPH H= H6H H5 J H818H H=r ȢIME1fDH5AH=C1kIHt111HuIMHEDH AfDHI5H H5SI H818fDHH 1MPHHHULEHZYHErCH_xL8;P<H8CHH,H5 H5@ H81p/p6L?,@ Hy H= oH]1H*`Ff.fifHHEHHCRHHRHEfI11ҾH= (1ID$I$LPI$LPfDID$I$LPI$LP fDHy61H5- HT1ZDHHEtHE@L`LPk2H3f.U@HAWAVIAUATSH8dH%(HE1dHH(L-*L98H@H}HPL}Le<9H[I~M>I9t&MtH=0AD$HtMfIEH thMt=IT$IL$HH9tZH=/|AD$PAT$HEdH+%(H8L[A\A]A^A_]ÐH-DID$I$LPI$LP뫐AD$I~BDH! H=^ 舖E1jH)H H5= H818,H H= HE1E1DH H= fDL@01111}1x1UHfHAVAUATISHFH9t>HXHHyH1f.HH9H;TuH;5n(H^(Lv HL--MC6HI|$M4$H9tCHt貓I\$HSHKHH9MCPS[LA\A]A^]HH9KHuH;29@J6HI|$M4$HtLID$[A\A]A^]fC6HI|$M4$H9.Mu&CHD2fCI|$HfHHHCPHHP[LA\A]A^]f.HH&H H5: H81x)H H= 舓Hd H= kHG H= N,I.I.I.I.I.UHAVAUATSHL%&L9oHfH~HtH=}+@Ls(C Mt=IVINHH9 H=D+AFPAVHLvLk8HC0M9tYMtH= +AFMt9IUIMHH9H=*u=AEPAU9Ls8I$LCXH[A\A]A^]f.D@Ls(C M DfD'fAFLk8ML@IHuLIFPILPHuH$H H58 H818'H H= HH1[A\A]A^]fIELIEPIELPLHu,HuaLUHAWAVAUATSH8L5#dH%(HE1L9HL~HLo(HG M9t]MtH=$)AGMt=IUIMHH9H=(AEPAU0L{(HHC0o@)EHEHtH=(@H}9LeIMtAIT$IL$HH9 H=p(AD$PAT$MttM9uRH{8Ht)Lk8HEdH+%(H8[A\A]A^A_]j&fH5ѢLuIM=HEdH+%(0H8Hy H= [A\A]A^A_] f.@fAGLo(MQfID$I$LPI$LPfDH!H55 HT H81P$;0fDIEHuLIEPIELPHuL`oL%f.LHu4Hu(H)f.UHAWAVAUATSH8L5 dH%(HE1L9HL~HLo(HG M9t]MtH=$&AGMt=IUIMHH9H=%AEPAU0L{(HHC0o@)EHEHtH=%@H}9LeIMtAIT$IL$HH9 H=p%AD$PAT$MttM9uRH{8Ht)Lk8HEdH+%(H8[A\A]A^A_]j#fH5џLuIMXHEdH+%(0H8Hy H=ٽ [A\A]A^A_] f.@fAGLo(MQfID$I$LPI$LPfDHH52 HT H81P!V0fDIEHuLIEPIELPHuL`oL"f.LHu4Hu%H&f.UHAWAVAUATSH8L5dH%(HE1L9HL~HLo(HG M9t]MtH=$#AGMt=IUIMHH9H="AEPAU0L{(HHC0o@)EHEHtH="@H}9LeIMtAIT$IL$HH9 H=p"AD$PAT$MttM9uRH{8Ht)Lk8HEdH+%(H8[A\A]A^A_]j fH5ќLuIMjHEdH+%(0H8Hy H= [A\A]A^A_] f.@fAGLo(MQfID$I$LPI$LPfDHH5/ HT H81Ph0fDIEHuLIEPIELPHuL`oLf.LHu4Hu"H#f.UHAWAVAUATSH8L5dH%(HE1L9HL~HLo(HG M9t]MtH=$ AGMt=IUIMHH9H=AEPAU0L{(HHC0o@)EHEHtH=@H}9LeIMtAIT$IL$HH9 H=pAD$PAT$MttM9uRH{8Ht)Lk8HEdH+%(H8[A\A]A^A_]jfH5љLuIMOHEdH+%(0H8Hy H=) [A\A]A^A_] f.@fAGLo(MQfID$I$LPI$LPfDHH5, HT H81PM0fDIEHuLIEPIELPHuL`oLf.LHu4HuH f.UHAWAVAUIATSHXH UydH%(HEHH9HH(yHHLgIH8AoM)MHEHtH=O@H}sLuIMt=IVINHH9lH=AFPAVMH:#H9Ch1HuHHELeLmI $ItTIMt>HHMtkHHtSHEdH+%(HXL[A\A]A^A_]fDLgDLWIMuH?DH u H*H H=_ E1of.@fH=HjwH5kwFRHH6ILIFPILPH=aDQHL{fInfInflM}HCIHH HEtZH}1HuLm)EIItI $H]dLDL8)EHfoE@H u HI $tf.LOHUH;=HAVAUATS(HLnHLg(HG M9taMtH=AEMtAIT$IL$HH9H=AD$PAT$Lk(HCH5HHHIM+H"I9D$Ml$MI $tLk0HC8[A\A]A^]fLDefAELg(MUfHaH5r' H} H81[H H= A\A]A^] I%fLgIHt)HIIf.Ip!Hu II $tYtIu`Mcl$ID$I$LPI$LPbfDL}OLGDL7bIuEl$AMcLItIt*LI/El$AD$II IEl$AD$II UHAWAVAUIATISHdH%(HE1HHEHEHEHHHHPHHHLyHEM~H}fHhDžhƅl)p)EHHIH I9H9HC(~C HtH=d@H}fHnfl)EHtV|"AWIDMdIu0H}HU0foEHxf)M)pHt{H}Ht{L HpإIHLH "LLeMtAIT$IL$HH9H=\AD$PAT$FLxMIT$IL$HH9)H=IAD$PAT$uLzuHEHuADHHH^ L mAH H5{ H8AT1XZH+ n H= 1|HEdH+%(tHeH[A\A]A^A_]DLyH M\1HI9H;LuHPHHEH%I6DHH>H}(f@: AHhH H5r" H81DL  H' H= {E1H HE1IM9dJtHϺHXSNHXtBHPJfffID$I$LPI$LPTfDID$I$LPI$LP`fDH7 H= (zAH H; H5"! H81f fDHH 1MPHPHULEHWY^PufLw-HDH97UHAVAUATISL7HMJLoMIUIMHH9H=AEPAUpL+I$HM}Mu0Mt=IVINHH9H=cAFPAVI}IEH9tIEHp&8LM4$fH>uL3AoD$fI$AL$C[A\A]A^]Ð)fIELIEPIELPL+@HMH L9tLHD f.ILIFPILPLuLuUHAWAVAUIATISHdH%(HE1HXHEHEHEH HHHPHHHLyHEMH}fHhDžhƅl)p)ENHHL- IM9L9HC(~C HtH=2t@H}fHnfl)EHtu@XXI5MnIt$0H}HU foEHxf)M)pHttH}HttLHpOHpSIH'H LLeMtAIT$IL$HH9H=5gAD$PAT$QLxMIT$IL$HH9\H= AD$PAT$~LXstfDHEHuADHqHH L fAH # H5 H8AU1 XZH  H=I 1tHEdH+%(|HeH[A\A]A^A_]DLyH uM\1 HI9H;LuHPHHEH-I&DH&H>H}f@AHH H5 H81 DL% H H=R sE1H H IEE1IM9\JtHϺHXFHXt:HPJfffID$I$LPI$LP}fDID$I$LPI$LP$fDHǿ # H=M r' fDjAHH H5 H81OfHHe 1MPHPHULEHhPY^0mfLXp H|DUHAWAVAUATISHHxL5dH%(HE1HTHEHELuHILHSHHHIHEHL}f1)E) IHL9 x Hs0IHM9H5LHHH;hH;L9HIAƅ~H ELHHHLkLuHIEL}LLLPH}H>H}LsSHH7IT$ ID$HHH9>H95IT$HHHID$H qHcf.HHMH# H % HHLH H?L yaMLHH5HL@H5 1H:ATH zXZH H= E1oHEdH+%(HeL[A\A]A^A_]HIMHL=1DHH9TM9|uIHHEHv@HLAH H, DH= nI$E1HI$HH]Ht;HSHKHH9H=+CPS+MIELPL>L}@MDIA9DLSfHfnfHfHHHCPHHPFLpH}HSAHк H= xmH]E1E1HHaH H5k H81E1AFfLxLxHsHH( 1LPHUMLELKY^P@E1 IL9tKtLHhLpLx)@LxLpHhtZKXI$M@ApDAiDHxjH5H=R 1HHt111HdH轍Af H*ALxhx gHNHvHHEH=HHqf.DUHfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuLhH_[LA\A]A^]HaH# H5k H81H! MH=ݜ (jIEH7ILIFPILP HDUHfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;`t@AEMt$MtINIFHH9tnuLgH[LA\A]A^]HH H5 H81H %H=} hIEH7ILIFPILP HDUfHAWIAVAUATSHhHxdH%(HE1H;5#)E)EHk|H~IH9t:HXHHqH1fDHH9H;TuHH5LH#HHH@H5HHHwIHHM3HHH0zH )gH9HOHgHHHL IHqIMeH nL;%ML;% L;%L*ÅPI $<IFH5[LHHnIHkH51H衫I$HȅyI$HqH$yH fH9HHeHHHiHu1E1H9C1HHELmIMtL蠇HHM>HHEID$H5ILHHHI$HHI$HIHILp@IHHxH5.HLLHIHHӆLˆLÆH5DL蔆HH"H1H9GLgMLoI$IEyHu1LHELezHMtLJHL9H;z1ҾH@IHH H]LHZLeHL)H}HtbHNLƅH}L)HEHuII&DLfDHsf.I $LDLH HHwfA1I $H DH=e XcHt H H}H]HtaHtHaHEdH+%(nHhL[A\A]A^A_]fDL^H[H5  H8H t6H #H=˕ bfHXfHDLwfLgffH}Hu)E (H}Ht`HEH]IIHfBHDHH9KHuH;D9fDH5H=1軆HHt111HYH}H %H=” afH H= aIjDA"H t(1IMtML?H4fAH 'Hh DH=( afDDH=H`H5`V0HHAIM\LNH=ͧ`/A)A"^H 1A" H53H=|1=HHt111HGXHH H=D 7` H^ H= `E1H1A 1rH5H=1襄HH111HWHcH !H= _LH=H@_H5A_.HH@H "H=e X_ H=-LkM"LcIEHI$ԀLHuA"ILA#IHu1E1IHu1LE1A#LA"!`DHf.fUHrfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-'HPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuL[Ho[LA\A]A^]HqH3 H5{ H81(H1 dH=m 8]IEH7ILIFPILP HDUHrfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;pt@AEMt$MtINIFHH9tnuL-ZH[LA\A]A^]HH| H5 H81H H= [IEH7ILIFPILP HDUHofHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=~uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuLXHO[LA\A]A^]HQH| H5[ H81H H= ZIEH7ILIFPILP HDUHnfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-wHPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;Pt@AEMt$MtINIFHH9tnuL WH[LA\A]A^]HH5 H5 H81xH H== XIEH7ILIFPILP H"DUHmfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=^uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuL}UH/[LA\A]A^]H1H* H5; H81H H=Պ VIEH7ILIFPILP HDUH0jfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-WHPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;0t@AEMt$MtINIFHH9tnuLSH[LA\A]A^]HH H5 H81XHa @H=u hUIEH7ILIFPILP H2DUHifHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=>uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuL]RH[LA\A]A^]HH7 H5 H81Hѡ H= SIEH7ILIFPILP HDUHAWAVIAUATSHHL%wdH%(HE1HHEHELeHILH@HHHIHEH LmHDžfHDžHDž HDž(HDž0HDž8HDž@HDžHHDžPHDžXHDž`HDžh)p)EM9H=eH5HGHH HH>HH9G LwLM LIIHHufInfIn1Lfl)EHIMt IHDžIMHIJHDžL9 Hs8LHIH! H;H;PM9LAą IoHDžE#HxxH0IH(H $HcH SH9H HSH, HH H9H( HHu1E1H1HEL}fHIMtL/rHDžMHrH5 LqHLHqHIHmHHXIELh HIH2H3H5HHLLLtH8IHHKqLCqHHDž,qLuLHDžHDž8L'HELHHQ H}HtMH& HHp! H HtpH(HDž HtpH0HDž(HtxpHDž0?HgHMMHÔ H Ŕ HHLH( H?L @MLHHHL@H5 1H:AVHs XZHw H= E1%NHEdH+%( HeL[A\A]A^A_]HIMHL-1DHH9M9luIHHEH@L9HC Hs(H}H]E1E1HE-HpHHH8HHH.HDž8IH)LuHHHHpLH} H]H8IHZHHDž8HXI$L` Mt IM Mt IHt H  Mt I $H}HtKHxH;J1fDL.Lm&@M DHIDžE11E1E1H8Ht HH H=. KE1fDoLfLfLwfHgfLWfLGDž1E1E1DHH1H(@#DHC Hs(H}H]E1HE+HpH_LfLhlHDžHQlHHt@lH8HDžHt$lDžE1ID$`fH 'MHH8ID$hAD$`HHID$pID$pHHH\H9HL5LMIDžL9LkHHLHTHDž8HDžHDžI|$xH0H(H VLE11MIL/f.LEfLLHHH# L1PLHUMLE&^_H H=} HH8HLHj!gHHHIHHjH{8HIFHGH; HG @u"tD‰HWHZI~ HJ"HIF(IEH;xHBIELA @u tE9HCHAHpIN0HHHHHAH;IV8HHF @u tEHHV9LIv@C˾VHHHLhH=HI HHLh111H>HohDžHH{xHhH`HXNHPHHHH@AzH hH{xHhH`HX/HPHHHH@›HDž@HDžHHDžPHDžXHDž`HDžhVE1IL9KtLHLLLLHt^KDDžE1L*DDžE11E1fDž@HH7 H5 H81E11E1E1DžfDHH H5 H81DžE11E1YfIHuE1k@DžfDžJH=_H GH5 GIH;Dž@H=yHFH5FnH|LGH}HDžLWH=TfLxLMHHIHHH#eHuH=^ HL%aH;+PXHHLHdDžRDžHC`fH@HChC`HHHCpHCpHPP{DžHH;LPXHH_H;nHPXHH7HHcDžL};H;;H5TH;RH5TLnCH;TH5THHEHHf.UH@WfHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-'HPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuL>Ho[LA\A]A^]HqH H5{ H81(H1 H=t 8@IEH7ILIFPILP HiDUHAWAVAUATISHHL-dH%(HE1HHEHELmHILH@HHHIHEH LuHDžfHDžHDž HDž(HDž0HDž8HDž@HDžHHDžPHDžXHDž`HDžh)p)EM9H=SH5HGHH HHHS HH9C L{MHCIHH HHHHufInfIn1Hfl)EIMtL_M H .ID$H5bLHDžHHIHoHL5HHIHIH9HH;AH;~DRL9IHADžE H lHDžEM9ID$ H}H]E1It$(HEaHpHFDHwH]HH+ H - HHHH H?L .HLHH=HL@H5 1H:SHܰ XZH H=>q 1R111Hp(H(R:DžII|$xHhH`HXHPHHLH@rLQI|$xHhH`HX~HPHHLH@}HDž@HDžHHDžPHDžXHDž`HDžhHH(H HHxxf~HE1E1HLHQH H5[ H81E1E11E1Dž!?fDDž)H^f.HufDDžHnPHDžL[PfDH1H}0H/Dž'HHaH H5k H81DžE1E11RH=IH3*H54*HHDžY@Dž#Dž2DžLH=UI`HL胪|HLTH;VH5>H1GH;"H5x>LIIfUH?fHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-HPHL9HS Lk(I$MtH=nuDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuL(H?[LA\A]A^]HAH H5K H81Hx {H=^ *IEH7ILIFPILP HDUH@>fHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-gHPHL9HS Lk(I$MtH=uDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;@t@AEMt$MtINIFHH9tnuL&H[LA\A]A^]HHs H5 H81hHqv 6H=] x(IEH7ILIFPILP HDUHx<fHAVAUATISHFHH9t/HXHt|HqH~f1HH9tSH;TuHL-׺HPHL9HS Lk(I$MtH=NuDAEHMl$HHHte[LA\A]A^]ÐHH9tHuH;t@AEMt$MtINIFHH9tnuLm%H[LA\A]A^]H!H{ H5+ H81ؼHt H=%\ &IEH7ILIFPILP HDUHHAWIAVAUATISHL5DdH%(HU1HqHEH"fHnHEfHnHLuH]H7fl)EH]HJ4IHI0MtcIHHIHEHLm@IHPoHIHU)UH)LmLu@HIH=H1 HH9dI;|uHHHEHHHl@ItrItdItnM1H=e s@Hr YH=pZ %1HUdH+%(He[A\A]A^A_]HVHULpLuL(LmH5:I9ut I9H(ƅ@ƅLHDHDžpHPDžxIFHDžXHDž`HDž(IFHH|HRHHNLeHcAH9}fDAAHHq jH=Y #1L`MIL$It$HH93H=AT$JAL$VLH!H;@E1DHI9IEL0LLP(L0L8HHI9It$H@LLL(H0#HdHHMIMIuHH9bH=AUJAMLH HDHmHLHHH[HEHHJHԃHLHH6HHEHf.AFAVHH HcAH9eHH5\ H8fD ffAFAVHH HHcAH9ODoHI)]E1IL94KtHLHhHLHtHJGfEvAHuHH” 1MPHHULELZY6fI$HLID$RI$LRHIUHLIERIULRHA1H1 Lߛ$fEvA@L(IHHIALf.HH H5 H818H1m kH=T H1kH m H=T (1{HH: H5# H81длHm@HEjH@HKHHsL!H0u,H HuLYܸHFH!H+f.@UHAWAVAUATISHHHdH%(HE1H|HDžxHEHEHHHH@H%HHLqHxM LxHHfL}H;_L}HEE)PH0H (H9HH(H2HHXHuE1H9C*fInfIn1HflLm)E貓IMt IHHHMHH}LLmNHEHMHUHL9H9o]HuHM]H HEHuHEH}H9tHEHpѵLm׽HI$HI$HhHwHHLmH`LHp0 fo`HXf)`)PfH~HtXHhHtGHPHHP THHH}L9tHEHpLXMIT$IL$HH9H=۳=AD$PAT$}LOsDHDžxHu>fHiHHO L AH a H5 H8AT1XZHg f H= 1HEdH+%(HeH[A\A]A^A_]DLqL=}M_1HI9L;|uH@HHxH%If.HL&LxLmH8fDH? Hf H= Lm1-@HuLmL@L{fHHHfDH9oeHMeHUHUHfE1IM9$JtLtH@JL1ffHMHHt>HȃtMHuH}HHHMHE2DID$I$LPI$LPfDHiHt LmH5o H81`H=H#H5#LmHH*f fDH=aLmfDLsMHCIHH H@t'H@HuLmHLmHqHH΋ 1MPH@HUHLxmY^zEHMHEHLHLG_1ǃL:L>9rBMLLHuH}0LfLHuH}_HUfHAWAVAUATISHHhdH%(HE1Hǂ)EHfHnHEfHnfl)EHHHEHHHt`M1H= e1H c H=m 1HEdH+%(oHeH[A\A]A^A_]fDLkL=|M1HI9DL;|uHMHHEHIM<HHuHYHHEI@HtHHFHEH>H}HGHGHHHgHHGWHH HcAH9H}H3Aă7f)EEALXHDDHϭHfHnfHnHXflHPH{HHHS HWRLH])EHt;HSHKHH9H=CPS(H}OHHH;LeMIT$IL$HH9H= AD$PAT$L5c` L-ɇ HZHcAH9AfȴH H}HtAf)EH5QH=17HHt111H H L5_ L-$ LLLe1MLLf.oLk)]M~/HHۆ 1MPHuHULEHjZY+H}ʳH@GWHH HHcAH9H:H5 H8DHiH[H$@ H{ Ht8HW JO u!HHx)ERHxfoE@HC SHLkHEE1IM9TJtLt@HEJH5$HrOH /fDDo]ffID$I$LPI$LP2fDDoAT@E1HHAHH A#HBDHHHCPHHPMHHAW XfDHEH@L_bHJH @ H'HK HJH鰮I齮HƮUfHAWAVAUATISHHhdH%(HE1HW{)EHfHnHEfHnfl)EHHHEHHHt`M1H=8 )H[ H= 1HEdH+%(oHeH[A\A]A^A_]fDLkL=5uM1HI9DL;|uHMHHEHIM<HMzHuHHHEI@HtHHFHEH>H}HGHGHHHgHHGWHH HcAH9H}H3vAă7f)EEA&X|HDDH_H8fHnfHnHXflHPHkHHHS HWRLH])EHt;HSHKHH9H=CPS(H}GHHH;HLeMIT$IL$HH9H=AD$PAT$L5X L-p HZ:HcAH9AfXH H}Htf)EH5كH=1K0HHt111HUH L5NX L- LL> Le1MLL f.oLk)]M~/HH 1MPHuHULEHZY+H}ZH@GWHH HHcAH9HʡH5 H8DHH[H$@ H{ Ht8HW JO u!HHx)ERHxfoE@HC SHLkHEE1IM9TJtLJt@HEJH5!HQkOH /fDDo]ffID$I$LPI$LP2fDDoAT@E1{HHAH'H A#HҟDHHHCPHHPMHHAW XfDHEH@L_HJHl@ HHKHJH鈧I镧H鞧UHAWAVAUATISHxdH%(HE1HlHEHEHEHiHHHhHHxHLqHEMHuLefL)p)EūH]LmǨfHnfInflHH})EHLpHHuLLBH}H~L?H5H]Ht;HKHsHH9!H=!SJKaHxHHKHsHH9H=֞SJKHHhGHhtfDHEHuADHYHHwz L }AH L H5 H8AT1XZHS H=); 1HUdH+%(He[A\A]A^A_]LqL=iM\1HI9tpL;|uHhHHEH)IH&H6Huf.LpHHuLLHE1IM9JtL2tHhJbSffHHhHHCRHHRHhHHhHHCRHHRHhyHbQ H=9 Mt1IUIMHH9t4H=luAEPAUtb1DIELIEPIELP1DHP H=8 h1L1HHw 1MPHhHULEHHY^efHHh1HhDLH}u1 HH!P H=D8 1L牅h2>h4H鄢HmH鐢UHAWAVAUATISHxdH%(HE1HfHEHEHEHiHHHhHHxHLqHEMHuLefL)p)EeH]LmgfHnfInflHH})EH*LpHHuLLH}HL.!H5H]Ht;HKHsHH9!H=SJKaHxHHKHsHH9H=vSJKHHhHhtfDHE:HuADHHHu L AH F H5 H8AT1.XZHM H=5 <1HUdH+%(He[A\A]A^A_]LqL=edM\1HI9tpL;|uHhHHEH)IH&H6Huf.LpHHuLLE1IM9JtLtHhJbSffHHhHHCRHHRHhHHhHHCRHHRHhyHL H=M4 Mt1IUIMHH9t4H= uAEPAUtb1DIELIEPIELP1DHrK H=3 1L(1HH~r 1MPHhHULEHY^efHHhHhDLH}u1 H'HJ H= 3 W1L牅h8hԗHjHSHvUHAWAVAUATISHxdH%(HE1HFaHEHEHEHiHHHhHHxHLqHEMHuLefL)p)EH]LmfHnfInflHH})EH,LpHHuLLʡH}HL2H5H]Ht;HKHsHH9!H=aSJKaHxHHKHsHH9H=SJKHHhHhtfDHEڜHuADHHHo L AH KA H5; H8AT1ΐXZHFH H=0 1HUdH+%(He[A\A]A^A_]LqL=_M\1HI9tpL;|uHhHHEH)IH&H6Huf.2LpHHuLLПE1IM9JtLrtHhJbSffHHhHHCRHHRHhHHhHHCRHHRHhyHF H=/ 8Mt1IUIMHH9t4H=uAEPAUtb1DIELIEPIELP1DHF H=. 1L1HHm 1MPHhHULEHY^efHHhqHhDLHH}u1 HNJHaE H=- 1L牅hr3htHPH9H\UHAWAVAUATISHxdH%(HE1H[HEHEHEHiHHHhHHxHLqHEMHuLefL)p)E襛H]LmfHnfInflHH})EHj̕LpHHuLLRH}H^L.*H5H]Ht;HKHsHH9!H=SJKaHxHHKHsHH9H=SJKHHh'HhtfDHEzHuADH9HHWj L ]AH ; H5ۅ H8AT1nXZHB H=+ |1HUdH+%(He[A\A]A^A_]LqL=YM\1HI9tpL;|uHhHHEH)IH&H6Huf.ғLpHHuLLXE1IM9JtLtHhJbSffHHhHHCRHHRHhHHhHHCRHHRHhyHBA H=) Mt1IUIMHH9t4H=LuAEPAUtb1DIELIEPIELP1DH@ H=M) H1Lh1HHg 1MPHhHULEH(Y^efHHhHhDLH}u1 HgH@ H=( 1L牅h.hH6HHBUHH)HAWIAVAUATSHH(L'HGL)H9PHH9THUHHMdHMHUHEL4H9uEDBHHL9t0o HSHtH=PtBHHL9uDMoIHI9u%{AD$HAL$t=HI9tULcMtID$IL$H9H=ىtuLHUHI9ufDIHtIwHH)~EfInMwflAH([A\A]A^A_]LwLL)H9HIHMHVfD@Mt3IvI~L9oH=9Av~A~%IEHIHHMuIEHCL9tHtH=t@MuHL3HHXHZAFMt3IMI}H9H=fAMyA}Mt$HIHH LsMl$I $M9tMtH=tAFMl$fDID$I$LPI$LPH@HɸMwHOIM9:HM$DCPS&IM9iI]HtHCHSH9gH=_tf.MwL9tULLFHHH9t0oHpHtH=tFHHH9uDL)IMwH([A\A]A^A_]7f.UfI6HELHUHMIFVI6LVHMHUIHErIMHULLEHEIEQIMLQHELEHHUDMgH([A\A]A^A_]DHHHCPHHPHILHEHUHMTHEHUIHMfLHULEHEHULEHHEHHH=" f.fUfHAWAVAUIATSHHhdH%(HE1HL)EHfHnHEfHnfl)EHHHEH7HHt`M1H=` UH8 H=` 1HEdH+%( HeH[A\A]A^A_]fDLcL=`ML 1HI94L;|uHMHHEH Mt$MLe@H>H$H~L&H}LeHtHG/HGHH8HHHzDyHcЉH9> fDH58I|$f)EH9t9HXHHJ1HvfDHH9H;tuH>H H9HL-MIEHHuE1I9EfIn1LLeH)E`IMt IIEHIEMHL-|M9H5 bH=ъ1zHHt111HH< H~6 H=^ t1\DH~L&H{H}LeHH] 1MPHuHULEHIZYH}LefL&Le8HLsHEMzL{H AIM1@HI9H;LuHMH{M9CI$(iM9qP|fH@IH@ H@H@@( H@@@0HcA]HHIEID$(AoT$ )UHnH=>H@HuHUI}09HހHfHnHfInLkflHCHWHHIE Ht@txH})EHtH}M"HHI $H}H@H]HuHUI}0HM@fHQMmHC I} Ht0HG PW uH)EPfoEf.I] /E1IM9JtL躹tHEJHH3 H=Z 1L|f.L|f_E1IM9bJtHϺHxHxt8HEJ<DGWHH HcЉH9H|H5 H8N؆H%f.GWHH HHcЉH92뫋_&fD[IHtH IML{HDHH93HuHԂH9HHH93HH9#HuH9ufHfDL?{MH1fDL {H5IH9H5Y\H=1HHt111HH}H0 H=_X 1HvH#P H5 H81fyH0 H= X v1M[H=U;HH5²IH'HuHUI}0MuMM}ILIMHuLDI$H:|fH@HH@ H@H@@(H@@@0H~HHID$(Aod$ )eHH={I@HuHUH{0L{IfHnHrfHnI]flIEHʂHIEHC Ht@fDHzH[H{AE H{ Ht&HVG PW uH)EPfoELk h@LmHuHUH{07M:-H=b9HEH1GH5}LBtS dI $LwH tH}M H5 H81vH- H=zU UHuHUH{0gnfC G H9(H*sXZH[* H=R L1HEdH+%(HeH[A\A]A^A_]DLqL==RM^1HI9L;|uHMHHEH*I`H&L.LmWf.Hs]H!L5) HBQ (LH{1@H5H9t'H1HH9H9tuL=.oM9IDLs6fLrfE1IM96JtLZt"HEJL5( HiP %LHH}Ht$LH3H_rf.HDHH9cHuHtyH9NHdHH9HH9HuH9Hf.HHO 1MPHuHULEHY^ZXH=2HH5HHH5H9DH5RH=z{1;HHt111HEHL5D' HN ,LH/1BL;IEHqsfH@HH@ H@H@@($H@@@0HjHHIE(AoM )MHJH=:rI@HuHUH{05L rIfHnHfHnI^flIFHoHIHC Ht{@ttH})EHtM9ID$HuLzHI$L@LuHuHUH{0MQDfHQqH[H,AF H{ Ht/H G PW uH)EPfoEfLs 3HuHUH{0f.HAkHN H5K H81mL5% HL /LH1 LsMNL{IHILHu+H=/lH5LX9IML}nAF G L5^$ HL *LHI1\qIxHyIxIxIx@UHAWAVAUATSHHdH%(HE1HWIHID$H5#HHLHHHuH9CLkMLsIEIH <LHu1HHELm*MIMt IMxHM HH0I9L-iM97AD$T*ID$LuLLP(L}H]wIHtHEILLP H}-H}khIEMHt;HSHKHH9H=nCPSHEdH+%(HeL[A\A]A^A_]H'lfHHu HlH" _H=I E1fHkILkIEMh@Lkzf=fHHHCPHHP%HrHE1H  L  H5d H8R1HEI 2jZYLHyj1H5!I H$Q LbHXHu1E1f.Hu1uDHfH, H5z H81iHA! aH={H H'LH}u0H}&fH dH=)H VLEEmH bH=G #HHSHKHH9tCH=ku)CPSHf.DHHHCPHHPItItItItUHAWAVIAUATSHHHHdH%(HE1b^IHFHHuE1HpqH9CFfInfIn1Hfl)EHIMt III $A@MH HH IH9HH0HHHpHuE1H9C/fInfIn1Hfl)ECHIMt I $AAMH H}LMgLeH]rHIHIH H1I~M&H9t$HtH=iCHtuI^IMHt;HSHKHH9'H=GiCPSHEdH+%(HH[A\A]A^A_]DHWgfLGgcfH7gkfH H DH=E !Mt14DHu LfH AH=D DLfI~M&H9fLffLffLf1fHHCHPHEdH+%(HHH@HH[A\A]A^A_]fCI~TH'fH DH=D !@jf.H @H=C LL{MLkIIEH LHufDH=6HH5HHH AH=ZC jDLcML{I$HILHuH=5̜f.HEdH+%(u'HHH[A\A]A^A_]Hdth>p9p4p/p*p%p pppp pppooooooDUHAWAVAUIATISHxdH%(HE1Hs0HEHEHEHHHH`H_HHLyHEMH]H5^H9st H;_LefHL)p)EuLuH]GnfInfHnflHH})EH? lkHe_II9Iu0HULnfoEHxf)M)pHtH}HtL^HpfHpjH1H]Ht;HKHsHH9H=]dSJKHxHHKHsHH9BH=dSJKHHhHhxf.HElHuADHiHH-@ L AH C H53[ H8AT1`XZH C H=? 1HUdH+%(*He[A\A]A^A_]LyH -Md1HI9H;LuH`HHEH-IcDH&HH]Uf2iH+]II9nH ]H& H5*q H81_fL\H b H=> fD1ff7fHHDE1IM9H} f. H H=] 1HHDE1IM9JtHϺHx3HxtHpJLftfHHxHHCRHHRHx_HIMH H5Sa H81PWLL DHHa/ 1MPHpHULEHZYkTHJ]UHAVAUATSHH`dH%(HE1HHEHEHEHIL,HkHH>HyH}OHSHGHGHHHdHZHGWHH HcAH9S@fP)E5RfH@HÿH@ H@H@@(H@@@0HXDcHHHQHfHnfHnHXflHPHRHHHS HRH])EHt;HSHKHH9H=P=CPSH}BHH H=8- 1f.HH%HHH HHHH( H?L HLHHUHL@H5G 1H:SH, MXZH8 H=, )1HUdH+%(KHe[A\A]A^]LqMf)EHH]HtHKHsHH9H=DOSJKmHHE踴HEXH>H}@DHcAH9lAfWHDfHAHLHHHEI@HHs+ 1LPLHUILEYH}^ffGWHH HHcAH9AHLH5#^ H8P/DHNH[Hl@ H{ Ht0H?W JO uHHE)ERfoEHEfHC E1(DgHHEHHCRHHRHEffDgAT@ RHHIH跀H A+HbKDHHHCPHHP^f>bfDHX8UH@W f@ HLwUH=cNHYWHoWUHAWAVIAUATSHHhdH%(HE1HHEHfHnHEfHnHLFflH])EHHIHXHHHt`M1H=( MH H=j E1HEdH+%(HeL[A\A]A^A_]DLyH Md1 HI9\I;LuHXHHEH5MoMLuIzHH,LfLeL6LuNoLy)]M~2HH' 1MPHXHULELZYLuLeH5I9vt I9fLHxDžxƅ|)EIH{HELHH`LmL}SfInfInHXflHYH})EHӯ5PIH9hI9HhIV0HMH`HpHPH}LCHII $H]HHSHKHH9XH=HICPSHIDrOIH9h=4UHcCHk H5mW H81FMLBHH= I $LFLXDMIWIOHH9H=dHAGPAWLڭDHLiHEM$IL$H=H1HH9I;|uHXHHHEM}@E1IM9KtHϺH`苂H`tHXJhf.LEf!fE1IL9 KtHPH`H`HPtHXJHHHCPHHP_1H L/*X=fHH= E1 HH=m I $LXffILIGPILP@:OHr@RRH@H H5T H818C8JfDHENH/QH`5H}u H`h~hGHPHPHPHPUfHAWAVAUATISHXLndH%(HE1HN)EHfHnHEfHnfl)EHHIIyMMH=UbHPH= HUdH+%(.He[A\A]A^A_]IuH^Ln H]LmH5H9sL5>t L9M9L9HCLuHLP L}H] MfInfHnflHHuLL)E L}LmHtH轩LfInfInflHPID$LLH@)EH}HHtzH)H AD$P1ofH)eMHH]LmHMH5HIHVIFHEHH5)HHVFHEHWIFfHFHHE7MIf.H@*f1H=H%M9SfDH<H[ H5P H81?H9QH=U 蠩H<H'$ H5P H81P?HRH= `aHRH= @H=HSHKHH9t@H=Au&CPS H,DHHHCPHHPHAQH=] 訨MIUIMHH9t@H=Au&AEPAUpL蒦cDDIELIEPIELP-HHS1MPHULE1HZYNfDIHAfDbIHNBHKIKIKHKHKfDUHAWAVAUIATSHHdH%(HE1HH HEHEH5:HEHIL\AD$PAT$LxMIT$IL$HH9H=>#AD$PAT$LuHHHHSH UHHHHH?L HLHHeCHL@H55 1H:SH{ :XZHH= 1跤HEdH+%(wHeH[A\A]A^A_]fHIL5}7H~L5m1 HH9M9tuIH(HEH9@2CL;-+7HHAH"7H^ H5,K H819D@H6H/QH= ףI $uK1L:Ht;HSHKHH9VH=6<8CPS1L6Lun@L5Y6^@f.fL:kfL:*fID$I$LPI$LPPfDID$I$LPI$LPfDHNH= 訢I1MDHOH=] xI $ffHHHCPHH1PGCHHHH 1LPHUILELY^fDL5H1HSH= 訡I $1E1IL9TKtLHPLXtLXHPtKF ;IEIEUHAWAVAUIATSHHdH%(HE1HHEHEH3HEHILL;-2HIu0HpLHPPfoEH}f)M)EHt.H}Ht H?2H}~HH2I $LeMtAIT$IL$HH9H=7\AD$PAT$LxMIT$IL$HH9H=7#AD$PAT$LHHHHH HHHH8H?L )HLHH<HL@H5. 1H:SH +4XZH H=N 17HEdH+%(wHeH[A\A]A^A_]fHIL50H~L51 HH9M9tuIH(HEH9@<L;-0HtBAH0H H5D H81Y3DX:H'0H H=l WI $uK1L64Ht;HSHKHH9VH=58CPS1L6Lun@L5/^@f.fL3kfL3*fID$I$LPI$LPPfDID$I$LPI$LPfDH H== (I1MDHP H= I $ffHHHCPHH1PGB=HHHH0 1LPHUILELyY^fDL舙5H1vH H== (I $1E1IL9TKtLHPLX\nLXHPtK? G5Ix?Ia?UHAWAVAUATISHxdH%(HE1H~HEHEHEHHHHhHHHLqHEMVH]H5qH{f)pH9t8HXHHJ1H\DHH9H;tuHvH H9HL%֣MI$H8HuE1I9D$fIn1LH])EHMtIMuL0fDI$HI$HRHL-+L9H+Hp H5@ H81.L=L5 ULL轘E1LH HxHt;HSHKHH9PH=12CPSL=WL5  MfL3L0jHE9Hu?H6HH L AH 3H5#( H8AT1-XZH3H= ėE1HEdH+%(HeL[A\A]A^A_]@LqL= M^1 HI9L;|uHhHHEH/I@DH&HH]2fL.fH5QH9t'H1HH9H9tuL-)L9IE@H-HBL=L5e OLL蛖HxE1HCE1IM9>JtLit*HhJfHHHCPHHPHDHH9KHuH$4H96HHH9HH9HuH9ufHHHi 1MPHhHULEHsY^xL^,H5H9lfH5 H=J61 HHt111HHͶL=L5 SLL_H=H4H55PdIHSHw{HoS HC()UHtH=:-@H}Hu7foEHxf)M)pHtH}HtH}HtڒHpnIHL9yH5HbAVI $t?L=L5 DLLE1H.>f@.L*Ml$MKMt$IELIJMHu&H=r-bH蠑 H5H IAO18L=;L5 QLL&-L=L5 VLLHH 8H7UHAWAVAUATISHxdH%(HE1HHEHEHEHHHHhHHHLqHEMVH]H5ѥH{f)pH9t8HXHHJ1H\DHH9H;tuH֤H ?H9HL%&MI$H1HuE1I9D$fIn1LH]n)EuHMtIMuL~(fDI$HI$HRHL-\$L9H\$HH5f8 H81'L=2L5 {LLE1LH HxHt;HSHKHH9PH=p)2CPSL=L5 MfLYL萐jHE 2Hu?H.HHB L AH H5 H8AT1&XZH3YH= $E1HEdH+%(HeL[A\A]A^A_]@LqL=M^1 HI9L;|uHhHHEH/I@DH&HH]2fLw&fH5H9t'H1HH9H9tuL->"L9IE@H&HBL=L5 uLLHxE1HCE1IM9>JtL:bt*HhJfHHHCPHHPHDHH9KHuH,H96HtHH9HH9HuH9ufHHH 1MPHhHULEHlY^xL$H5H9lfH5!H=.1kHHt111HuH-L=tL5? yLL__H=CHH5\IHSH{HoS HC()UHtH=%@H}Hu#foEHxf)M)pHtVH}HtHH}Ht:HpIHL9yH5H*bA|I $t?L=]L5( DLLJE1H.>f@.L#Ml$MKMt$IELI読MHu&H=ZH H5HlIAu18L=L5f wLL膋&L=rL5= |LL]HH0H{0UHAWAVAUATSHHdH%(HE1HHH;H;^SPHCH}HޅP(LmLew,HH)HEIELuLHpLHP(H}nL}d/A+,ID_'H}Mb$LK*HHMtAIT$IL$HH9H="AD$PAT$MtL蔈HEdH+%(HeH[A\A]A^A_]PLeLmg+HH (HEI$LuLHpLHP(H}L}T.A+IDO&H}M(fID$I$LPI$LP fDH'HE11H BH52 H8L R1HY^fHy1H5H褪1DHE11E1H5/ HE1H81M#HH=N]Mt5IVINHH9tIH= u/AFPAVHtH覆1DILIFPILP@E1,[M1E1E1(CLE1E1E1$*f.L踅`L訅bLH}tLE1E1E1&f.LH}tLE1vE1E1*!IG,Ie,I,I,I7,I-I,I,I%-I,IC,H,I,I,@UHAWAVAUIATISHhdH%(HE1H#HEHEHEH~HHHpHHHLyHEMaH}HGlHGH?HH;H1HHcAH9fA0'HuAAHE'Hu#DMH=蚠H= H=?21HUdH+%(cHe[A\A]A^A_]fDLyH M~1@HI9H;LuHpHHEHMIDHFH>H}fE1fL;-)EIu0H}DfoEH]f)M)EHHSHKHH9H=CPS.H]Ht;HSHKHH9H=CPSH]HkH=H5HGHHIH Ic|#IH H"I9EWI]HJMeHI$IM%MHufHnfIn1Lfl)EIHt H  IKIEHMIEHFH=#Hu1HHELeHI$HI$HgH^111HzH WT HH=x1$@fH~HH}YHH]HHKHsHH9hH=unSJKHHx-HxGWHH HcAH97HH5f* H8"DkfGWHH HHcAH9IMLEHL.fDgE1IM9JtHϺHxsTHxtjHpJffHHxHHCRHHRHxcDgA@HHHLH AHBDHHHCPHHP fHHHCPHHPH]DHH,H5' H81Q CfDLf.LfHH 1MPHpHULEH]ZYmfS fDL7fH'fLfHufHfH(}H} /Hh H$TH7%f.fUHHAWIAVAUIATISHxH5 L5mdH%(HU1HwHEHhfHnHufHnLufl)EM=J HpI8INMtYM11H=*AH1GH= E1}HEdH+%($HeL[A\A]A^A_]ÐIL$LxHH8f1H)EH!IHVH; AH;DDHM9?LADž[I $/EHL HpHHj IHn1HHH!IHCIL;% L;%usM9tnLADžybH u HI $NAyIDHDH= j|E1I $LDDI $zEHIHHI<$H L9xM9LHHxHxIHIu0H}LHSfoEH}f)M)EHt$zH}HtzH}}IHII $ H]HHSHKHH9H=ACPSIHGyLfH}LeIH5LmIHPHEH;=oHuH}HxHFPL}LeHHL1HkHEH HE HEHELELHpHILLPPH}qLuEF}Iz H}M?M9}HxLH]H;MIT$IL$HH9H=AD$PAT$L9k@oMo)UMH}LegfDHHEIGHxHfDE1II9KtL@tpHEJH_H; HL1@HI9|ItL?thHEHHH=} kMIT$IL$HH9t~H=fuHc1H EHDIIMLxI@oHC)]H~2HHx1MPHpHULEHGCZY|HELeH5HxfL%y1DHH9L9duHpHHyHEHHLxLeH57YfHHHxHML Le<DHHKHELWfHpHI$HHmLA fH?fLH;QHI $HLL9x?HxI9hHIHIu0LmLLHPXLeLLlH}Hfb\DfA H 0HH"HxHxHHAHpHHx9HHxHEHAfDHHHCPHHPKHfLx#@LA zfH5H=1[HHt111HeZHLA 7E1IL9JtLHhLx5LxHhtHpJ*@HbfA DjHSA DHHH5 H81`fDH"HmHH5w H81$A HrI $tILLHmfUH0ufHAVAUATSHFHH9t6HXHHy1HHH9H;TuH;5Lf(Ln ML5MAD$HH{L+L9AD$Ht^LcHH9tHuH;HuH[A\A]A^]fDHFLcL+MtLe^HCH[A\A]A^]ÐAD$THu-H{L+I9t9MWAD$H{HNNHcH= j_MWIT$IL$HH9t8Mu#AD$PAT$#LU]DID$I$LPI$LPH[A\A]A^]HHH5 H81HH=M ^HH=0 ^|I I IIIf.@UfHAWAVAUATISHHxdH%(HE1H)EHfHnHEfHnfl)EH1HHxHHHteMH=xHH=d ]E1HEdH+%( HeL[A\A]A^A_]LsL=UM< 1 HI9TL;|uHxHHEH IFL{HpL-M.1fHL9L;luHxHHEHLpIfDHL.L~LmL}H5pI9uL5t M9 M9HpIGH9t4HXHHqH1 HH9H;TuH56L}HHH5cL}IHoHHVHxHHHHHHaI $GLxLÅLV}IMHH9CHuH;1fDH1nH aH9HHqaHHIEH5zLHHlIMHJHuE1H9CfInfIn1HflLpLELx)ELpLxIMtI uLLxIM&H M9ID$H5LHHHHuH;H;L9HiH xIEH5EHHELHHEL9]os HC()uHtH=Q"@HEHuHxHxHEH}L}HpHt XH Rƅ`M9PHH]fInIU ~pLsHCHflHHIE(L-Ht M @Mt MAGHC0`HC C8HHS(HLsHCC@MC H[ HpHxLH}fInfHnHpfl)`!fo`)UMdCHx?H}IHtVMtE1I $~MMtLVMtLVHdHVWo>Ls)}MLmL}fDL#fH%fH _E11AE1HnDH=7 jWMFE11fHHEHCHpI fDE1IM9<JtL*t(HxJE1IM9JtLJ*tHxJ~IMDLtfHwzf1HaLofAH tE1E1E11DH'DHfHfAGfD@CHErHAfDHH1MPHxHULEH3ZYLgfHWfHaHͧH5kH811AH=dHM[H5N[$HHE1E1Ax@C H{ H0O &HPE1E1A AHcIH=I#H{@"HHHHHHHELHHx^HEL}HpHƅ`@HEHH H[A\A]A^]LwfMd$H= AE I|$Ht'H=G PW u HPDMl$fD@|11H=lSiH 19fH=~H*VH5+V6IM I$HfDH=~LIH51H=1KrIHt111HUEI $Q@ID$I$LPI$LPefDH)HH53H81HH= LH1LgfAE MfDfLJMd$MDG !HHHf.UfHAWAVAUIATISH(H5dH%(HE1ID$LHHHHoHH9CLsML{IIH LHu1HHELucIMt IRHHHMHIL;%OaID$ I]Md$(IEI9t\MtH=AD$Ht;HSHKHH9BH=CPSRMeHEdH+%(HH(L[A\A]A^A_]fHfL4fHwfHHH= _J|f.AD$I]GfLf#DHu1E1YfHu1LDHHPH5H81TfDHHHCPHHPHfHGHbDUfHAWAVAUIATISH(H5&dH%(HE1ID$LHHHHoHOH9CLsML{IIH LHu1HHELu蓿IMt IRHHHMHIL;%aID$@I]Md$HIEI9t\MtH=AD$Ht;HSHKHH9BH=CPSRMeHEdH+%(HH(L[A\A]A^A_]fHfL4fHfHH#H= G|f.AD$I]GfLGf*#DHu1E1YfHu1LDH!HH5+H81TfDHHHCPHHPHfHDMHDUIIHHSHHLFHRHNJH9IyI2H9sIzH9(IzH9sgH?L)H9LLCLHHHH9HHPHSHPHSH@H@HH]fD11L3HKH HHpH9HHPHSHPHSH0@H@HH]ffDHPHrsS@WPS@BT1fAT0HP0f.fDH|I|1҉׃L 9M 89rfDHPHzs;@~PS@T>fT9HPLDLD1AЃN N 9rPST1AT0HPIPST>T9HPH=5uf.UHAWAVIAUATSHLoHI9tsI@AD$PAT$t9HI9tELcMtID$IT$L9t]H=MtøuLHAI9u@IHtPIvHHH)[A\A]A^A_]%f.ID$I$LPI$LP`fDH[A\A]A^A_]fUHAWAVAUATISHH8dH%(HE1HHDžxHEHEHlHHH0HHkHLyHxM#HxH0HOUfL}HDžPL}H MHEE)@H9HL%uMMUI$ID$H5H]LHH#II$HI$MH3H]IHHnH5ϰHnvH5׹LLtfIHoIMI $IH(HIVH8H;X<H#TH |LH9HML%cLM~I$HgHuE1I9D$YfIn1H]L0)E·IMtIH0HII$HI$MHLeLH]LrHLuH}HEH9tHEHpHIEHHIEHH8H`HHp0%fo`HpHPfH~fH~)@HH)H9HmHRH KH9HL-JMIEH5OLOaIHL[aH5Է1LjdIHvL6aL.aHoH hHIH8HHOHDžxHu>fHHHL 0AH [H5KH8AT1XZHo H=iw E1>HEdH+%(HeL[A\A]A^A_]@LyL5UM_1HI9L;tuH0HHxH%If.HHH0HxA I $`IMHDH=v H] >E1H}L9tHEHpLHH@I9tvIAD$PAT$t9HI9tELcMtID$IT$L9t]H=-tøuLH;I9u@H@HwHPHH)_ID$I$LPI$LP`fDH]LfDH]LjfDLLf.H]LfDH]LfDE1IM94JtLt#H0JH]LC(fDH=ѱHrGH5sGH] IHA~ H]LGHuH]LH=hH] LgA H=%H]T IHjA gtIHsA KHH11MPH0HUHLxfY^3LUA~ HHȸH]H5%H81]H=fHFH5FH]G .H1H]LID$IHMl$HLH]IE>\MHumA IMGLT9H=HiEH5jE IHkA H=A I $uLIfUHAWAVAUATSHXdH%(HE1HGHH{fHE)Ef)EL5IL9zHs0H}H]LmLHEH]LmHE1IHIL9uHCHHtH=J@LmfHnfHnfl)EMt7IEIUL9+H={AEPAUHUHt`H}'qIHID$I;D$ }QIT$IEL,HID$IM@LmHI9;H]bfIID$MI;D$ |LLtI $gIM HH=b8LmH]E1H}Ht6L9txI!AFPAVt;HL9tGLsMtIFIVL9}H==tøuLH5L9u@H]HtHuHH)HEdH+%(HeL[A\A]A^A_]fL'fILIFPILPA5L&5(I $L@IELIEPIELPHUL`f.HHE1L H |H5H8R1H"XZE1Hyz1H5HXaf.H HH5H81L LH}H1GHf.DUfHAWAVLuAUATSHH0H8dH%(HE1H;=HLuHDžPHEE)@HGH *CH9HL%CM+I$H5HuE1I9D$fIn1LmL8)E萫HMt II$AHHhI$HH}HLmfHEHMHUHL9H9{oUHuHMUHHEHuHEH}H9tHEHpLmHHHHnHHH0LmH`LHp@@fo`HpHPfH~fH~)@HH)H96HhH?CkHHH;c5HVH}L9tHEHpLHL@M9t{I!AEPAUtH\ȃtM HuH}HHHMHELDLmLcGfDH9to]HM]HUHUH fDH1HH1HLmH57H81AH=rH>H5>LmSIHH=FLmuM|$MI\$ILLmHNRIHuAH WLmHaEA:H8H=cnHHt111H((HQAHBH >H9H=H=HHH5{H{QIHHQH51LTHHLbQHZQH8H=跩HHt111Hq'H)QAGEHMHEH=HHIAH5aCHѐAHLHLGR1ɉσL:L>9r7AIH=YH<H5<&bMLLHuH}LfLHuH}HfUHAWAVAUATSH8dH%(HE1H;=AIH IuIUfHEII)E)EI)7HL9LSIuIUfHnILe)EH9H)HfDAHHH9t0o HJHtH=(tAHHH9uDLeHuHULLLs0LeLuM9tsIAEPAUt[A\]ÐDID$I$LPI$LPHfDH[A\],e@L'f.@UfHAWAVAUATIHSHH(H5dH%(HE1HGA$HHIHuL5L9HC H[(HHHCI|$I$H9tHZCHt]'I\$IMHt;HSHKHH9^H=.@CPSHEdH+%(H(L[A\A]A^A_]fH5HHHKH;pH;L9HQAHHPEHHEOL}LLH}fLuH]EHHI|$M4$H9HHI|$I$H1fHHHCPHHPC&HD@L4fHHHvDH=N&IMDL6@HuBH=&jTDCI|$fH$HGEuI|$11I$Hf1iH>fLH]Hu6H}E1Ht$H}DLza710L`ajE11ZHtEH=$%IMLI\$1I$Ht H1IMLMI!IPI[IIjII;IIIIHI IIUfHAWAVL`AUEATASHHH8LH(L dH%(HE1)@)PxLuH8HLHEH]H0~0fHnflHHXA)PH"LoEH}fo]MxhHf"EHH}EEfEHCHEHt;"L@H0H(LPAUH8ELAWH H}XZ=H0H@H8H@H)HRL*lH?{fHE½HJA$fDHH1MPHxHULEHZYHEbHNH4fDUHAWAVAUATISHXdH%(HE1HƇHEHEHEHYHHHEHHkHLqHEMH]fL%gH)ELLHHHڹLmIHLwH}fL}H]E~AEIfInfHnDfl)MgMH}fo])]HLH}UHH]HHKHsHH9H=SJKHHE$HEtfDHEzHuADH9HHL ] AH _H5۩H8AT1nXZHfH=qR |1HUdH+%(DHe[A\A]A^A_]LqL=M\1HI9thL;|uHMHHEH,I@H&HH]f.LH}SxfE1IM9JtLtHEJjifHHEHHCRHHRHEfH5H=:1H}Gf.H}NHH]HOHKHsHH9H=u|SJKHHE^HEDbfE1IM9lJtLjtXHEJfHHEHHCRHHRHEwf*HLm1LBDHHHCPHHPfLϨH]HH}E1HRH}LMDH^H=K Ht/HSHKHH9t4H=TuCPStl1'DDHHHCPHHP1H_^H=uJ P1fHh1LLò1E1fHH1MPHuHULEH Y^@H$b1H]H=I 16I|IfIyf.@UHHAWIAVAUIATSHxH5dH%(HU1HqHEHfHnHEfHnHXHuHMH fl)EHMMJ HhIMiIHIOHEHH`L%efHʞL})pI$H9LDžhL;%MH"H1H9XHHHHCH5rHHHIHHMHHLLjIEHȃIEHH|!H5H9pKL-lMIEIEH5dZLHHqHIEHHLIEHIHI$L`IH6HלH58dHLLH2H`HHHPHHHcIJIM0I $L`LmhLLLH}fLuH]EfInfHnflHHx)pH HpOHHHI $HxHT JIHPoMgHU)]MH}H;=H;=L}Le H;= f)pI$ƬHDžhMgL5}M$1HI9,M;tuHhHHEHIL$WIItI2M1H=ł'HZH=1 HEdH+%(HeH[A\A]A^A_]HZ'H=bh I $ofDHtH 1;@HpdFHlHhLH`HbHEHHoLe\fHHHIhMt IMA%fDHH=zH9sL%L8L}HxH;=H;=;H} H;=C@ L`LeHDf.LWfo IO)e1@HI9ItLRtHhHL[fH`3H`HH4cHhLH`VHt?H`HELaDL/1f.LiHTHH1MPHhHULELZYfHH9H=ifDH=lH H5 HH^HW$H=djmLGfH7qfH=kf !DA$H HHWDH=~I $1L֞rj]DA$IMHVDH=~LwfLgfLWfHGfH=jH H5 IHHaV%H= ~DA%CDҕDH=Qjf.L_LuMH}1H6H}7L#B*fDA%cDHU(H=Z}`I $g1FH/)f.LfHEH/EE1L~A1E1HU"H=|[ձ˱ƱI鳱鹱鴱鯱骱饱頱雱閱鑱錱釱UHAWAVAUATISHXdH%(HE1Hf[HEHEHEHHHHEH}HHLqHEMLmHfH )EIEH9HH HHHCH5ZHHHIHHHMHNHHuE1I9D$fInfIn1Lfl)E{HMt II$HnHI$ IMLeHLH}dfoEMH})EHtUH}*H}Ht<H};HJH}>IH5H @H]HHSHKHH9H=؛CPSu{HTqfHEHuADHqHHzL AH #IH5H8AT1XZHOH=yE1HEdH+%(HeL[A\A]A^A_]@LqL=XM\1 HI9L;|uHMHHEH0IPH.L.LmGf.HfLfLfHLHNH=xE1H IH;HI$uL@E1IM9\JtLtHHEJL7ffHHHCPHHP`H=ydH H5 fHH>DIDH=9dtfMt$MbI\$IHI $tGIHu@L<LffDHOsLAffDHHEw1MPHuHULEH3Y^"8HZHZf.@HHtHwHH)%Xf.DUHAWAVAUATISHHXdH%(HE1HbHDžxHEHEH\HHHPHH HLqHxMLxHXL}H;EL}HEHH H9H=H H]HHHuE1H9CUfInfIn1HflLm)E]uIMt IlHHHMHH}LLmq0HEHMHUHL9H90oUHuHMUHHEHuHEH}H9tHEHp|LmHI$HI$HH2HXH`LmLHHp0xH2H`HHtHpH) HHH}L9HEHpyHDžxߞHuFf.HHHGtL AH KCH5;H8AT1ΒXZHI H=)6 1HEdH+%(jHeH[A\A]A^A_]DLqL=E_MW1HI9,L;|uHPHHxHI-f.HQHyLmH5WH81@HI H=]5 Lm 1DHL&LxLmHÒmfDHuLmH@HuLmLqfLwfH9t{o]HM]HUHUHPfDE1IM9JtLtHPJLfHMHHt>HȃHtMPHuH}HHHMHEDH=mHH5LmHHIH=lLmfDLsMHCIHH HPtHPHusLmHܐHH.q1MPHPHUHLxY^EHMHE HLHLG1ǃL:L>9rMLLHuH}LfLHuH}IڥIʥUHAWAVAUATISHHXdH%(HE1H[HDžxHEHEH\HHHPHH HLqHxMLxHXL}H;EL}HEHN H WH9H=H>H]HHHuE1H9CUfInfIn1HflLm)EnIMt IlHHHMHH}LLm*HEHMHUHL9H90oUHuHMUHHEHuHEH}H9tHEHp LmHI$HI$HH2HXH`LmLHHp@H(,H`HHtHpH)HHH}L9HEHpuyHDžxoHuFf.H)HHmL MAH <H5ˆH8AT1^XZH{CH=/ 1jHEdH+%(jHeH[A\A]A^A_]DLqL=XMW1HI9,L;|uHPHHxHI-f.HHdTLmH5H81@HBH=/ Lm1DHL&LxLmHSmfDHuLmH6@HuLmLqfLfH9t{o]HM]HUHUHPfDE1IM9JtLBtHPJLfHMHHt>HȃHtMPHuH}HHHMHEDH=fHH5LmHHIH=qfLmfDLsMHCIHH HPtHPHusLmHܐHHj1MPHPHUHLxY^EHMHE HLHLG1ǃL:L>9rMLLHuH}LfLHuH}I鄟ItUHATSHdH%(HE1#HHtVf@@(@@@8@H@XԓHC(H{HHC 7HC0HC8fC@kHEdH+%(u HH[A\]ҌIf.UHATSHdH%(HE1#HfH@@(f@8@H@8@@X@h@xHCHH{hHC@wHCPHCXfC`H=tGx#HEdH+%(u1HH[A\]H t 1H15݋IDUHATSHdH%(HE1!"HfH@@(f@8@H@8@@X@h@x$HCHH{hHC@HCPHCXfC`HH=}HHFx"HEdH+%(u0HH[A\]H t 1H15݊IDHHtHwHH)%f.DUHAWAVIAUATSHLoHI9tsI@AD$PAT$t9HI9tELcMtID$IT$L9t]H=-tøuLHI9u@IHtPIvHHH)[A\A]A^A_]%ڈf.ID$I$LPI$LP`fDH[A\A]A^A_]fUHAWAVIAUATSHLoHI9tsI@AD$PAT$t9HI9tELcMtID$IT$L9t]H=-tøuLHI9u@IHtPIvHHH)[A\A]A^A_]%ڇf.ID$I$LPI$LP`fDH[A\A]A^A_]fUfHAUIATISHH^H+HGHH9HfHnHI\$A$IMIUH9trH)H4fAHHH9t+o HJHtH=tAHHH9uIt$H[A\A]]D1tfHIt$H[A\A]]UHAWAVIAUATSHLoHI9tsI@AD$PAT$t9HI9tELcMtID$IT$L9t]H=-tøuLHI9u@IHtPIvHHH)[A\A]A^A_]%څf.ID$I$LPI$LP`fDH[A\A]A^A_]fUfHAWAVAUATISH8HLndH%(HE1HE)E)EfHnfInHPHEflHUMtH="dAEH]HULHUHHLuH]I9tI"AD$PAT$t=HI9tMLcMtID$IT$L9H=tuLHI9uH]HtHuHH)WMt=IUIMHH9H=7 AEPAUH]Ht7HSHKHH9t}H=CPSHEdH+%(H8[A\A]A^A_]AEfDID$I$LPI$LPfDHHHCPHHP@IELIEPIELP"DfLH#IH/HLMMtLH}HtHH}HuH)HtĐUHATSH?HLgMt=IT$IL$HH9t:H=ju AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]#@Lf.@UHHAWAVAUIATISHHhdH%(HU1HIHEHfHnHzHEfHnflHU)EMH HMH.HHt_Iع1H=^H6H=9! 1HEdH+%(HeH[A\A]A^A_]DMuL=CM1HI9M;|uHuHHEHIML}H=y@HNH%H~H;=yH}L8L}[foM})]M~/HH]1IPHuHULELZYH}L}H;=iyH5H9wtfL;%Fy)EDID$0HE!IHzIGHnLƜHHID$0HH@HpHH+p@HHc蹈HHtIHLұÃwLuHuLLQH}fH]LmEL}MtJIWIOHH9RH=}AGPAWLHEH fHnfInflH`H})EHpH} HHI $H}H*? f.H=wI|$8H5 ?HGHHkIHHI9FWI^HZMfHI$IMHufHnfIn1Lfl)EZIHt H IHIM HlH5]Z1L"IHH;rH;ZL;5vMLԃIHPx_HI;H=lLt]HHt111H.HHm2H= (@IHLHC2H= E1I $ MtLj1Hu LyH1H=W 1HMuHEMMUH YDM1@HI9I;LuHuHHHEM~H}/ Sf.1@HI9ItLHMvHMtHEHLx f.E1IM9,KtHϺLxHMHMLxtHEJD1HMqH}V]H}DIDʂH8HW0H= 1@HtH4CH5 H81vH0H=u 1LwfJHwH/H=6 1fH@LOwf.fL'w fLwfHzHH/H= @ILIGPILPHEDLWvLm1MJL8BE1f.H5YWH=1KHHt111HUH H.H= E1}fDHEʀHbx@HuLmHu1HuLLuSyH-H=H H-H=+ ~lgb]wSNIDH172-(UHAWAVAUIATISHhH qdH%(HE1H?HEHEH]H3ILL 1LkH' H=, OMtIT$IL$HH9t=H=pu#AD$PAT$;L5.DID$I$LPI$LPfDbyHyHH N1LPHUMLEL赵Y^FfDHgn+fL2|EE1HELff.E1IL9TKtHxLEH}~H}LEHxtKS@E1Hu1`f.Hu1KDz{EAxHEIE1Y(q鼃փ鲃H韃饃頃雃閃鑃錃釃邃}@UHAWAVAUIATISHhHidH%(HE1H7HEHEH]HILLfH}LefH5)EiHHVH5LiIHbkLuIHLLbH}fL}H]EmqA4nIfInfHnDfl)MViM-H}foe)eHL_H}@HH]H"HKHsHH9?H=dSJKHHEHEf.L^H}yfo.Ls)mMH}LewfDHHEHCHxIfDE1IM94JtLžt HEJE1IM9TJtL肞t@HEJFf.HHEHHCRHHRHEf:lHH5Lqg@lHHH=~ Q1fL\HnH=L HtHSHKHH9t;H=bu!CPSuH1@DHHHCPHHP1pL/`H]HuMH}E1H H}LHH=u H1 LnAE11fDHEjHAfDHH?1MPHuHULEHZY.@HERjHinmA5jI1HH= s15cvvv,vvvuuuuuIuuuuUHAWAVAUIATSHHXdH%(HE1H8HEHEHEHVHIHEHHpHLyHEMH}f11)E_IHefIH[ZH9I9LuHs0IT$ LcUH}HEfH]EHEkAhIDc~EfHnflMH})EHdLYH}HHI $|LeMIT$IL$HH9H=_AD$PAT$|Lr@HEgHuADHdHH=L ŷAH S H5CVH8AU1[XZH3H=9=1HEdH+%(HeH[A\A]A^A_]DLyH U6M\1HI9txI;LuHMHHEH4I@H.H>H}f.LWH}>wfL\vfE1IM9KtHϺHMvHMtHEJRcf.ID$I$LPI$LPfDHH=;1f:iHiWH&H5skH81 Z aLVHnH=t;1I $eHtHSHKHH9t?H=\u%CPS[H1 @DHHHCPHH1PZhHVHH5jH81@YLYH]Hu\H}HEHH} L@LUHFH=L:LzgHEA1fHH= :I $.LYHH91MPHuHULEL蛠Y^p6gAcHEI1A\HI)}LIVfE1IM9|KtHϺH0sH0tZH(J fH0cSH0HlHH(LH0膇HtH0HELqSH'fDHH(1MPH(HULEL`ZYHARH1MufRHME11۾RE11M9fDE1侯&HGfLGfLGHEH0H-H}E1HH}LfDHB1۾E1侳H=HH5nIHVDIFH0HIVHLHH(kL(HufDH=)D~fHE"QH->TAQID9LfE1)0&LTAPHDž0IE1ILEI]I]I]I]I]I]I]I]I]I{]Is]Ik]DHHtHwHH)%Hf.DUHAUIATSHLgHI9t+@H;HCH9tHCHpGH I9uI]Ht$IuHHH)[A\A]]%Gf.H[A\A]]DUfHAWAVL}H@AUATSHdH%(HE1HpL}HH`HEHDž0HDžhƅpHEEH) DIfo@HP1HfH~H0HEfI~Hf֝fօ) DIHkI9+HELIHHL&@H`HH9HHUL9GoMHpH`hHHEHMHEH]LuH`HHhLuHtHuH}Hs111JH}HL9tHEHpEH&ID$I;D$ FIT$HHHID$H I L9H}L9tHEHpIEH`HH9tHpHp%EHLHH9t&H;HCH9tHCHpDH I9uHtHHH)DHEdH+%(%HL[A\A]A^A_]@HAf.L9oUH`hHL}I=HL\>I $zH HEHyH=HvE1fDHUHHtRHЃITHTFr1ƃI<7H<19rH`HuHHHhHErfDI $ML@?HHL9HEHp?Cf.H@fuqsAZATfTH`HuJf.EHUH`HhHEL@xAATTH`HuCIXIXfDHHtHwHH)%XBf.DUHAUATSHHH?.Lg LoMtAIT$IL$HH9H=@AD$PAT$MeMt=IT$IL$HH9H=@u0AD$PAT$H;H[A\A]]DDyf.ID$I$LPI$LPMeMW@ID$I$LPI$LPlfDHH[A\A]]fDLxLh1UHATSH?HLgMt=IT$IL$HH9t:H=?u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L谤f.@UHAWAVAUIATISHXdH%(HE1H HEHEHEHHHHEHH HLyHEMTH}HGHGHHH~HHGWHH HAHH9f)EfEIMBIEH5 LHHDIHHLIHIHIHHI $H;N4H;DH;7H+EAąH gECL;-7HIuLmDLHP0H}ifLeL}ECIA FID>AfInfInflMH})EHH6H}HHH}HtZ蕢SHEEHu#DMH=[HJH=71谣HEdH+%(*HeH[A\A]A^A_]LyH EM~1@HI9H;LuHMHHEHPICHNH>H}:f.z1HcAH9<Atf)EEH5H=7jIHIcBIH9HAI9D$M|$M Mt$ILI6HufInfIn1Lfl)E1HMtLLHLH=QCHu1HHEH]IHH111LޙLHZH=SΡ1GWHH HHcAH9H8H53JH8<$CHfA)E{H4H}N*ff1)EAIE1DDGHYI $HH=h1@DE1IM9JtHϺHMtHMtHEJ.Lg7efLW7KfHG7fIc/@fDA,@=HHHGlH AH6DL6f.DAH2H:H5FH815D<H]2Hv]H=荟1sfDZtfDLW6[fH2H(]H=?MhL1輝L5LeMH}E1H腝H}qLrdDAYH tHDH=<跞1H5DHH!1MPHuHULEH|ZY}@H9_H=P16fIu L"5fHYH=1,BA?IE1LsBAE1E1_H$_?H'MHuE1MZMHuAZ 8LLLLLLLLHLLLLLLLLLLLf.UfHAWAVAUATISHHdH%(HE1H )pHfHnHEfHnfl)EHCHHHHH,HtgMH=詷HxH=1?HEdH+%(HeH[A\A]A^A_]f.LsL==M1HI9DL;|uHHHHpHIFL{H0L-M1HL9L;luHHHHxHL0IHH>LvHpLxf1)P藗IHL`LLrH`Lh<HH9HHH9H XfInfHnHEHEHfHnfl)EfHnfl)`MtH=3[AEHELuLLH0LH:H}fL}H]EH}Ht轘H}HhHt衘HEHt LL>A;fInfHnIDfl)06MHXfo0)PHt2HHM,HP|HH}MtLHXH!fDo>Ls)pMHpLxDAEfDHQ(LLDž`Hh]IH7HHLuLH7H OHELHEHfHnLfHnHEflH0H)E,H}fL}H]EHEHt LL=A9ID5fInfHnflMmHX)PH__HHpHCH0I0E1IM9JtL:ktHHJ&E1IM9JtLjt{HHJ8H6HH=91fHpH=MtL1處@H@H=1dHDžxo8HmAGHHC1MPHHHUHLptZYK%fDH0-L}1M!E1H0&HH(HnH=H*H1蘔HDžp7Ho{7HHH=o躕16HH[(kH0$,L}MuJH}1Ht!H}H0HEH:AE1E1/IDHEIEIDHDIEIDHEIEI!EI/EI=EHDI;EIdDI\Df.@UfHAWAVAUATISHHhdH%(HE1Hg)EHh fHnHEfHnfl)EH1HHEHHHt`MH= RHB>H=1HUdH+%(He[A\A]A^A_]fDLsL=M1HI9L;|uHMHHEHIFL{HxL-ZMa1DHL9L;luHMHHEH0LxIfHH>LfH}LefH5)E/HHVH5L/IHb1LuIHLL$H}fL}H]EM7A4IfInfHnDfl)M6/M-H}foe)eHL$H} uHH]H"HKHsHH9?H=*SJKHHEHEf.L_$H}tyfo.Ls)mMH}LewfDHHEHCHxIfDE1IM94JtLdt HEJE1IM9TJtLbdt@HEJFf.HHEHHCRHHRHEf2HH5`LQ-@1HHUH=11fL"HWXH=HtHSHKHH9t;H=s(u!CPSuH1@DHHHCPHHP1pL&H]HuMH}E1HH}LHZH= (1 L3AE11fDHE0HAfDHH1MPHuHULEHlZY.@HE20HiN3A0I1HTH=8S15(>>> ?>>>>>>>I>>>>UfHAWAVAUATISHHxdH%(HE1H)EHfHnHEfHnfl)EH1HHpHHHt]MH=:蟨H H=L15HEdH+%(OHeH[A\A]A^A_]LsL==M41HI9tL;|uHpHHEHIFL{HhL-M1fHL9L;luHpHHEHLhIfDHH>L~H}L}f1)E計IHLuLLcH]Lm-HHm*fHnfInHhfl)EMtH=i$+AEL}LLL(H}YHEfH]EHpH}HtH}SH}Ht50A,~pfHnIDfl)p(MH}fop)mHt襉HhH}mHH#MtLvH}Hdo>Ls)}M+H}L}_fDAEfDHLLEHEIHY)L}HhLLLH}HEfH]EHp.A+ID&~pfHnflMH})EHf.HHEHCHhIfDE1IM9JtL ]tHpJE1IM9JtL\txHpJ^*HHa H= 1fH@ H=MtL1i@H H=踈1HEB*HJA$fDHHc1MPHpHULEHfZYLHE1HpHHDžpLfHh H>H=H*H1hHEb)HLeN)H>H H=荇1fDLHEHpHujH}1Ht4H}'LHh *+(Iĉ#HDžp1L+A(HDžpIm!I7I7I7I7I7I7I7I7I7I7I{7I7I7I7I7IV7I7IC7I;7DUHAWAVAUATISHXdH%(HE1HHEHEHEHIHHHEHHcHLqHEMH]H5̚H9st H;f)E$H;|I&LmHHs L1H}fLuH]E*A&ID "fInfHnflMH})EH莃LH}gHsH]HHKHsHH9H=KMSJKHHE迂HEwfHE&HuADH"HHL uAH H5sH8AT1XZHl> H=Q1HUdH+%(He[A\A]A^A_]LqL=Md1HI9txL;|uHMHHEH4I@H.HH]f.L/H}nf}ffE1IM9JtLVtHEJZHHEHHCRHHRHEf1HHf)E!I6z'HHH5)H81``L/H] H=_1uLH\ H=l/HtHSHKHH9t;H=u!CPSuH#1 @DHHHCPHHP1L?LuMuMH}1HH}9L,fH^ H=X1nLټ&A1E1fDHHh1MPHuHULEH+_Y^J%A"IE1|22222222{2v2q2I^2d2_2Z2f.@UfHAWAVAUATISHHHhdH%(HE1HE)EHfHnH-HEfHnHEflHE)EHHH`HHH'HHEHCHXIL-M 1fDHL9<L;luH`HHEHU HXL{HHXL5aM 1@HL9L;tuH`HHEHm LXI'fHVHFo.LkHE)mMK H}}LkL=M1HI9L;|uH`HHEHIEL{HXHHFoH>HE)]HGHGHtaHHHKH{ HcAH9H @AuHaAE1LeH52fLm)pID$H9HXHHJH1@HH9H;tuL5vM9K I$H5IEH9t4HXHyHJH1 HH9H;tuM9, IEHL9hjM9M9HhLuIM DMD$ LHp0HP@H}HEfH`HEEHhu!A<IDp~`hMjHx)pHzH HpG_HH IMqI $MtLzHxHzHH9kHuH;5YfDHH *xH9H:HxHoHHHuE1H9C]fInfIn1Hfl)EDIMtIuLNfDHHMHHML5.M9H5~LM E1IMHAH=YzM 1@HH9HuH;5fDHyH vH9HHvHHM9oHHuHDž`H9CID$8fIn1H~`HEfl)EH`IHtHHXHHHHMHH7M9H5L HEWHu fMH=H H=1xyHEdH+%(HeH[A\A]A^A_]H Hp;\fDGWHH HcAH9/HUH5!H8 HfGWHH HHcAH9Hf.E1IM9JtLKtH`J.E1IM9JtLKtH`JHCo6HXI)uDL5 M9{LkH  H=5w1LfDE1IM9JtLJtH`JGAKL7fHH-HWCH AHH HH5H81 A H t2H"DH=9vME11fH DjAH HH5H81O DNH H H=MvE11H' (fL !fAH HH5"H81 DHH% H=:u{HERHAHH1MPH`HULEHSZY1AH_HH5iH81 BfHEHAaHHE H=ZtLh1Lg HEH`HHDžhH}H.sH}LH=gHpH5pCHHH H=Ht1'H="BLsMHCIHHH`轕H`HuhLH( H==sH=HpH5p!CHH H H=sE1TA H=cBHCH`HH{HHHXHHXHuL5AHDžhIHDž`WLxHDžhAHDž`"HTH@ H H=r6$$$q$$$$$$~$$t$K$j$e$H$X$f.UHAWAVAUIATISHXdH%(HE1HHEHEHEHvHHHEHHHLyHEM4H}HGoHGHBHH>HHBHcЉH9#fDHuBfHEHu#DMH=:H* H=gp1HUdH+%(He[A\A]A^A_]fDLyH eM~1@HI9H;LuHMHHEHPIHNH>H}f.1f)E(L;-!I8Iu0LmLHPHH}fL}H]EAID fInfHnflM0H})EH:nLYH}RHH]HHKHsHH9H=u}SJK{HHEomHEffDLH}R@GWHH HcЉH9H>H5H8 ~fGWHH HHcЉH9r볋_hE1IM9lJtHϺHMAHMtPHEJHHEHHCRHHRHEa_fD IHH7:I $LDHH>H5H81 LgH H=,m1L?HǺ H=omHtHSHKHH9t;H=u!CPSuHck1@DHHHCPHHP1hLH]HuMH}E1HYkH}LFH H=-l1LSAE11fDHH1MPHuHULEHkJZYRA I1CH H pkfa\WIDJd@;61,fDUfHAWAVAUATISHHHhdH%(HE1Hu)EHfHnH-HEfHnHEflHE)EHHH`HHH'HHEHCHXIL-M 1fDHL9<L;luH`HHEHU HXL{HHXL5M 1@HL9L;tuH`HHEHm LXI'fHVHFo.LkHE)mMK H}}LkL=M1HI9L;|uH`HHEHIEL{HXHHFoH>HE)]HGHGHtaHHHKHHcAH9H @Au HaAE1LeH5b}fLm)pID$H9HXHHJH1@HH9H;tuL5M9K I$H5|IEH9t4HXHyHJH1 HH9H;tuM9, IEBHL9hjM9M9HhLuIM DMD$ LHp0HP0H}HEfH`HEEHh Al ID~`hMjHx)pHfH;HpwJHH IMqI $MtLeHxHeHH9kHuH;5LYfDHyH zcH9H:HacHoHHHuE1H9C]fInfIn1Hfl)EtIMtIuL~fDHHMHHML5^M9H5zLM廤 E1IMHqH=١fM 1@HH9HuH;5$fDHxH BbH9HH)bHHM9oHHuHDž`H9CID$8fIn1H~`HEfl)E/H`IHtHHXHHHHMHH7M9H5GyL HEHu fMH=Hg H=g1dHEdH+%(HeH[A\A]A^A_]H/HpkGfDGWHH HcAH9/HH5 H8=HfGWHH HHcAH9Hf.E1IM9JtL*7tH`J.E1IM9JtL6tH`JHCo6HXI)uDL5!M9{LkHP H=b1LfDE1IM9JtL*6tH`JGAKLgfHH-H.H AH2H>H&H5H H81A H t2HRDH=aME11fHDAHH%H5H81D~HMHծ H=:}aE11HW(fLG!fAHHHH5RH81DHHU H=`{HEHAHH1MPH`HULEH>ZYaAHHH5H81FBfHEHAaHHu H=ڛ`Lh1LHEH`HHDžhH}H^^H}LKH=H \H5!\ /HHHЬ H=5x_1'H=R.LsMHCIHHH`H`HuhLHX H=_H=HU[H5V[Q.HH H H=z^E1TA H=N-HCH`HH{HHHXHHXHuL5AHDžhIHDž`WL訙HDžhAHDž`"HHpH H=u]6{Kqlgb]XN%D?H_2f.UfHHAWAVAUIATSHL%#dH%(HU1He)EHfHnHEfHnHHLeflH})EHJHHIM)IHHEHAHHL=H1fDHH9LL;|uHHHEHLIMLmH]LH5dpM9AI9uD1HLE1@IHVo&LqHU)eM~2HHr1MPHHULEH9ZYHELmH]HM9H5oAI9uDHH5 oH9pt L9eHfHH) H5nHCH9HXH?HJHZ1fDHH9DH;tuL9HHLL9tLp(HL9E[L0LL}HsIU MLLH}fLuLeEH@HtHPH)AIDfInfInflMwH() HpsXHH  A~D$`A~L$PAD$hAL$XffAL$PAD$`HH}H8H9HEHp|fDHDžxHuFf.HHHL =AH {H5kH8AT1XZHc!H=1 LHEdH+%(HeH[A\A]A^A_]DLyH MW1HI9H;LuH0HHxHIif.IFHu #I9H#^HJH9PL%JMrI$HgHuE1I9D$fInfIn1Lfl)EIMt II$HMI$HvLeLL|H}LH}HEH9tHEHp *HIHIHH,MmHELuH0IELM}pPHI9}.MHXLI9IMHIEPHXHIII IE~IIMcM<>III9|H0ILMIEPHHPHHIH?H=HHIU0H)H :IEPIEhfHL6Lx6MHHLI9IMHI$PHHHH`H`菄Mf.LnfLUH)0HH=H1E1IM9 JtHϺH8H8tH0JfL7|fL'fH=HzGH5{GIHL-<-I $-L8DH=9tfH=1dIHzLfHH1MPH0HUHLx%Y^C2fDL7_fH5 H=:1kHHt111H?Hh2?HH3H5H81.fHHH5H81H=HEH5EfM|$MbID$ILHH0hL0Hu30;H`H`H(H(襁cH`H(HHhH5ƁH HH H H5H LL(LL8LHPLLHPHtUH`(HLH`L9IH(HM10H`IAIN<7HsH~HVUHAWAVAUIATSHHHMLb HhHRI}PdH%(HE1IE8HpHLEHJDHxPLEHxLH9}$HH}HH9HLHPHEHIcLHxII HI9E1H}nLLxMMI@Kc4CTDHpH)HHLcHLvHCPLH 4HHPHHIH?IH=HHHS0H)HCPHChL9eHLHMTHxHEHALHƒHA*DHCPH 4HHPHHHIH?IH=HHHS0H) ~C`~KPChKXfFf FC`KPL9eDH}1I|$foUA$HyyCfAFI|$HI}1ID$I$LPI$LPfDL 3LL="H}uH}HHPH}L@pEHH H}L H UHAWAVLpAUIATSHHƐHHLf`HdH%(HE1HHHH9HfL) D$H H)0)@HpH H0HLHpHHCP1fHsCPLHHH@HC@HpHHH~@HC`fHnflH)EHtH=Gfo H()eHtH=@fo0H8)mHtH=t@fHE0)pHp0L}HpHuLLefAHHL9t0foHJHtH=tAHHL9uDHChHxHHHPHHHPhLE1LHHH`4Hfo`f)`HxHta0HhHtP0HXHt?0HxLpL9H@GHOt;IL9taI~HtHGHOH9H=tŸuHI_/HHL9ufLpMtHuLL)HMt$IMt3IFIVH9KH=[-AFPAV M9uHHI|$IHulM9uHHHt,H8Ht,H(Ht,HHHHpHuH)HtiLeL}Z,UHHAWIAVAUATSHHH5dH%(HU1H^HEH fHnHEfHnH HuHMH /fl)EHMHJ4H8IMvIHHKHEHH}HHH}L}H5;@H9wt H;=HDž@fHDžH)P)`gIHL;=L;=NL;=TL/LeILHLH}H]HEH@AIDMH&3HDž@IH/I\$ID$HeHH=1I$AD$L*HXfHnfInfl)PHt)LHPcHHgL@HHHtHPMI$LPfDIHPo HKHU)eHHHߢ1MPH8HULEHZYfLkL5M1HI9L;tuH8HHEHIMHXIIIM1H=WEHwH=)1HEdH+%(HeH[A\A]A^A_]xLeILHLH}LuHELHH}HtHPH}+AHD&HLpHDžxMHHU-HxHDžHLsHHCHHH He'Hhfop)`HtE'LdH`谴HHHHHtHPHhHt'HXHt&jHPHUHPHUH8H}H@HH8HH0H0HsHEHHHDH8HH0H_H0HEHf.E1*Hx1Do(HK)m{E1IM9JtLtH8JHtH="'L@1f.H@1HͶH8ӢH8 RH*tAD$DL7JDLHDžHLuME1LaLǼHDž@H]HH}HHPH}La@LDfDfDHEBHw@H0#H0HgHS1ZH}HmHDUHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]`@L"f.@UfHAVAUATSHH H:dH%(HE1HFH9t;HXHHyH#1HH9 H;TuH;5Lf(Ln MtH=sMAD$MuL+LkM9MH=>AD$MLM"LcIUIMHH9H=AEPAUHEdH+%(H H[A\A]A^]HH9HuH;<fDLuLH}fLmLeEjHHBpH=Ϛ"MZIT$IL$HH9sH=UAD$PAT$L H5۝H=d1 GIHt111HLCHoH=-"M@MofAD$H{Htl LcMMMLO HClfIELIEPIELPIAD$DHkHH5uH81"HnH=X2!LUfID$I$LPI$LPL}LmMu)H}E1Ht6[H}LH\L;\EE1E14E1&II"I5RImIPIeI@fUHHAWAVIAUIATSHHPH dH%(HU1HtHEHxfHnH@ HMfHnH`fHnHEfl)EfHnHflH]H])EMNfI6PIIH IH`LpLuH0HuHp0HpH@HxHXHhHI9ELmHHHtH`H;5@H;5@ H9މ<ʈ;uH`RH0H DH9H*L%+MZI$HHIHMIHfLp\IH@H5QLH5LLL'BII$HPMI$HvI]IDI9RHH?H5&H81ӲE1 HqjH='MbMt I $Hht Hh=HXHH%ILnLmHHH`HMf.HIMHEH%H%HuIIH`oFo&IU)e)EH~.HH 1MPHULELL ZY+HEHuLuLmH`@oIM)]HHHuILuH`DHVo.IMHU)mH HEHuILuH`H )IIH`I$HI$/IVMt I8E1E1 HELh{Hu!fDM1H=À6H6h H=E1HEdH+%([HeL[A\A]A^A_]DIE1HIEHzIfHH1!IHFH@ E1H(H0H9PmHPLp ̾IljɶMpH@I{ H@(tL{H@H]LL(LHHHQLH}_A&HDZHQH0;uH`<<MI$HPI$MIM3HDžhM*LׯfLǯxfLfLfL`IILhf.IM9KtLtLL`MLhsK E1E1 HMIELHDžhHPIUHhHhH.HdLLHhHhHt7HEH>fHXE1 E1HDžXHhHhHhHf.H1yLLHh7HHhHEHQf.H5H=21;HHt111HH8E1E1 DrH}E1E1 fI*jHHlH5H81PP H0iHzHH5 H81fHDžh +I$MH[LMJL׬fLǬfLfL|fH= aH H5 IHHDžhE1 fH=`fI $NL=@I $%L fDHH}gHPZHDžhE1 LH5*Ljvt#IE x t4xt.H@E1H(lIMLpsHDž(E1>HHHrH|H5 H81 ƮIHIOIdIt@UHHAWAVIAUATISHHH5dH%(HU1Hs`HEH`fHnHEfHnH(HuHMH fl)EHMH'NufoHK)]LFf.E1LhIL`MI@IM9DJtLRtLLhML`K2A It]1IMtuHXtHXHH`HHtBIHYDH=R Hip@L'DHDLDHhHhHHmLHHhHt5HhHEHQHAY H= 1:HzHHk1LPHUMLEHY^iFDHX H=@ Hh1Hm1fH@HHYH5H81ȠHqX H=A 11MfH@LȚt)Hp/LHH!IHA A DH=UHH5IHNf.HW H= 1=IHW H=X 1H=9UfIA ZfI?1A f.HE2HWmA HX)HA LH}tLDE1@HV H= 1EfMHuOL1A wH HH5H81HJV H=1f.H!V H=A L_HDžXMA H5Hit HC x t-xt'H@H0H HHDž0IA H鿽HkH陽HϽHpfUHAWAVIAUATISHhdH%(HE1H [HEHEHEHHHHpHoHHLyHEMH]IFfH5SL)EHHwIHH2I9D$gMl$MiM|$IEII $MHu1LHELmq}IMt IMI$MHI$eIDL;5]IFLmLLP(L}LeIHHLHEHMHpHx~pHxH>H})EHtpҤIIHULLPH}LkHEHxHHYH}HtMt|Lr@HE HuADHɣHH}L AH {KH5kH8AT1XZHRfH=} 1HEdH+%(=HeH[A\A]A^A_]DLyH ]XM\1HI9H;LuHpHHEH%ISDHHH]EfHI$u LNHQsH=|FH}H-E11Lf.LfL/fLךSfE1IM9TJtHϺHx3Hxt2HpJfjDHu1E1fHu1DHaHpqH5kH81HPtH={(HPtH={MIT$IL$HH9t>H=vu$AD$PAT$E11#@DID$I$LPI$LPfHH {1MPHpHULEHxY^%fHOzH=z(1LHML+H}u4?LHsOyH=^z<DLxR=xTH5OvH= zHxHx1H}1H[H|H鱷H鴷H麷H陷H鼷HsHH˷HԷHԷf.DUHHAWAVIAUATISHHH5iwL-ʓdH%(HU1H`HEHhfHnHufHnLmfl)EHBJ H`IuIMt^M11H=mHMFH=x14HEdH+%(nHeH[A\A]A^A_]HKLhIHqf1L)p)E7HHH; AH;D=L94HAHEtHH#EWL9hzM9LiHH,HhPIH+LuIt$ LHL H}`H]LefEH]LeH}HtzH}ZyfHnfInflHHxf)p)MHt2Hp覊HHHxHFL};@M/H5bHHHL=>L9xH@HhHLoHIEHuHh1LHEHEzjHpHhHtH?HpL)H5*dHHHL9xsLxMgHXIHHu1HHEL}iIMtLM0HLpLLHH LLyHчIHH\ HH/mH5H81XH@aH=.hE1H?aH=.HMtLfLpÓLpHHHjL1PLHUMLE^_HHu1fHpHHhHHt7IMAgH(?DH=-E1HgDE1IL9,KtLH`LhLpLpLhH`tKDDHf.HׇfHLJfHHH>lH= -ML. fLHEHpH3H}E1HH}L+fDH=nH=,(HtH=iH=V,d@L׆5fAfIM)1LHH  HDH=VHH5IHH<fH=+bHpHHhHHu H+H<gH=}+ }DH=aV謽qE1Hu1fIEAfHIE @Hu1DI]HMeHLI$@MHuBA ID=fE1)p&f.L)AɏHDžpIE1HDžhIHu13AgIHu1E1HHu1HpHHhHHt(H H:HHprHʤH遤H鿤H鼤HɤH鏤H釤HdH驤Hkf.@UHAWAVAUIATSHHL5dH%(HE1HNHEHELuHILHHHHHIHEHL}Hfƅ ƅ,H$)H0H`HDž8HDž@HDžPDžX`fopfH@ Hd)0H$l,HExH@HPo]EH}XHHt-M9TL&HHH0kIM9LuIu0H L/H}fL}H]EAŌIDfInfHnflMH)HvL}HvHxH@Ht;HKHsHH91H=1SJKQHHHKHsHH9H=SJKHHWHHGH-HH+0H -0HHHH5H?L HLHH=HL@H5y1H:SHbXZH67 H=&1HUdH+%( He[A\A]A^A_]DHIMHL=)K1DHH9LM9|uIHHEH@oeEHXafDL{Ht fDL>L}>@M2(fcfHHHHCRHHRHHHHHCRHHRHH5LIHi5` H=$1AfHqHzH>RH5H81}LgzH4d H=$1L~H]HjH}E1HH}&L"DLyH4c H=#'HHSHKHH9t?H=u%CPSH1@DHHHCPHHP1L3LHHHL_1LPHUILELY^H3e H="81iHHQHDL!ҊAE11DE1IL9[A\]ÐDID$I$LPI$LPHfDH[A\]\@Lf.@UHAWAVAUATSHHdH%(HE1HHHf)EeyH;^mIH[0{LmHHLuH}fL}H]E~A{IfInfHnDfl)MvMH}foM)MHdLlH}HiH]Ht3HKHsHH9tiH=)ruOSJKmHUdH+%(He[A\A]A^A_]LlH}DHHEHHCRHHRHE@HqwHE1L $H -H5iH8R1HQnX1ZLfHy:1H5QH!1DZ}HkH&H5H81@n@uLkH%H=?1LjHy%H=HtHSHKHH9t;H=pu!CPSuH1@DHHHCPHHP1LnH]HH}E1Ht>H}9L,H$H=81x{AxI1HHE4HE~L{A1E1wq;61,'" If.UHAWAVAUATSHHdH%(HE1HHHf)E%uH;iIH[0cwLmHHLpH}fL}H]EzAvwIfInfHnDfl)MrMH}foM)MH$LChH}BHiH]Ht3HKHsHH9tiH=muOSJKmHUdH+%(He[A\A]A^A_]LgH}DHHEHHCRHHRHE@H1sHE1L ] H H5dH8R1HMjjX1ZLfHy:1H5`MHT!1DyHIgHY"H5S{H81jqLfHi!H=1LfH9!H=HtHSHKHH9t;H=Clu!CPSuH1@DHHHCPHHP1LiH]HH}E1Ht>H}9L,Hb H=1xwAtI1HHEHE~L@zwA1E17m<72-(# If.UHAWAVAUATSHHdH%(HE1HHHf)EpH;dIH[0#sLmHHLlH}fL}H]EovA6sIfInfHnDfl)MXnMH}foM)MHLdH} HiH]Ht3HKHsHH9tiH=iuOSJKmHUdH+%(He[A\A]A^A_]LcH} DHHEHHCRHHRHE@HnHE1L H H5`H8R1HLI*fX1ZLfHy:1H5 IH!1DtH cHH5wH81elLbH) H= 1L_bH H= HtHSHKHH9t;H=hu!CPSuH1@DHHHCPHHP1LeH]HH}E1Ht>yH}9Lf ,H"H= 1xzsAApI1HHEHE~L :sA1E1h=83.)$ I f.UHAWAVAUATSHHdH%(HE1HHHf)ElH;`IH[0nLmHHL@hH}fL}H]E/rAnIfInfHnDfl)MjMH}foM)MHL_H}HiH]Ht3HKHsHH9tiH=ieuOSJKmHUdH+%(He[A\A]A^A_]LG_H}FDHHEHHCRHHRHE@HjHE1L H mH5]\H8R1H EaX1ZLfHy:1H5DH!1DpH^HH5rH81ahLO^HH=1L^HH=tOHtHSHKHH9t;H=cu!CPSuHC1@DHHHCPHHP1L_aH]HH}E1Ht>9H}9L&,HH=x1x:oAlI1HHEtHE~LnA1E1d>94/*%  I f.UfHHAWAVAUIATISHHL=}\dH%(HU1H/5)EHfHnL}HfHnHEflHM)EMbL4HBH)HUH8IMH}H%HupLE1A1DHHPo(IMHU)mH~.HHA1IPHULELL蹦ZYH}Ht3H;=WH;=Uh:L91hH}E1HtnH;=WH;=hL9DPehDPeH}L9E1-fDIME1HHPH}L9AH52H9wtfE)`IHHpLHpLxHP iHHPfHHMDDHEHPH@HefH} HEfLmEHPH}HtH}kAhI~PfInDfl)PcMHhfoP)`Ht&HHAYH`=HHLhMt=IUIMHH9BH=^AEPAUMIT$IL$HH9H=^AD$PAT$uZL PH~dHIع11H=>Hn H=1HEdH+%(HeH[A\A]A^A_]HHuH8H}D+HPHUHPHUfDH(LLHPHHHEHPH0LLHP譚HPHHEHHHn&LLHPtHHPHEHkffo0IM)ufID$I$LPI$LPfDIELIEPIELP1HSDHHP?HPDH:eHH H=yLhME11H H=@MIT$IL$HH9t>H=[u$AD$PAT$E11@DID$I$LPI$LPÐHHkUH H=M!IUIMHH9t;H= [u!AEPAULDIELIEPIELPH@XHEE1HPHH@xfAycHDžPIE1H H=2DP3cHH}DPL9ADL萿3H@Xf.bHH}AfLHHPbHPHLfDHP{bHPH.$fDZbH F[Lh1M/IyIzIyIyI8zI zIWzIyIyIzIzIyf.DUHAWAVAUATSHXdH%(HE1HHHfH;R)E)EHC Hx HtH=UXG?HC HS(fHnfHnflHtH=$XBH})EHtc^LmIHuLw`H}fLeH]EdA`IfInfHnDfl)M[MH}foe)eH{LQH}HH]Ht;HKHsHH9,H=H}Zf.Hh{EH}z2DHI4fE1IM9JtLtHpJZfUfIELIEPIELPHHHCPHHPfHH=*رE1HDH_,H5XH81GHH=*蘱E1E1H Mt9IT$IL$HH9t;H=Iu!AD$PAT$[E1DID$I$LPI$LPƐHH=)E1^HhCHH=)˰4fDLOGHEHpHu-H}E1H"H}LL9rHLHLG1ǃL:L>9rMLLH@H}MLLH`H}LfLH@H}VLfLH`H}L<Is\Ic\f.DUHAWAVAUATISHdH%(HE1H{HDžXHEHEHCHHHHHHBHLqHXMLXHLpHDžhL`H]ƅpH9XH=HHL}LLy=H}HMHUHEHUH9%HMHMHMHMHEH`HMHEHEL9HMH99oUHpH`hHHEHuHEH}H9tHEHpD9H}H}HEH9tHEHp9H}AIHyHhH`=3IHAH>H9CHufInfIn1Hfl)EIMt I $iIMoHHMHHH`L9HpHpS8xHDžXO@HuFf.H =HHL -AH H5.H8AT1>4XZH[H=gE1IHEdH+%(HeL[A\A]A^A_]@LqL=mMW1HI9L;|uHHHHXHIf.HL&LXL4fLw4fL4H`HUHEHUEHL9HMH9to]H`hHUHUHN@H4.fH2H=MyH /HH=לE1HMHHtAHȃKtMVH`H}HHHhHEE1IM9JtLot{HHJ>HMIHHyYE1@@B4C4H=HH5.kHH9DH=Ljf.LLcM3HCI$HHHH HHLDFfGI;DDEAEHMH`HH1MPHHHUHLXxY^bfDH1fA1AAN N D9rL 2HHLHLG1ǃL:L>9rMLLH`H}LfLH`H}4H=UH=UfDUHATSH?HLgMt=IT$IL$HH9t:H=Z2u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L耗f.@UHAWAVAUATISHXdH%(HE1HfHEHEHEHYHHHEHHsHLqHEMH]fL%H)EL06LH5HH0z7LmIHL.H}fL}H]E=A9IfInfHnDfl)M5MH}fo])]H蓖L*H}H}VH H]HHKHsHH9H=E0SJKHHE蹕HEqHE9HuADH5HH%L AH H5s'H8AT1-XZH# H=1HUdH+%(<He[A\A]A^A_]LqL=5Md1HI9tpL;|uHMHHEH4I@H.HH]f.L/)H}}H])HDE1IM9JtLitHEJblf.HHEHHCRHHRHEfH H=%踕1H5i H=61kHHt111HuH ?Hs H=d1D6HH@ H=11Mf.L'H H=lH>HSHKHH9t?H=o-u%CPS H1@DHHHCPHHP1L+H]Hu7H}E1Ht~H}LH$+L8A1E1@HH 1MPHuHULEH rY^8Am5I1].OON OOIONNNNNNNfUHAWAVAUATISHXdH%(HE1H~HEHEHEHHHHEHH3HLqHEMH}f1)ETHH1LeIHL)H}fL}H]En7A54IfInfHnDfl)MW/MH]fo])]HhHSHKHH9H=*CPSL$H}-H$H0f.HE3HuADHA0HHL eAH H5!H8AT1v'XZH H=!脑1HUdH+%(wHe[A\A]A^A_]LqL=Md1HI9L;|uHMHHEH0IH.H>H}f.L#H}H}CHH]H;HKHsHH9H=2)uxSJKHHE誎HE[A\]ÐDID$I$LPI$LPHfDH[A\]l@Lf.@UHAWAVAUATISHXdH%(HE1HHEHEHEHYHHHEHHkHLqHEMH]fL%:H)EL(LH(HH)LmIHL+H}fL}H]E/Ae,IfInfHnDfl)M'MH}fo])]HL2H}HH]HHKHsHH9H="SJKHHEDHEtfDHE+HuADHY(HHL }{AH H5H8AT1XZH& H=a蜉1HUdH+%(DHe[A\A]A^A_]LqL=M\1HI9thL;|uHMHHEH,I@H&HH]f.LH}~xfE1IM9JtL:\tHEJjifHHEHHCRHHRHEfH5H=r)1HHt111H%H kH#5 H=1D)HH8 H=1rf.LH; H=t诇HtHSHKHH9tCH=# u)CPScH蟅1DHHHCPHHP1LH]HucH}E1H葅H}"L~fH= H=Ȇ1YHL;u+A1E1HHT1MPHuHULEHdY^&+A'I1 B BIABAAA BAAAAAA@UHAWAVAUATISHXdH%(HE1HHEHEHEH HHHEHH#HLqHEMwH}f1)EԁHHJ$LeIHL%H}fL}H]E)A&IfInfHnDfl)M!MH]fo])]HXHSHKHH9mH=MCPSLLH}fDHE&HuADH"HH<L uAH H5sH8AT1XZH# H= 1HUdH+%(He[A\A]A^A_]LqL=Md1HI9L;|uHMHHEH0I H.H>H}f.L/H}HH]HFHKHsHH9H=SJK HHEAHELfE1IM9\JtLJVtHHEJ|fHHEHHCRHHRHEgf $HS!fDHHHCPHHPLH H=Ht3HSHKHH9t3H=uCPS1kDHHHCPHHP1:L'H]HueH}E1HH}BL5fHG H=-81HX1L詼%AE11fDHH1MPHuHULEH^Y^Z@H~%AI"I1H H=|臀1<<<<)eHGHGHtaHHHHI HcAH9@AuHAE1H]H5:L%H9st L9f)E)EL9HC(~C HtH=YC@H}fHnfl)EHt-|IL9xHxLeHMDLHp0H}fLmH]E"AIDfInfHnflMH})EH{LH}胥HH]Ht;HKHsHH9H=VSJK]H]H+HKHsHH9H=SJKHHxzHxLH}辤6fo.Mg)mMKH}wfGWHH HcAH9H%H5v$H8KffGWHH HHcAH9Q@HHEIGHhIYfDE1IM9$KtLNtHpJFE1IM9lKtLZNtXHpJDwDwAt@ HHeHFH AKHb=DHHxHHCRHHRHxHHxHHCRHHRHxe1HHf)E)EfDH HMH5 H81HZ H=y1fZH H2H5 H81@@L HN] H=w?y1NL H] H=GyHfHSHKHH9t?H=u%CPS3Hv1@DHHHCPHHP1LH]HuHH}E1HvH} L޳HHxjvHxL足AE11HEHqAKfDH_ H=w1fHH1IPHpHULELUZYfHEH.AI1H]HNI{3I3HL3fUHAWAVAUIATISHXdH%(HE1HHEHEHEHvHHHEHHHLyHEM4H}HGoHGHBHH>HHBHcЉH9#fDHuBfHEHu#DMH= H H=u1HUdH+%(He[A\A]A^A_]fDLyH 5M~1@HI9H;LuHMHHEHPIHNH>H}f.1f)EL;-I8LuIu0LH}fL}H]EASIDfInfHnflM0H})EH sL)H}HH]HHKHsHH9H= u}SJK{HHE?rHEffDLH}~@GWHH HcЉH9H H5_H8~fGWHH HHcЉH9r볋_hE1IM9lJtHϺHMFHMtPHEJHHEHHCRHHRHEa_fD[IHH?I $L DHHZH5H81hhL7Hv8 H=gr1LHN8 H=?rHtHSHKHH9t;H= u!CPSuH3p1@DHHHCPHHP1hLOH]HuMH}E1H)pH}LHw: H=hq1L#AE11fDHH1MPHuHULEH;OZYRAI1CHH o e-`-[-V-Q-L-I9-?-Y-5-0-+-&-!-fDUfHAWIAVAUATSHHxHxdH%(HE1H8)EHfHnHEfHnfl)EM*HHpHMHHt^IعH=T(Hϼ H=)o1HUdH+%(.He[A\A]A^A_]@MgL5UM1 HI9$M;tuHpHHEHID$MoHhL5.M1 HL9M;tuHpHHEHLhIfDHo&H>)eHGHGHtaHHHHIHcAH9@AuHAE1H]H5L%CH9st L9f)E)EL9HC(~C HtH=C@H}fHnfl)EHt}l IL9xHxLeHMDLHp0pH}fLmH]EgA.IDb fInfHnflMH})EHkLH}ӕHH]Ht;HKHsHH9H=SJK]H]H+HKHsHH9H=^SJKHHxjHxL?H}6fo.Mg)mMKH}wfGWHH HcAH9HuH5H8-KffGWHH HHcAH9Q@HHEIGHhIYfDE1IM9$KtL>tHpJFE1IM9lKtL>tXHpJDwDwAt@[HHeH7H AKH=DHHxHHCRHHRHxHHxHHCRHHRHxe1HHGf)E)EfDH)HH53H81H H=Yi1fHHH5H81L_H H=i1NL/Hn H=_iHfHSHKHH9t?H=u%CPS3HKg1@DHHHCPHHP1LgH]HuHH}E1HAgH} L.HHxfHxL@ AE11HE HqAKfDH/ H= h1fHH|1IPHpHULELFZYfHEb H~ AE I1H]+ HNIK$IV$H$fUHAWAVAUATISHHdH%(HE1HHEHfHnHHEfHnflHE)EHHH`H^HHt_M1H=#He%H=|f1HEdH+%(HeH[A\A]A^A_]DLsL=M1HI9TL;|uH`HHEHMnMLeL5H^HLvL;5LuL&LeVo.Ls)mM~2HH1MPH`HULEHCZYLuLeL;5VH5 yI9vt.fLx)EL)EL;5LtI^(H5{xID$H9tLH]HoHDž`H}H0^H}zLqmLHCH=>_iHJH=!_I $L{1@HEHH5FH=L_1XDLHDž`H]HL觚HDž`A1LHĬHH=^^L^XWHDž`I1HL*dX1HDž`L]%~ytojeIMSI@FA<72IA% f.UHAUIATSHH:IfHnH bfHnI\$flID$HHI$HtHC Htc@t\I]AEHt;HSHKHH9H=CPSH[A\A]]HH[HAD$ H{ Ht.HG PW uH)EPfoELc KHHHCPHHH@H[A\A]]fFfAD$ nDHH[A\A]]ZfDG ]HHt HHPHHf.UfHAWAVIAUATSHHdH%(HE1H)EHXfHnH-8HEfHnHEflHE)EHHHXH H(HHHEHCHPIL%M 1DHL9, L;duHXHHEHM LPIMDžPL}LmHY_f1LHh)pHHh賣IH H1L蚣IH H5mH=`n1GIH HH5oLIFIGHH HXHXHmH OdH9H L%6dMm I$HXLlHHI $VH;7H;JH;=HAąiH -EWHplH cH9H HcH HHXHIH H L;%L;%!L;%'LpÅv I $lHkH bH9H L%bM I$HXL#HHL_zHAąLHDzE;HTkH ]bH9HHDbHJ HHXHIHHyLoÅLyHjH aH9H L%aM. I$HXL4HHLpyHAą]HUyELHejH NaH9H H5aH HHXHIHHxL耄ÅLxHiH `H9H L%`M I$HXLEHHLxH AąnHfxE]HviH ?`H9H H&`H` HHXHIHH xL葃ÅLwHXHHHHHH5H=1zHHt111HMHw"DHH~LnL>H{H}LmL}3HHC1MPHXHULEH3ZYH}L}LmfLkL=ձM41HI9L;|uHXHHEHIEL{HPHHt`M1H=|%pH̡H=XE1THEdH+%(HeL[A\A]A^A_]DH~LnL>H}LmL}HH;=H;=?uXH;=ItOP:H+QfDLnL>DžPLmL}f‰PfHXHHPHHI $u L@HH=CSIM&Mt IHxHtRMHeH=VSHXHHHHH3L;=$[L;-pv1ɃPHHIU Iw LpHLL;5pIFLL]HuIIIMLt7IH DHjD1IMuLODHAHXH2(HHHEI|L}LmE1IM9$JtLr%tHXJoLk)UL"f.LfE1IM9\JtL$tHHXJ.HXHHPHHH jH\@LcfHfHH=sPE1HE1H`ffDLfj#H=HOZH5PZHHHXHHPHHvH=H*ZH5+ZIHLJfHfHErHAfDH=I\f.HXHHPHHHXfHXT,H`HH5jH81zHzLHEH/H=}HXH5XZHHbH=R}HH8H5H81HH=.NHHH5H81FHXGH=ӕH,XH5-XIHTH=H=H=AHWH5W^IHHXHHPHHt@H=cH=ҕH[WH5\WHHHXlH=HS[A\]ÐDID$I$LPI$LPHfDH[A\]鬇@L@Jf.@UfHATSH@dH%(HE1H;=)ELeHw L0H}-foEMH])EHt;HSHKHH9pH=BCPSH}H]HtHHSHKHH9qH=CPSHEHlHH}HH]Ht3HKHsHH9t;H=SuySJK7HUdH+%(YH@[A\]HHEHHCRHHRHE@HIH=eHJ1^fffHHHCPHHPHHϘH5H81kfDHHHCPHHPHEDL LffDHHEGHEHGHpG*HHDUfHATSH@dH%(HE1H;=)EQLeHHw0LH}foEMH])EHt;HSHKHH9H=-oCPSH}H]HtHHSHKHH9nH=0CPSHEHHH}HH]Ht;HKHsHH9|H=lSJK,HUdH+%(NH@[A\]HHH5H81XH5H=hG1aHHEHHCRHHRHE}f]fffHHHCPHHPdHHHCPHHPHEDLGffDL(rHHEDHEHDHD-HHDUfHATSH@dH%(HE1H;=)EQLeHHw0L=H}foEMH])EHt;HSHKHH9H=MoCPSH}H]HtHHSHKHH9nH=0CPSHEHHH}HH]Ht;HKHsHH9|H=SJK,HUdH+%(NH@[A\]HHH5H81xHדmH=:D1aHHEHHCRHHRHE}f]fffHHHCPHHPdHHHCPHHPHEDLgffDLHrHHEAHEHAHA-HOH?DUHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]L~@L@f.@UHAWAVAUATISHXdH%(HE1HֱHEHEHEHyHHHEHHHLqHEMH}fH5')EHHLeIHL)H}ffLuH]EA_IfInfHnDfl)MMH}fo])]H @L,H5UTH=W1YHH#L-DL93H@HuHH_HIHPHHH^H]HHSHKHH9H=vxCPSuyH>oHERHuADHHHeL 52AH ÆH5H8AT1FXZHdH=E1Q@HEdH+%(KHeL[A\A]A^A_]@LqL=}M\1HI9thL;|uHMHHEH4I@H.H>H}f.Lo>fHfE1IM9JtLtHEJjfHHHCPHHPHfDLHG|H=d~>Ht3HSHKHH9t;H=cu!CPSoE1DHHHCPHHP@LLuMH}1H\HHzH5H81H0H=M}=IEH@HH=%}=HHL-ygA.I1E1HHD1MPHuHULEH{Y^*@Hp;Iĉf1)eoHPwH=m|<<HfUHAWAVAUIATSHHhL5zdH%(HE1HDHEHELuHILL9H}fDf.1HeH}nH}\DHHEHHCRHHRHEH/fDH HH5H81LHH=dx81JHCHH]1LPHUILELY^NfDLHH=wG8HtHSHKHH9t;H=u!CPSPH761DHHHCPHHP1LWLm1ML8srAE1 HЄH=wx71AE1IL9KtHxLEH} H}LEHxtuK3Lr|HOH=v61*% +IUfHAWAVEAUATSHHHhHPL`dH%(HE1H; O)p)EvHA Hq(H}HEaHEfLm)EHHH; -HIHHHH <H9HPH;HpHHCH5HHH_IHHMHHID$H5LHH0HI$HHI$H>H'H@HCLMH=H@1HAIgMfH L;%.L;%‰L;%L ÅTI $z~HfInIAƈfl)EMtH= AEH]L}HML`fEHPHEHhH}"HEfLuEHhH}Ht2AfInHDh)PHHxfoP)pH:2LYHpH8H@H)HHpwLHHbH}Ht1HxHt1MtL1HEdH+%(HĘH[A\A]A^A_]LfHf4I@HDžHE1DLg f.Hf*H)H tlH'H=9rd21DHfI $uL!Lxf.HDHH($H=q11gfZHHTH/H=|q115AE/fDH=H7H57HHDH=af*DDH@1H>IHDHLuMHDžhH}He/H}f.HHlL_H*H=dp0HhHh1/fDHDžhE1HyH5H8Q@H}H!HDžhAE1H@1H=o/1wrmhc^HJQLGB=83.)$I PDUfHHAWIAVIAUATSH dH%(HU1H)@H`)`fHnHfHnHfHnHEflHHH)EfHnHxfHnflHM)EfHnHlflXHp)EMJ4I H JcHHP0HpHP(HhHP H`HHHXL(MfHHXHHHHHHPL@I( HIJcHI} H CJcHfDMfHLHfH@H. HIM HPLhL@HHHHXHH`HMuL;5LL;5oL;5bL) HpH5,@HH9pt H;B HIEHHHHDžHDžHDž HDž(HDž0HDž8gHH3 Ht0H1HDžHtHIH H ~H߾HDžHI9H5LQMHHHHIHXHHIH;HMLHDžMLHDžXAƅLLEI$H=LH5ZLLHHHLLLpIHH;=Ͻi XI}H5 ?-XUL;%:H5LLHIHH5HKHHHH5LKH5~HH9sH[fInfHnHfl)HLpHHILKLHu1fo)E蠠HIuKHDžMQLYKLHDžVÅ<L4KH5=zLJIHλHHH#I$L`HIHHcV]H`H H H`H5L H`JHH5LHDž b LHLlMHH H} L-JH%JLJLJLIEMHDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžHpHtHzIHHtHfIHHtHRIHHtH>IHHtH*IHHtHIHHtHIHxHtHHHHtHHHHtHHHHtHHHHtHHIMMtLHH{HHEdH+%(a8HeL[A\A]A^A_]fDHMfH@1Do0Mf)@MHɇLHJHHHPI fDHPo8MfHP)@MHLHHHHXIMHLHHHH`IMPHLH}HHHhIHPL@IHHHHXHH`H"DMOH`LhHDHV0HpLp(LhHX HH`HpHHXHpHHPHXL(HHL@MDž‰fHWtfLGnfH5ipLa`H6H5*H9pH)HHHHHfInH5$H9p*fInfHu1LHflϺ)MLHE)EiH IHDžML#EfDDžfHAHpXDH)1MH=^>yH@rH=՜"E1#HHHHLHHHHpIMDHHrM1PHUL@L[A\cH1H>KHfDH1LHHHHHIHHDž`IHDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžpDž\HDž`IHDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžHDžpDž\H`HtHAH8HtHAH@HtHAHHHtHAHPHtHA\H-oH=™E1rHzH5HHfDH5HDHpL.DžHHVL@HHHHf.H5!HHH[HDž`Dž\HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžHtH?MtL?ML?HHH%yHHH6H5iL~DEB L;%H5iL?HHHHHH9GeH_HHQLwHIL>LHu1ɺHEH]ޓHHH >HHDž L>HDž H5hLM>HHHHHػH9XLpfInL)MLxIHIL=LHu1fo)ELHH =HHDžHL=HHHDžH@H9t H;O HH{HHHKHH9|HC HHHHHH=HDž H5wH=/HDžÅL2 HH;LtzHIHH3>ÅL2!HLLS~HHa HW2HHLL~HHt HH;^EH#HH9XrHHHHH5qH1HIH-H1H5?Lq1IHL!L}1HLHDžBÃp LO1HH;q*H5pH1HIH)H&"H5H9pX1HHH(HHH5pH0H`H H)2H0L`H50rLh0HHHH(Lf0LHDž S0HHDž@0HDžI9PH5oH/HH0H=J/LH5aL/HHH0L/HHHHH;H5dHt/IH+H H zH9HG*HaHH*HHH5p[H/HIH(H/LLHHH:.L.L.HHDžf:Aƅ-H.HDžEy-H5pH|.HHH,H5jHQAƅY+Hb.1HE*HHH*HiH*H9)H5XHsm>+H H5H9p+HHH2+HHHH ;1H9H*HfHnùLHEHcL)EfInH)PHt̀Hc1)EiHIHtH2-M.L!-1HHHHHxE1LLLLLCHHHfDI$LHDž`HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžDž\HDž`HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžDž\-HHH.>fDHDž`HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžDž\+H=cHSH5THHHHJHDž`E1HE1HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžDž\2H=b$HHDž`HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžDž\hHHXHHH)LwHIL(fInL1HfofHnHuflκHE)M)E|HH I'HHHcHLHDž`Dž\fHu1ɺL)ELt|HH xL2$H5daL&HIH&6H8HFH8LH`H H'L&H8HDž&H`'2Aƅ&H`&HDž EL;%DE1LLLHEHAHHHDžHDžHDžHpH;7 H5*^H=%HHH HH AH9H LpfInp)MHXIHHHn%HHu1foHHE)EdzLHH.%HH%H %HHDžHDžHIHDžHDžxHDžHDžHDžHDžfHu1ɺLH)ELHEyHH H E1HDžPE1HDžHHH`HDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžDž\LHu11HELHExHH H E1HDžPHDžHLH`HDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžDž\BHxH Dž\HDžPH`HDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžDž\SH HDžPHDžHH`HDž@HDž8HDžHDžHDžHDžHDžxHDžHDžDž\HHHHHHnH E1HDžPE1HDžHHH`HDž@HDž8HDžHDžHDžHDžHDžxHDžHDžDž\ HDž`E1HE1HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžDž\JDž\HDžPE1HDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDž`HHDžPLHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžHDžDž\HDž`E1HDžPHDžHHDž@HDž8HDžHDžHDžHDžHDžxHDžHDžHDžHDžHDžDž\HHDž`HDž\LHDžPHDžHHDž@HDž8nHАIH?HIGLHDž HHIHHLHHH#LHоH" LJHL$Dž\HDžPHDžHHDž@HDž8Dž\E1E1L`H=FHH5HHHHrDž\1H`HPHHH@H8HHHHHxH:HH+Dž\1HPHHH@H8HHHHHxHHE1E1HE1LPE1LHL@L8LLLLLxLLDž\lHH5H81tH=xD;HpH=(H;=i"HGH1P0HHHS!H%Aƅ(&H1HE L;%  H5SLHHH+H H5nH9pW+HUHH*HHHHIH)H:H1H%1LH$Å.LI H H H9H.HHH-HHH5CHHHH-H1H5HHH9py,~HHu1ɺH)E]mI1HM+HE1H5 LLX+E1LLAL1HLHIHL8H5>HLdLE1|LHLH54^dL8L`HLLZHHHHE1LL E1LL E1LH HDž\SLH`ZH LDž\$H`6E1Dž\VE1E1LPLHL@1LHPHHH@Dž\V1E1HHHHxHHHHH1LHPHHH@H8H LE1Dž\VH`1HPHHH@H81HHHHxHHHHH]H 11E1HPHH`LHHH@H8HHHHxHHHHDž\VʱLpHM)HXIHH1H1HuHuLuFLHH o H=6^HHHHE11LE1H`HPHHH@H8LLLLxLLLLDž\VְH=x5HAH5BmHJE1E1LE1L`LPLHL@L8LLLLxLLLLLDž\T"H5H0E1LDž\UL`1LE1Dž\UH`HPHHH@H81HHHHHxHHHHHrH 1LDž\%HPH`HHH@H8HHHHHxHHHHHݮH=lϛHHHHNH E1E1E1LPE1H`LHL@L8LLLLLxLLLLDž\%/H=HH5ӛH?H E1E1E1LPLH`LHL@L8LLLLLxLLLLLDž\%釭H E1Dž\&LPH`LHL@L8LLLLLxLLLLH Dž\'H`1HPHHH@H8HHHHHxHHHHH阬H HE1Dž\'H`1HPHHH@H8HHHHHxHHHHHLpfInL)M`HXIHHHHHu1fo)E@LIHH LE1Dž\'H`1HPHHH@H81HHHHHxHHHH H=,HHHHH 1Dž\'HPH`HHH@H8HHHHHxHHHHHH=,HH5HIH Dž\%H`0H5~AH=wi1 HHH H`HtH111HDž\.E1LPLHL@L8LLLH LDž\)H`HH@HHHWHHH 4cH 11E1HPLH`HHH@H8HHHHHHHHHDž\+鸨H Dž\-H`E1E1E1LPLHL@L8LLLH Dž\-H`1HPHHH@H8HHHHHHHHH LE1Dž\-H`1HPHHH@H81HHHHHHHHoH 1E1Dž\,HLH`1HPHHH@H8HHHHHHHH=ՓHHPDž\8H HtH1H HtH1HHtzE1LHtHcI~`H5e3|f\HH=a>HHLH 5NHHHHH HHH;WHH5qH9pnL=XMIH5u9LuHHL}H5H=JH@HHH5H$HHIH@H5HHPHL HbH9CLCHfInP)`MH[ILHHHu1fo`HHE)E9HIHP{M Hj'UH@HHLxH9cHHC T]IHH9H5r1HTDHLHHHH HHLHHHH QH9HH8HPHFHH5HZIHH56H?H@HHLDTIH|HXC\H@HHHH5*#HR#HPHLHHHPLHHHtHE1HL t HHE1LHtHaI~xH8H0H(UDž\H1HHI~xH(H8H0MH 1HHLH`HHH8YDž\IE1LHLDž\HE1LHLfDž\IE1LHLFDž\IE1LHL@LH={ >HPHPDž\H1E1HHH@HH=1 HH5覎HPDž\@1HPDž\G1HPHH{Dž\G1HPHH\Dž\@1HPHH=E1H@LPLHDž\EHP1Hu1H@HUHEHHHE+5I|Dž\FE1Dž\F1E1HPDž\E1E1HPHHDž\@1HPHHH@cH=g3肌IMDž\@1HPHHH@HH="3HH5ILYHHHHE1Dž\KL LL1E1HPHHH@HDž\;Dž\9H 11E1HPHH`LHHH@H8HHHHHHHDž\5'H5HH Dž\6H``H="HH5藋HE1E1E1Dž\NLPLHL@L&Dž\N1E1HPHHH@H81HH E1Dž\MH`1HPHHH@H81HHHHHHH 11E1HPHH`HHH@H8HHHHHHDž\M鉜E11E1Dž\MHPHHH@H8LLLLL31E1E1Dž\NLPLHL@HHHHHHݛ1HE1Dž\ H`LHPHHH@H81HHHHHxHHHHHP11HE1H`1E1HPHHH@H8HHHHHxHHHHDž\̚Dž\_1E1LPLHL@E1HHHHxHHHHHHgDž\_1E1HPHHH@ÙH=#HHH H!E111HH`E1E1HPHHH@H8LLLLxLLLLLDž\_邙H=1HH5&H>E1E1HE1LPE1LHL@L8LLLLxLLLLLLDž\]ؘXH]Dž\^HE1H5oH1E1HE1LPLHL@L8E1HHHHHxHHHHHHDž\^ HDž\8BH E1E1E1LPLH`LHL@L8LLLLLxLLLLLDž\)pHHHH5\H81iKH Dž\)HLH`H 1E1E1LPLH`LHL@HHHHHxHHHHHDž\P鞖H~JHIHH8gIFLLAHHHLAH`H HLA׾H賖LE1LHPH:HxDž\Q1E1L`HPHHH@H-BH5]H81IE1L輋H H`tE1Dž\QALh1E1HE1HPHHH@1L`E1HHHHHxHHHHHDž\QÔHu1ɺH~)MI)E)H811LE1H`HPHHH@H8HHHHHxHHHHDž\R 11LE1HPHHH@H8HHHHHxHHHHHDž\Q陓1LDž\SH`11LE1HPHHH@HHHHHxHHHHHDž\SHL11E1Dž\SHPHHH@HHHHHxHHHHHvH HLH`nUHHHAWAVIAUATSHHdH%(HU1Hf#HEHfHnH(HEfHnH0fHnfl)EfHnH CflHMHMHM)EMH HHHrHteL1H=)HyH=)覯E1HEdH+%( HeL[A\A]A^A_]fDI^L%U$H 1 HH9M;duHHHEHV HCHHH-L}L%B/DHHHLfLeL8L}HAIfH1L)0H(II$Dž$H()@)PkHHyI $THHHLUIHH;=H;N%L;%ALgNAI$HPEHI$E HH;=@)11IHV H;@y H@ Iv(HXHP̍HPfL;-@HHX)PHL;-<L;-7MALMAąEDIHIIT$L:IHK~HHfHnfl)`HtH=]EH@1HEDxIfyLH?H`HDžpHHBIH}fLmH]EHhHt֪QAMHDIHH>Hu:IEHHpHHH@ H@H1:H}>HEfLmEHH}Ht'H}&MHfInHH8)0HtݩH01PHDžIHsMt II $HHHHHHHtHqHHtH]HXHtLHtH?HHHt.H8HtHHHIFoHH)]HHEAHHI^JL8H1 HH9M;|uHHHHJDIAHuH?H]L}LeLmH5 H9sH;<1Hj9Ht%E1 IL9t0KtL|txHJ6JH4N@I$DHI$L?EH$LbIHIMDHDžE1L;-;HDž"$AKI$HZM1E1HDžHDžHDž@H&H="%E1L?HHEIFHHHHHL}HHHEHHcL}LeE1 IL9KtL{tHJ&)LG>TL9>I $-L>H>'L% :+L=L1E1MHDžHDžHDž@ YHHSE1MHDžHDžHDžGHEHHHa 1LPLLEHHeAYAZvL=iHDž1MHDž8H1HDžHh<LmMH}1H]?H}MH(IbI#cIAcIbI%cIAcIncIkcIbIcIhcUHSHHH>u.HFHGfHHFFHGHH]fDH9fHH]UfHAWAVAUATISHHdH%(HE1H)pH8fHnHEfHnfl)EHCHH8HFHHtgMH=艽HpH=E1HEdH+%(HeL[A\A]A^A_]fLsL=M1HI9L;|uH8HHpHIFL{H0L-M1HL9lL;luH8HHxHuL0IHH>L~HpLxf1)@)PpHHtL`LL"JL`LhnBHuM\?H8H?H 1fInfHnHEHEHdfHnfl)EfInfl)`MtH=8AD$HDž0L}LuLHLLDH}f~EH]MfHnH0fl)PHtHzH}LmMtQIUIMHH9H=K8AEPAUHEHXHeHhHtHEHt LL(DA@ID#<MHHfHnf~P)Pfl)@Ht薝H81H@IHMtLdHHHOf.o>Ls)pMHpLxlDAD$HXH0#1LƚH8Hu8=H0L}LuHH=H LHEfHnH8LHEH۩fHnfl)E5LL@HPLHXHhHtIL衴HEHt LLeBA,?ID`:ML@HLuXH00LHXIIHHSH輛FHHpHCH0IpE1IM9 JtLZptH8JfE1IM9JtLptH8J>H}H$H=NY,H(H=-8MtL軚E1OpfIELIEPIELPHEHXV@HDžxo=HAgHHC1MPH8HUHLpyY^EfDH8#.3HH=HSHtH֙E1]fDL11f@LHG6H=fDHDžp<Hk<H|H+H=誚_H0P-HX-!6L襘 5HZHZH[HZH[HZHZH[H#[H<[HG[H7[HA[fUfHAWAVAUATISHHdH%(HE1H )EH8fHnHEfHnfl)EH.HHhH)HHtbMH=G謴HH=aE1AHEdH+%([HeL[A\A]A^A_]@LsL=EM1 HI9L;|uHhHHEHIFL{HPL- M1fHL9dL;luHhHHEH]LPI fDHH>L~H}L}f1)p)E豔IHLmLLfALuH]9HM6fInfHnHhfl)EHtH=r0CE1LuLLL;H} f~ELeMfofInfl)P)UMtL H}H}HtH}H}Htؕ<A8ID 4MTHxfoPf)E)pHt茕Hh)HpIHoHt;HSHKHH9H=G/CPSIHxHF<Do>Ls)}M[H}L}fDCL}u1L覒IH5LuHhLLL+LL9LeLLPH}HtqLɬ:Aj7ID2MLpLLPHhN(LLe芾IHMLHHEHCHPIfDE1IM9JtLhtHhJnE1IM9JtLZhtHhJfHHHCPHHP*6HLHH=i,HH=eHHtH˒E1HE5HAfDHH 1MPHhHULEHrZYsuHh{&HH=諓MtL.E1fDL*E1fLHH=mPHvHE4H4HHLH=ZHh%Lem-ITIeTITITITITITITISI;TI%TISf.UfHHAWIAVIAUATSHL-0%dH%(HU1Hz)PH0fHn)@H˜)`fHnHfHnflH0HE)EfHnHfHnflLP)EfHnH flH]Lp)EH JHI H 2JcHDHV0HpHP(HhHP H`HXHpL`IOHHPHHXLHH@I HJcHIOHeHLHfH@H HHHHLHfHHHHHHm HPLXH@LHHML;=L;=0EM9<Lc0 @H`H@H;=1AH;=/DL90Aƃ HhH H;=H;=v/L9/m HpH5ϢHH9pt L9 H5HCf))) H9t;HXH(HJHC1HH9,H;tuL9 L9M HID$H9t5HXHHJH-1 HH9H;tuM9I$HHCH9t;HXH HqH;1HH9$H;TuL9 L9k8 M9` pv'IID$ 1LHPHC8Hp  0LLL/H!'IIL$ HS LHY3&HבHfInfHnflLxHPH%)HHHtLLDIH; L9 H5L谮IHw HM,I9G MoM IGIEHIH LHu1LHELmHMtLQHe I HHHHHq IH W LI $IMt IH(HtŠHHt豊HHt蠊HEdH+%( HeL[A\A]A^A_]HH9HuH;5*fDEGHH5ٌH9p L5MIIHL HHX8&HHHm HH5HN HH5H/ HLLٯHH L襬L蝬H葬H52L9HyHE1E11AHH9HuH;5(fDHAH5H9p HqHHHHIH I$L`$IH. HH5HHLL譮IHHuLmLeM9,H5LM1MAE1HE1DIWHDžE1MUILL6 >HH9HuH;\'fDH5H=**1ӭIHt111H݀L蕪HH=͈E1H #HHhE1HDž!fDDžyfIH _JcHHV0HpHP(HhHP H`LxLXHXL`HHPHLHH@fD‰}f.‰fE&L}HIL$ LHS LL0LL*H LHH~BH8HtLE,A(ID$MqHLL+BHfHYHLH\HHHPHHSHHLH\HHHXHHPH@ILHHHIOH@Do.IO)@@HVo6IOHP)@@f'H@HHHLH[HHRH`HH2HHLHr[HH4HhHHH0HLH2[HHHpHHHHL1PHHUML@c_AX|u+|&Hu1MH=a uHH=褄E1@LGf.LgfHHLHHHLH5L9oHH5|SE1E1AIHtHHHHHt`HtHHHHHt5HDH=觃HMD10fDrHgDH1HN[ fD$H9vx$HbwXLfHLDžLHH@IH=QIH8HH=謂E1aH=HQHH=HH=kE1E1H#HHUH#HH3@LHHLVLH5CH="1荦HHt111HyHOHH=臁H*H5˕HH=SHOH7H5Y(H81HeH=[DHH~H5(H81E11A`H=HH5.PHHH5'H81qHH=聀L$HH=TH=)HRH5SOHDžE11AE1E1A'H4H=*E1E1Ai!HA1H=GHH=ʿH5ѓH!:HDžAE1H HHsEHDžA Hu1E1?Hu14H6H=,~6Hu HHHkHH=~0 HhIE11AHDžE1Hz@H@H@H@Hb@UHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L{f.@UHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]\@Lzf.@UHATSH?HLgMt=IT$IL$HH9t:H= u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]霷@L0zf.@UHATSH?HLgMt=IT$IL$HH9t:H=Ju AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]ܶ@Lpyf.@UHAUATISHHH>tg8tHIHSI}AEIEIEHsHoK(AM(IE0Ht\H=`uN@M,$H[A\A]]@HoFGHGHtH='u-@H[A\A]]@M,$H[A\A]]D@H[A\A]]þ8L] f.UHAWIAVAUATSHH?LwH_HGHEI9tqIAD$PAT$t9HI9tELcMtID$IT$L9teH=MtøuLHwI9u@I_HtHEHHpH)I?u=H[A\A]A^A_]fDID$I$LPI$LPXfDHL[A\A]A^A_]骴f.UHAWAVAUIATSHHL5 dH%(HE1HHEHELuHILL9H}f@vHHHCPHHP1HeHHkHH%IML-HXo?HHH4H5 H81pH}HH1LPHUILELNY^JL#fH}HE)E:LHHE1IL9^KtH8L@HHCHHL@H8tK LrH~2Hn2@UHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]鬪@L@mf.@UHAWAVAUATSHdH%(HE1HHHfH}H)p!-H]LeCIH L}H`HLH}HEoo]fE]fH~LmEH}HtlH}H}uH}HtlH}\hfHnfInՋhIfl)P M-HxfoP)pHt=lH`XHp蔖HHxHt;HKHsHH9H=SJKMt=IL$It$HH9H=u8AT$JAL$HUdH+%(He[A\A]A^A_]úDufHHhHHCRHHRHhMPf.I$HhLID$RI$LRHhNH HE1L H uH5eH8R1HiX1ZHy=1H5EH$1DH h H=UkMtLKj1E1&L8H`KHӸj H={kMt5IUIMHH9t?H=u%AEPAU1f.DIELIEPIELP1rDHELHhHH}HhaLm1MqE1Hh;vfDHl H=j1LHhhHhcDHHhhHhDLhh1HEHhdfH]LmEH,H-H&-H-H-H,H-H-H -H,fDHH>uoFfNGÐUHN]@UHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]l@Lgf.@UHAWAVIAUATSHLoHI9tsI@AD$PAT$t9HI9tELcMtID$IT$L9t]H=tøuLHifI9u@IHtPIvHHH)[A\A]A^A_]%f.ID$I$LPI$LP`fDH[A\A]A^A_]fUHAWIAVAUATSHH?LwH_HGHEI9tqIAD$PAT$t9HI9tELcMtID$IT$L9teH=tøuLHYeI9u@I_HtHEHHpH)I?u=H[A\A]A^A_]fDID$I$LPI$LPXfDHL[A\A]A^A_]:f.UHAWIAVAUATSHHL5wdH%(HE1HAHEHELuHIL,HpHHH\H ^HHHHH?L WHLHHnHL@H5'1H:SH?XZHH=weE1HEdH+%(=HeL[A\A]A^A_]f.HHIHEHDH}L9H5xH9wtMffHDžpHE)`)P)E苠HHHHM9ILuIw0HLUH}HEoEHEfMHE)EL}H]I9tpIAD$PAT$t=HI9tELcMtID$IT$L9yH= tuLHbI9uH]HtHuHH)H}KAHDHxHpfo]fIL`Lh)EH@HE)`MHpHEM9uaCPStL9H}f@fHHHCPHHP1HHHHH%IM.L HH1H5H81ppHH;fH\H2H^HH1LPHUILEL4u.HFHGfHHFFHGHH]fDHfHH]UHATSH?HLgMt=IT$IL$HH9t:H=:u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]̗@L`Zf.@UHAVAUATSHPdH%(HE1HHHf)EH;ILmHsLH}8LuHH{AIVHCHCIvH蟑AoV(S(HC0HtH=@H]H]Hk^H}AtHɖHEHH]Hu9oEH}fM)EHtYH}.H]E1HtRHSHKHH9H=_!CPS7HEHALGH}MHH]Ht;HKHsHH9H=SJKHUdH+%(He[A\A]A^]fH HkH5H81LHH=Y1;`ffHHEHHCRHHRHE0fHHE1L H H5uH8R1H/X1ZHy(1H5 Hz1DHHHCPHHPHE"DLP#fD@DL'fHHEVHEBHVHI%IIDUfHAWAVAUATISHHHHdH%(HE1H)EHfHnH-HEfHnHEflHE)EHHH@HHHHHEHCH8IL-{M 1fDHL9LL;luH@HHEH] H8L{HH8L5!MP 1@HL9L;tuH@HHEH L8I'fH.HFo>LkHE)}MY H}}LkL=]M1HI94L;|uH@HHEHIEL{H8HHFo.H>HE)mHG"HGHtqHHH+H;HcЉ@H9 f@u)QH.Dž@@Dž@LeH5#kfLm)PID$H9HXH6HJHQ1DHH9<H;tuL5M94 I$H5JiIEH9tH8I)}DL5M9LHsHЛ^H=xNLWfLGfG@fE1IM9~JtL!tjH@JG؉@0[HH(HH @Hf.AeH t-HDH=MME1BfDH_D2HaHH5kH81LHohH=$M1HHIMdHHE1DHfLfHEbH?AfDrHHH5H81XXL'HiH=dWL;fHH_1MPH@HULEHH*ZYuHH H5H81l@HEzHWA1HUHEHHHHDž@H}HJH}H L#HhH=`SK@fDH=yHFH5FHHUHb`H= KH=6A`BL{M/LsIHI}lLHu LdHcH=JE117H=sHEH5EHHHeH=YLJE1H=-HCH8HHKHHHH0kH0HuL葫DHDž@HDžH@HQHL,H̖kH=tIXn i d _ Z I7 | H C > 9 4 I O " f.@UHAWAVAUIATISHXdH%(HE1HHEHEHEHvHHHEHHHLyHEM4H}HGoHGHBHH>HHBHcЉH9#fDHuBfHEHu#DMH=r*cHH=LjG1HUdH+%(He[A\A]A^A_]fDLyH UM~1@HI9H;LuHMHHEHPIHNH>H}f.1f)EL;-I8Iu0LmLHP(H}fL}H]EAsIDfInfHnflM0H})EH*ELIH}9HH]HHKHsHH9H=u}SJK{HHE_DHEffDLH}8@GWHH HcЉH9H.H5H8~fGWHH HHcЉH9r볋_hE1IM9lJtHϺHMHMtPHEJHHEHHCRHHRHEa_fD{IHH'I $LDHH H5H81LWHߑ.H=D1L/H.H=d_DHtHSHKHH9t;H=u!CPSuHSB1@DHHHCPHHP1hLoH]HuMH}E1HIBH}L6H0H=C1L CAE11fDHH1MPHuHULEH[!ZYRAI1CHH \WRMHCI06P,'"fDUfHAWAVAUATISHHHHdH%(HE1He)EHfHnH-HEfHnHEflHE)EHHH@HHHHHEHCH8IL-ۙM 1fDHL9LL;luH@HHEH] H8L{HH8L5MP 1@HL9L;tuH@HHEH L8I'fH.HFo>LkHE)}MY H}}LkL=M1HI94L;|uH@HHEHIEL{H8HHFo.H>HE)mHG"HGHtqHHH+HHcЉ@H9 f@u)H.Dž@@Dž@LeH5UfLm)PID$H9HXH6HJHQ1DHH9<H;tuL5~M94 I$H5SIEH9tH8I)}DL5M9L訚sH0H=8LfLfG@fE1IM9~JtL tjH@JG؉@0HH(HgH @Hf.AH t-HEDH=7ME1BfDHDHHH5H81xxLGHτ H=w71HHIMdHHE1DH7fL'fHEH?AfDHHuH5 H81LH H=6;fHHܰ1MPH@HULEHZYuCHrHkH5|H81)l@HEHWA1HHEHHHHDž@H}H|4H}HiqLH  H=ݯ5@fDH=نH21H531HHUH‚H=j5H= ABL{M/LsIHIVLHu LĖHLH=4E117H=ӌHd0H5e0@HHHH=֮4E1H=HpHCH8HHKHHHH0VH0HuLDHDž@HDžH@HHLH,H=3XI^oje`[I0vIf.@UHAWAVAUIATSHHhL5JdH%(HE1HHEHELuHILL9H}fDf.1H5H}>H}\DHHEHHCRHHRHEH/fDHHH5H81L_H|H=p/1HCHH-1LPHUILELm Y^NfDLHo|H=L9H}fDf.1H5H}>H}\DHHEHHCRHHRHEH/fDHټHH5H81L_HvH=j)1HCHHP1LPHUILELmY^NfDLHovH=dj)HtHSHKHH9t;H=u!CPSPH'1DHHHCPHHP1L'Lm1MLdBAE1 HuH=iH(1AE1IL9KtHxLEH}H}LEHxtuK3Ltc|HuH=i'1Z/*% 0 IUHAWAVAUIATSHHhL5JdH%(HE1HHEHELuHILL9H}fDf.1H5H}>H}\DHHEHHCRHHRHEH/fDHٶHH5H81L_HpH=֝#1HCHHÙ1LPHUILELmY^NfDLHopH=^#HtHSHKHH9t;H=u!CPSPH!1DHHHCPHHP1L'Lm1ML^BAE1 HoH=H"1AE1IL9KtHxLEH}H}LEHxtuK3Lt]|HoH=!1Zpkfa\WqMHCI061,UHSHHH>u.HFHGfHHFFHGHH]fDHfHH]UHAVAUATSH?HtLcfE1MIT$IL$HH9H=JAD$PAT$6Et MtLFH[Ht;HSHKHH9H=CPS[A\A]A^]4LcfAIM(if.ID$I$LPI$LP8fDf.YfHHHCPHH[A\A]H@A^]H[A\A]A^]LUHAWIAVAUATSHHhL-*dH%(HE1HHEHELmHcILHH\HHIHEH|LeM9%HH"AoW LsHCHγ)UHHHEHtH=\@L}LLH}HtM9t!L^IHLLH=ŶCH=4(OfInfHnIflHfHL9H=})ESC@I$MHPI$I$HH@H8HgHMHHcH cHHHHiH?L HLHHŻHL@H5~1H:SH XZHqjH=zE1HEdH+%(HeL[A\A]A^A_]ÐHIMHL%Q~1DHH9$M9duIH@HEH@CH=2MsCHxt ucHE~EI|$ fHnflAD$HyoL&Lec@MW@rtI|$ fomIAl$HtsMLBBDLf.HHLH5H81hHhH=ٕxZLULUH(HH1LPHUILELUY^VfDH`h!H=iHE1HHHSH5H81Hh"H=IEE1HfE1IL9KtLHxLULELELUHxtKfڻHHqgH=zHIIIHII7IGIPfDUHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]T@L@f.@UHAWAVAUATSHdH%(HE1HHHH;HCH}HP HELmHp8HH|~IHELH`HIEP8H}pHEfHEHEEHxAֹHED H}L߲ͪHHH}XH}ֻHhH>HEHuH}PHpH8IHHx*HH9P?L%MI$HuH}HpH8HIHHI9D$HEHu~EfIn1Lfl)EHMIHtHHXHHIMAI $LLHIIMHEHEH9hHE1IHPHHHLMtLHEHtHHpHtHHxHtHHEdH+%(HeH[A\A]A^A_]fDLfLfH׬^fLǬfLfH9d2H=IMI $t8E1HcH=V}HHE1HfDLGDH7GfHHE11H5aL `H8H Y[R1H^_Hyc2H==KfDHy1H5H61rDHH7H5H81`HEHDžpE11۾HDžxH=inH H5 IHIMLf.HpHEH=nfH`{HEHxH7H}HEHEH}eH`.OTfBLxHEHDžxfDID$HEHMt$HLI4H`MHEE1{IMI]LܩOLǩ4fAaHEDHEHDžxs@H`NVAHEHEHDžx/HuL4AHIH[HnInIHH8HHHHf.UHATSH?HLgMt=IT$IL$HH9t:H=*u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]L@LPf.@UHAWAVAUATSHXdH%(HE1HWHHH;̣H7H[ D Ht!HHaH5Z1HHu EL}HLHEffoMLu)EH}MH]EHtAݱIDH}MT֢H=%fHu1H)EʆfInfHnIflH@PL@MLE1HL)EH5aLEfoEHt]I)EHtH=!CLLH}IHt MubH\H=PJIEHMI@)EHtH=çCLLH}IHt M@It[IEMIEHt;HtHn HEdH+%(HeL[A\A]A^A_]fLDLDHHE1L %ZH TH5H8R1H؈2XZE1jHyZ1H5H0Af.HHptH5H81ȣHq[H=mO fDHK[H=GO HYHSHKHH9tBH="u(CPS&H fDHHHCPHHPHZH=N E1%CCwH=111H_ZH=[N qYPIgIuIIIIIIUfHAWIAVAUATISHhH}dH%(HE1H+o)EHXfHnHEfHnfl)EM-HHEHHAHtdMH=R'HOYH=sM1 HEdH+%(KHeH[A\A]A^A_]MoL5cM1 HI9tM;tuHMHHEHIEI_HxL5"nH1DHH9M;tuHMHHEHLxIEfHH>LfH}LeҟIHML轟HEH_HEH;vHuL}LHFPLuLefInfInHflH)EMtH=ۢAD$LuHMLLLaH}~HEfLmEHEH}HtIMH=fHu1H)E莀~EfInHflHlH@H@)EMtH=#AELHH}IHtMoIVHCPE1H]HPHHHH]MtLMtLMtAIT$IL$HH9bH=DAD$PAT$"MLMo&Mo)eMH}LefDAEfDH_8f.HHEIGHxH fDE1II9KtLtHEJLf1@HI9DItLJt0HEH^AD$mDfID$I$LPI$LPfDHE1E1E1E11۾_@ڨHE1E1E1E11۾7@HE1E1E1H5!H{uE11H81œHfTH=HHHHEHxfDME1E1E11۾L'LmMu^H}HEHH}_LAR@ME1E1XDE1E1CHEE1HEH3A fDE1HHT1MPHuHULELZYLHE*HH}Hv HEIE1kIH HV&WH .IfUHATSH?HLgMt=IT$IL$HH9t:H=ju AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]?@Lf.@UHAWAVIAUIATSHHL%tmdH%(HE1H^fHDžPHfHnHEfHnLXfl)EMHH HUH+Ht^M1H=6RHQH=hE1HEdH+%(HeH[A\A]A^A_]@M}H iM 1 HI9dI;LuH HHPH} IGH0H0LP%HtH*LfLXL6LPHpL}H;HDžhH0H`ƅpL}HEEH{xIHH<LsxHaH9P7L-HMIEH|HuHDž8I9E~8fIn1Lfl)ExH8IHtHH HHIEHMIEHHELHH 3HEHMHUHL9H9noeHuHMeHHEHuHEH}H9tHEHpҚܢH8HIHHIH7H`H0H9HHUL9omHpH`hHHEHMHELH5iI$"j I $CPE1rH IH`D`H}HEfLeEH8 ̡Iŋ LM ÒHC1MHPID$HEH8fHnfHnfl)@HtH=d@L@HLfInHHI8) HtMI[HCfo H8H)@HtH=ALHHHIHtMIM LchH8H{p5HHHH}L9tHEHpqH`H0H9tHpHpMH8H,H5"Do>M})PM~5HHML1PH HUMLPq^_LPLXi@HHfLfLfLhfH5ygL\AI$HEI$HE)I$H5(sLBI $CTAHHPIEH0H0TMeH _M1DHI9\I;LuH HHyL0HXIrLד%fLǓfE1IM9KtHϺL0H8H8L0tH JR@H9tSo}HM}HUHUHfDL9o}H`hL}L}LW@HMHHt>HȃtM HuH}HHHMHEYDH5IqL$AI$HE@I$H@EI$H5dLmLHAHCPHUHHtAH=ЃHtUH`HuHHHhHE E1IM9$KtHϺH8H8tH Jhf.HqHkH5{H81(HGH=<811H80H=PH H5 vIHH{GH=;LǐfIMtHFGH=;sLDIEH8HfIUHLHH L Hu8fDH= P9H8@eItHFH=:@LߏDH59bLTqH5GbL2AŅ[LOEIH5QH=$HHH9XbHXH8HNLhHHIEH H8fIn1LfHnfl)EnIHtHM*LH=LqHHLz111HHdH5EH=9_*H@H8F H LeMHDž8H}HH}H 2DHDH=8H8H8`HIDH=81fHDžP/HLgfHCH==8X1YM)L+fDMEHMHEBHLHLG1ɉσL:L>9rEHUH`ITHTF1҉փI<7H<19r^ "HDž8IE1CH 0* E1M$MLLHuH}QUATTH`HuIHuATfTH`HuLfLHuH}TH?HHH H0HýH3H鯽f.@UfHAWAVAUATISHHdH%(HE1Hb)PH`fHnHEfHnfl)EHCHH8HH,HtoMH=8nH*AH=5HDž8HEdH+%( H8He[A\A]A^A_]DLsL=YM 1 HI9L;|uH8HHPH IFL{H L-`M 1HL9 L;luH8HHXHu L IHHH HPHFH0HXHDLuLpHDžhL`H `ƅpLuHEEH9HWH0HHHUHuHDž8H9Co~81HL` )EhH8IHtHHHHHHHMHHELL`HH8#HEHMHUHL9H9omHuHMmHHEHuHEH}H9tHEHpL`HI$HI$HHTH`HUHL9L9o}HpH`hHHEHMHEH0L`苅IH.L`YHH8LLwH},HEfH]EH0AH8DH8H1H=fHu1H)EfIHH I|$xHHSH H0fHnID$xHHID$PID$HPHAHEfHnfl)@HtH=CH@LHH~0fHnHHfl) HtHHH*HID$fo H)@HtH=UCHLHHHtH H HHH0I|$pHID$hL8I$I$HFH}L9tHEHpH`L9tHpHpHHv@HHp;H=/11H8f.HtkH<;H=/ʐo>Ls)PMHPH HXH0CL`H0L`LhCcHofLfHHPHCH IE1IM9lJtL*t[H8JLwfH9ouHMuHUHUH'f.:DE1IM9JtL蚿tH8JL9omH`hLuLuL;@HfHfH+fHMHHt>Hȃ4tMHuH}HHHMHEDHUHHtAHЃGtUmH`HuHHHhHEFH=AHH5L`'HHfDH=YAL`=HCH8H}LcHHL`I$ LHuRfڋHH7H=&,=@H8HEH0HuzH}1HsoH}H8X%r|H;7H=+HH!EHMHE#H8$7HDž0A8@HDžXߊHAgH6H=+HDž8HHc1MPH8HUHLPZYCfDH<6H=*I$HI@֐HDžPH;AH8HDž09rEHUH`ITHTF1҉փI<6H<19rUATTH`HuMLLHuH};ATfTH`HuLfLHuH} I:IAIbIjI'IIIUIIcIIHIVUHATSH?HLgMt=IT$IL$HH9t:H=Ju AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]!@Lpf.@UHAWIAVAULmATISHHHdH%(HE1LHLIH}pHEfH]EHEA]I~MfHnDfl)MLMsExL5~xM9ID$fo]AD$PH)]HtH=}CHuLH}IHtMI $tdIMHt3HSHKHH9tRH=}uxCPSHEdH+%(HHL[A\A]A^A_]ÐL{DHHHCPHHP@C:DLzHEHEHvH}1H'H}kL^f.vH1H=7&Ht3HSHKHH9t6H=v|uCPSE1fDHHHCPHHP@HvH4H5H81HyH0H=%X5H0H=m%8HX=Iʼnf1)eYHLhAiHEI1Q}II)IVH/ITIlIVIrI=II$I IfUHATSH?HLgMt=IT$IL$HH9t:H=zu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\],@Lf.@UHATSH?HLgMt=IT$IL$HH9t:H=yu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]l@Lf.@UHAWAVAUATSH8dH%(HE1HgHHH;|sHnH[@D HHHH5o1V{HEu H/LmHIL,pH}IfLuH]EӄAID|LMrrH=fHu1H)EVfInfHnIflH@TH@)EH@HtH=xCHuLH}IHtMIMI$MI$HthHt;HSHKHH9H=wCPSHEdH+%(HeL[A\A]A^A_]LuDEzH=p111H&,3H= Lgu*fafCHHHCPHHP9H|HE1L )H U$H5EnH8R1HxXsXZE1HyJ1H5DXH1f.HpH)DH5H81hsH+2H=xfD"pH*6H=RHYHSHKHH9tBH=uu(CPS&H>fDHHHCPHHPL_sLuMH}1Htw:H}L'fH*8H=xE18H):H=XI$H @HpC}Iʼnx1E1'DLҀA}I1E1vH I)I[I/IYIcI6IrIaI#I)I`If.@UHAWAVAUATISHHhdH%(HE1HoDz$zHxLmDLL"mH}HEo]fE]Lef]EH}HtcH}H}=LuMtJIVINHH9H=)sAFPAVgHEH4A{~MfInIDfl)MwML-%mL9H{Hfoec@HtbHxlIELCTD{\HUdH+%(Hh[A\A]A^A_]@xHxE1Hx[l롐xZ~HlH@H5H81@o@vHx lH&H=;Mt9IT$IL$HH9tMH=qu#AD$PAT$1fDDRfID$I$LPI$LP1@ILIFPILPHEDL LuLL(sH}t\LnLeHEMZE1LcLP1+fL8rHEfLeEHELuHݣأUHATSH?HLgMt=IT$IL$HH9t:H= pu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L0f.@UfHAWAVAUATSHHh\HPdH%(HE1HEHDžxHE)EXyHHHXxDL#Mt L;%ii+H[HuHDž`E1H=]H5V+HGHHbHEHH5HYuH9G_LwLuMNLIIHL}HufIn1Lh)ELHxIMt IHEMIzL5hHEM9pHDžxHt H Mt I $H`HtHHHHH\HhHEHHDL9zH@H}HP HEL}Hh}vfInIhH{H})EHtCM9M} @vHluHHEHULH`H3vH}KHEfL}EHhH}HtH}tu~hfInflHFMtH=lAGH}L)EHt|HEHuHhHPHhHHxIH LMt IMMtL(MtLH}Ht HEdH+%(HĘH[A\A]A^A_]D:j^DI\$I$LH3hH`LixH5L4yLHDžxH}HEHtzLHH5sHEI`;A1H H=MHMHULHx>L5WeHxIHtHDžxH}HtH}HEHtHHH`LHHEHxx MHEHuE1HhLhfHhfLhfHh1fHwhRfLgh AHHH`LHHxx? E1E1E11HxHt HH}Ht HH}HtHt~HDH=KHfE1E1A?gLhE1AAG*E1AH`fHEE1HhHH`} qHhHu(fE1AH`H {E1E1A^HX[A\A]A^A_]fDMuIELIbI@dHd=fd'DHELdHErf.LgdM fLGdMfL'dH;LeH5&nI|$`褄L111"I|$xLLLIH=lHmH5n9HH뒾THH=VG1~LWfHnfInflMHGIHHHEH}1HuLU)ECLUII tH}fLcH=ɚHKHELLLIHxxO8bLU)EbfoELU]afUHAWAVAUATISHhdH%(HE1H:HEHEHEH1HHHxHH@HLqHEMdLmH=f)EPfID$I$LPI$LPfD2mHa[HBH5koH81^eLZHH= IMI$L1PmH^fDHHB1MPHxHULEHإY^jCbHtH=8 H Hi^1LY^VHHf.fUfHAWAVIAUATSHhLfdH%(HE1H~)EHfHnHEfHnfl)EHHISIMMH=H#H=_ AHEdH+%(HeD[A\A]A^A_]IuLnH^ LmH]H5^H9sfHu1H]H=,HEH)EbHxE1E1~&[HjHrH鞍H/H靍HaH#HHf.@UHATSH?HLgMt=IT$IL$HH9t:H=Xu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L谽f.@UfHAWAVAUATISHhLndH%(HE1H)EHfHnHEfHnfl)EHHIIIMpMH= "HS H=躾AHEdH+%(.HeD[A\A]A^A_]IuLnH^ LmH]H5VH9sfHu1H]H=\HEH)E5HHmLuLLHHzHH;PL{ _IHv(_HHELLHxHWH}fL}LuE:_fInfInflHL;%tP>ID$)EH@MtH=UFAFHuLH}IHtȻMIMmAD$TAH =Mt=IVINHH9H=}UAFPAVH}H I@o^H)]_H%H]LmH_H5+HIHVIXHEHH5HHVXHEH?IFf>fAFfDRDHFHHE^If.HRE1LRf.H! H=舻H Mt5IVINHH9tCH=Su)AFPAVuMA*DILIFPILP@ILIFPILPH5H6cH9H=蠺ADHH=}xE1E1DHH=UPfD_AH@MH H5JaH81ODV^H( H=ADVE1@LHPFDHxKPLuMH}E1H!H}Hx DHH1MPHULE1HmZYlfDH9LH4H5C`H81NHH=ME1mDZHAfDL~ZZH@BZE1f.HxZE1E1 SH鴅H鸅HՅH鿅H˅HHȅH鱅fUHATSH?HLgMt=IT$IL$HH9t:H=zPu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\] @L蠵f.@UfHAWAVAUATSHHxL%9JLvdH%(HE1HO)EH8!fHnHLefHnHEflHE)EHKIIFM_I1HFHHEYIH5LHVSHEHIMH}LuHGHGHNHHzHPHDHcAH9A WHA@Iu^HF(ofHHE)eXHKHH1MPHULE1L̓ZY'ItrItdM1H=HH=aHEdH+%(He؉[A\A]A^A_]HF(HEH~ LvH}LuE1L}H5I9vt M9kH5\I9wt M9EL,IHM9?HpLHhHIFP HpLxH`!V~`fInflHGLuHhIcL)pILXH}fL}LmEHxHt裲UfInfInflHL9HCH@)pMtH=iLAEHhHHxIHt7MI $,CP1M&L DHVH5"LIHVIOHEHTH@GWHH HcAH9HMJH5[H8NGWHH HHcAH9H5LHV4OHHEIGfDonH)mUIfDDoOLOIf.HH=m8MIUIMHH9H=Ju*AEPAULfDDoA@+OIHH}IA{LHmD1HLw-| f.IELIEPIELP1HAL'-Bf.AE?fDH5,H=R1HHt111HŨH HMH=贰NBRHHH=聰@HyCH+H5WH810FHH=u@HG^f.HH==MIVINHH9tHH=xHu.AFPAViLWDILIFPILP LFLmMH}E1HH}&f.LH1BH$H5;VH81DHH=-cfDPH [@jPHHA%fDHxH:PE1fE1E1EHoPH`QIH{H,|H;|HM|H{H|H:|H{H3|H{UHATSH?HLgMt=IT$IL$HH9t:H=jFu AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L萫f.@UfHAWAVAUATSHHxL%)@LvdH%(HE1H?)EH8!fHnHLefHnHEflHE)EHKIIMI1HFHHEOIH5LHV IHEHIMH}LuHGHGHNH4HzHHN:HcAH9A MHA@Iu^HF(ofHHE)eNHKHH1MPHULE1L載ZY'ItrItdM1H=HH=QHUdH+%(He[A\A]A^A_]f.HF(HEH~ LvH}LuE1L}H5I9vt M9kH5LI9wt M9ELIHM9?HpLHhHIFP(HpLxH`L~`fInflHGLuHhIcL)pLNIH}fL}LmEHxHt蚨KfInfInflHL9HCH@)pMtH=`BAEHhHHxIHt.MI $CT1M-IMIuHH9H=AAUJAMLh^hH/LH5pLIHVI}EHEHOJH]s@GWHH HcAH9H?H5NQH8CGWHH HHcAH9q%f.H51 LHVDHkHEIGnfDonH)m6KIfDDoL>gfHqH==اM~LWqfDoA@EIHHsIA{Lr>mD1HLg#| f.IUhLIERIULRh1H7L#:fAE8fDH5q"H=H1HHt111H赞H H=H= 褦N2HHH H=q@Hi9HxH5sMH81 <HH=0H=^f.HH=]MIVINHH9t@H=h>u&AFPAViL\DDILIFPILP(fL;LmMH}E1HգH}f.LH!8HH5+LH81:HH=M\fDrFH [@ZFHHA%fDHxH*FE1~fE1E1EHFHQ>HyrHrHrHrHQrHrHrHhrHrHjrH?HtH`ff.DUHATSL'Mu!HHHtHPL#Mu[A\]fI|$0Ht1I|$ID$H9tID$Hp=[L8A\]%=UHAWAVAUATSHXdH%(HE1HL586fIHHE)EL9HvH;5;cH 3DHn؃H5uH=C1HHt111H衚HYHzH=&葢3@HH9t7HXHHyH1HH9H;TuHHHHHIHH5 HuAIEHEsIEH6EHC Hs(H}HEM9I|$8foUAT$0Ht513H9gH8fAD$0Ht1H}HtHPHEdH+%(HX[A\A]A^A_]f.Ha<LmHUELHHEFH}H]HEH]BHM9HE9:HI|$8fHnfHnflHXHPHx@AD$0HHH IMu La7HBH=YH5HIHtH5HsADžxLE(H5H=A1HHt111HʗHHH=O躟\DL6fH >HNHxH5H81T5HMH=dHY2HH5cFH815H H= H2HH5&FH814H H=xH]ߞH}H߻YH}GHDHH9cHuH;<QfDH51H=?1KHHt111HUH H. H=E-DH11HH5;EH813HH=L4H]HEHu+H}HHPH}L@1H 9H5 H88 8H&lHFlHlUHAWAVAUATSHHhL-=0LvdH%(HE1HHEHHfHnHEfHnLmfl)EHIIkIMMH AL eH$;HHH5,H8AV1m2XZH H=r{yIIuL~ L}H~H})fDo^H)]N?HH}L}H54H9wHE,A=IHM9LhxL}xDLt9H}LuHELu;=IHL9MM4IHHEMt$ID$H@HH=3I$AD$L踙H{(fInfInflC Ht蘙1Mt ILPHEdH+%(He؉[A\A]A^A_]@H=H50LIHVI-7HEHPM4H}MjM(HFHHE=IM~H5LHV6H*HEIGDHY5L}HUDELHHE?H}LeHELeH}HtHPH}s;HZL91LeHEM 3IHߝH}MeLIEH>HIEyLH{(foec Ht1V:HL9tbf.E1E1D:H~HHD11PHUMLELwY^LH+HH5?H81.! H4H=蠘L}fHHxxHxU@ f9HH iAL ҉lH}1Ex9H/" DL.LuHEMH}HwHPH}@L8AD$L}aH*HoH5>H81H-$ fDL-LeHEMOE1LYfE1x1HfH'fH ff.UHATSL'Mu1LgHMtL)L^0L#Mu[A\]ÐI|$0HtAI|$ID$H9tID$Hp#0[L8A\]%0UHAWAVAUATSH8dH%(HE1HIH H=OHH#H@H= ߂ 5L;-)IIuLmLHPH}L}HELuMtL(LI/H}x:A?7IDs2MH;{(tyLkL{MtLS(L.L (H{AH;t+HEdH+%(HeH[A\A]A^A_]fDH+D9AH(H!H5 <H81*D1L'HH=z赔H MtL'L .1FfH2HE11L H H8H5$R1H[ *XZHy1H59H1DHH=18AH&HJH5:H81)D0Lm&HH=b蝓H H{*1A@H HHH=$_1L)L}MaL7A4ID/E1|H=y3111HH=ޒa9af.DUHAWAVAUATISHXdH%(HE1H^HEHEHEHHHHEHHHLqHEMH]H=IHH@H=D~HHHH;,$H@H}HP L}Lu2IH/HEF2HHELHEH&H}LmHEL}MtL#L8*H}g5E.2}Ib-ML;%j#I|$Ml$HtH};#H})H}"Ml$MI<$H LMMtL"L)MIVINHH9H=b(AFPAV~L؍tfDHE21HuADH-HH L AH H5H8AT1&%XZHH=X 12HEdH+%(HeH[A\A]A^A_]DLqL=M\1 HI9L;|uHMHHEH0I0H.HH]'f.I $DE1E11E1AHVDH= oMt I $HEME1+@H/%"f.L%fE1IM9lJtLzatXHEJfILIFPILPE1E1AH H'E1E1H54AH81D#E1AL9$L+$fDL}L#H} L1E.}I)nAH}VHH1MPHuHULEHjY^"X@j1AHHH53H81O"DN)AyH5H=,1IHt111H%LݭA &H}qE1>H[HR[Hf[H=[@UHATSH?HLgMt=IT$IL$HH9t:H=:$u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]@L`f.@UHAVAUATSH?Ht0LcfE1MIT$IL$HH9H=Z#AD$PAT$6Et MtLH[Ht;HSHKHH9H=#CPS[A\A]A^]*4$)LcfAIM(if.ID$I$LPI$LP8fDf.YfHHHCPHH[A\A]H@A^]H[A\A]A^]騇L蘇UfHHAWIAVAUIATSHHL%}dH%(HU1Ho)EHfHnHpLefHnHEflHU)EMJHIEMI`HHEIEHHH=Hg1HH9I;|uHHHEH5LIM2HELuHdfDIHVo.MuHU)mM~2HH1MPHHULELfZYHELuLeHMML5M1HI94M;tuHHHEHIAIMHItrItdM1H=艢H H=!E1HEdH+%(JHeL[A\A]A^A_]fLfLeLpHLuHHEH5 I;vH5I9t$tL-M9f)'L-M9M|$8At$IL$AT$$AoD$(MtH=*AGAo\$@AD$PhL9H{H@s(Lc(HK,S4L{HC8Ht藄ohC`HHcPOHHHZ$HfofIV H)0) HtH=#M@L}L0LH MLH}HEfLeEHH(Ht较H8Ht譃)&fInI~fl)!Mkfo)0MtH=WAD$H{t?*t%|DH{ fo0{HtH!IEMtLHH*ӂ fD@AGfDHHLYHHEIDE1II9KtHH7WHHtwHJ%o6Mu)u$ZD1@HI9\ItLLHVHLt,HH@1HL&fAD$!D1H?Lf)#HHH5)H81XHQH=]hE1HaHH5k)H81HH=(MtL諀 !H{ fo0IcHt膀MwLUjHH=ȁ[LOLeMHDžH}HSH}CL 6DHH4H=@KMLDHE"HA"Hf.HE"HHDžE1?[HYPHPHIPH4PHSPHwPHPHCPHPUfHHAWIAVAUIATSHHL%}dH%(HU1H)EHfHnHpLefHnHEflHU)EMJHI%MI`HHEIEHHH=HG1HH9tI;|uHHHEHLIMHELuHdfDIHVo.MuHU)mM~2HH1MPHHULEL]ZYHELuLeHMML5M1HI9M;tuHHHEHmIAIMHItrItdM1H=虙H@H=V1~E1HEdH+%(YHeL[A\A]A^A_]fLfLeLpHLuHHEH5I;vH5"I9t$tL-M9f) L-M9M|$8At$IL$AT$$AoD$(MtH= AGAo\$@AD$PhL9H{H@s(Lc(HK,S4L{HC8Ht{ohC`H HcP_HHH:Hfo IV H()0HtH=>8@L}L0LLLH}HEfLeEHH8Htz!fInI~fl)Mnfo)0MtH=AD$H{t r!t8H{ fo0{Ht6zHQIEMtLzH(HJz@fD@AGfDH!HL"QHHEIDE1II9KtHHgNHHtwHJEo6Mu)uDD1@HI9\ItLLHMHLt,HH@1HLFfAD$D1HoLf) &?H H8H5 H81HEH=yE1H HH5 H81HHAEH=}XyMtLw;H{ fo0IcHtwMwL腟jHFH=x[LLeMHDžH}HMwH}QL:DDHK HdHH={xMLvDHEHAHf.HEHH8HE1HDžHDžE1\HGHGH HHGHHHHHGHGHHHHf.fUHSHHH>uVHVHGHoNHFHOH]HFHGHV HFHF HWDH fHCH]f.UHAWAVAUIATISHdH%(HE1HhHEHEHEHHHHHLHHLyHEM>HEHP)HxH|HJHH~H H[HcAH9W@Af)0)@)P)`EEH5H=2IHIcuIHHI9FM~MMnILIEHufInfIn1Lfl)EHMtL˖LÖMAxHL詖H=Hu1HHEH]IHzHm111LlLWAx@HE*Hu#DMH=貏H3eH=1HtHEdH+%(HeH[A\A]A^A_]LyH M~1@HI94H;LuHHHEHMI~DHFHHEpf.fE1)0)@)P)`oIHIEH5LHHIHHLHIHIHHI_H;XH;H;H5AƅH qEL;-HIuLmDLHP8LpLLfopfoUH})fI~) )P)`H}HtpH}HtpH}AIDMfofo fH)P)`)0)@fo)pMtH=G iAGfo )efH~HtH= ^@LH}HHtoHxHtoHxHhHtoHXHtoHHHtoH8Hox@HH HcAH9uof)0)@)P)`.Lf.LrfP@HH HHcAH9HH5H8j HfA)0)@)P)`kD=HAwI(HɽDH=Wo1D`E1IM9JtHϺH CH tHJf.IcHGfD`A\@AGfo@) @Hx HHH$;H AHf.LfAHHH5H81oDn A|H7AxnLOSf.A{Lȩ AwH )HHHU1MPHHULEHLZYwfA~DIu LfAwDz:H%H MHuE1MHuAxH=H=H>fUfHAWAVAUATSHH8dH%(HE1)))) H;HHsHHH@ H9H HuH(H5)^LeHLLHL L{H}HEH9tHEHpHtv8LIAIWI}AEIEIEIwH Aoo(Am(IE0HH=e@H{HEH`LmLL}HHf1HLL}HDž %1H fooHstatus: HEHUfooHU@HHH HELEHhH}I0L9HUH`H9bHpH9HpH9BH11H HPH@HHpH9SHHIHyH1@t A@tD fE H A D@HLmHL踥LHfo H)fI~fI~)Htxhfo0H))HtQhH}&H}Ht8hH}Ht*hH}YA IDTMHfof))HtgHfof))HtgHMfo) MtH={AGfofofH~fHnfl)0HtH=@@HH8HHtgH(HtgHHHtfHHtfHHtfHHtfHEdH+%(bH8H[A\A]A^A_] HHKH5H81HkHH=Xg10@H?L)H9HLHHPH@HHpH9H@HPHPHPL@LHHH0H@@LPLGH:HLeLH AG~H@LH=111^^@TfDHHHLAHAsE1At AtD fD HA  E1AσL >L :D9rH< HA1AAȃN N D9rL H"EH=cWH|6I66H6Hh6I6f.UHSHHHHuZHH]DHHE1L ͰH ]H5MH8R1HXZH]1Hyt1H5HȆufUHATSH?HLgMt=IT$IL$HH9t:H=*u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]鼟@LPbf.@UfHAWAVAUATISHHXdH%(HE1HG)EHfHnHEfHnfl)EH)HHEH_HHt`MH=~HCH=Zc1HEdH+%(oHeH[A\A]A^A_]LsL=uM 1HI9$L;|uHMHHEHIFL{HEL-=M\1HL9L;luHMHHEH(LuI@HHLnH]LmH5vH;sL}HL:LuLefInfInHflH)EMtH=pAD$LuLLLPH}HEfLmEHEH}Ht[`eIMH=w轒~EfInHflHfH;H)EMtH=u AECCAEHxtHUHEH{ fHnfHnflCHt_HH]E1HPHHHH]MtL_MtLs_MtLf_MuLU_ho6Ls)uM[H]Lm/fDD|H{ fo}I{Ht^MCLɆ6@H7AfHHEHCHEIMfE1IM9JtLz3tHEJ>E1IM9JtL:3tHEJAD$D1H?HfME1E1E11۾H.H=wJ_HDHHEH!fDME1E1LLmMuFH}HEHt]H}La@E1E1[HEE1HH H5H81`E1HE HAfDHH}1MPHuHULEHKH}wf.Hgzf-TH{ foeIcHtXM,L血@E1IM9JtL-tHEJ#f.ID$I$LPI$LPfDBHHjH=ޞY@*HCH=ZYMIT$IL$HH9t@H=u&AD$PAT$TL@WGDID$I$LPI$LPfDL_LeMH}E1H5WH}L"DHYH=͝pXE1pHaHH5kH81HH=(XIHL襓IE1E1DfHH1MPHuHULEH5Y^@JIʼnE1E1.HQ)Hd)He)HO)H^)H})HN)Hx)H,)H$)H%)fDUHAWAVAUATISHXdH%(HE1H>HEHEHEHHHHEHH HLqHEM'H}1SHHXLmHIL?H} fL}LeE6Iʼn3LM/H=:lEfInfInHflHL5 fHL9)EMtH=AD$C1fDHEjHuADH)HHL MGAH ۛH5H8AT1^XZHU:H=lUE1HEdH+%("HeL[A\A]A^A_]@LqL=mM\1HI9LL;|uHMHHEH(I0AD$HxtuHEHUH{ fHnfHnflCHt=SHIHPHHHtxM&IT$IL$HH9H=AD$PAT$LvRHvH>H}wf.Hzfr-H{ foeIcHtrRM,LAz@E1IM9JtL"'tHEJ#f.ID$I$LPI$LPfDHH HH=!S@HMH=RMIT$IL$HH9t@H=hu&AD$PAT$LLP?DID$I$LPI$LP fDLLeMH}E1HPH}LDHPH=RE1pHHcH5 H81HQH=]QIHLEGIE1E1>fHHk1MPHuHULEH/Y^@"IʼnE1E1Ha#Ht#Hu#H_#Hn#H#H^#H#H<#H4#H5#fDUHAWAVIAUATISHhdH%(HE1HCHEHEHEHHHHpHwHHLyHEM9LeIFH5LHH?HHHCH5޲HHHsIHHHM HID$H5LHHDIMtLLHHIIMIH;H;UH;[HAŅH EWLmLL&L}H]fInfHnIflH0L;5C)EHtH=z|CAo^)]HEHtH=VH@L}HuLLH}HEfLuEHxH}HtMH}HtLIH!H=JdU~xfInIflHfH;H )EMtH=,AFAD$I$LxE1HPI$I$HLxMtLKLMtL>LMtL1LHt{H$LqfHE"HuADHHHͦL ?AH H5H8AT1XZH H=E1!MHEdH+%(HeL[A\A]A^A_]@LyH M\1 HI9TH;LuHpHHEH-ISDH&L&LeEfH DCHj5DH 2E1E1E1E11H%H=ALMpHDžxI$HI$HJL<Hf.L\fLCfAFHxtVHUHEI|$ fHnfHnflAD$HJfE1IM9JtHϺHxHxtHpJpfxHxfDE1E1E1E1f.HfHxHLxf.HH!jH=LHH%LE1i1H11-@H1iME1E1E1DzTI|$ fouIAt$HtwFMLFnIE1E1E11dHHH5H81h!XfDLHEHxHH}E1HEH}L˂fDME1E1 DM#H HkH5H81E1$HDžxE1E1E1E1E1a@I2ILfH;LPXIM ,H;Nu3LPXIMc땾H;JtH5 YLIH;,sH5XL~IdE1E1E11۾uIlIIII\IIIfUHATSH?HLgMt=IT$IL$HH9t:H=u AD$PAT$tVHHu>[A\]ÐDID$I$LPI$LPHfDH[A\]l@LCf.@UHHAWIAVIAUATSHHL%ٻL-dH%(HU1H\HDž`H fHnHHEfHnHUflLhLp)EMJ HIcMI~IOHHH`HH`M9H51WMAI9t$HD:Dž@H@HDž HLHHDž(HDž0f8wEVAD$L It$(LID$ H{{AD$@Ao\$HAod$Xf8)@)PL99HS8H{HfHsXo(oH) HDž0S8k@sPHtfH)*fo@foPfC`L{hkx}IHLc@HLc(LrHHH)L}HHLLKH}HEo}fE}LefֽEH}Hta@H}H}sH}Ht=@H}Zl0fInI~fl)IMfo)MtH=AD$H{tH{ foHcHt ?HCHHIEMtLj?H H~H0H)JiDI$HVo>IOHp)`H;H`LhLpHM9H5SAI9t$DH5SI9wM9|1HLcIOH=H1HH9I;|uHHH`H}HHHf.IIIM1H=ZH8H=<O?E1HEdH+%(HeL[A\A]A^A_]HHLHHJHhHHTLhMHHH`XL~LpL`LhAD$\D^tH{ foIsHt[A\]ÐDID$I$LPI$LPHfDH[A\]u@L@8f.@UHAVAUATSH?Ht~LcfE1MIT$IL$HH9H=:AD$PAT$6Et MtL_H[Ht;HSHKHH9H=CPS[A\A]A^]4LcfAIM(if.ID$I$LPI$LP8fDf.YfHHHCPHH[A\A]H@A^]H[A\A]A^]6Lx6UIHHAWAVIAUATISHL-HbdH%(HU1H$HEH`fHnH°LmfHnH fHnH]flHE)EfHnflLm)EMtrJ4I4H JcH@IT$H HLfDHH9 I;LuHHEHHJI tIzIMHHHHMHHHEL9H5FJAH9sD H5;JI9t$t M9E}fDIFLfLeHXH]@HIL$HEHLLHL9HEH5IMAH9sAHE Dž0@yH`HDž@H8:HDžHHDžPfXHHE CHs(0HC H8H@HHmD{@o[HocXfDX)`)pM9 IF8I~HfIvXoHo8H0)@HDžPAF8An@AvPHt H)fo`fopfE~`LA~hAnxUpHHI^@HHXx@L#Mt M9H[HuHDžE1HqGH)H9P L=)M IHHuHDžI9G/ ~1L)E HHHtHHHHJHIKHHHHH>Ht H @Mt I $!HHtHHHHHHHIV(1HH HHL9jHbHH_ 7IHLL}Iv(HHLH}D HEfH]EHH}Htx1H} kfHnۋI~fl)M fo) HtH= CI~t* I~ fo A^Ht0LIvL LHPLZH(IHt0MM9IH IEMHHHHHHtHF0H@HtHPH)*HEdH+%( HeL[A\A]A^A_]DoIL$)]HCHEHf.HPoIL$HU)MHHEH]HfDo@o IT$)e)EH~'HHxM1PHULELZYx~HEH]LeHHEHLMHELLLHu!f1H=~KH~&H=Mv00E11HLDD=fDIL}Iv(HLwH}ZHEfH]EHH}Ht/.H}z^"fHn㋽I~fl);Mfo) HtH=CI~tI~ fo AfHfI\$I$LHH'HPf.LfHfHLHjHHHL HEHHXH|*H=Ft).HHHHHqE11H@HH$DHMHLIHLfIM9KtHtLHHLLJLHHHHHLHLLHHHHHLtWHEHDHf.HfLfHwLHHHHHLfHLLHH HLHHHEHQHHXzH5H81Hy)H=q+HHHHHE1MHHyH5H818H5?LLI $u L(AAHyDH=9q+E14LLLH5I`tQ111L胀IxHLHy7H=QwH2H53IH%HHLHHxxsyHTx,H=pk*H7x0H=kpN*HHHHHE1"IGHHIWHLHHKLHuH=pv(1HExHťE11H$xHD蝥DXblI~ fo IAVHt'MLOC I~ fo IAVHt'MLdOCpHILXHE1HHL2dlHDžA8L/@A=LHE1HHLc HDžLc=LcyLHHLHHu4H=m'ZIDI_IBIjI`I=IZI9IHKf.@H=uGfGf.UfHHAWAVIAUATSHHdH%(HU1Ha)`H fHnH˜HDžpfHnHfHnHEfl)EfHnHflHM)ExMt'H HHw4H HcHfDH6 H$HIع1H=̣wAH^u# H=\l&1HEdH+%( HeH[A\A]A^A_]HaHLMnH`H> IEM~HL-M 1HL9M;luHHHhH HM~HHL%M 1fHL9M;duHHHpHm LIMHhHLxL`HL;=HH5,8I9wtH;H58H9st7 fH) )0_HH- H&7H /&H9HU L%&Mu I$HHIELhܾIHH!H5HLHLHIHI $H IML;5J Hf6H_%H9X{ L-F%M IEHHuE1I9E fIn1L)E HMt I $t H IM HH; H5H$H9X L%$MZ I$HHu1I9D$ fHn1L)EeIHtH5DM I $L;-c L;=VJL;5CH HH;(5 L;-( Aow HME I^ )@HHL` HtLLHELLHHIH@H{HPLHL0HLHXHt Ha8HHHt;B H LHQHH,JIH H; H5LBHH# H1H9G( LM, H_IH{BH1HHEL}yHMtLEBHA H)BHBI$ILHIHHHHHMt IMMt I $H8HtH(Hf.oFoM~)`)pMYHhLxL;=L`HHpHH@HH`IFHIH~HNL.M~HHpHHhL`MHIfDHV oFo.MnHU)`)pM)HhLxL;=L`HH]HpHIFo&HI)`-f.HaIHxHpL(HHpHHhL`sfDH^ H]LxL;= LxfI $DH }LMtoE1E1E1A HDžH HlDH=c1MtIu LHV4@I $E1E1E1A HDžf.HIHL*HHxI]fE1IM9lKtLzt[HJE1IM9TKtL:tCHJ.LnfHwOfLg] HfLײ%f1HkLϗ!\HHHH1IPHHULL`ZYRfHWufLGf1H}rH?#fʼHE1E1E1A HDž@fLfH=gHH5FIHODH=agdL@L fL;5{HHH;`L;-S+HHELIM HIIv HHS DHPLH!L0HLHXHt~H0:A HsE1L~fHDžp/H_A<HDžhH7AH5-LzMA E1E1HDžI $`LRH=eHH5`IHsHDžE1A H=leoMeM\I]I$LHM:IHu8LE1E1A H'=H5,HzQLA E1HDžH=dHH5IH7E1A 3HHgH5H81A LA E1I\$HMl$HLIE79MHuH=dVH5+LyMA E1H6H\fH5@H81A :A 5H5H+HxA A A HHu1E1HHu1jHHeH5H81PPA /H^HeH5hH81A bH#HIeH5-H81ګڲA 'HHH HfUHHAWAVIAUATSHHL%dH%(HU1HuHEHfHnHLefHnH8fHnLeflHE)EfHnflLe)EHJ4H I;H XJcHL{H -MLHI9T H;LuH HHEHILMmHuH H|H HEII& IItLMHpHHuL(LmH5'I9wtb LPLHXH5H]LH(HL1H`Hھ HHګH}HEH9tHEHpHX&fH -)0H9Ht H H HHHuHDž H9C ~ fIn1Hfl)EH IHtHHHH`HHMHHM9 HLLZHXHPH ?H M9L9` rfHnH M9 H )PHtH=۪ @Aoo )@HHHtH=s @H(Iu LH@I~H`H HpHYHEL`H Mt*I0Ht2I8LH]ouH`fHEhEH(G(HHHtHXHtHH`8wH`IHSIAIGIGHsHGo{(A(IG0HtH=` @L}H(LH0LHHXHtH(l'IH: Hb9HH& L9u HCH5"HH HIH HZI9D$ M|$M ID$IHI $H%LH(1LHEL}茆H(Mt II$A HH(JI$HoH(HHHHBHIEIHIEH H8Ht MtL H HtH H`HpHtKHSHKHH9H=kCPSH`HHEdH+%(i HeL[A\A]A^A_]fDIHvHHuLxM9L}@HL{LHEMLLmMfo@o0L{)u)EM~2HH1MPH HULEHZYHEL}LmHHEM9Ho8L{)}MHEHNHPo(L{HU)mML}HELLmM9HfDLMLDH}H(H@HEHu DM1H=b (H[ H=S E1DohHpHEeHH=@fDHDž IA 1I $E1DHg[DH=R E1MtIMhHfHע fL9H7Aǃ fHnH M9 H )PHtH.H(Iu LDIPH`HHpH HEL`HMt;I0Ht IIGH9tIGHp8L{H]o}H`fHEhEH(!mH`fLfHwfMIM9|JtHϺH(H(tZH Jpf^fHfLfH4H|H HHtTHEI^f.Lf1HYL蟅fLwJHHjH HJHHEI:DHHHCPHHPH`ff.HDTH=UH H5 NHHyE1E1A HDž fD@HCH HXHKHHHH#*HH('H=TiL@H5LiMHDž LA E1H E11L 1A HDž fL fH @f@HEIHeL9H(H2DHGYfH}^@L}xE11A H5yH=21+HHt111HH(E11A DH5)HyhA nfDHufE1A bfZHHUH5H81@@H E11A E1Hu1`fHXhHu1CDHEIHPL9H5SH=1*HHt111HHo'E11A IHE11A `ZHHTH5H81@@H E11A ݦH.rɟHHHdHHHHHHHf.UfHAWAVAUATSHxHxH H}HpHUdH%(HE1HHH9HHlH#HHCH5%eHHHIHHMHHHSHu1I9EfHn1LE)EzIHt H MIMHH H9H[L%M+I$ID$H5cdLHHHI$HHI$H6HHuE1H9C fIn1Hx)EyIMt I $M H IFH5eLHHIH?IEH5deLHHHH3HLIIHMpIHH L;%L;%:oL;%@bLÅWI $RHHH9PHHHH5;^Ls#IHHI9D$ID$HEH M|$HII $^MHuHE1LHEHEHxH}It H}#MI $H5]L"HxHHxHeH9PHHHhHL`HHI$"HuHh1LHEHEwHhHEt Hhd"H}I $H؟HuHDžxH9CHMfInH~xHMfl1)E#wHxIt Hx!IHMHHxHHgMH!Hpo)UL}M LH}HuLRaHELH]HENXHLW!HEHUHHHxIIM=HtHHEdH+%(HHEHx[A\A]A^A_]f.L'ofHfLfHfHfLוfI $LDDLxIAEH tnMtI $tSHxHHHpHHuHKH=DG1L'fLDHDIHDžxEH_fLהH[E1CfDHuHEKH=VD1f.H'fLwfH=1OHzH5{HHHJH=CBDH=Nf.DEE1E1HDžx`I]HMeHLI$MHuM1E1AI $t4H%JDH=3CMtIt$MfDLWDLGDH=NH:H5;IHHIH=B1E1HpHM1HHrHyHSH}HuLP]HEH]HEL#f.L@fE1AfZDH=!M)HDžxEL'fLcMLkI$HIELHufDHDžxE1EKfDA1!Lf.HfLafHHH=Ax1IHDžxEeuHGH=@?1 vDH=@HH5vHHH{GH=@1RH=@|f.HEHu1DHu1 DLxHu1HDžhLxHu1{fDHCHxHLcHHI$LHuAkWRMHC>94/*% 9I }xsnid_ITRMHC>94/fUfHHAWAVIAUIATSH8HdH%(HU1Hl)`HPfHnH]HxfHnHxfHnHEflfHnH)EfHnflHu)E)pMt&J HIH zJcHI 0IILHIHHHH9HpHpL HHhL`HH5 H9ptB I9H5 I9vtO fL))))1HHw H H5H9p L=M IIH I$L`IHCHSH5hH|$LLLHHIHPHIHIMI $H9w H8H5H9p L-M IEH|HuE1I9E fIn1L)EkIMtLM IMQ I9 L0HLLgLLLeH8Ht4>IH HL3H9F hHH9 I9 foH) HtHIO0Lh0Hox )HHtHHH@HLHHL LHj݆H@XZHE HEoHfHmHHLLLLqH8HtH4 H@uHPHtH@tH.HHtH(Ht~ LLL߭HzLIH' H9O H5~gL&HHU HÑH9G LwM LgII$ H1LHELu iHMtLH LHHIEHHHHIL@ILI&HH HHMLpLxIHIIMGME1E1HDžI $LfHIHkDHUHLM~H`HWIGIVHH (fHE1fID$H9IK;LuHJHhHLIMHpHHL`H9HLxDo@o M~)`)pMDHpHL`LxH9HHhHRHH`IFHHHP o@o0M~HU)`)pM~8HHEMLH CmHL`HQ1Y^HpL`LxH9HHhHHEHHpL M~HHhL`MHIHHPo(M~Hp)`MHUHLHHxIjIHu@M1H=*l H=H=B5mE1HEdH+%(HeL[A\A]A^A_]DL'MHe=H=4E1fDLif.LׄffLDŽLfL2fE1E1H<H=u4Mt I $1Mu\Mt IMHHtHHtHHtHH1IEL 7H `HLHHpIfE1IGM9IKtHϺH/HtHJfDLof.LWfLGfH1H7<;hfD1HUCLhHvHQMHLHoHEIDbHzH:H=v2@Lf.H=!8HH5ֺIHF'DH@H=7f"HH9I9foH) HtHH@IW0IHHL Hp0H@HEHEoHfHuHHL}LLLkH8HtH.H@SHPHtH@4H(Ht fDH|H9H=0LE1H=X6[IH\H8H=\0LE1BHq|HKH5{H81((VfDH6fDHDžhHA^H5IHJnLE1HDžIMt E1WDLDH=A5HH5KHz{HJH5H811~1_H[ME1MeMM}I$LI MHufIL~H5*LzIME1IHu1TH6H=a.H6H=D.oHH5HHrHl6H=-LvHEHHHZHa"HEHHHy/FH%H5HOH锼H餼f.@UfHHAWAVIAUIATSH8H@ydH%(HU1Hj[)`HPfHnH]HxfHnHxfHnHEflfHnH)EfHnflHu)E)pMt&J HIH "pJcHI 0IILHIHHHH9HpHpL HHhL`HH5H9ptB I9H5I9vtO fL))))? HHw HH5!H9p L=M IwIH I$L`OIHCHAH5eWH v$LLL HHIHPHIHIMI $H9w HH5AH9p L-(M IEH HuE1I9E fIn1L)ElZIMtL<M IMQ I9 L0HLLzLLLH8Ht΄IH HLèH9F HH9 I9 foH) HtsHIO0Lh0Hox )HHtH9HH@HLHHL LHjH@XZHE HEoHfHmHHLLLLH8HtpHH@uHPHtIH@tH3HHtH(HtY LLLoH tLJ IH' H9O H5VLHHU HSH9G LwM LgII$H1LHELuWHMtLjH LYHQHIEHHHHIL@ILI&HH HHMLpLxIHIIMGME1E1v HDžI $LfHIHkDHADHLM~薵H`HWIGIVHH THE1fID$H9IK;LuHJHhHLIMHpHHL`H9HLxDo@o M~)`)pMDHpHL`LxH9HHhHRHH`IFHHHP o@o0M~HU)`)pM~8HHEMLH [HL`HQ1:Y^HpL`LxH9HHhHHEHHpL M~HHhL`MHIHHPo(M~Hp)`MH&DHLGHHxIj~Hu@M1H=ZeHL,[ H=#E1HEdH+%(HeL[A\A]A^A_]DLsMH+v H=#E1fDLsif.LgsffLWsLfLGs2fE1E1v H|+H=-#0Mt I $1Mu\Mt IMHHtHHt{HHtjHHU1IELr7HNHLzHHpIfE1IGM9IKtHϺH迮HtHJfDLqf.LqfLqfH1H*VfD1H1LV4|HvH;HLbHoHEID{HzH)t H=.!1@Lqf.H=&HZH5[fIHF'DH@H=q&tfxHH9I9foH) Ht-HH@IW0IHHL Hp0]~H@HEHEoHfHuHHL |LLLH8HtjHH@SHPHt?H@4H(Ht k fDH#kH'H=SVLE1H=$IH\Hf'w H=LE1|HkH4:H5 H81mt VfDHn6fDHDžh?yHA^H5H%9nLE1v HDžIMt E1WDL'nDH=#HjH5k膦{H jH=9H5~H81ls _qxH[Mw E1MeMM}I$LICMHufIL^mH5L 8Mw E1IHu1THk%y H=HN% H=wHH57H7 rH$ H=Lv } HEHHH:pZHHEHHH p/oHgHwH鑫H֫Hf.@UfHHAWAVIAUATSHH8dH%(HU1HB)`HxfHnHHDžpfHnHfHnHflfHnHE)EfHnHxfl)EfHnH Ogfl)E?gHMxMH HHH ^HcHfDHV(HUHP HUo@o Mn)`)pHHx^HcHMnH=8HL薩H`HEIHAHLmHhH= MfIL=HM 1fHL9M;|uHHHpH IMHxH;fL`LhHHpLmHHEHHH5=H9ptL;-eH5I9utX fL)))) HH@ H)HH9Xh L5yM IdHH I$L`lIH$H%/H5DHcLHLHHINH 5I $HH;dL H`H H9H} HHY HcIH ILxlIH H].H5.DHb LLHHH HLLHH;c HHH9X L5MG IHoHuE1I9F fIn1L)EAGIMtLM LL;=Ac L0HLLgHLHȕH8HtqIH HH薕HH;bnHHH;b+ HH;b L;=b foH) Ht HMG0Lh0HHX0Hox )HHtLۧLH@LLHHHH HjPmH@Y^HE oHfLmHuLLqHLH讉H8HtLuH@ HPHtH@ HHtH(Ht LHL'H`LIH> H;`? H5BLjHHI HmH9G LwM H_IHQL1HHELuSDIMtL#M HL HI$MHHHHfDHdfDH~tHHH _HIHXH;_HHxHpLxL HHpLhL`HuRH~(HH}Lh LmfHa_HIHmHu!fDIع1H=IuH\ H=2 E1HEdH+%( HeL[A\A]A^A_]DHNL~L&MnHHpLhL`MPH^HIHHMnH`Do.Mn)`MHY>HL:H^HxIMH1HLHuHEIMHX,HL٠HHEIMHHeHI1PHLmLL`L¨AXAYlLwafHgafLWaf1HHfHL9ItL貝tHHHHfDH1HE4fDHDžE1A8 HDžIXE1HtHDžH LHMt I $Ht H HDH=bHE1HtHHHH HtHHHHHMtIt\MtItbHHtHHtpHHt_HHJDL_DL_DHw_lfLg_fHW_fLG_fH7_f1HmL/DfiHE1E1A6 HDžHDžrf.H=H H5 6IHHDžE1A8 HDžfDH5ZHDE1E1A8 HDžHDžH=yMhHAH=躕HHE1E1A9 HDžjeHHH;YHH;YL;=YfoH) Ht0HH@HIO0HLL HP0HHp0jkH@XZHEoHfLmH}LLiHLHH8HtlLH@HPHtEH@sH(Ht&q`AE H.XHDžpfH1AH5H&HE1E1A8 HDžHDžH=HH5X}fHHDžE1A9 5MA9 E1HDž 4fHaPiHWH&H5kH816Z6aAB eHlHMLE1HDžE1A9 bH5XH%1LA9 E1HE11H=jHH5IHE1A: MfMI^I$LH%IHuH=LA: E1HDžE1H5L$MA: E1 H;M? H5/L:HHI HYH9G LwM H_IH!L1HHELu#1IMtLM HLHI$MHHHHfDHPfDH~tHHH LHIHXH;LHHxHpLxL HHpLhL`HuRH~(HH}Lh LmfH1LHIHZHu!fDIع1H=6EH, H=*ݸE1HEdH+%( HeL[A\A]A^A_]DHNL~L&MnHHpLhL`MPHqKHIHHMnH`Do.Mn)`MH)+HL H^HxIMHHL؍HuHEIMH(HL詍HHEIMHH55I1PHLmLL`L蒕AXAYlLGNfH7NfL'Nf1HHfHL9ItL肊tHHHHfDH1H24fDHDžE1AS HDžIXE1HtHDžH LHMt I $Ht H HDH=2HE1HtHHHH HtHHHHHMtIt\MtItbHHtQHHt@HHt/HHDLgLDLWLDHGLlfL7LfH'LfLLfHLf1H= L0fVHE1E1AQ HDžHDžrf.H=QHH5IHHDžE1AS HDžfDH5QGHDE1E1AS HDžHDžH=ĂyMUHAH=节HHE1E1AT HDžjRHHH;FHH;{FL;=nFfoH) HtHH@HIO0HLL HP0HHp0jFIH@XZHEoHfLmH}LLUHLHmH8HtELKAŃH H(1HDž(D,IH* H aHʽL;5=H@H9R H=Hl H6=HHf I$L`pEIH IFH5aLHHIH H5aHL<IM# LLHLIHHLLL;=<HƼH=OH9xlL-6MIE><IHHHID$uDHHH5LHH H5sHH; HHLLHH LLHHH;;LPLL/RH@LHmaHXHt\fJHHL0HL4aGL;=;HHH;t;HH}IO LHHLL@ uAH}xfLeH]EH:EIHH HLpHxL`Lh(HMH`LhHpLxHLmH>fHDžE1DžHDžH.HDžHDžH HHHtHHHHMt I $Mt IMH/H=bMtE1ITMHHtHHHHHHtHHHHIHHtHHHHtHtHH8HtѤHEdH+%(HeL[A\A]A^A_]@o8L{)`LHPo8L{Hp)`LfDHL{H`yDE1LHMMI@IFM9IJtLxtMHLJVf.H; HHH HHH[DHuE1H9Xd fInfInH1fl)EIMtLMHpL;=7 H|H=H9x H̤HH HHHuE1H9X fInH1)EHMtLH HHH;7 BL;=6IWHH;6CHH}IW LHHHH FH} fLeH]EH}HtH}t HKL 6LPfInfHnfl)PHtH{L)HXHHt衡HH* H;5d H5HqIH =HIH H1H5HU4H5~LL[IHL'HLHHDžHIifDE1LHMMI@IM9JtLutMHLJfDE1LHMMI@IFM9IJtLutMHLJ CHuMH=虼HH=E1.fE1LHMMI@IFM9IJtL^ttMHLnJf.L7f7DHw7fLg7fLW7dfHG76fH77fL'7%fAHL)5@AHDžI1AHDžI1E1HDžE1E1HDžHDžHDžYaDH2HH5FH81G5G<L2HDžE1E11DžHDžHDžfDL6fLL+LM[DžE1HLE1fDHDžR:@HHA"Dž1HDžHDžHH1MPHUL`HT|Y^?DH=HzH5{vmHH1DžE1E1E1HDžHDžHDžHER?H`A:fDHDžDžHDžHDžH=lIDžE1E1E1HDžHDžf.HE>HADžE1E1HDžHDžVHDžxO>H]A7HDžE1HDžDžM+HDžp=HAH=HuH5vkHH1E1E1E1HDžHDžDž5H1E1E1HDžHHDžDžLhHMLxIEI1LHugH= j:HDžE1HDžDžH5LiLE1HDžE1DžE1HDžHDžHDž%H=vHH5+jHH%HDž1E1E1HDžDžH=HÚH5ĚiIHHDž1E1HDžHDžDžwH5oL DžM1%HDž1HDžHDžDžH=behQL`HMH81L-L4H*HDžE1E11DžDžI1XDž1E1E1HDžHDžUH5MH1HDžHHM-LeMu]H}1HtE,H}gHV2)HE1E11Dž&7E1*H1E10znnnnInIn_n~nInInIoInoIoLn+oInnIoInf.fUHHAWAVIAUATSHHHL-m(dH%(HU1HLhHfHnHHfHnfHnH(HDž`flHE)EfHnflHMLpLxLm)EHt*J4HIH JcHDIH JcHfDL{H MBLHI9D H;LuHHH`HIHMLLLo@oL{)`)pM H`LhLHHpM9HHxHH5I9vtfIHDž0) -+HHHHSHHHH=IHHMHHH5LlIH H86I $y%IH HHID$HIMt$ HID$(-HH HH57HV$HH5H7$HLLAHH I $H HH8"HH@H9t H;" HH HH~HH9w HF(Lf HHHHHI$HHHHHvM9 IVHؾ2 UM9 H&HH Ao|$ I|$(fHDžP)@)HtiLL@HHLL2L tHHHt腏HHttLM٣HIIHHtHHHHHHtHHHHIL(H I9t{I#AD$PAT$t=HI9tELcMtID$IT$L9H=(tuLH%I9uH HtH0HH)a)HEdH+%( HeL[A\A]A^A_]@HP o@o0L{HU)`)pM~5HH 1MPHHUHL`umY^2H`LhHHpM9HHxHHEHF@HL{HH`MLMLLDo L{)`MHHHdHCHpIMzHHHPdHHxICHPo(L{Hp)`LMLLHHH`3LLHxHHpLpM9LhLLLLHHHHx@ID$I$LHPHLP@H?$HDž`.HuM1H=iHY| H=E1(fL#fH#fL#KfH#2fL#fH#fDž HDžHDžH HMHDžHDžHDžI $HtHHHHHHtH tKHHtHHHHt=HH=qċ1M@H"D"Lw"VfHg" fHW"]fHHH:aHHhIfMIM9JtHϺH^HtHJf1HLfHDžE1HDžDž HDžE1HDžDž HDžHDžP+H-HWHH `HHEIHDž1HDžHDžHDžDž HDžHDžDž Hp HHuHDžHDžHDžHDžDž 4HQ LC L5 * H5vH貪HIH$|HHHHHHC#IHH5LHn$LHLxHH H@H4L,M*HGHDžDž )HwHDž 11HHHHHH5/H81MDž H5LjHHDž HDžHHHHL`HHHsDž Mo(HHHHƨLID$LHHHHLHLIHHHHHAHHHǾhDž  H!HHH'HDž MYHDž1HDžHDžDž Dž M1Dž MHMHDžDž HH5/H81oaHMMDž HDž5AH D]t/Hu:HDžDž AE1E1LDž 1HHH^HR^H[^f.UHHAWAVAUATISHHHL-dH%(HU1HLhHfHnHHDž`fHnHfHnH˜flfHnHE)EfHnHflHE)EfHnflLpLxLm)EHNqI7KI KIKUfHAWAVAUATISHHdH%(HU1H)EHpfHn)EH8 fHnHfHnHEfl)EfHnfl)EHJLs)})E뎐DžmE1I $HHHHHlMt INH=H \HDžE11ۋH$H=#fMtE1IMMMt IHHtHHHHHHHt]dH8HtLdHtH?dMtL2dHxHt!dHhHtdHEdH+%(HeL[A\A]A^A_]E1IM9DJtL8t0HJHEHuMH=j7HճXH=E1dGE1IM9|JtL8thHJE1IM9JtL7tHJFL'2fHDfLfLfHfHfMIM9JtL:7tHJ>H5HHH H5HIH0HH5HۄHHAHxI9G?MgMIGI$LHH跄HufInfHnH1fl)EIMtL~HvMHaLÅLGHH9HH@HDžE1E11Džb fDDžmE1I $u L1DHDžE1DHEHAAD$ I|$ HEO ;HP0H5vHRDžvLE1NLHun@HH1MPHHULEH`?Y^,JHEH2A DžvlH=0H9gH5:gU0IHHDžE11Džm~f.HEBHAH=2/ZuL\DžgE1E1HDž HE11DžgDžgE1H=HDfH5Efp/HHQHDžE1DžvDžm13DžvbH=[.DžgLE11'LHuHDžDžgE1E1&LHuDžvDžvH5H= 1HH111HVH蟀HDžE1DžhH5sLsDžkE1E11HDž]DžxE1KDžy1:HDžE1DžpHoDž|E11H1DžpDždE1E1HDžDžpLHuE1DžpL1XDžp[H5yH=1kHH111HqUH)HDžDžqH5qH1E11DžtHDžhE1E1HDžDžq1E1H67|7w7r7m7h7c7^7Y7T7O7J7E7@7;76717,7'7H)7777 777666666666666666666666666I5|6w6r6m6h6c6^6Y6T6O6J6E6@6;66616,6'6"66666fUHHAWAVAUIATSHdH%(HU1H0HDž`HfHnHHEfHnH0fHnfl)EfHnHflHhHpHx)EHJ4I IH gJcHfDMt$H,Mm LfDHI9 I;TuHH`H; IM H>LH80H8H HhI IV I I H ILxLhL0L`HEELH5HEHH HEI$H赺 <I9 H=mH5HGHH IH IFH5xLHHH8H8H HMH9GLwM^LoIIEHH0HH L8HuH81HELu{IMt I M H8HH0HH HI9G MwM IGIHIH8D L8HufInfIn1Lfl)EH8Mt I IMH8 Ij H9 H5vH8JyIHHI9EMuMPM}ILI.yHufIn1L )E+IMtLxMLxH8xL8I9 H5H8xIH H8I9Eq MuM M}ILIxHufInfIn1Lfl)EzHMtLJxH L9xH8-xHIE1E1HDžHDž0HPHHHLMtIEHIEHH0HtHH8HHI $H HH8HHH}HH9tHEHpUMtLHTHHtH4THEdH+%(HeH[A\A]A^A_]IfHvH HxL`Lpj@H5)hH=ri1FIH HYH@1LH@H0HI9OLI$I$HL HHDžHƅLL H8He IHHI H8HP藾HPH HXHIH^ H90 H0H@0P(uHLeZX I9 HfH^H9X H^HP HHHuH9Cy fInfIn1HflLe)EnIMtL>uM HLe)uH]LHH胅LH興HIHLtHi `w~ HHfHnfl)Ht H+H0ox ) f֥fH~HtH\+HuHULuHLuH譈HFHS(HMHCHCHHSHHEL9p HCHEHC(fo HK foLuHEECHc8sPqHJVI}(fHnfHnflHXHPHAHHAE Ht[PHIE H0I}8IE0HSsIEH8I]8LLHPH IU=fDHPo0Mt$Hp)`MH L`LhLp^o@o8Mt$)`)pM~.HH1MPHUL`L/Y^HxL`LhLpH HMt$H`MSH L`IIo0Mt$)`M,H L`ILhufH IIWfDHDž`L8HuM1H=}=kH H=J1O*fDITH8LDž H 1E1HH=wOH)HE1HHHH@L/:f.LfHfHfLfHnfMH MIL8IM9KtL""tMH MML8kJLWfLGfH8H8HFHLH8 $H8HHpIfDLf.HDžE11E1Dž HDž0HDž8HMH8HHxH8H8H8HjHALH8*#H8HHxIHDžE11E1HDž0Dž M:I1L#LfLfH H@HI$HHHH cfDLO_f.HDžE11HDž8Dž yH5gH=81pIH111HCLmH8LDž H 6H5H=1pH8HH111HCH:mHDžE1Dž HDž0f:DHDžE11E1HDž0HDž8Dž kDHu1E1-fH8E1HDžHDž0HDž8Dž fDHuE1sHu1DHuNf.HDžE11HDž0HDž8Dž LE11HDžDž hHDžE11HDž0Dž >H8L1HDžLHDž8H Dž Dž E1E1HDžHDž0MHuE1HjHRLeH5pH81H8LDž H HDžE11HDž0Dž JMHu Hq@}AC(@lAD6fD2Z@MHuE1HDžE11HDž0Dž rH8H8H>SMHuH0HLeH56H81H8LDž H RH=XHRH5RLe9HHE1H8LHDž8Dž H H=Le+H8LI1HDž8H Dž UL{MzHCIHLeHHhHHuHHE113MD>LD:1AN N 9rpH8LE11HDž8H Dž HDžE11E1HDž0Dž H8LE11HDž8H Dž xAC(AD6D2H H+!H HV!H!H3!H!IK!H!UHAWAVAUATSHG H}<n<<oHEHHL HML9u9fDI<$It$HtH)fI(L9eAD$ E11ofBL9 Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tlL1H)Hx1HHoHHH9rI)LfHnfHnflMu"MuAEH([A\A]A^A_]f.Iu)ELL)foEDIHULLEMLEHUHIHpfDL93eHH9HGHIH=s3UHAWAVAUIATSHhdH%(HE1HHF@HIH9|1IEHEdH+%(HhL[A\A]A^A_]@H~^HV H}HrHVH)HPH}HHHC H@HP Ht"HHH)ʀx t xtHxH1UHCH HSI9IMLeLHC@HL}MLuHEHELuLLuMtUHDHELpHPHHH=H@HHE>HEx C0t xtLxL{8Hs H;s(fHnHEFHs H}u$H}HnHPH}]fDL0{J@H}LuHtHx >LuHxHHE=MFILP8DH]HH}JH5#1=LeHLHtLL+H}HEH9HEHplHUH{:H}H@=I}VLL+H}uH}H0HPH}LyHHH4HIHELHH.HuHE1HHELL`HHHDž%Hfo>LHstatus: HEHUfo>@HpHHUHHEHLjLHHH@@AUATTHH8LLtHf.fUHAWAVAUIATISHxdH%(HE1HۭHDžxHEHEHHHH`HLHHLyHxMLxHHEHEHhHEEI9tH6KH 7H9HL%7MI$ID$H5LHHII$HMI$HIM9I9rMeI$Ml$pLPHI9&I$I$ffo <ID$PI$HPHHHIH?H=HHIT$0H)H ( A~D$`A~L$PAD$hAL$XffAL$PAD$`HH}HhH9HEHpr@HDžxHu>fHYHHOL }(AH }H5H8AT1XZHcH=~16HEdH+%(HeH[A\A]A^A_]DLyH MM_1HI94H;LuH`HHxH%Ivf.IFHu CI9HHH 5H9PL%4MI$HHuE1I9D$fInfIn1Lfl)ETIMt II$HMI$HLeLLggH}LkjH}HEH9tHEHpHIHIHHLLpIuHMHULHpL[pfDHL6LxMLpLI9LIMHI$PHpLOcf.L7BH)rHsH=$|41E1IM9JtHϺHhcHhtH`JfL\fLfH= H2H52IH9oKH%H=]1/HEdH+%( HeH[A\A]A^A_]M}M;ƅL-mADžL5EH$4 4HDžHPHH HEƅDžƅHDž(HDž0HDž8HDžHHDžPHHXHDž`HDžhHDžpHEHE@xu&LLuƅiL;5kL;-L;-"L;-(rLLj2ƅuLL:ANjLcHH|/H=)-1HNHHufDoM})]M~.HHc1MPHULELL[ ZYCLuLmL;5jAL;5D L;5 ЈxfDƅE1D LEH;IoS HC()HtH=1C@LLHHLLLHwHHt*LHH;lL;-mL;-HL;-;LJ:IILHPIIHHXHXHH`H9t H-H H HH(H9HH 1DHI9I9LuIHHEIMLuL;5]AL;5L-D L;5 Јdf.LnLmL6Lu@HCH5UHHHIHH@H5LHHIIEHMIEH<L;5L;5ulL;5tcLjAIHPEyRIHu LA:H+yDH=e)HI1HfDIDHIEH5}|H%KHHDHH9GFLMPLoIIE KHu1LHEL} IMtLJMLJHJ'fDL,f.HM}HEHx&H=O(1HH;Hs2HfHaLL^HHEIf@L-l@H5yH=B1LLHHt111HLHIH&w'H=^'1HɺHvH5H81LLHHv3H=x'1E1IM9KtHϺLHHLtKIHu1DLf.L׽5fL)HtLTbHHtC% _DzD*H"@A:IMtLOff.*A<FA<IHu1E1H2H<HEUHAWAVAUATISHHL5dH%(HE1HHEHELuHPIL1H:ATHʶXZH0nH=iE1 HEdH+%(HeL[A\A]A^A_]HIH/L;5ALA2fDH>H}@H=1DHH9I9|uIHHEH@@%Lf.HHHHHu HڶHHHHHH m H=gHE1HHHHfHwH;HdHlH=}gLIE1H'efH5H=B1LCHHt111HLH@H/lH=gLfDbHHH1LPHUMLELY^ fDJHyHrmH5H8100HLHwkH=OfLfDLfHfHYH5 H8@HfHfE1IL9KtHLHHLHtKH7fH=HH5IHHDH=yfګDHLγ fLHtL@XHHt/BҺDºDHH@HHHWHHH=HuHjHtH}HHwH0H9tHH%sf.DUHAWAVAUATSHHH(LwL'LL)HH9M9HѺIHEIHHEM)H*HHEHEH1HQIIGHLIYQL9HUID$LCH HHJH HzH I9tEHrHxH2HHH9uHOdt8@>HxfHEHL)HH L9HCMFH$fH2H0HrH LJH I9tUHpHzLHH:H9uIq@RtDD@tDT0fDT7fI)L~EfHnflMtIu)ELL)foEHEH]AEHHI]H([A\A]A^A_]AN\N\"E1EAN<N<A9rfLL8LL>1ANN9rHx3fHHuHMHMHEHM\8ɉ>||HxDDDT0DT7[|f|HxHH}H9HFHEHHuH=z HH}uLɴ۷HuH}HH>HݻDUHHSHHHH;{t'HGHHBH6HH>NHC H]HHH]%f.DUfHAWAVAUATSHHxH1HHdH%(HE1HEE)) fHDžHDž0HDžHDžHHEHE)IH H;. H)H#H9P L=#M ILHL~Hà ICHDž`HHH5)H=0/1QHH HHH1H=)H5gHCH5~L #sLpw=H_AXH H HL+HH/ HHHHHH1HH IE1E1LHDžHDžI;EIUHHHHHCH;Ht H;y;H{H]H;^LcL{ I$IH HDžHHtHHHHMt IHL%LHMI;E)L6HHDžHtHc6MtLV6HHDžHHHE1HHHHHpHEHHDžHhLHAHpHH HHHHtH5HHDžHxMtLa5Hz&H# HDžH9PNL5MILHI9FLHh1fHn1L)EHHHtH4HxL4HHDž4HHHDž=Å>'HHHHHLHHCHxHHC u8H4HDžLxDH/f.HfLfbHaHDžLMHDžHDž%@HDžHDžHDžMtLR3MLA3E1E1E11DžHDžHtHHHHHHHt HHHt HHHtHHHHHHtHHHHtHHtHHHH H]H=&YDžMt IMHHtHHHHHt H xHHtHHHHFMt I(Mt I $ Mt IH}HH9tHEHp}HHtlH `aHTaHEdH+%(He[A\A]A^A_]@HDžE1E11DžfDM|$M$$ fH51nH0HHHjHsH9GH_HLgHI$L0Hu1LHEH]超HHHtH{0HLe0HDžHDžf.LwHDžHO~f.H7Df*DDLfLfLfHפfHǤzfH\fL*fHfHDfDHDžHWifHGLMLHDžHDžkH*I]Hk HH: 1HwHUIEE1E1HDžhHHhH9IEHHHCHHH9t H;FH{HWHKHxH9HC HpHxHHHHpH)HDžMtL)HDžMtL)HHdH9PHKHHL%L9`DH1HfHnH1xH)E[~HHHtH )HHDžH(LHLHR9H}HVcE1Mt IMMtLH}HH9tHEHpLhH`I9t-H;HCH9tHCHpH I9uH`HtHpHH)^H8HI@HMHHt~Hȃs3tZ tILfLHuH}=fHLHLGr1ǃL:L>9rHuH}HHHMHEHDHL6LxpH=afHH5FHHHIu LA0 1H9MlHHIEH(@H=e,f.L{MLcII$H LHufDLf.HfE1L(MIfDIM9JtLZtLML(KaH]EHMHEsLA0 H@Hu LLHuH}*HHEILH mLxHHQ1LY^HLIHt|H@HH HtsHDž(AH݄H:\H5H81DE1L_IA3 LHE1A/ E1ItXA/ HGnI݅Df.ItI $L(w͋LL H"fHcfUHAWAVAUATSHHdH%(HE1HSHDžxHEHEHHLH8H)@HMHHt~H,ȃs3XtZ tILfLHuH}=fHLHLGr1ǃL:L>9rHuH}HHHMHE8DHL.Lx`H=A\HH5&HH8IMA;L@H9}MtHHIH(DH=[fL{M(LcII$H LHufDIMI $t8ADLf.HsfLDE1L(MIfDIM9JtL tLML(iK1HQ-LC@EHMHE5LAHHu LLHuH}HHEILH cLxHHQ1Y^G L_}IHH@HH HHDž(JAHxzHPH5H81/}D.E1LyIAL}f)P|E1AE1IMt AHcIޅL}sHMH[f.@UHAWAVAUIATSHHdH%(HE1H0<HDžpHfHnHZyHEfHnflHx)EHL9rnL(~HQ#LtEHMHE8LsLrIHH@HHHHDž(HDžp@~HwHuA^OLesAZH7)LyH8AA]A\ LLHuH}MHl)YH=W>E1IMt"AYLfLHuH}LrHXIޅL:]?vE1wH逺齺H麺fHUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=s@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)sfoEDIHULLE]sLEHUHIHpfD@L9L9+]HH9HGHIH=slUHAWAVAUATISHdH%(HE1H@HEH8fHnH-HEfHnflHPHE)EH/HHHXHHHLqHEIMd LefID$H;LiHE)Ef)`t H;hp I$E1HDžXE1HXID$H=hI9|$I9ID$JIHH;EkMt IAHrHCH9tM.1 HI9L;|uHXHHEHI DLhf.H`H}_f.LvL @HuH5H3DžXg IL@IELIEPIELPIELIEPIELPHL&Lef.I9HWH`HH; H=iVH/IHo HMfIEH;EHEHE)E)Et H;E} IEMHDžXHDž`1HX"IFH EH`I9NH9<IFL$HH`I$Ht H ID$ID$HIHHL CHЉH9.VHJHHH@HHXH5IH`HHH9HuH`H;ERH`HVH5fYHHHSRH81IHDž`ALfCHJHCwfDHPHHH`lH`AHHXHHeH7JWfHHHUQf.HwAHAH9HuTHufDH1JH5ZH8MEAHHo[THtHPH2H9KHPAHhHII9}HPJ\IH @H`;IfDL'IlfLI5HCVH5H8MALH@`HyHHiLH@HTH@H;SH@HCHHH8HHu HqH1HUHEH@H9tjHHELHDž`E1ALSAD$Љ9HoHH5YH8'L~fHGfHHUH)H0H H9'H HDž8Ht-HH H9HFH9HHBH8H8IH HfHnшHDfHnfl) HkH0pH8fo H)]HMf.AD$@HXLIHQHHtH3NH0H9IIHuH}Ht H H}HuHt H)'IHpfomLeHuH])pLeHt H)HQHWLHHHp HhHMt ILPH96MdHHI$H`HH0PHH@H0HH)KHHfHPH`ATHHhHHHPATgHDž`APpH5&H=oO1HHt111H"HHDž`AQ'HOHbfDLDLA0IHHIDH=躭HtQHH@;HЉH9:H@HH8HHHSDH@@Љ9tHDH55UH8DHH@HH8HHHCuH@@8H@YJHtHH8EfH8HH HH-HCHCLCLPAVHPBHPHHPH@LMbInH`AVHDž`HDž`E1ALHDž`AVLAIH8H@HHXHHDž``H(HPASsLoBMHHIH53H8dFHLH (MA/@H@H5GH@HbH@H@)E1HPASH`1ASH`H==1yH/H=腪HH,uMHf.@UHAWAVAUATISHdH%(HE1HCHEHfHnH=HEfHnflHE)EHJHHI~IMt_M1H=%HYH=%E1艩HEdH+%(HeL[A\A]A^A_]@LyL5eMg1HI9LL;tuHHHEH0MwMH}L-;vI.I%LnLmH>H}No&Ly)eM~2HH$1MPHHULEH跆ZYH}LmH fH@LHHHDž0HDžPH) )@VHHHHPfo@fHfo LmL`HDžPHpLLHH0)`HDž0HE)@)]) CHHIHt;HSHKHH9RH=@CPSL}H]I9tyI$fAD$HAL$t=HI9tELcMtID$IL$L9qH=?tuLHI9uH]HtHuHH)G@H`HtHpH)+@wMH@HtHPH)@L(H I9tuIfAD$PAT$t9HI9tELcMtID$IT$L9tmH=>tøuLH)I9u@H HWH0HH)]??L-8@ID$I$LPI$LPPfDID$I$LPI$LPHfDJ<4DHLqHEM4LkH QM'1@HI9H;LuHHHHEM~(@E1IM9JtLBxtHJtHH= 舤E1fE1 IM9t_JtHϺHwHtx9HJ(f.HHHCPHHPEH3IHEEH-Hr^>HHHfDUHAWAVAUATISHdH%(HE1HHEHfHnHP6HEfHnflHE)EHJHHI~IMt_M1H=%DH{H=E1٢HEdH+%(HeL[A\A]A^A_]@LyL5Mg1HI9LL;tuHHHEH0MwMH}L-E5vI.I%LnLmH>H}No&Ly)eM~2HH01MPHHULEHZYH}LmH fH@LHHHDž0HDžPH) )@HHHHPfo@fHfo LmL`HDžPHpLLHH0)`HDž0HE)@)]) T2HHIHt;HSHKHH9RH=R9CPSL}H]I9tyI$fAD$HAL$t=HI9tELcMtID$IL$L9qH=8tuLHUI9uH]HtHuHH)9H`HtHpH){9MH@HtHPH)Q9L(H I9tuIfAD$PAT$t9HI9tELcMtID$IT$L9tmH=7tøuLHyI9u@H HWH0HH)8?L-2@ID$I$LPI$LPPfDID$I$LPI$LPHfD54DHLqHEM4LkH M'1@HI9H;LuHHHHEM~(@E1IM9JtLqtHJHH=؝E1fE1 IM9t_JtHϺHqHtx9HJ(f.HHHCPHHP>H3IHE>H-HIr7H~H~H~fDHUHAWAVAUATSH(LoL7LL)HH9>M9IIHE1HL@L)HH11HLDM)fHnHK(fHnfl)EH*MUMu5foUI\$A$H([A\A]A^A_]fLEHL=MkIt$LL)5LLL>Mtf.HHHUHM-5HMHUHH.fH}LL>fDHH9HGHH=P.UfHHAWAVAUATISHHdH%(HU1H)`HfHnHHDžpfHnHfHnH-flHE)EfHnflHx)EHtkN,IwqH &JcHLsL=6M L HI9L;|uIDH`HL{INIIt[M1H=荵HVH=E1"HEdH+%(jHeL[A\A]A^A_]HVHHxHHLpHLpHHhHH`HEfEHEHH)fHDž0HDžPHE) )@HIFH;)t H;I)IE1E11pfDH~HHHpLg'HHH#H(H;0HHH(LMuIFH :)I9NI9IFN$II$Ht H  ID$I\$H2HDžH(H;0lHH O&ao6HK)`IHM 1@HL9 H;TuIDHpH LqMH`LpHHhHH*HoFo>Ls)`)pM\ H`LpHHhHHxHHVo6LsHp)`MMHLHlH HxItHHKH`IHM 1 HL9 H;TuIDHhH L{H|H )HfHg-ID$DH@`H HH LHHn H 8H9HP H!+HHHHHHH,HHj7HHDA\$HcA\$AD$HH HA\$AD$HH HcA\$LAIH6Ht#H|3H2H9 b.f.IHH;'CH@H;h%t H;$8 HE1HDžHHEHHHH %HBH9JL9HBN$II$Ht H 6H/H H9H,L-MIEHHl31I9EafHnfIn1Lfl)E IHt H MIM+H}LHEHHUH9HHMPH9oeHuHMeHCHEHuHEH}H9tHEHp,4H IHHH;PHGHUHHuH@HH HL_HHIHw4HHtH&1H0H9  ,HhHyH H9HBL5ٌM|IH5LIIHMCIHq$IHCLI$L`,IHTHH5BH#5HH5;L#LLL褶HHLlLdL\LTH5H!IHeH5HAŅLE2LHLHAUHHLL@L  4HXZLKIHHIHbfI9OdII$fDL'fL'HHH;PLH@HHUH,OH9omHMmHUHUHf.H'fH'M)fDDžnLIuJE1L&Mt9Iu4L&Mu+3DžqIIHItItMtIMLI܋HH=zE1Mt I $qHHHHHH}HH9tHEHp(LHH@I9t4f.H;HCH9tHCHp(H I9uH@HtHPHH)^(H HtH0H)B(HH-vHMHHt=Hȃ-t nHuH}HHHMHEfDL%fE1LHMMMI IM9JtLbatMHML}KDDžkE1E1LHMMMIfIM9|JtL`tMHMLWKDf.H$H=HlH5mx\IHL#L#BL9HNdII$FI]H M}HLIKMHuoH=.[qLMDIM9JtL_tLLID-HHH1LPHUML`HjY^sL"HLHLGl1ɉσL:L>9rQHDž`5-H4JI $tHDžrHLD"HDžp,HAL IH-H@LMIHDžh,HA~EHMHEyZ,H?HDžH< HHHH@HHHBIH=HH5YIHDžqDžqIE1H=uXIIDžqUL DžqI4H5H&HHfHu|HogDžjE1I{H'H5:H8k$HDžyH LLHuH}DžrHIHmDžjE1E1H1DžjHH5pH=)1芭IH(111H萀LHHDžsHL蓋HAAUDžv3$rDžmLE1I)DžmLIE1 HDžmHLfLHuH}HHHHHtDžnE1?"DžnLE1DžsaLH7iHeiHiif.UfHHAWAVAUATISHH8dH%(HU1H)`HfHn)pHfHnfHnH HflHUH)EfHnflHEHU)EHtJ4Iw%H vJcHDIIt[M1H=)襡HqH=E1:HEdH+%(HeL[A\A]A^A_]HN HHMHHHxLhHHLxHpHHhHH`HPfƅPH@HH)fHDžHDž0HDžH)) HHHIEH;t H;<yIEE1E11jH>H|HZH0L_HHHHH;HHHLM-IEH 2I9M I9OIEN HE1E1HHEHMHH fHBH9J I9HBN9rHIDžH?Ld IHH@LMI\HDžxH!AHDžpHAEHMH@,HHDž HDžhHADžIMHu HH#HH@LMIH=HlH5lBIHDžIHH=ADžIE1E1XDžIE1E1dH5HHHHupHgDžE1E1IH=HkH5kBIHHIDžHHIDžHH=@HH5H84 HIDžH3 LLH@H}DžE1E1E1DžE1KLIE1E1DžDžIHDžHHH8bLE1E1IDžeLfLH@H} HIDžHHH{H5H=1褕IHt111HhLfHIDžHHHHHHDžHCH5FH=o1IHt111H"hLڑHIDžHHHHHHDžHHIDžHHH5= LrHAuFAHIDžHHHLE1LʪHQHQHUR@Rf.UfHHAWAVAUATISHH8dH%(HU1Hy)`HfHn)pHfHnfHnH HflHUH)EfHnflHEHU)EHtJ4Iw%H ^JcHDIIt[M1H=uHwH=E1 nHEdH+%(HeL[A\A]A^A_]HN HHMHHHxLhHHLxHpHHhHH`HPfƅPH@HH)fHDžHDž0HDžH)) HHHIEH;t H; yIEE1E11jH>H|HZH0L/HHHHH;HHHLM-IEH I9M I9OIEN9rHIDžH?L4IHH@LMI\HDžxH!AHDžpHAEHMH@,HHDž HDžhpHADžIMHEHH#HH@LMIH=ϾHUH5U*IHDžIHH=)DžIE1E1XDžIE1E1dH5HHHHupHgDžE1E1IH=H TH5 T)IHHIDžHHIDžHH=(HtH5ӥH8HIDžH3 LLH@H}DžE1E1E1DžE1KLIE1E1DžDžIHDžHHHbLE1E1IDžeLfLH@H} HIDžHHH{H5H=1t}IHt111H~PL6zHIDžHHHHHHDžHCH5H=?1|IHt111HOLyHIDžHHHHHHDžHHIDžHHH5 LZHAuFAHIDžHHHLE1L蚒H`:H:H::f.UHAWAVAUATISHdH%(HE1HcHDžpHfHnHHEfHnflHx)EHHJLgHH=3FIMAEHMHE#LLHHHH0H=EHH=~EHwHDžp!H(>HH IHH@LMcHIHHMAINHHDžZHLHLG1ɉσL:L>9rHbHHHH@LMnIH=H]>H5^>IH HH=EDE1H=IE1A/LIE1AH5 HHHfHuHE1IAIE1A0IHu1I $LoE1AE1AtIHu LLHuH}LHH=!CIE1HhH=ӌBHeH5ĎH8LE1AE1S1E1ALEHI $t!HۏH=FqBK@LOLfLHuH}Y!LE1IA2LIAE1H[H=ƋAE1Lq}@vH%C&H@&H}&fUfHHAWAVAUATISHHXdH%(HU1H)`HfHnHHEfHnHxfHnfl)EfHnfl)E!)pHtuJ4IH JcHLkL=MLfDHI9L;|uHH`HL{Mu@IcIIM1H=[HfH=\W@E1HEdH+%(HeL[A\A]A^A_]HLsH`MHM]1f.HL9 H;TuHHhH(MnM~IHyHHHH-HpI&o0Lk)`MH`L=qLhHLHH5RH9pt H;AHEfHDžHHEH)E) )@fHDž0HDžPHE))#IFH;t H;7IE1HDžE1mI~IIIpHILI#HH;L&HHIHpIFH I9NI9IFJIHMt I $HCLcM/HDžHH;iHH^HPo0LkHp)`M H`LhL=OHHpHo@o8Lk)`)pMg H`LhLxHHpHf.LHCfDH@`HHH HHHH H9HHHIHHHHH8LIHIDDcMc@DcCII LfDDcCII Iff.DcAHLHH2HtHH2H95@IL;=XH IGHH9t H;4 IE1E1mII$IIHWILIcH(H;0L&HH(IMIGHI9OL9IGJIHMtI $tVHCtaLcM9rpLIHH@LMcIRHHH,HH@HHHHDž[H&HDž"HH H5H81ZAC4H5|H8HHA=HLE1A2HuAHH{~H5H81DAAHA@LXf)H=i111#Hx/H=u+$H5HBHHDL?LB LLHuH}]HH5vH8#LE1E11A2Hw2H=t*zE1A:1E1A:vLA6E11ZHDžA6IHH5TvH8LfLHuH}4M1A:HWH0HHPUHAWAVAUATISHHdH%(HE1HHDžpHfHnHHEfHnflHx)EHHHH.HHHH H9HhHHIHHHHHLIJHIjDDkMcH@DkCII L;fDDkCII If.DkALAHHMHt#H\H2H9Bf.IHH;ѶCH@H;t H;?E HE1HDžHHEHHHHHAH9Q I9HAJIHMt IMH6H H9HL%Mb I$HHLE1I9D$d fInfHn1Lfl)E譙IMt IMkM I $HH}LTHEHHMH9HHUH94o]HuHM]HHEHuHEH}H9tHEHpлIH$ IHXH;`HGHUHHuHXHX HIYHHHHp_HHtHH0H9 HBCfHY4HH9PO HHH HHH5HBIH^ HBH5}HBHHHZ {IH4 HXHHH H|H5vHsHLL}EHHLEBL=BH1BHJ3HH9P L5M IH5LAHHH LAH5|HAIH zIH LxIHp H{H5vHyQ LLHDIH7 HKALCAL;AH5 sHAIH HHMLL HPHHATL0PHAXAY L@LUHMHAfI9K\IHHLsHpML{H^tM1 HI9H;TuHHHxMn'LWfLG2fL7HXH;`"HPHHUH4%HfHE1@IM9JtLRtHoJH9t[omHMmHUHUH,fDHkfDLWfLGfHMHHt=Hdȃt  HuH}HHHMHEfDL׳A1LE1E1HE1HHHHHLMt I $HItHHHHHHiDH=rfMHt&1HHHHHHMt IHHtHHHHMt IH}HH9tHEHpQLXHPI9t0fDH;HCH9tHCHpH I9uHPHtH`HH)H0HtH@H)ڴH(HH5fL3fHfLfHױ?fHDZofL5E1LILMIfDIM9JtLtLLMLJE1E1AHDžH=HH5IHLA+HDžE1AHrI95HJ\IHgH=-`HDžAE1Ml$M[M|$IELI#;MHuiE1E1AHDžHDžItE1HjDLE1AE1HDžHAE1E1HDž1/LE1AHDžpLLH9HHE@H5qH=@9HH:H H9H:HXHLxHHI9HuHfHn1L@)E֎HHtH9HL9LH=ָLHHLb9111HHL9AE1E1HDžE1E1A HBHDžpHHH֬IHHHMIVLMIhHHDž&EHMHELE1AHDžgHLHLGz1ɉσL:L>9r_HHHHH@HHHIH=yHH5HHME1AHDž{LE1AHDž%H=+yfLE1E1AHDžUH5yHVHHyLAH:RLLE1E1HDžAHu*E1E1ALHu1E11E1AHDžE1E1AHDž$H=*xHH5IHEMA*LHuTLE1ALME1AH=w뚋 LLHuH}LLE1E1HDžA1LE11AHDžHDžH8H5_H8Ȯ莐E1LE1E1LAMALfLHuH}V1LE1E1HAE1ME1AHDžH?Hu-1LE1AHHjNHIHNf.UHAWAVAUATISHHdH%(HE1H0hHDžpHfHnHZHEfHnflHx)EHHHH.HHHH H9HhH衢HIHHHHHXLIHIjDDkMcH@DkCII L;fDDkCII If.DkALAHHMXHt#HH2H9f.IHH;qCH@H;`t H;ߜE HE1HDžHHEHHHHHAH9Q I9HAJIHMt IMHH H9HL%Mb I$HHE1I9D$d fInfHn1Lfl)EMIMt IMkM I $HH}Ll=HEHHMH9HHUH94o]HuHM]HHEHuHEH}H9tHEHppzIH$ IHXH;`HGHUHHuH@HX HIYHHHHpHHtHH0H9 H+fHH"H9PO H HH HHH50uHx+IH^ H+H5mfHQ+HHHZ IH4 HX]HHH H[eH5_HHLL.HHL*L*H*HHH9P L5M IH5/tLw*HHH L|*H5MeHI*IH IH Lx\IHp HadH5^HQ LLH#-IH7 H)L)L)H5[H)IH VHHMLL HPHHATL0PHAXAY Ld)L<;HMHAfI9K\IHHLsHpML{H\M1 HI9H;TuHHHxMn'LfL2fLםHXH;`"HPHHUHԢ%HfHE1@IM9JtLtHoJH9t[omHMmHUHUH,fDH fDLfLfHMHHt=Hdȃt  HuH}HHHMHEfDLwA1LE1E1HE1HHHHHLMt I $HItHHHHHHURDH=:OHt&1HHHHHHMt IHHtHHHHMt IH}HH9tHEHpLXHPI9t0fDH;HCH9tHCHpH I9uHPHtH`HH)H0HtH@H)zH(HeH5fL3fHfLfHw?fHgofLW5E1LILMIfDIM9JtLtLLMLJE1E1AHDžH=PuHH55IHLA+HDžE1AHarI95HJ\IHgH=tHDžAE1Ml$M[M|$IELI#MHuiE1E1AHDžHDžItE1HjDLE1AE1HDžHAE1E1HDž1/LE1AHDžpLLH٢HHE@H5QZH=)"HH:H 6H9H:HXHLxHHI}"HuHfHn1L@)EvwHHtHB"HL,"LH=vLzHHL"111H4H!AE1E1HDžE1E1A HHDžpHHHvIHHHMIVLMIh/HHDž&EHMHELE1AHDžgHLHLGz1ɉσL:L>9r_HHHHH@HHHIH=2bHH5HHME1AHDž{LE1AHDž%H=aLE1E1AHDžUH5:bHHHyLAHzRLLE1E1HDžAHu*E1E1ALHu1E11E1AHDžE1E1AHDž$H=`H;H5<IHEMA*LHuTLE1ALME1AH=X`뚋 LLHuH}LLE1E1HDžA1LE11AHDžHDžHؚH57HH8h.yE1LE1E1LAMALfLHuH}V1LE1E1HAE1ME1AHDžHHu-1LE1AH"H 7H4IaHkf.UfHHAWAVIAUATSHHxdH%(HU1HW)`HfHn)pHfHnH fHnHflfHnHE)EfHnHfl)EfHnfl)E)EH JHI H JcHHV(HUHP HUo@o(Lk)`)pIH|JcHLkHMHHH`HIHVHHHhHLcIL=RVM1DHL9L;|uHHHpHtHSIL=gHE1fDID$H9bIN;|uHJHxHIMoH`HpHhHHpHHxHHEHHEHHPfƅPH@HxH)fHDžHDžHDž0HDžH))) HHHHHHH@H;t H;.*HE1E1HIH E1HII\I2IHHH!ILIHH;L&HHIMIFH I9NI9AIFJIHMtI $tXHCtsLcM.HDžHH;vHHkfDHLHɍHCu@H@`HmHH]H HHHH:HyH9PkHHHmHIHHHHH HHLIhDHHH I:DcMc@DcCII L fDDcCII If.DcAHLHAHH H HHt'HH2H9dH HhIg HH;=QH;=ߔH;=wH HHB HHnH9P L=UMIH5b@H LHIH H LHHHu1I9E\fHnH 1LH)EjIHtH HHM H LHHH HgLHH@H;t H;HE1E1HIH H|@IFIIbI8HHHILIHH;L&HHIM'IFH \I9NL9iIFJIHMt I $|HCLcM&HDžHH;nHHscfDI9'K\IHHLH HCmH@`HqHHaH HHHH>HH9PHHH襆HIHHHHOH HHNLI8DHHԒH I DcMc@DcCII LfDDcCII IfDcAHLHAHHH H,Ht'HЎH2H9H HH LHHH;4pHH@H;t H;yHHDžHDžHIH HHHEHqIFH@I9V HH9IFHHHHMt I $HKH$H9P* L= MIHHE1I9G fInfHn1Lfl)EeIMtLMX ILeLL !H@L $H}HEH9tHEHpPZIHw IMH(H;0HGHHHH@H$H( HIHLHH܏HHtH}H0H9FcLVfDL9K\IH4I I/It|M1H=i H;hH=8E1HEdH+%(UHeL[A\A]A^A_]LGYHCHHHPHxHHHHHxHHpHHhHpH`HLkH`o6Lk)`MHVo>LkHp)`L"H~(HH}Hx HH}EH E1E1DžHHDžLHDžHDžMt IMt IMHHtHHpHHHHtHHHHHHtHHHHPH 9H=6E1Ht H 4HHHHHHHHHHHHHHHtH@HxH9tHPHpHtLLHHUL Hs%fDbHTHFL8L*)H=1LIfDIM9JtLjtLLHH,H HHH@Hg\H5@H=j IH'HI9GI_HMoHLIEHuHfHn1L@H)EH@HE]IHtHM_LH=L`IH*Lv111LL`DžLHDžHDžHDžZDH E1DžE1HDžHDžHDžHHH@Hu HHHHلHHH9HHH HH H9g 19}IH HH@H;kut H;u HE1HDžHI1HIEHuI9UL9 IEJIHHHtHH5GHvHHCH5g[H9HHAH;CHAHx H HsHHH2HL HHHHLHH0ЅHHtHqH0H94 W}LHtHH=sL ^IH LLÅLm)1U{IHHH@H;tt H;~s H1HDžHIE1HIGHsI9W? H9 IGHHHHMtLH5FHHH" H5YH9' HHBH;Ȅ HBHx HHqHIHOLL L4HLHLHH HHH=THH5薱IHHDžE1HDžHDžDžH tH>HH9I\HHHHHDžHDžHDžDžbMgMMoI$LIE MHuH=SDžE1HDžHDžHDžM+LqH2AI]HMuHH LHI]MHufH HH)vIHB H@HHH HDžXDžE1H1 f.@}HHHxH HHuIHH@LMIHDžxNHAHE1HYH&DžLHDžHDž6H HڀHHDžHDžpHTA1Hb5H=QBH H~H HHVtIHuH@LM>IH HHHDž L9=KDIHH/HnHCYHH H5BHzHHdfDH HHZ~H E1E1LHDžHDžHDžDžHSH;&HHHLHDžHDžLDžDžE1E1H5(6H=tIHRwIH@H{HH|kIHjH5?HL7nL'H5XLL5IHLLH=b}LZVIHL111LLDžLHDžHDžHDžHH H5?HxHHBHu`H HHaXH E1E1LHDžHDžHDžDžHH5c4H=IHuIHHyHHIiIHH5=HLrlLbH5LLpIHLHHHlHceHHIILpmHHH.H5(H[dHHH]HHH!HH HLHYHLHE1DžHHgIH(H@HHhH HDž"isHHtH pH0H9;jLPHtHCH7rHHLAUHAVMHHLHpPkH HLIHDHLLHYHhLHHLoHDžHQH5KH=q1pIH}111HvL.DžE1LLLL1HhLIHHjM1LHHHDžLLDžLE1E1E1LLLLDžDžE1(H5JH=p1GIHtm111HQL DžLE1LLL DžE1E1HLDžE11HLIHLDžHH=%2`HHw1H1HMt1H=1HH5֝1H[Aă2H i EHH5"6HGHHIHH@H;MXt H;X I$ME1E1I$H1rHHDHHLOVHHHH8H;@HHH8LMIFH "XI9NP I9IFN$II$Ht H ID$I\$H2HDžH8H;@lHH07Uaf.L]fHYHDH]ID$mDH@`HHHLHHH hH9H%H[HHHHHHmH@]HHdgH HBDA\$HcA\$AD$HH HA\$AD$HH HA\$LAIH&@gHtHcH2H9^fI' HH;aX;H@H;PUt H;UHE1HDžHHEHHHHUHAH9Q I9HAN$II$Ht H HH H9H L-MQ IEHHc1I9E fHnfIn1Lfl)E?;IHt H M IM#H}L_HEHHUH9HHMH9go]HuHM]HHEHuHEH}H9tHEHpc]meH IHXH;`HGHUHHuHHX HL_HHIHwdHHtHaH0H9|\HLH5.LIHH5LHHH5`LhHIHHAą LZHsHPEM H;L=MIH5"LHIHELTHHHHuE1HHHILp HIaI9D$fInH1)E8HMtL|HpHvHVL;5T4HL蚹HHL"HH;\T0LHLXIHLLLHH 8H9HL-MhIEH5k6L{HIHLmXHHH@IHuHE1L0HHHH_I9D$BfInH1)E17IMtLHMHH5yLHHLHH H9HL=MIH5+L_HHLg$RIHHHHH\ZIHeHaH5HQFHH5*LP'HLLHHHLLHH5yHuIH#`LHHLL HLLPATL0aHZYL3LHHIHHDžkI9OdII$fDLUf.LTHXH;`LHPHHUHYOHLsHpM]L{H^M1 HI9H;TuHHmHxMnLWTfH9omHMmHUHUHEf.HTfHE1@IM9JtLrtHJ_HSfHSTfHMHHt=Hjȃ t  HuH}HHHMHEafDL7SDž1LE1E1HHH@HHHHHMtIM@H, H=dǻHt6HDžHHHHHEHHMt I $8Mt I:HHtHHHH(HHtHHHHHHtHHHHH}HH9tHEHpvTLXHPI9t-H;HCH9tHCHpFTH I9uHPHtH`HH)TH0HtH@H)TH(HvH7QfL'QfLQfHQfHPfHPfHPfLPH=C,HH5(IHDžL^E1LILMIf.IM9GJtLҌtLLML!JI9HNdII$&DžE1E1HDžHDžHDžHDžE1HDžHDžDžfDHI]HM}HLIMHuH=*HDžE1HDžHDžDž9HFDžE1E1HDžHDžHDžI-LNHDžE1E1HDžHDžHDžDžMIUFHE1E1E1DžHDžHDžHDžEHMHELLHXHHDžIE1HDžHDž&H5H=CIHKHUI9D$I\$HM|$HLI$HuHfHn1L@)E-HHtHHLHH=WH-0IH5H111LۭLDžE1E1HDžHDžHDžHD/HDžE1HDžHDžDžHDžpVH.DHDžLJII$HMXIVLMIHLHLGp1ɉσL:L>9rUdVHHDžsHFJHHHH@HHHI/H5eH!QHHHDžIHDžHDžDž%HDžIHDžDžHuIHDžIHDžDžH0LE1E1IHDžHDžHDžDžHDžE1HDžHDžDžIDžE1E1E1HDžHDžHDžMHu1rMHuaDžE1E1HDžHDžHDžH=:IHHDžIDžH=HkH5l跁DžI@LE1HDžDžDžIE1HDžHDžH=M舀IHVHDžIHDžDžH= HH5LIE1DžLIHDžHDžDžgHLMLgII$LHuLE11E1DžHDžHDžHDžBHDžE11HDžHDžHDžDžHDžLIDžnH=IHkDžIWH=H1H52DžDžE1IWDžIE1 DžE1IHDžHDžHDžHNH5tH8J LLHuH} DžIHMH5jH8^JDžIHDžLIDž%HLoMLgIEI$LHuDžIE1E1HDžHDžHDž+^1E1E1IHHHDžLfLHuH}LIHDžDžNHLH5HiH8'IDžLIAHIDžHLHuN.HIDžHMILsDHL鑖HȖHԖHHUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=[E@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)|EfoEDIHULLE-ELEHUHIHpfD@L9L9+]HH9HGHIH=C >UfHHAWAVAUATISHHXdH%(HU1HA )`HfHn)EH )pfHnH$fHnflH(HE)EfHnHpfHnfl)EfHnfl)EH- J HI* H 6JcHHV(HUHP HUo@o8Lk)`)pI@ H6JcHLkH-HHH`HIH HHHhH L{IH M~1f.HL9d H;LuHHHpHMHSIH H E1fDIGH9 IJ;LuHJHxH IMH`HhH(>HHW HxH}LpHH5=HHH}H*=HH HH5H9pt H;; HH5ռH9pt H;h; fID$H;8HDž0) f)))))t H;8 I$E1E1E1H@H>fo@Lf)@)MIFIVHH9H=<@NAFPAVLLHMt=IFIVHH9CH=?AFPAVHHH(H;0foHHtH=?@HH(IMID$H S7I9L$L9ID$JIHMIL=HH Jf.L膤Lf@H(*ILIFPILPILIFPILPWLAHHhGHt#H DH2H9| >f.I $ L@HLNLLL]HHHtFHHo HL].DL(L fHHDžPL)@L)HHH9 H@>HfHnH)@HPM9 M)IfDBHIL9t0Ao$IT$HtH= =tBHIL9uffoHLH)HtH=<:@LLHHHL9?foHf))HtfHHtUHHtDLHL@M9H"AEPAUIM9WMl$MtIEIUH9TH=;tAgI $IE1HYDH= Mt I>M]E1HHtqHHt`HHtOHHt>HHt-L(H I9}I%f.AD$PAT$t=HI9tELcMtID$IT$L9yH=:tuLHEI9uH HH0HH)};L97K\IHaCHHDžH}HHDžItrItdItn1MH=Q裼FHH=;E1HEdH+%(9HeL[A\A]A^A_]HV(HUHP HUo@Ho8HxH)`)pfDo>Lk)`MfHVo>LkHp)`LfDHLkH`jDLW7:fHHH:vHHEIMHHH vH HEIMHH1MPHHUHL`}ZYID$I$LPI$LP@fDE1I$HI$HLh6E1IFM9IJtHϺHrHtHJfDL@MtHPLL)8L-1L90HHH@ H`HPHpHW~H8IHH>7H֢ HfInfHnflL`HPH6)HHHtH0H9IHL9H5L西HHHB=H9GH_HLoHIE苿Hu1LHEH]IHtH\Mt9LOLGI$HPI$MtII$HMIMAr7fE1IM9JtHϺHpHtHJ`fIELIEPIELPHYDL3^fL3fJ>HGIH1Hw{3fDH1HS fD1aLMK=HuA1H=PF@=H2HL20LH3HDžx7=HuAoL&1IH\H@LMeIJfHDžp<H6A<HIHu17<HD<HsE1AiL1M(IU?H-HH5AH81e0e7H0-E1AnHAq=H5H$LAqD1H LAfkHMfH=1Af4H齃H-H9HOHUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=1@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)1foEDIHULLE1LEHUHIHpfD@L9L9+]HH9HGHIH= +UfHHAWAVAUATISHHH5*dH%(HU1H)`HfHnHuHfHnH$fHnHflfHnHDžp)EfHnHHflHE)EfHnH DflHxHxH )EHpHMHkJ4HIH c#JcHfDHP(HUHP HUo@o0Lk)`)pI*H4#JcHLkHm HHNlH`H:IH\HH%lHhH LsIL=OM1f.HL9 L;|uHHHpHIMH`LpH]HHhHXHxHxHEHpHH5H9pt H;c( fL-:5HDž))f)HH;t$L9HDž)1H;'$HC5 HHHH;HH5HGHH] IH; H3HH "HHq HL8II$HM I$HH L;5x#M9?L;5'2LY4Å"Dž}E11ME1f.Ig E1M+ IB HH=詓E1Ht H Mt IMMt I $HHHHHHHtܑHHtˑLHI9tI@AD$PAT$t=HI9tMLcMtID$IT$L9H=q+tuLHI9uHHtHHH)!,LHI9tuIfAD$PAT$t=HI9tELcMtID$IT$L9QH=*tuLHEI9uHH6HHH)}+IH5HIIH2 L0HHz HHh HL5IHM I $>H L荾Å) L* IGH;!t H;! IE1E1H@HDžHhfHhH&fo@H))HtJT2IHxHH;fofH~HtH= )@HHHMIGH I9O;I9IGJIHH;#HHHH`HHH&fD1m'HHUHH8THHHHHy&@IdIJI`HQ"HHHxHxHxLxHLpHXHhHH`@IHX(H]Hp HpHuDHHHdH HxIMH/HHdH HEIMHHHadH HEIMHHF1MPHHUHL`MlY^]L0IHH@LMILHL HHHLHHtwHHaH`H>1IHHHH@H;W t H;HHHDž(HDž0HH@E1H(H@H  HFH9NH0H9(HFL,HH0IEMt I $ HH 1yH9H7HyHHHME1HuH9CfInfIn1Hfl)EIMt I M) H IGI;G IWI$L$HIGI $H(MH@H(IHHtHH2H9H@MtLӘHHǘIGH;t H;IM1HDž@HH@HDž(HHIFH 8I9N}H99IFL,HIEH(HtH2L LL@!HHLq1HHttH0HHPHHHH(-1LPMHH;HXfInfHnflHtH=lV@HHH@L(H@LIH-HHtHH0H9LHH;NH@H;=t H;,HE1HDžHHH8HHHDžSH8HpHCH9SL9qHCN$II$HHtHfHHuH9XHuH0HHH0E1HuHH9XfInfInH01fl)EHMtLHH0˕LHL)H@L*L9H0HBH胕H@Hp[HHLH8HHIH)HHtHH0H9 H8 E1E1L0HH9fo`HH;)fH~HtKH E1IH HHHLHL HL9L衉HHHt(qHHHRL HLHLHm-HHtpH0H@HH"H8HH8H;$H5H8{HHHH9CjHCH@HVLkHHIEWL1HcиHHMHH)L@H41Lu>IMtLMHLH8LHL(HH{DHP o@o0LsHU)`)pM~.HH1MPHUL`HNZYH`H]HHHhHHpH8HxHfHLsH`M}H*HHHCFHHH HhIMDHaHHH FHHH HpIM HPHHHEHHH_HxIfo0Ls)`s@HPo8LsHp)`H}HH8HHHHH`HKHHPH8HpHxHHhHHH8HHxHHx@HIGI;G /LLu.LDLf.HH98 H5 H=1GIH 111HMfLDž(@E11E1E1HDž@E1HDž0HDžHDž8H@HtHH HHHt H Mt IMt I%(HH=m1Mt I $ Mt IM HHtHH@HHH0HtHH@HHH8HtHH@HHvHHHH@HHH@HH9tHPHpuHHtdkHxHtSkH觃LLM9t{I!AEPAUtH H@HzH]H@H=HHHEH/H111HZ]HLH1ME1HDž@HDž0HDžDž(JHHHHHHHHHHHHrHHHDž(@E1E1HDž8HDž0HDžDž(BE1E1HDž8HDž0HDž~Dž(F1E11HDž0HDžLHE1ME1Dž(JLIHWH@HH@HHH8H@H;t H;/ H8E1HDžHHDžHE1HnH8HHCH9SL9 HCJIH0HHHHtHׄH0H@;HuHcH9ZHcH-HH; H0HH@H@1H腇IHuHQH@EL;5a ML'ML;%eHH;Ao|$ ID$(>HtH=g@HHH0HHHH8HH@H H@H0H0H;H0HMLUL0%LE1LHL(HDž@L8E1HDž0HDž(NH0E1HDž0H@LL(E1LHL8Dž(NH=vHaH5a[0H0H0R1E11H@L9UH8NdII$LhHM5HXIEH5H0H H=/qH41MxHDž01 H;!H@PXHH,H59tHhH0HM1IopIT$ HL9H8JDIH0HWHHH5H81J1LHE11L(H@LL8Dž(UE111LHL(HL8Dž(RH=H_H5_].H@H@HtH0H@@HH=^-LHL(IE1Dž(hE1LHE11L(L@Dž(TL811LH1ML(E1E1H@H0HDž(GHH8HH8H@HHHHIE1LH1E1L@MDž(G1LHE1E1H8E1H0HDž(G1LHL(E1Dž(hjE1LHL(1L@L8Dž(MV1LHL(E1H0E1HDž(MH5+qH8w1LHE1E1H8L(H8Dž(g1LHL(E1H8E1Dž(gHt5H E1IH HHHKHH9HH9HnHH;H@H;\Hr\H8HOHIHHIH1HHI$HHM~RIHPILfIn1HHHH:H9uAt LHL4J)INtL|HmH[H9P<H[H@HH1HHHH@H E11H9JNPfInH@H)HHHc)EH41CIMtL|HH|M%H@{fHnfInH@IflH@IHHH5RHH8LL~HHH8g{L_{LW{E1LHL(L8e1LHE11L(H@H0L8Dž(MH;mH5(lH@H8H8HH8H@HHHtYIItHHtHH0H9H8WzHHE1LH0;E1LHL(1L@L8Dž(P1LHL(E1H0E1HDž(PTH5XlLȹMMH0H@HE1HHE1E11LL(LHL8H0Dž(PE1LHL(1L@L8Dž(RLjHMLrIEIxL@1LHL(L8Dž(]H=%H@H@LHL(E1Dž(]L8MH=HGWH5HW&11LHL(H@L8Dž(]1BH=L%H8H8LHL(E1Dž(]WH= HVH5V%H;VL5VMI1H@HYHhHVH9XHoVH8HOH1DHH H8H E11H9JPfInH8H)HfHnHcflH41)E HHMtLvHv1HHH8vH;E11I9^PfInLH)HHHHc@H41H])E~HMtLNvH@BvHH6vH1LHL(E1H8Dž([sMnM[I^IELHuI޸;H="H8H8Ht$IH=HTH5TV#LHL(E1Dž([LjHMAHBIEHHHMuHHH81LHL(L8Dž([H8GH=9!H8H8LHL(1E1Dž([H=HSH5Sk"Hw?H??H>?H??H?H>f.UHAWAVAUATISHdH%(HE1H[HEH8fHnH`HEfHnflHE)EHHHHHHHHt_M1H=vTmH;DH=RQE1HEdH+%( HeL[A\A]A^A_]@LyL5ŝM 1HI9L;tuHHHHEH{ MwMbLeH=UHHH~H;=)H}L&LeVo&Ly)eM~2HHq1MPHHHULEH/ZYH}LeH;=H54dH9wtfHE)Ef)P)`HHHID$H;kt H;"I$E1E1E1MID$H=I9|$I9ID$JIHMt IHbdHCH9t4HXHQHqHl1 HH9\H;TuHpHfopLhf)p)`MIFIVHH9H= {AFPAVyLxMt=IFIVHH9H=AFPAVHHuH;ufo`HhHtH=j@HHuIMgLAHH=HHtHH0H94L$pfH]HHIHuH3LpHLPL`LL> HxHtLHeAHDHKHPLHLHvIHhLHHMAu\fDHH9HuH;fDH=zH5HGHHIIHHI9EQM}MUMuIIIMwMHufIn1LC)EIMt IMdIM{H=Hu1HHELuwIHI111LtDIMAnE1I $!Mt IMHhHHHDH=4LE1Ht H HHHtHJHXHtJLuH]I9tuIDAEPAUt;HI9tGLkMtIEIUL9-H==tøuLHII9u@H]HHuHH)uH=O#DL/)f.H`H}?Yf.L&ILIz@HuILIFPILPJILIFPILPsIELIEPIELPI9K\IH ApI $LHLqHEMHKL=ӫH1fDHH9L;|uHHHHWHEM~E1IM9JtL tHHJHW?LIL;wL-E1IL9JtLH@H@tHHJ 1HHHHHLAn{L L{+H1Aj+AoHLRfDL7`LIHH@LMIHEHHuE1HuI $t@It AnYLLUHhLAsHH7Lh1AlE1HDžH1Al)LE1Al4I4H4DUHAWAVAUATISH8dH%(HE1HHEHfHnHHEfHnflHE)EHHJL}Lo&Li)eM~0HHM1PLEHHUH#AXAYL}LmfHI 1L)`fH)IIEHDžpH)) )0ӍHHIM H*YIGH9t`HkÅpH` H5%L_HHL9hH@HH`LkHHIE_HuH1LfHn)E辴HHtH_HLt_Ll_H1HIHH9#HuH;5fDHAPH= ;H9xL-:MH IEHIH7 HHXHHt HH5HZ HLLaIHT IMHHHHHHHHHHL;%)H5PL$MDžE1E1HDžHDžfDL-)@I9I ID$8H5 HHHHHHHHfDLf.DHLqHEML{HM[1@HI9H;TuHH3HEMnH7 fHE1@IM9AJtLtH&JIT$ H`fH2fHfLf@HhI9HJ\IHH5L聛 HMH7H9XW H7HH% HIHU ILxHHH HH5٬H LHLL^HIH LH[L@[H8[L;5y- IH HH@LHHZML'fHHIfDHH?H5#H81LDžE1E1E1HDžHDžHDž1H@HHtHHHHHHtHHHHHHtHHHHHHtHHHHMt IMiHSH=8Ht6HDžHHHHHHHMt I $HHtHHHHMt IHt H IBHHtHHHH8H8Ht5H(Ht5HHt5HHt5LhH`I9txI DAD$PAT$t9HI9tELcMtID$IT$L9t]H==tøuLH4I9u@H`H(HpHH)ID$I$LPI$LP`fDLf.DH$fL1fHCfLAfH?fLfHHfHwZfHgfZDE1LILMI@IM9JtLtLLMLJHDž1E1E1HDžHDžHDžLDžHDžE1HDžH=yH"2H5#2IH&E1HHDžI1L|LLHHHH=yDžE1E1HDžHDžM HDžIMtE1LDžE1E1HDžHDžE1DžDžHDžHDž1E1E1HDžHDžmHDž1E1HDžHDžHDžDž-HEuHF\HLuHMHPHLiH}!oufE)@L0H@LHHHt0LHSEHXHt0SHLHHHKHHHH>HDžE1HHH5H81LLDžL8HHHH@HHHI3HDž1E1HDžHDžDžgHDž1E1HDžDž=HHu1HHDž1HDžHDžDžHHuHHHu1C1HDž1HDžDžHHuHDžHDžHDž1E1HDžDž7LHu11hDžnHDžLHu1:HHDžE1E1HDžHDžHDžHDžDžHDžIDžHDž1HDžHDžKDž1HDžHDž+H=zH{,H5|,7HHHDž1E1E1HDžHDžDžIHuHDžOHDžHDžHDžDžxHDž1HDžHDžDž+H=y$DžE1E1HDžIHuDžE1HE1HDžHDžHDžL f)@DžE1E1HHDžHDž|HݿHDžE1HE1HDžHDžHDžDž Dž1E1E1HDžHDžHDž1E1E1HDžDžHDž1E1HDžHDžDžH5@HHDžHfDDžE1E1HDžŨc11HE1HE1HDžIIHHIu@HUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)foEDIHULLE]LEHUHIHpfD@L9L9+]HH9HGHIH=s ۼHUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)foEDIHULLEmLEHUHIHpfD@L9L9+]HH9HGHIH= UHAWAVAUATISHL=zdH%(HE1HHEHfHnHEfHnL}fl)EHJHH(IIMt_M1H=1DBH4t[H=r&E1HEdH+%( HeL[A\A]A^A_]@LqH tM 1HI9H;LuH(HHEHMnM L LuƅIIHFH L9HEL6Luff.o.Lq)mM~2HH1MPH(HULEHZYHELuH L9H H59H9pt kfIFH;lHE)Ef)@)P)`t H;ƵtIE1HDž0HEE1H(4oS HC(HtH=@HHuIH0IFH RI9NHI9IFJIHL90MI $L|L9{HuH;u]H(HS ԼqƅL 2D@Hu=H0LHHgHtHVH2H9<IH HG Hw(HhH`HXffo`)`)PHt!"foPIHX)pHtH=@LmHUHpL$H}HEfH]EH0HxHt!A~0fHnIDfl)0MaHHfo0)@HA!L`H@IH1MIDH)8HH{H9t:HXH"HqHJ1fDHH94H;TuMI $xDH H:H5H81MA1ITHt H 6HoDH=l!1MDE1I $MHtH HhHt HXHtHHHtLuH]I9twI AEPAUt7HI9tGLkMtIEIUL9tYH=tǸuLH I9uH]HHuHH)CfDIELIEPIELPgI9K\IHHLiHEMHKH=Hv1@HH9H;|uH(HHGHEMu@E1IM9TJtHϺH0KH0t2H(J8f.LfHwfLgfE1IL9JtH H0H0H txiH(J H 1HxH6lH=j1M6E1H@f\HfDLWH@HH5,jH8ALSsHEH9ERHHXHPHpHPbHf.HH9HuH;<fDHaHJH5^HWH81ALkIH4H@HH0HzIaHE"HjL#HjH=hSMLԳH]HHDž0H}HtmH}LXH5GH=1AHHt111HH{>H jH=gHxHhHDž0A1{HiH=gkHDž01IH,JIt"HiH=dg'rE1LACI I I H I @HUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH^E11oBJDHtH=˳@L9Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tdL1H)Hx1HHoHHH9rI)LfHnfHnflMuMuAEH([A\A]A^A_]fIu)ELL)foEDIHULLELEHUHIHpfD@L9L9+]HH9HGHIH= UfHAWIAVAUATISHHdH%(HE1HE)E^HHTHO{L5|HEHfHnH`HEfHnH>flLuHE)EHK4I7 =M$IIL{HEMLeH\fI\IGAoL{HE)]M H]LeH;LuH5 ,H9st H5I9vt L;5q3 fHHDžp)`f)0I)@SHH H5QH H*H H9H HH5 HHCH5]HHH IHHM* HHR lHH) H)H5H)H5HL/hHA>MZKfDAo'L{)efDLE1IHf.IM9JtLtLHJLWfLGHHHHfvHHHHt HEIFHHHf.HHUMHHlHLEPZYLA:H It E1@LoD1H]HgE1H7fHL,fH1E1A2o1E1A7^HP+PDYZD[QRSTUVWXfIHHtACfLL HLHTH=vHH5CHHE1A8'H=7RHE2H%A8H H\A:H5oLÅL-qL;5bIMH=48LCHH5L-H=HIHH-111LLn-HHAFLKIHH@HHHIH`LH-ISA8E1A;HHAAAIHНzAJ1E1A>iE1A>JHŇuAFLAFL%0=I$ IOHTf.Ht{HUHAWIAVAUATISHH_HII)LHH)HHGH)HL9r8IHfIfHI9uMgH[A\A]A^A_]ÐL9-M44L9IH6H9HULILE.LEIwfHHUIJ(IfHI9uH9H{1E1H)HHo I HI9rHMH)HLEעHMLEIfHnMGIfInflAH[A\A]A^A_]IM9MFIHtHH9HGHIH=  DUHAWAVAUATSHdH%(HE1HIHIFfHE)EfH5lH)`)pHLHHHbAăOHHHL}LmIcHHLL)HH9HHH9jԦH@HƚH8EHuE1E1HPI9/AEPAUH}LmMtJIEIUHH9H=AEPAUHEH>\\IMMILmM}M}L9t\HtH=oCMt=IGIWHH9H=>`AGPAWLI]IL9HIIvHPDHP0H}fL}H]EMIEIUHH9H=|~L*H}vHPlAqHHHPHHHu HE11AHsSDH=PE1MtL HtHHhHtLmH]I9tuI DAD$PAT$t9HI9tELcMtID$IT$L9tmH=tøuLHI9u@H]HtHuHH)WHEdH+%(*HeL[A\A]A^A_]fID$I$LPI$LPPfDHHIM9LfH{HtHI9uLm^LVIELIEPIELP{CM}HHF~H5H81E11AH@[fDIELIEPIELPHEDLxA뱐HP1E1ffHH}H)9Hf.ILIGPILPULxIH8H9oP )UH}Ht[HEHUHPHuHoH}f~ELmMfInfl)pMtLHPjH}HtAͣIDML`HpLH@LmLxIHQA-H1HE1L ]MH GH5ݑH8R1H{jXZE1Hy1H5k{HT#f.L'fLh1E1I9LHH~H5(H81Ֆ՝1E1MA2AHړHTH5H81DHP9E1f"A$HIIII IIHUHAWAVAUATSH(LoL7LL)HH9>M9IIHE1HL@L)HH11LDM)fHnljK(fHnfl)EH,MWMu7foUI\$A$H([A\A]A^A_]@LEHL(MkIt$LL)LLL)Mtf.HHHUHM}HMHUHH.fH}LLءfDHH9HGHH= UHAWAVAUATISHHXdH%(HE1HPHEHEHEHHHHPHHHLqHEMLufIFfH;HE)`)M)pt H;IHDžHHDžP1HHIFHFI9VHPH9IFL$HHPI$Ht H hHXH5jTHGHHVHH8HH9CL{MLkIIEH LHufInfIn1Hfl)EsIMt IMH SI $L=žM9}MeM[HXH5`HGHH!HH+L9x9LxMH LLHHHHHIMHWAăDeHuH;uD&HHHHuHHLIHBrHtHH2H9L\IHXH;HXLmHULHp0;LuLLLpLLaH}HtL[L`LL,LˍLLxIIHKA'f.HErHuADH1HHwL U AH @H5ӊH8AT1fXZHGH=8wE1qHEdH+%(HeL[A\A]A^A_]@LqL=ULM\1 HI9TL;|uHPHHEH-IDLߐL=M9}L5HHt'HtH II@H?IHof.HWH=;:1L2HƅIHH@LMI7fHUHHEHHHE1HED`H}GWHH HcAH9uOHUHHEHHHd HUBRHH HHcAH95HH5חH8>H}HHEHHrgHE@AĉEAH}^HEHHt H}AHHEHHL[DHHH5c:H8H</H=9gHH@H}H5ARHEH~HEH@Hj//E1"H8f.fUHAWAVAUATSHHhdH%(HE1HmHf=AHE)pH=H H9HH}HLHHCH5@HHHIHHHMHsHDHu11I9D$!1LHEH]cIHt H I$M%HI$0HyH H9H HHHHCH5MHHHIHH HHu1E1I9F 1LHELebHMtI $uLfHIIMH I9z1rIH^HH H9HL-MIEH5%L IHIM]H6H _H9HL-FMIEH5%L IHIM|}fInfInHflHHH@ HDž`1H`HDHHPHtk HH H9H[L=MIH5qEL IHmL HHuE1I9D$fIn1LP)EaIMtL M:L HH H9HL%MWI$H5I$Li IHLu LLIHLP LADžL5 E,HEH >H9HL%%M,I$H5#L IHL LL|IH1L L@ADžL EH5{L/IHHADžLX LP EtHPLjH`HDž`HPHI{HHUdH+%(9He[A\A]A^A_]@HPf.L~fH=X褶HHDžhfDhH,9H=|3HpHuH)Ht61QH~f.Lg~fHW~fLG~H sH5~eL'~DfHHE1L 2H U-H5EwH8R1Hc|X1ZHyQ1H5cH81wDH=WHH5L}vfHDžhE1E1HW}MuZ]:u,DLH= TH*H5+薵IHDžhE1DItCMtIt)MI $L|L|DL|DHXHPLH]LHmfoULefHHELe)P)p)EzHLH4xH9hHhfoPHDžpHEHyHq(YLa(HHhH)t~HhI\$HMl$HLIE^MHuDHI$Lr{DH=aUHH5γHHDžhE1E1E1IMtHH LzDH=QHH5^IHE1DžhHPyMufrPDDžhE1E1cH=T<)HvHV`H5H81?yDžhyDžhME1E1MfMI^I$LHIHuH=PHH5NIH@DžhE1E1DDžhE11l@H=iPDfDžh1GH=APADžhLE1"H=PH"H5#螱IHDžhE1E18DDžhE1!fDH=O蔰fDžhE1fDM|$MkMl$ILIEVMHuFH=^O9DžhE1E1gH=8OH)H5*ŰIHDžhE1bDžhSH=N̯DžhE1E1E1-{Hq@UHATSHPdH%(HE1HIHH5@qI9t$fH}LHE)EyfoMLe)MLe)MHuqH;jstHH{(foUHs8Lc8S(Ht H)y1HUdH+%(HP[A\]H)sH.H53H81uH-H=+H}LH)Htky@HDL[H+{H5/H8z3zHf.UHAWAVAUATISHHXdH%(HE1Hd1HEHEHEHHHHPHHHLqHEMLufIFfH;nHE)`)M)pt H;@oIHDžHHDžP1HHIFHoI9VHPH9IFL$HHPI$Ht H hHXH5*5HGHHVHH8HQ}H9CL{MLkIIEH LHufInfIn1Hfl)ETIMt IMH SI $L=M9}MeM[HXH5eAHGHH!HH+L9x9LxMH LLHHmkHHIMHAăDeHuH;uD&HHHHuHHLIHB2~HtHzH2H9uL^{IHXH;MoHXLmHULHp03{LuLLjLpLLH}HtL4L`LL謕LnL˾LxIIHKA8 'f.HE2}HuADHyHHGXL  AH !H5kH8AT1&qXZH( H=Y'E11HEdH+%(HeL[A\A]A^A_]@LqL=-M\1 HI9TL;|uHPHHEH-IDLqL=|M9}LlHHt'H4zH II|H?IH/qf.HqHYH{c0Lkqc0Hkqc0HkqsHcL*gH}tLUqoc0Lqk;qHHssHHsHsH%uf.UHAWIAVAUATSHH8LgLw(dH%(HE1HFL/L9 HHH<HHHUjHU1HdHIGH;HC~CIGLHAoO fHnHCHUflHCK )EucMtH0I9tJ4L>jLeMt:DLM$$H{HCH9tHCHpj0HjMuHEdH+%(uRH8[A\A]A^A_]DLJ1E1c$HG0H0 H=t g"ajHEL}Mt6IIIWHEH9tIGHpeiL0WiL}H}7aMtH誳Ls(L+LcHCH;1H cnHYqHrUHAWAVAUIATISHdH%(HE1p HDž8H0HEH(HPHE@ƅDDžHƅLHDžXHDž`HDžhDžp?HDžxHEH HEHEHEHEE?HEHEEoI$L5`aID$PID$ I$ID$A\$AD$AD$AD$ID$(ID$0ID$8AD$@?ID$HID$PID$XID$`ID$hID$pAD$x?IDŽ$IDŽ$AƄ$M9H5k4LHHL9fHIHH H5:=LL0CHHL=\L9H;mˆRL9IH`mH H5@CL8HHnL9H;lˆL9HlMH DH5)L9XHH<L9H;/lˆL9H|lH *H5 L:HH L9H;kˆ\L9SH lH H5*=L;tHHL9H;Kkˆ L9Hk H H5?L<HH L9H;jˆu,L9t'H.kH ,H5FL=HHL9H;Wjˆ6L9-HjH H5T6L@HHL9H;iˆDL9;H2jH xH58LAHHL9H;siˆL9Hi_H H56LB*HHNL9H;iAL9HQiADž3H H5LDDHH(HA4H HtD>H 6H9H,HHdHH5HIHHH5"LHH;LH5 A1HIHHLF#LH5D)L?IHL9H5(H5"H0HHPI|$ AD$LI$H8AD$IT$@AT$DAT$mHuI|$XmEA$IWH}=\H}H H9tHEH4`HP\HPH(HXH9t HZ`HEdH+%(sHL[A\A]A^A_]feHg] fHG]\f H']fHdH5:H8aH@oH=6@H\f.L\fH\fH\df!Hg\fmHG\fH7\zfH'\fH\8fH5H&AoE1H H.DH=&MjfHOAofHpH=lHw[ffHrApE1cHqH=HeHAqE1#HWrH=MeHArE1HsH= ZeHAsE1HtH=\H52LUIHHHIEIGH;[ILP @u tEHPIE LH H HIE(0HHLdH=HgIH}H@H=cL HpHLHHxH9H(H*H5-L`IFHHw HH0 IFH5mLHHR HHK HMHHH;IH;QZFH;WM9HZ 'HH nYHH9HL1HufHn1L)E0HHtHH IM HLHLHHpHH HH5H H5HHHHXH9C LcMLkI$HIEHufInfIn1Lfl)E/HMtLHLH5&H=ZhIH9KIHmHHXxSIHHH5.H5J HVXH5O LJ LLL HxH LLLE1NH5HIH& WIH HL.\HHJLjLbHAąHGE HJHH5H=YHH HVH9C LkM LcIEHI$Hu1LHELm-HpMtLHp LLpLÅ LmtƅMFH5LwH50L HtHH HH5"HxtsHxH;7I,HxH5'HdWH9GhHpLpM7L6AąKLE" LL 3  IE1HuH TI9NEfInH1)EO,H@MtLH@ HHH5n"H=VHpIH GIH! HHXOIHH@H5 HFHH5bLiFHTH5LJFLLLTHHLLL LH5Iy H5'LIH HL;- G H56 HIH H5gHoIH L{H5L7AąLTE 8KIHHIULHHH=IHULH5yL#HpIHHHLH5l&HIH I$H 'RLI9L$fInHt1LmH)E~)IMtLNLFM H1H%LpH@I$LxH~H=0@DžHbE1E11HDžHDžHDžDHH1H=THt6HDžxHHHHHHxHHHtHHHHUHHtHHHHCHt H EMt I $FMt IHKFHHPHH9t HJHFHHXHH9t HfJH(EH(H`H0H9t H5JHEHHhHH9t HJHEdH+%(X HHĘ[A\A]A^A_]L'GfHGfHGfHFfLFfHF-fHF-fLFfHBE11DžHhHfDHDžE1E11HDžDžHdkB>D2>DHDžE1E11DžHe&fDNL;5 BIAoV I~()Ht蟇HHHLHLHFH:HHt8LWAf.DžHrE11|@HDžpDžHtHDž@I $;ML@Mt IM2Mt IHpHHHHHHDH@E1DžHHpH@E11DžHjHfDIE1Hul@E1IHu1aH藰HAtHHHtDžH|ADL?1E1%DLDfLCfLCfOHxxHIHHH5HFHHTHKH9G H_HNLgHI$,Hu1LHEH]-#HHtHHLH=$?HHtHHDžHtHHDžHtHDž)fDH>E1E1E1HDžp1DžHiHTDHXHHLhHIE HuSfRJL;5K>IAo^ I~()Ht߃HHHLHLHDH@HHAtHlHHt[DžHA fH=HHJH=H@BDžHvE1E1kDžHsE1E1SE1E1E11HDž@DžHvDžHsE1E1E1HDžpDDžHtE1E1H<E11HDžpDžHjH[DžHvE11@IHu1tfIHuWHi<E1HDžpDžHjHDDžHtE1E1HDžpH<IE1HDžpDžHj1HHDžpE1DžHtDžHE1E1H! HHH@KQfDDžHE1~H@E1E1DžHH[I}`H5KI__111LI}xHHHHvFH5'cH8CDžHH@E1DžHHDžHE1E1HxEDžHE1jLLE1E1E1HDžpDžHHLoMLgIEI$LHuKH:H=H5!NH81<CDžH}I}xHHHH91DžHlHuLHu~I1Hu1 GKHv9HoH5MH81-<-CDžHIHu1H@LE1DžHH @H@LE1DžHHH@1E1LHpHDžHzH@1E1LHpHDžH;H@LE1DžHHUH@LDžHLpH*H@LE1DžHH1LE1DžHHpH@HHpLMHGIHHY1H@1E1E1LLDžHHHpHpH@H鯖H鳖H鎖IyI麖HǖDUfHAWIAVAUIATSHHXH}dH%(HE1)E>HIH"HEHfHnH6fHnflHE)EM-IDHEHH0HtkIع1H=谾I $HH=>1HUdH+%(He[A\A]A^A_]f.MoH MT1HI9dI;LuH}HHEH MuMIOH=H1HH9\I;|uH}HHHEMn0fHHIUIuMfAoUMo)UM~0HHuHULHILELP7ZYHuHUH},HI $HEL8HEIEMwHEMHuH4fDL8Df.H4,@E1IM9KtHϺHMtHMtHEJfE1 IL9t`KtHMH}tH}HMtx;HEJqfDH0H=mHEԠHEbBH|T@HEBBH4.;f.@UfHAWIAVAUIATSHHXH}dH%(HE1)E;HIHHEHfHnH3fHnflHE)EM-IDHEHH0HtkIع1H={I $HH=螟1HUdH+%(He[A\A]A^A_]f.MoH EMT1HI9dI;LuH}HHEH MuMIOH=`H1HH9\I;|uH}HHHEMn0fHHIUIuMfAoUMo)UM~0HHuHULH=ILELP|ZYHuHUH}HI $HEL,5HEIEMwHEMHuH1fDL4Df.H0,@E1IM9KtHϺHM6qHMtHEJfE1 IL9t`KtHMH}pH}HMtx;HEJqfDHH=#HE4HE>H|T@HE>H47f.@f.DHUHAWAVAUATSH(LL'LL)HH9M9IIHEHHM)HH>E11ofBL9 Hs11L)HHAo H HH9rHfHnL)HtfHnflL9tlL1H)Hx1HHoHHH9rI)LfHnfHnflMu"MuAEH([A\A]A^A_]f.Iu)ELL) 5foEDIHULLE4LEHUHIHpfDL93eHH9HGHIH= K.UfHHAWAVAUATSHHXH*HdH%(HE1HF))0fHDž HDž@HDžHDžHDžHDžHDžHDžHDž))H9t H;* HOH LwH9* LO LG(LHw0HO8HW@HGHLLHHHIIIHHHHHL;5,y HH.HPH H jHHDžHp.HHH H FHHDžH<.H@H H "HLHDžHDžHhHLHDžHDžH`HDž7HpH H E11L;=+LLItHHHH;N+MI $L$/H;%+HDžHH; oK HC(HtH=0@HHIL9pHHh6H8H H8HpE1H0HhH;x*r HhLsHIHy H0HDfopHf)p)p)Ht袕HxHt葕8H/ L蚸HDžL8L;@\ fopA$fH~Ht\oIL8IL98H`H;{)tWLpH`LkLLLOHxHtؔ7H HLZOL;5) foH)HtnL8L0fHE)pLL)Hp^ HH9 Hp/HpfHnH)pHUM9LL)HBHIH9Ao$$IT$ HtH=-tB@HJHS Hg9?f.H+fH+fH+HK3H5OH8/A1E1HHt HHHt H&HHt HHHt HHHt HHHt HHt H tHkDH=Mt IMt I $Mt IHhHtHHpHHH`HtHHpHHHHt.HHtL8H0I9}I%f.AD$PAT$t=HI9tELcMtID$IT$L9AH=+tuLH5I9uH0HtH@HH)q,LHI9tuIfAD$PAT$t9HI9tELcMtID$IT$L9t}H=+tøuLH虐I9u@HHtH HH)+HEdH+%(HXHe[A\A]A^A_]ID$I$LPI$LP@fDID$I$LPI$LPxfDHAHH(fDH5IH1MfHDžDHDžfH)pH; NHHHHfHnHfHnHfl)EfHnHfHnHfl)EfHnHfHnHflHE)EZ&IHH@E1LLAHH3JDIHIuLAHǾrHLAE1E1E1HDž`HDžhsfDL'fL&fH&CfH&fL&f&DH&~f&cD&>Dz&Dj&DZ&D(HAE1E1HDž`HDžh8H5LpLA1E1HDž`E1E1HDžh1I>MNMFIvIN IV(HIF0LILHHH10Hp0H%MLA1E1HDž`E1E1HDžh@/HMuE11E1AHDžhHDž`eA1E1=LpHL)#HxHLHA1E1E1HDž`E1HDžhHA1HH0L'L6eHHcH5/7H81"`1=Hq+H5"HH8(IHA1'IH1AIHA1LTLHLPHHxLxAo~ I~()Ht eH@LpHPHMLHHpHLX%HXHpXZ3EHHt肊HHtqLٛLќHHtPIL耧mIHA11E1E1AHDž`HDžh1HeHH5o2H81!IHA1zIHA1c%"I $H}}H>~HLp'~HO~H}Hz~f.DUHAWAVAUATISHhdH%(HE1HHEHEHEHHHHxHRHHLqHEMDHuLefL)EH]Lm+fHnfInflH'H])EHVHSHKHH9H=["CPSHuL.fHE"+HuADH'HHuL { AH H5H8AT1XZHsH=,$1HUdH+%(lHe[A\A]A^A_]LqL=}Md1HI94L;|uHxHHEH-IpDH&H6HubfHuLL{H]Ht;HKHsHH97H= SJK'HH]HHKHsHH9H= SJKHHxHxf.bfE1IM9JtLZtHxJcfHHxHHCRHHRHxHHxHHCRHHRHxHHHCPHHP&HH=踆Mt1IUIMHH9t4H=,uAEPAUtb1MDIELIEPIELP1DHwH=0(1LH1HHx1HxDHH1MPHxHULEHcY^}fH؃= HyHyHyf.DUHAWAVAUATSH(HUdH%(HE1H9HEIIHHE#IujACI LcH B'M9teMeH{H;MuLeIvHu1H(HHHEHCLL'LeH;fMtfH]@HEdH+%(uH(H[A\A]A^A_]!HH9]tL}L LH HE#H)&H'UfHAWAVLuAUATSHdH%(HE1HEH;=EHDž`H8HEHELuHEE)PHGHE1HDž@LHLH)HHH@L}HXHPHH P+HHHK"IHHHIHMH$HHH}L9tHEHpH}H8H9tHEHpLPLXMM9t)I<$ID$H9tID$HpKI M9uMtH`LL)*HEdH+%(HH[A\A]A^A_]fL}1 HHH@I9"HxLHHHHLsHEH8H9HHUL9oMHMHUMHHEHMHEHHH9PL%ێMI$HuH}xH0HdH I9D$HDž(H~(1L0)pH(IHtHHHHZH0HH(HH}MI $HCH;C IHSIEL,HHCIMHH HHH9@pxfDHMSHgufL9tKoUHUULuLuLqfDL/Pf.LcfHUHHt>HЃtAHMHuHHHUHEDHfHHL}HH9HPH zHHIHLHMHHLPLH@H`HAHbL}H5G&H8HPH 1AHPDL}HH=H ~1AH=HH5NIHH .E1H~MtIMt3AMtI $jL}LMXE1DL7E1AH3L}HH2H=aZ ~H uhfH=L@HAHHfID$H(HMl$HLIEWMHp4LH/H IMAaEHUHExITHTFR1҉փI<6H<19r7HxAATTHMHu ATfTHMHukE1,HqH8qUfHHAWAVAUATISHHdH%(HU1H)`H0fHnHHDžpfHnH("fHnHEfl)EfHnH"flHM)E xHtJ4Iw3H JcHDI IIM1H=Hi H={1HEdH+%(HeH[A\A]A^A_]HHH8LsFQH`HHL{H8IVL5HMG1HL9dL;tuHHhH$LsHL= M1fDHL9L;|uHHpH|LrMH`LpL= H(HhH HxH8H(H5H9pt H;I ; H H9pt H;/  H I9^t L;5  H8H9Xt H;  I9_t L;=  ffHPL;5 HDžHDžHDžHH@HDžHƅP)P))))` I1I~HPIHE1H07oS HC(HtH=Y@HHM9fIFIJIHH; ;MbIMXLH; BHH;nH0HS o@o8Ls)`)pMHHH8NNH8HHEIMH`LpL}H(HhH HxH8@HPo0LsHp)`M9HHH8MH8H HxI+HHSH`ISf.o8HS)`IwfHP o@o(LsHU)`)pL=! L8HHLpHLpH HhH(H`L~ L}HHH8Hx@@HH5HQ~ MIMH HH5H818 HQ E1A E1IHDž0aMtE1I $gMH0HtHH8HHH9DH= uHtE1H LMt IMMtI$HI$HPHhHt/tH@HH9tHPHp HHtHH)LLMM9t/fI<$ID$H9tID$HpI M9uMtHLL)LLM9tvIAEPAUt8IM9tGMl$MtIEIUL9tpH=@ tƸuLIrM9uLMtHLL) HXHrIELIEPIELPOE1LH8MH0I@IM9JtLZGtLH8MH0IJE1LH8MH0I@IM9JtLFtLH8MH0I|J?IHIHH8H;HE1HxHDžHEHH8HLH@L,IEHt HxHH *tH9HHtH0Hx HH0HE1H H9H fInfInH01fl)EIMt IM H0HHHHLuLL)H@L*H}HEH9tHEHpo yH I $LL;ID$HHLI$H@H谧I LH8HHH9AsH8 L;=K,IIE1?f.ƅOE1HH;D6HHM9gIGLN,IIEHt H[IEIEHtH'H]AEA9lDOAbH A@fDLGfH7gfL'ifL=!D@HfH(1H \H5DL8E1DLf.LH8EH8HHH1MPHUL`HMZY)fH 1HVKfDHf.H=HL e@L$f.H8HH!H@HL @AEjDHOH RLHAH9HHnH5H8& ƅOf.H2HLE@L@ HtHH8,'H8AHH0HHHfDH H5H8H H=vYm1f:}L,LLю H8H(H;BH H;.LfLHDž0) M)LfHnN<0HLL0L) >LLL(fHDžM)M)eHL9LHfHnJ:LLH)LLfHHDžM)M)HL9%LfHnJ8H)M9M)J 0fBHIH9Ao4$IT$0HtH=tBE1HDžp HwATIA LE1H8 H8Hv6DHDžho HAH=,HlH5l:H0H0L8E1A *HH HLuL LHMLHP0H(H0MHp0HpLH(H H(L`L%HxHtwhLπH0|L[H HtH0H)?芊BLPLL$H8;L{IHOH;` H5;LHHkHH9GELMXHGIHH8ÊLH81HEL}H0MtL荊H0H8sH0gI$MHPI$HMtIMI$HLA H8HH(HHwE12LpM4L`IHI$L0HuH=6oL8A  H%;11&HDž E1HDž08 AHHH5H81mDlA H81E1oME1A A H5{H1L8A H0fDA HuMA L8H8Hu1E1A H8Hu1LLHHr(H)[H[H[HL [H[HLL ZH[HT[[f.@UfHHAWAVAUIATSHHL%dH%(HU1H)`H("fHnH"HEfHnfHnfInH fl)EfHnfl)E)pHt}J4IH JcHLsHM LHI9\ H;TuHH`H HSM~@I~ IIzM1H=H H=d1HEdH+%(HeH[A\A]A^A_]HL{H`LH HB1HH9H;LuHHhHMwM~LHYHHX:HXHHpIUo8Ls)`MH`LXLPLhH@H@H5xH9pt L9 H%I9]t M96 HPH9Xt L9` HXH9Xt L9 ffHEEHDžHDžHDžH8HEHE)p))))M9Z IE1I}HPIUHE1HH=fDoS HC(HtH=G@HHM9}IEIJIHL9wMILLL9HH;wHHHS N@o@o0Ls)`)pM$H`LhH@HpHPHxHXfDHPo8LsHp)`MH`LXLhH@HpHPfDLXLPLhHLhH@H`xHNHXHxHHHPHpf.@HZH5QuH MImfHHH5H81LE1A E1IMHDžHlMtE1IkMHHHtHHXHHHDH=@`HtE1H LMt IMtIHIHHHt^H}H8H9tHEHphHHtHH)LLLMM9t,fDI<$ID$H9tID$HpI M9uMtHLL)LLM9tvIAEPAUt8IM9tGMl$MtIEIUL9tpH=tƸuLI]M9uLMtHLL)QHxH<]IELIEPIELPOLXE1ILHIHPIM9 JtL1tLXHPLHJf.IEHIEHHPL9HE1HxHDž0HEH HPH0LH@L4IHt H+HTpH ^H9Ho H^HHH HHHH E1H |H9H fInfInHH1fl)EIMt IMM7 HHHH(HHjLmLLH}LH}HEH9tHEHp4>HC ITLL;IEHULIEHuH}IE HHPH0H0H9A~HP}HXL9HHxE1@@ƅoE1HH;D>HHHXL9hHXLH@N4IIHt H.IFIFHtHH0AFA9?DoAUH$ A8HXHXHBH*HHX0HXHHxI@MHPIHLXIM9KtH.tLHPMMLXJGHW3fLGKfL7$fH'LPE1LfLHXHXHDHH1MPHUL`H7ZYDH@1HfDLXF@HWfH}HLI@Lf.HPH?H}AHUHLHXHHCHDž`LX0HfAF ZDHoHrLHAH9HH^H5H8ƅof.LXHtHHPDHPAHHHHHXHJfDH H5H8H H=qW1cf.JL<GL.MHXxHPL9@LfLHDžP)@M)L?fHnJ 8HLHPLHX)@^HXLLHHfHDž0L) L)HX!HH9GHXHXfHnLLH) H0HLLH(fHDžL)L)HXHXHH9HXHXfHnH)HM9M)J 8fBHIH9tlAo}IU8HtH=tBfDHDžhHOA,E1IA LE1VHH@LmL@HL HL0LHXHp0LHLHHHHHHHH@HHtRLOkHXSgLۥH@HtHPH) uzH@LpLHPL|IH8L9BH5LcuH\H H9HeL`MmHHI$HHHXBuLHX1HELe@HHMtL uHH!HXtHHtIMHPIHMtIIHLH=uHNTH5OTZ"HHHH|LPE1A A HPHHXHHkE1H=9!LhMLxIEHItLHHu LPA 11HDž@1HDžPvLXA E1_AHH+H5H81DA HPsE1]A RA H5eHA1LPA HHA Hu(lHXHu1E1HXHu1MA LXLLHHFHH@L H0FHH@H0FHFHfGHGH0GFHNFfUHAVAUATISHHHIH<L4LIL1HMT$0It$E1ML$ID$Ht-H1H6HA(HIDH8Ht?HHHHHuI<$It$L9t HNI\$M,$[A\A]A^]@I|$H9IL$LH9tKLILo0HG0MN@H=tlnHIEID$(HHf.fUHAWIAVAUATSHHS dH%(HE1HpEHH@HG0HHEHHEHFH;G HDžHHDžPHDžXHDžhHDžpHGHGHGHG(HG0HE`t H; IHDžHDžH@1LmHHHEH(H YI9OIGHH9IGL$HHI$Ht H H(LHEHHUHH9L9oMHMHUMHHEHMHEH}L9tHEHpHHuHULmH(HHXH}LuzHPHjL I@HHDL9suHsMtLLuLL L9tHEHpHLHLIHHHtHH0H9pIHHHIMtI $fH}HH9tHEHpbHH@HHHH9t H1HEdH+%(HH[A\A]A^A_]DLL iLPHH1HHHXH H@LMM0LMLILH}HLIv(MI-fDMMDIs(1MHHI9,ML9uM;~uMt-LIvLHHLuI8LLLLf0HPHIHMHPHEL9*IFHEIFHhINH`LmHXHEHHEH8HքH H@I^(H HHHIHL0HXH}LLLLL9t#oUHUULmLf.HUHHt?HЃtAUHMHuHHHUHE@HKfHH8H1HHH @HPLPIHtH@(1HHL4HPHf.I3HۖH=MHMTI $JDH9MdHHI$Hg@Hqr3MD=LD:1AN N 9r@2AEAF@AD5fD2oDEHUHEHUHE/ITHTFF1҉փI|5H<19r*L$?LH@HHIHt9H@HHHHDžLH@H;H=HFH@HAEAFAD5D2RAUATTHMHuQ7!IܻATfTHMHuH@E1HH+=L=H=H#=f.fUfHAWAVAUIATSHHHHdH%(HE1)pHD%VJ H HHHXHDžDƅDžƅHDžHDžHDžDž?HDžHDž HH(HDž0HDž8HDž@DžH?HDžPHDžXƅ`HpHfHHHHHHHDžxDƅDžƅHDžHDžHDžDž?HDžHDžHDžHDžHDžDž?HDžHDžƅHHHDžHDžHDžDž?HDžHDž)HCH;t H;F HHLHHsHH0LhHHpHHx $(,HHHHӀHLHH@躀EL爅HhHEHpH9t HH}H0H`H8H9t HLH0H HpHHHHxHHHH(H(H@HH L;-~`L1 HPH IEH;t H;? IEHDžHDž8E1H/IEHI9]1 H8H9TIEL$HH8I$Mt IHSH<H9XL5<ME IHHuHDžXI9F ~XfIn1LLfl)E'HXHHtHHHH"H1IPHPLHH @HMHLLIHbHt HH2H9 LLL>aMtL1aHPHLGHLLHn}HeHH@HH9t HH HP`H(L}HH;\1tHPHD HCH;t H;0 HHDžHDž8HIE1H!IEHI9]H8H98IEL$HH8I$Mt IHPH:H9XL5:MIH;HuHDžXI9F5~XfIn1Lfl)E莴HXHHtHHHHH8I7HPH_/H >HMHLIHHtH{H2H9D aL^MtL^HPHHLHzHHH@HH9t HBLH HPG^H LzH5H="]HH1HH9G@H_HCLgHI$]Hu1LHEH]HPHtH]HPL]LPLiÅLy]tƅHzH;HHpHs HC0pfopfHLhH)pLH`)HfHhHHt9HH`|HIH=HDž0AfLLHf.H=H6H56L IH1ALMkHPHHXHH>MHt H Mt I $HxHH0HCDH=G9HE1HHHH@HH(H(H HtH58LHHHH9t HH@HHHH9t HHHZHHHH9t HH )H(HH0H9t HuH(HHHH9t HDH0HtH07HEdH+%(HL[A\A]A^A_]IM1LM#L7fLL ^Hf.HHfDLMLE1HALyALa@HHHHLH5,H81e1AHDž0LkfDLO-f.L7*fH' AHPHHXHHE11E1HPfDHAf.H8H9#MdHHI$H8DIFHXHI^HLLH#XIHufH=ѨLALHDž01H=H 2H5 2vIH1AMLE1fDH8H9MdHHI$H8rDIFHXHI^HLH2WIHuH=YLMAVfACDH;!1HHHHB Hr(H*HpHhfDH{IHH@HHHHDž8@ALnfDA]DLIH"H@HHHHDž8 AI $A1IHu1IHu1HHH5H81AHHcH5H811ALqL6HhAtLnHHt1A=HHA6E11ALAE11AAHLͯ1M6jH賯M1AUHq*H*H>*H*H@*f.UfHAWAVAUIATSHHhdH%(HU1H)EHfHn)EHfHnHHEflfHn)EfHnfl)EHJ1MPHxHULEH ZYHEBHApfDHEHnAHUHAWIAVAUATSHH9sHIIDM7HCfH+ID$A $PHIH9RHQHfHnIMl$A$HCH3II)H9tHLjHLIL$C H(AD$ I(I9AD$ Lk ItFHCfH+ID$A$HIH9HHfHnIMl$A$HsH;ID$C SfDID$HSLI$H3HaIHL[A\A]A^A_]1E11fDHyDHyHHL7;LHּM9tLI(HHf.UHAUATISHHG Ln IMHFfH+HG'IHL99LHfHnILkID$I4$II)H9tHL2HLHKAD$ C H[A\A]]@C H[A\A]]DLnfL+.HGHL9LpHfHnILkIt$I<$HCDHGIT$HH6H_AD$ C H[A\A]]D1E11ff.I IH:9HLfUfHAWAVL@AUATSHhHL-dH%(HE1)pfHEEHDžHDžHDžL0HDž8ƅ@HDžHDžHDžHDžHDžHDž)L9 HG HHH@HLp> HPÃw fHDž )\L`fHn}fInLeflE)EJ Hp)pfHuHELpLe)EHt H)HEHHH9:H -H9H H-H HHH wH9H LHuHDž~1LLp)E辝HHHHtHHDžHI $LeHHDžLpLLXH0LLe[H}L9tHEHpLpH9 H H0H8H`HPHPLpHDžH'\HPLeHXH9HEH`HE}HULpHPHDžXƅ`ELHL EHHPH9tH`Hp(HHXxDL#M9t MH[HuHDžE1HLLPHHLHPfoXXH)HtR#HPvH`Ht3#HPWH#HkH^IHdHHLHHDžHxx*sHHt"H0L9tH@HpEHuAHLڙtH=8DHfHA`HHAhA`HHApHApHH=赖IHtH11HjL">A5LHu@H\HhHHLf.ff.UffHAWAVAUATISHHXHHdH%(HE1HpƅpHH`HEHDžhHDž0HHEHEE)) )YIH H;MH=x/Hu1HHEH]IH H@HH@HHHHHj IpML;%tLH H ~IHfHnfl)HtL5 M&@HDžPfE1)@H@LHHLHcHa HfInfHnflLhHPHHyHHHtmHHHtTH1HIH}HH9tHEHp'HHtHtH L(H I9t}I%f.AD$PAT$t=HI9tELcMtID$IT$L9H=tuLH%I9uH HtH0HH)aHHtHMHHt<H`HH9tHpHpHEdH+%(*HXL[A\A]A^A_]HDžHDžMrLH;MILLHHIffHnHfH~fDHH9ufHnfHnL;%ӪH0fl) LHxI91GIH H1HH;tHDžHHIHHtH8MFH5+LDH5,LDH)H HH9H HnH HHHuHDžH9C ~fIn1Hfl)EfHHHtH+8H H8L 8LH5OL7HHkH;,iH@ Hs(HHCH7HHHL$HHdH7H;H(H 5HH9HEHHHHLHH?HHuHDžH9Cx~1H)EHHHtH6H6HH6HEHHHHFH}HIHZHHU6H`HuI`HHHHHHt H H`HhHEHHEHUJ~HH{HCHCHH)HhCHfoC8fCP5H HfHnfHnflHXHPHHyHHHtHůLLr9HHHI9LH@fL(H MHDžP)@I) HL9LSIfHnLP)@I9I)IBHHL9`oHSHtMtBԐID$I$LPI$LP0fDLOf.HDžAE1E11Hw_DH=_!MtIE1HIHt+MMJIALݨ3LǨH5kH=?R3HHLHHHH-1IHHH9CLsM#LcIHI$2HufIn1LLm)EIMtL2H2L2MbL2H=LHHHhLo2111HHY2AE1_E1AQHDžA;AE1E1HDžHDžL1HHLHML1AE1H5&H=14HHt111HH1AE1H5H5H8ŪAnHEH5BHHHAH}HDHHAHLE1H1HDAE1IAH5PhH==0IH(LHHDLIH2L7HHHI9GMwM0MoILIE>0HufInfIn1LHfl)EHE.HMtL/L/H/H!L/H=BH:HIHH/111LL/AE1AAbHƠH2`H5дH81}HLAIHDžE1HAsH=H H5 HHAAH1HH.HHH.H=fAyAHCHHtHKHHHHW.HHuFHLAE1HDžLHAE1E12H=-XH H5 HHLAIHCHHJHKHHHH-HHuH=WuA ~IHfHnfl)HtH.HfHDžP)@HfHnHHHHP)@FHHH9t o9Hq8HtH=tFH@HHLHHHL"}HHHtI IM1HPIHDžA8LE1AHDžYIHuE1#HE1E1HDžAHIHuMHuE1HME1E1HDžAHHDžAHE1AHDžHQHAE1E1HDžHHDžMHuAE11cH= II=HBIOIIIIIIIIIIHIH IIYf.UfHAWAVIAUATMSHHHxdH%(HE1HQ)EH9 H9H9ZH5sHi HHIHLHI9.HC Hs(H}HE HEHuI|$I$1cHEHL=7~IFIH;t H;ZIHDžpHDžxE1E1Hp8IFHҗI9VHxH9PIFHHHxHHEMt I $@IMt IMH5}LO7HEHRIHH ;H9HH"HEHHIHHEHIERHEHLHzIHH@H5SyLHHHEH}wI $eLeH5yH}LI$HhHI$=HUH}L*IHHUHHEHHIM0HUHHEHH HMHHEHHH}HH9GHW HGHHH9H9HWI$L$HHGHpML}HpLHEHHtHJH2H9<0L&L&HELeMHIAfH5~H=ҥ1{)IH111HL9&DžxE1E1HEHEHE]@L?qf.L(IHH8RfHEdH+%(HxL[A\A]A^A_]DžxHEIE1E1HEHEMt I $HuHtHHEHHxHPH=HtE1H tlH]HtHHEHHMtItVHMH]HtHHEHHtHH@HDHDLDHי@fLǙfHbfHEDžxHMHHpHHHEHEMt IHuIHtHHEHHtCMtIMtHHMH~HHEHHkH]f.HDLDH^fLטdfLǘfLfLfH}HfHwfLgfHWfH5{H=r1&HH111H!H"DžxHEE1E11HEHEHEE1E1HEX@H5 {H=1%HHo111HHY"Džx{f.HEE1E1HEHEL}DžxHxH9`HI|HHH}Hx f.H=LHzH5{vHEH}MHEE1E1HEDžx$fHH]fDHEE1HEDžxfDH=1L4yH}L)LeDžxHEE1E1HEHEfLϔIHH@HHpHHDžxDžxHEE1DžxfDDžxgDH59JH}LmdHEHH( 9Džx 1qDžxbDžxSDžxE1Džx1)L}E1E1E1DžxHEHEHEHE-LeE1E1HEHEDžxHzLL}MLeDžxH'Hf.@UHHAWAVAUIATSHL%7dH%(HU1H_HEHHfHnHLefHnH@ fHnLeflHE)EfHnflLe)EHJ4HIHH JcHL{H HMLHI9 H;LuHHHEHIM}H _HH#H9 HEIzfDI I.ITLLLxL}L(LmHPI9Et M9 fIH)fHHDžP)@M9j IEH HDžHxHMwL9t;IXHHqH1HH9H;TuH=p1IHH5pHHHH5)u1HHHHHHh HHB HHHHH HIHHHH I$I LMfML9#MuH;l fIvHpHHuHxHH} IEHHHHƘHHHHHH HHAH/E1HHLLLHH IEH HH;eHHH@H;t H; HHDžHDžHIHHDžIEH=QI9}HH9IEHHHHL9HtHHHHHzHYHHH9_L9&HHH;Po[ HC(HtH=@HHHHHHLHH6rHHtHH0H9 LYLHfH@HDžp)`MI) HL9E LŐfHnILp)`I9 I)J BHHH9 o#HS HtH=tB@Hߍxf.HL{HEMLLmMLfo(L{)mMLmL}LLo@o0L{)u)EM~2HHs1MPHHULEH^Y^HELmL}HHEH:HPo8L{HU)}MHELmLL}H@LMLHELHu"M1H=*sHB H=jCmE1HEdH+%(< HeL[A\A]A^A_]DHS H@O%f.@HHH5a HV`HHDž3 @Dž4 fDMt[HDžE1HDžIM0Mt I $1HHtHHHHLHAH=LBOE1Ht H Mt IHHtHHHHNMt IMOI&HHHHHLHH@I9t}I#f.AEPAUt7HI9tGLkMtIEIUL9tqH=tǸuLHI9uH@HtHPHH)HHIELIEPIELPOHωf.LfDLfHTfLwRfjDLWfLGfHHHfHnHHP)@HH9_I\HHHHTfDH5!KH=J mIHHHH.HHHHHHI9D$ID$HHNIT$HLHHHuLH1HfInHE)EgHMtLHHHHxHH=ڑjIHHM111LL7Dž5 MIM9JtHϺHHtHJfH!HG?H5+H81؅Dž7 )H]H+\HHHteHEIH51H,mLk(fIH)fHHDžP)@HHAQHH*HbHEIDHH5bH8HDžE1Dž" HDž1pDž" E11HDžHDž?fDL2f.Hw fHgfHWLODž) E1H1HHHHH[H MHIHH@HHHzHDžHDž1Dž/ FDž2 1HDž*HfoHHh) HtL0H`HH HL7L?H8IHtH(HtHMH5aLIH4H'I9D$I\$H1ID$HLHHdHuH1HEH]acHHtH-HHHIEHMHDž1HDžDž( HDžHDžDž) Dž# E1E11HDž1riHZ6HDžHDžDž6 MDž6 HDžHDžDž6 LHuHDžLHDžHDžDž6 xLHuDž5 HHDžDž9 _LHu11HLDž; .LHu1LHLDž; Dž3 E11HDžHDžHDž1Dž3 +fQ11HE1HHDž3 BH=se}H]xIIHf.UHHAWAVAUATISHL=w|dH%(HU1H1LHEHHfHnHL}fHnH@ fHnL}flHE)EfHnflL})EHJ ~LLHfH@HDžp)`MI)BHL9 L~fHnILp)`I9 I)J DBHHH9o3HS0HtH=\}tBILvLuLhLmoDH51H9t'H1HH9H;tuL9HHH;Pok HC(.HcH=|6@MDLzf.I9K\IHHDHH9HuH H9HLH9>HHH9+HuH9fH}Hu1H=wHHE(ZIHR111H.IA,I $IE1H_0DH=` E1HHHHHLHMt IMt I $MtLAHtH4LHH@I9txI DAD$PAT$t=HI9tELcMtID$IT$L9qH=ztuLHUI9uH@H}HPHH){e@HLsHEMJLLeMMo>Ls)}MHELeMMHvoFo>Ls)})EM~2HH _1MPHHULEHlZYHELeLmLuHHVo>LsHU)}MHELeMLmHLMMHEnHuM1H=S^H-H=)^E1HEdH+%(^HeL[A\A]A^A_]fDHH@,HS H@mfHHyfHnHHP)@o@AFHH@HHID$I$LPI$LPHHrH.H5H81nuI $AaLmvSHyrHr.H5H810uAH;=L"v`Hv2LvOMIM9JtHϺH[HtHJTHHJHH腴Ht,HEIrH+H=[lE1HHF@HH/HHEIHj+H=[HE1RMQ1HfoHHh) HtzH0L`HH LHʂHH8IHtH(HtLhH`I9t_HAEPAUHI9t/LkMtIEIUH9H=utH`HtHpHH){vMZH5QLcHHH|H9GGLoM,H_IEHIHu1HHELmJSIMtLMtH LI$MCH t A"H sIELIEPIELPHLrAHqIHH@HHHtaIZLHZ6}HA!^HHu1HHu1E11A"E1A$MrXLAuH=VWnHMHgHH"HHIVfUHAWAVAUATISHHdH%(HE1H<HEH@fHnH}mHEfHnflHE)EHHHHHHtoM1H=WtH'H=W HDžHEdH+%(HHe[A\A]A^A_]LsL=1M1HI9L;|uHHHEHsMnMHilL}HHfH HFHHEL>L}V@o.Ls)mM~2HHVH1PHHUMLE^_HEL}HHffHDžp)0H5IHHy) )`)@H9p HH HHLjAă5 H fEIGH57LHHNHH#HZwH9CYLcMLkI$IEH  LHu1HHELeNHMtLhHH a IH LIGH;~gt H;gILHDžHDžHDžE1HHCHgHH9SH9HCL$HHI$HHtHHHHID$HH5H9r8 L5MX IH;f I$LH1LLHH IHHHHHfHH;0il MiIM_LL;- iKHhH;pAo] IE(HtH=un@HHhHLuHHIH6wHtHsH2H9nH HHDžƅHIHe HLeLLF?H@LH脙H}HtvHC H HX Enfo H(H)PHtfHhH`fHE)EHH)HQ HH9HmHfHnH)EHUHH9 H)HHBHHH9o#HS HtH=ltB@HfHDL;%fI$MIMM@H5L4 H5/H=IHw H5LHHH L%rL9` HXH8 H@HHHHuH1HEH]IHHtHHt HrM9f~ I^H IFHLHHBHufHnH1)E;IHHtHHH H1f. i3DIU H`p1f.HhfHhfLh_fHLkHEM4L{H 3M71@HI9TH;LuHHHHEMu8@@HhtH9LdHHI$H@E1IM9JtLrtHJFLgfH5YIH=q1{IHv111HL9DžE1L-mcHDžHDžHDžHt H FMt IHHHtHHHH6HHtHHHH$HH=MHHt)H1HHHHHHMt IMMt I $XHHI(HHHHHHtHLhH`I9tzI"AD$PAT$t9HI9tELcMtID$IT$L9t}H=gtøuLHI9u@H`HtHpHH)9hH8Ht(H(H}fDID$I$LPI$LP@fDL7efH'efLefHeefLdkfHdfLdfHdfHdfLdfH5LQ/I$MMy@H=i%HH5֜IHID$L7dfH'dfE1IM9JtHϺH胠HtHJpfH=iHH56HHCHDžE1DžIf.L?pHHHDžHDžDžfH=9$Hc<DžE1E1E1HDžHDžHDžH=z蕚HDžDžrH5Hg-t*MLLHuDžE1HDžHDž lH3)DHDžHDžHDžDžHHHMHPLH/[TdH- H8fHnfHnflHXHPHqX)0HHHt9L聆HXHt H5qH=1+HH*H;\]HH0HCHPkHHLLHPHZHEkHHDžE1Dž?XHDž1E1DžHu1E1DžE1E1IHDžHDž:Hu1L_HHH@HHH?HDž1HDžL-\HDžHDžDžHDž1E1Dž1DžE1HDžHDžHDžLDžE1E1HDžHDž4HHu11HPDžE1E1HDžLHu1LE1E1HDžHDžDžHHu1HLLHDžDžLLLHDžDžHZHH5nH81A]LLHDžDž+LLLDžLLLDž_DžE1E1E1HDžHDžHDžyHDžE1Dž HC1LE1DžHHHu]`HH4HH`HqHHat leastat mostexactly%.200s() needs an argumentBad call flags for CyFunctionUnknown exception__pyx_capi__int (arrow::Status const &)maybe_unbox_memory_poolPyObject *(PyObject *)make_streamwrap_functimeunit_to_stringstring_to_timeunitpyarrow_unwrap_metadatapyarrow_wrap_metadatapyarrow_wrap_bufferpyarrow_wrap_resizable_bufferpyarrow_wrap_data_typepyarrow_wrap_fieldpyarrow_wrap_schemapyarrow_wrap_scalarpyarrow_wrap_arraypyarrow_wrap_chunked_arraypyarrow_wrap_tensorpyarrow_wrap_batchpyarrow_wrap_tablepyarrow_unwrap_bufferpyarrow_unwrap_data_typepyarrow_unwrap_fieldpyarrow_unwrap_schemapyarrow_unwrap_scalarpyarrow_unwrap_arraypyarrow_unwrap_chunked_arraypyarrow_unwrap_tensorpyarrow_unwrap_batchpyarrow_unwrap_tablepyarrow_internal_check_statusint (PyObject *)pyarrow_is_bufferpyarrow_is_data_typepyarrow_is_metadatapyarrow_is_fieldpyarrow_is_schemapyarrow_is_arraypyarrow_is_chunked_arraypyarrow_is_scalarpyarrow_is_tensorpyarrow_is_sparse_coo_tensorpyarrow_is_sparse_csr_matrixpyarrow_is_sparse_csc_matrixpyarrow_is_sparse_csf_tensorpyarrow_is_tablepyarrow_is_batch__loader__loader__file__origin__package__parent__path__submodule_search_locations_cython_coroutine_type_cython_generator_type__builtins__collections.abcbackports_abcbuiltinsboolcomplexdatetimetimedelta_cython_3_0_10Expected %s, got %.200sMissing type objectcannot import name %San integer is required_enabledarrow_arrayarrow_schemaarrow_array_streamkeywords must be stringsname '%U' is not defined__enter__dictionary_encodeused_dltensor__arrow_ext_serialize____arrow_ext_scalar_class____arrow_ext_class__pyarrow/lib.pyxpyarrow.lib.cpu_countpyarrow/memory.pxipyarrow/types.pxipyarrow.lib.Field.__init__pyarrow.lib.Schema.__init__pyarrow/table.pxipyarrow/tensor.pxipyarrow.lib.Tensor.__init__pyarrow/io.pxipyarrow.lib.io_thread_countpyarrow.lib.Buffer.__init____exit__pyarrow/ipc.pxipyarrow/public-api.pxipyarrow.lib.Tensor.equalsotherstppyarrow.lib.Message.equalsbuilderpyarrow/builder.pxiinit_schemaincluded_fieldsc_options_assert_openarrow.fixed_shape_tensorlist_typelist_view_typedict_typerun_end_encoded_typewrappedpyarrow/scalar.pxisp_chunked_arraypyarrow.lib.Scalar.__hash__sp_arraypyarrow/array.pxipyarrow.lib.Array.lengthinit_rz__del__pyarrow.lib.MemoryPool.initpyarrow.lib.DataType.__hash__map_typets_typetime_typeduration_typedecimal128_typedecimal256_typepyarrow/lib.pxdpyarrow.lib.Schema.__len__pyarrow.lib.Schema.__hash__pyarrow.lib.Schema.__iter__pyarrow.lib.Array.__len__pyarrow.lib.Array.__iter___ssize_t_shape_ssize_t_stridespyarrow.lib._Tabular.__len__pyarrow.lib.Buffer.getitempyarrow.lib.Buffer.__len__own_fileis_readablepyarrow.lib.CacheOptions.initpyarrow.lib.Codec.__repr___stop_tokenpyarrow/error.pxi_loose_versionpyarrow/pandas-shim.pxi_pd_types_api_compat_module_categorical_type_datetimetz_type_extension_array_extension_dtype_array_like_types_is_extension_array_dtype_lockhas_sparse_pd024_is_v1_is_ge_v21_is_ge_v3pyarrow.lib.UnionType.__len__sp_typepyarrow.lib.box_memory_pool__reduce_cython__set_memcopy_threadspyarrow.lib.Buffer.equalsbytes_allocatedmax_memorypyarrow.lib.Buffer.to_pybytespyarrow.lib._is_primitivepyarrow.lib.alloc_c_streampyarrow.lib.Table.__cinit____arrow_ext_deserialize__set_auto_loadkeysitemspyarrow.lib.Field.equalspyarrow.lib.Field.__reduce__pyarrow.lib.Schema.__reduce__pyarrow.lib.Schema.equalssp_schemapyarrow.lib.UInt8Scalar.as_pypyarrow.lib.Int8Scalar.as_pypyarrow.lib.Int16Scalar.as_pypyarrow.lib.Int32Scalar.as_pypyarrow.lib.Int64Scalar.as_pypyarrow.lib.FloatScalar.as_pypyarrow.lib.MapScalar.as_pypyarrow.lib.Array.from_pandasget_total_buffer_sizepyarrow.lib._codes_to_indicesfind_physical_offsetfind_physical_lengthiterchunkspyarrow.lib._Tabular._columnitercolumnsremove_column_is_initializedpyarrow.lib.alloc_c_arraypyarrow.lib.alloc_c_schemapyarrow.lib.Table.__reduce__pyarrow.lib._normalize_indexpyarrow.lib.Codec.unwrappyarrow.lib.Buffer.hexpyarrow.lib.Field.__hash__pyarrow.lib.DataType.__str__generator already executingpyarrow.lib.Table._to_pandaspyarrow.lib.Schema._fieldpyarrow.lib.Table.group_by__setstate_cython__maps_as_pydictslogging_memory_poollogging_poolpyarrow.lib.proxy_memory_poolproxy_poolenable_signal_handlers_reconstruct_record_batch_reconstruct_table_make_shape_or_strides_bufferis_seriesis_indexpyarrow.lib.get_valuespyarrow.lib.Table.equalsfrom_streampyarrow.lib.OSFile.filenohandlereadlinesreadlinetruncatewritelinesreadallpyarrow.lib.NativeFile.read1_assert_seekableis_seekable_assert_writableis_writable_assert_readablepyarrow.lib.NativeFile.isattyfrom_dense_numpy__arrow_c_stream__pyarrow.lib.Table.droppyarrow.lib.Table.__sizeof__from_struct_arraypyarrow.lib.Table.casttarget_schemapyarrow.lib.RecordBatch.castappend_columnpyarrow.lib._Tabular.takepyarrow.lib._Tabular.fieldpyarrow.lib._Tabular.column_ensure_integer_index__dataframe__pyarrow.lib.ChunkedArray.takevalue_countspyarrow.lib.ChunkedArray.castto_numpy_ndarraydictionary_decodevalue_lengthsvalue_parent_indicespyarrow.lib.Array.tolistpyarrow.lib.Array.to_pylistpyarrow.lib.Array.indexpyarrow.lib.Array.drop_nullpyarrow.lib.Array.takepyarrow.lib.Array.fill_nullpyarrow.lib.Array.is_validpyarrow.lib.Array.is_nanpyarrow.lib.Array.is_nullpyarrow.lib.Array.__sizeof__pyarrow.lib.Array.uniquepyarrow.lib.Array.castpyarrow.lib.UnionScalar.as_pypyarrow.lib.ListScalar.as_pypyarrow.lib.Scalar.__reduce__pyarrow.lib.Scalar.castadd_metadatapyarrow.lib.Schema.appendpyarrow.lib.Schema.fieldempty_tableto_dictget_all.0genexprpyarrow.lib._wrap_read_statsstorage_typeserializedpyarrow.lib.Buffer.__eq__pyarrow.lib.Tensor.__eq__pyarrow.lib.Tensor.__repr__pyarrow.lib.Array.__str__pyarrow.lib.Array.__getitem__pyarrow.lib.Array.__repr__pyarrow.lib.Scalar.__str__pyarrow.lib.Scalar.__repr__pyarrow.lib.Schema.__str__pyarrow.lib.Schema.__repr__pyarrow.lib.Field.__repr__pyarrow.lib.DataType.__repr__pyarrow/config.pxipyarrow.lib._build_infochunkpyarrow.lib.Field.__str__get_field_indexstruct_typepyarrow.lib.Message.__repr__pyarrow.lib.as_buffer__call__pyarrow.lib.NativeFile.filenopyarrow.lib._normalize_slicekeypyarrow.lib.type_for_aliaspyarrow.lib.large_utf8pyarrow.lib.utf8_get_pandas_tz_typepyarrow.lib._get_pandas_typelog_memory_allocationspyarrow/compat.pxipyarrow.lib.frombytespyarrow.lib.tobytespyarrow.lib.encode_file_pathpyarrow.lib._wrap_write_statspyarrow.lib.CacheOptions.wrappyarrow.lib.Scalar.as_pythrow_as_py_tupleselfpyarrow.lib.compresspyarrow.lib.Message.serializepyarrow.lib.Message.__init__pyarrow.lib.Array.__init__pyarrow.lib.Scalar.__init__pyarrow.lib.DataType.__init__pyarrow.lib.ensure_metadatato_pydictappend_valuespyarrow.lib._Tabular.__init__pyarrow.lib.Buffer.__repr___reconstructfunctionpyarrow.lib.DataType.fieldpyarrow.lib.UnionType.fieldfield_by_namepyarrow.lib.StructType.fieldpyarrow.lib._check_is_filepyarrow.lib.OSFile.__cinit__pyarrow.lib.memory_mappyarrow.lib.asarraybenchmark_PandasObjectIsNullpyarrow/benchmark.pxiobjpyarrow.lib.Schema.__eq__pyarrow.lib.Scalar.__eq__pyarrow.lib.Array.__eq__pyarrow.lib.Field.__eq__pyarrow.lib._Tabular.__eq__release_unusedpyarrow.lib.set_memory_poolread_pandasto_struct_array_detect_compression__pyx_unpickle__Tabular__pyx_unpickle___Pyx_EnumMetafrom_network_metricsset_memcopy_thresholdset_memcopy_blocksizepyarrow.lib.Codec.detect_datetime_from_intNo module named '%U'pyarrow.lib.UnionArray.childcython_runtimedoes not match__orig_bases__getinit pyarrow.libpyarrow.lib._pacpyarrow.lib._pcpyarrow.lib.create_memory_map__reduce_ex__pyarrow.lib.is_boolean_valuepyarrow.lib.is_float_valuepyarrow.lib.is_integer_valuepyarrow.lib.get_native_filepyarrow.lib._Tabular.sort_bypyarrow.lib.Table.joinpyarrow.lib.as_native_filepyarrow.lib.Array.filtermaskpyarrow.lib._empty_arraywrite_queuepyarrow.lib.Tensor.dim_namedrop_columnspyarrow.lib.decompresspyarrow.lib.Array.sortpyarrow.lib.ChunkedArray.sortpyarrow.lib.Table.filterpyarrow.lib.input_streampyarrow.lib.output_streampyarrow.lib._as_c_pointerpyarrow.lib.Schema.__sizeof__pyarrow.lib.StructArray.sortcombine_chunksfrom_buffersvalue_offsetsnull_bitmap__new__pyarrow.lib.unionsuper(): empty __class__ cellpyarrow.lib._from_pydict_handle_arrow_array_protocolpyarrow.lib._from_pylistcleanup_have_pandasis_availablesupports_compression_levelpyarrow.lib._to_pandas_dtypetranscoding_input_streampyarrow.lib._Tabular.__repr__pyarrow.lib.Table.join_asofpyarrow.lib.DataType.__eq___tried_importing_pandasget_rangeindex_attribute_check_importinfer_dtype_have_pandas_internalis_data_frameis_categoricalis_datetimetzis_sparsepyarrow.lib._is_array_likepyarrow.lib.wrap_array_outputpyarrow.lib.tablepyarrow.lib.Array.formatpyarrow.lib.Array.sumaggregatepyarrow.lib.NativeFile.uploadpyarrow.lib.ensure_typepyarrow.lib.DataType.equals__pyx_unpickle__PandasAPIShimpyarrow.lib.Array.difffrom_numpy_ndarraypyarrow.lib.Array.equalspyarrow.lib.Array.__array__pyarrow.lib.record_batchpyarrow.lib.runtime_infopyarrow.lib.primitive_typepyarrow.lib.string_viewpyarrow.lib.binary_viewpyarrow.lib.large_stringpyarrow.lib.large_binarypyarrow.lib.stringpyarrow.lib.float64pyarrow.lib.float32pyarrow.lib.float16pyarrow.lib.date64pyarrow.lib.date32pyarrow.lib.int64pyarrow.lib.uint64pyarrow.lib.int32pyarrow.lib.uint32pyarrow.lib.int16pyarrow.lib.uint16pyarrow.lib.int8pyarrow.lib.uint8pyarrow.lib.bool_pyarrow.lib.nullpyarrow.lib.Table._columnpyarrow.lib.wrap_datumpep3118_formatpyarrow.lib.map_sp_fieldpyarrow.lib.durationpyarrow.lib.time64pyarrow.lib.time32pyarrow.lib.timestampfrom_storagetyppyarrow.lib.Scalar.equalspyarrow.lib.Scalar.unwrappyarrow.lib.Table.slicesp_tensorpyarrow.lib.Tensor.initpyarrow.lib.Scalar.wraprandom_accesspyarrow.lib.Array.slicepyarrow.lib.StopToken.initpyarrow.lib.StructArray.fieldpyarrow.lib.convert_statuspyarrow.lib.check_statuspyarrow.lib.get_writerget_output_streamtensor_ext_typerelease_registrypyarrow.lib.Array.__reduce__write_tableresizeget_random_access_filepyarrow.lib.NativeFile.seekpyarrow.lib.NativeFile.closesp_sparse_tensorto_scipyto_pydata_sparsepyarrow.lib.Tensor.to_numpypyarrow.lib.Table.validate_export_to_csp_batchpyarrow.lib.Array.validate_debug_printpyarrow.lib.Scalar.validate__arrow_c_schema__pyarrow.lib.Schema.to_stringUninitialized Resultdownloadreadintopyarrow.lib.NativeFile.readmaximum_compression_levelminimum_compression_leveldefault_compression_levelpyarrow.lib.Array.__dlpack____dlpack_device__get_record_batch_sizepyarrow.lib.get_tensor_sizepyarrow.lib.foreign_bufferset_io_thread_countpyarrow.lib.have_libhdfspyarrow.lib.Array.to_stringpyarrow.lib.string_to_tzinfounregister_extension_typejemalloc_set_decay_msset_timezone_db_pathpyarrow.lib.set_cpu_countpyarrow.lib.Scalar.initsp_tablepyarrow.lib.Table.initpyarrow.lib.RecordBatch.initpyarrow.lib.ChunkedArray.initread_next_batchfinishsp_memo_export_to_c_devicegetvaluepyarrow.lib.Field.initpyarrow.lib.Array.initpyarrow.lib.DataType.initpyarrow.lib.UnionType.initcpy_ext_typefixed_size_binary_typepyarrow.lib.DurationType.initpyarrow.lib.Time64Type.initpyarrow.lib.Time32Type.initpyarrow.lib.StructType.initpyarrow.lib.MapType.initpyarrow.lib.ListViewType.initpyarrow.lib.ListType.initpyarrow.lib.Array.bufferspyarrow.lib.run_end_encodedremove_metadatapyarrow.lib.get_readerpyarrow.lib.Buffer.initwith_metadatapyarrow.lib.Table.to_batches__arrow_c_array__serialize_tosinkpyarrow.lib.Field.with_namepyarrow.lib.decimal256pyarrow.lib.decimal128from_tensorpyarrow.lib.list_pyarrow.lib.large_listpyarrow.lib.NativeFile.flushpyarrow.lib._cb_transformpyarrow.lib.Field.with_typenew_typepyarrow.lib.NativeFile.sizepyarrow.lib.write_tensordestwith_nullablepyarrow.lib.binarywrite_batchset_input_streamreplace_schema_metadatapyarrow.lib.list_viewpyarrow.lib.large_list_viewpyarrow.lib.NativeFile.tellpyarrow.lib.UnionArray.fieldread_atpyarrow.lib.RecordBatch.slicepyarrow.lib.Field.flattenpyarrow.lib.Schema.initpyarrow.lib._ndarray_to_typepyarrow.lib._ndarray_to_array_ndarray_to_arrow_type_import_from_c_capsule_import_from_cpyarrow.lib.infer_typepyarrow.lib.from_numpy_dtypeget_all_field_indices_flattened_fieldpyarrow.lib.Array.viewpyarrow.lib.repeatpyarrow.lib.nullsget_batch_import_from_c_devicepyarrow.lib.read_record_batchdictionary_memo bytes, have pyarrow.lib._allocate_bufferpyarrow.lib.allocate_bufferpyarrow.lib.as_c_bufferpybufout_bufpyarrow.lib.Codec.decompresspyarrow.lib.Codec.compresspyarrow.lib.NativeFile.writepyarrow.lib.Buffer.sliceread_bufferpyarrow.lib.Schema.serializepyarrow.lib.py_bufferto_tensorpyarrow.lib.read_tensorpyarrow.lib.tzinfo_to_stringpyarrow.lib.Schema.setpyarrow.lib.Schema.removepyarrow.lib.Schema.insertpyarrow.lib.read_schemapyarrow.lib.dictionarypyarrow.lib.Array.getitemunify_dictionariespyarrow.lib.arraypyarrow.lib.scalarread_allpyarrow.lib.Table.set_columnpyarrow.lib.Table.add_columnpyarrow.lib.Table.flattenpyarrow.lib.Table.to_readerdetachget_streamcreatepyarrow.lib.get_input_streampyarrow.lib._get_input_streamopen_streamset_output_streamunicodepyarrow.lib.Codec.__init__read_next_messagepyarrow.lib.read_messagefrom_batches_from_arrayspyarrow.lib.Table.from_pandaspyarrow.lib.fieldpyarrow.lib.Array.to_numpyvector::reserverename_columnspyarrow.lib.Tensor.from_numpypyarrow.lib.unify_schemaspyarrow.lib.structpyarrow.lib.sparse_unionpyarrow.lib.dense_unionfrom_pydata_sparsevalue_typefrom_scipypyarrow.lib.concat_arrayspyarrow.lib.chunked_arraypyarrow.lib.concat_tablespromote_optionspyarrow.lib.Table.select_init_signalspyarrow.lib.Array._to_pandaspyarrow.lib._restore_arrayfrom_densechildrenfield_namestype_codesfrom_sparsepyarrow.lib.table_to_blockspyarrow.lib._sanitize_arrayspyarrow.lib.Table.from_arrayspyarrow.lib.schema_cython_3_0_10.generator__name__name of the generator__qualname__gi_frameFrame of the generatorgi_runninggi_yieldfromgi_code__module__sendfunc_doc__doc__func_namefunc_dict__dict__func_globals__globals__func_closure__closure__func_code__code__func_defaults__defaults____kwdefaults____annotations___is_coroutineCythonUnboundCMethodpyarrow.lib.__Pyx_EnumMetanum_record_batches_use_legacy_format_metadata_versionpyarrow.lib.MessageReader__next__pyarrow.lib.BufferReaderpyarrow.lib.MockOutputStreampyarrow.lib.OSFilepyarrow.lib.MemoryMappedFilepyarrow.lib.PythonFilepyarrow.lib.StringViewBuildernull_countpyarrow.lib.StringBuilderrun_endspyarrow.lib.LargeBinaryArraytotal_values_lengthpyarrow.lib.LargeStringArraypyarrow.lib.DurationArraypyarrow.lib.Time64Arraypyarrow.lib.Time32Arraypyarrow.lib.TimestampArraypyarrow.lib.Date64Arraypyarrow.lib.Date32Arraypyarrow.lib.ExtensionScalarpyarrow.lib.UnionScalartype_codepyarrow.lib.DictionaryScalarpyarrow.lib.MapScalarpyarrow.lib.StructScalarpyarrow.lib.ListViewScalarpyarrow.lib.LargeListScalarpyarrow.lib.ListScalarpyarrow.lib.StringViewScalarpyarrow.lib.BinaryViewScalarpyarrow.lib.LargeStringScalarpyarrow.lib.StringScalarpyarrow.lib.LargeBinaryScalarpyarrow.lib.BinaryScalarpyarrow.lib.DurationScalarpyarrow.lib.TimestampScalarpyarrow.lib.Time64Scalarpyarrow.lib.Time32Scalarpyarrow.lib.Date64Scalarpyarrow.lib.Date32Scalarpyarrow.lib.Decimal256Scalarpyarrow.lib.Decimal128Scalarpyarrow.lib.DoubleScalarpyarrow.lib.FloatScalarpyarrow.lib.HalfFloatScalarpyarrow.lib.Int64Scalarpyarrow.lib.UInt64Scalarpyarrow.lib.Int32Scalarpyarrow.lib.UInt32Scalarpyarrow.lib.Int16Scalarpyarrow.lib.UInt16Scalarpyarrow.lib.Int8Scalarpyarrow.lib.UInt8Scalarpyarrow.lib.BooleanScalarpyarrow.lib.NullScalarpyarrow.lib.DenseUnionTypepyarrow.lib.SparseUnionTypepyarrow.lib.UnionTypemodepyarrow.lib.ProxyMemoryPoolpyarrow.lib.LoggingMemoryPoolLoggingMemoryPool()pyarrow.lib._PandasAPIShimcompatpyarrow.lib.SignalStopHandlerpyarrow.lib.StopTokenpyarrow.lib.CodecReturns the name of the codecpyarrow.lib.CacheOptionshole_size_limitrange_size_limitlazyprefetch_limitpyarrow.lib.RecordBatchReaderpyarrow.lib.NativeFileclosedpyarrow.lib.ResizableBufferpyarrow.lib.Bufferaddressis_mutableis_cpupyarrow.lib.RecordBatchnum_columnsnum_rowsnbytespyarrow.lib.Tablepyarrow.lib._Tabularcolumn_namespyarrow.lib.ChunkedArraynum_chunkspyarrow.lib.ExtensionArraypyarrow.lib.DictionaryArraypyarrow.lib.BinaryViewArraypyarrow.lib.StringViewArraypyarrow.lib.BinaryArraypyarrow.lib.StringArraypyarrow.lib.UnionArrayGet the type codes array.pyarrow.lib.MapArraysizespyarrow.lib.ListViewArraypyarrow.lib.LargeListArraypyarrow.lib.ListArraypyarrow.lib.BaseListArraypyarrow.lib.StructArraypyarrow.lib.Decimal256Arraypyarrow.lib.Decimal128Arraypyarrow.lib.DoubleArraypyarrow.lib.FloatArraypyarrow.lib.HalfFloatArraypyarrow.lib.UInt64Arraypyarrow.lib.Int64Arraypyarrow.lib.UInt32Arraypyarrow.lib.Int32Arraypyarrow.lib.UInt16Arraypyarrow.lib.Int16Arraypyarrow.lib.UInt8Arraypyarrow.lib.Int8Arraypyarrow.lib.IntegerArraypyarrow.lib.NumericArraypyarrow.lib.BooleanArrayfalse_counttrue_countpyarrow.lib.NullArraypyarrow.lib.SparseCSFTensorndimdim_namesnon_zero_lengthpyarrow.lib.SparseCOOTensorhas_canonical_formatpyarrow.lib.SparseCSCMatrixpyarrow.lib.SparseCSRMatrixpyarrow.lib.Tensoris_contiguouspyarrow.lib.Arraypyarrow.lib.Scalarpyarrow.lib.Schemapandas_metadatapyarrow.lib.Fieldpyarrow.lib.KeyValueMetadatapyarrow.lib._Metadatapyarrow.lib.PyExtensionTypepermutationpyarrow.lib.ExtensionTypepyarrow.lib.BaseExtensionTypeextension_namepyarrow.lib.RunEndEncodedTyperun_end_typepyarrow.lib.Decimal256Typeprecisionscalepyarrow.lib.Decimal128Typepyarrow.lib.DurationTypepyarrow.lib.Time64Typepyarrow.lib.Time32Typepyarrow.lib.TimestampTypepyarrow.lib.DictionaryTypeorderedindex_typepyarrow.lib.DictionaryMemopyarrow.lib.StructTypepyarrow.lib.FixedSizeListTypevalue_fieldlist_sizepyarrow.lib.MapTypekey_fieldkey_typeitem_fielditem_typekeys_sortedpyarrow.lib.LargeListViewTypepyarrow.lib.ListViewTypepyarrow.lib.LargeListTypepyarrow.lib.ListTypepyarrow.lib.DataTypebit_widthbyte_widthnum_fieldsnum_bufferspyarrow.lib.MemoryPoolbackend_namepyarrow.lib.Messagebodypyarrow.lib.IpcReadOptionsensure_native_endianuse_threadspyarrow.lib.IpcWriteOptionsallow_64bitemit_dictionary_deltaspyarrow.lib._Weakrefablebg_write_register_py_extension_typemonth_day_nano_intervalsupported_memory_backendstotal_allocated_bytesmimalloc_memory_pooljemalloc_memory_poolsystem_memory_pooldefault_memory_pool_gdb_test_session%.200s() takes %.8s %zd positional argument%.1s (%zd given)need more than %zd value%.1s to unpack%.200s() takes no keyword arguments%.200s() takes exactly one argument (%zd given)%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes no arguments (%zd given) while calling a Python objectNULL result without error in PyObject_Callmetaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its basesstrings are too large to concatPyObject *(arrow::Status const &) arrow::MemoryPool *(struct __pyx_obj_7pyarrow_3lib_MemoryPool *)PyObject *( arrow::MemoryPool *)PyObject *( arrow::Datum const &)PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::InputStream> *)PyObject *(PyObject *, bool, std::shared_ptr< arrow::io::RandomAccessFile> *)PyObject *(PyObject *, std::shared_ptr< arrow::io::OutputStream> *)struct __pyx_obj_7pyarrow_3lib_NativeFile *(PyObject *, bool)std::shared_ptr< arrow::io::InputStream> (std::shared_ptr< arrow::io::InputStream> , PyObject *, PyObject *)native_transcoding_input_streamstd::shared_ptr > (PyObject *, PyObject *)struct __pyx_obj_7pyarrow_3lib_DataType *(PyObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7pyarrow_3lib_ensure_type *__pyx_optional_args)PyObject *(enum arrow::TimeUnit::type)enum arrow::TimeUnit::type (PyObject *)std::shared_ptr< arrow::KeyValueMetadata const > (PyObject *)PyObject *(std::shared_ptr< arrow::KeyValueMetadata const > const &)PyObject *(std::shared_ptr< arrow::Buffer> const &)PyObject *(std::shared_ptr< arrow::ResizableBuffer> const &)PyObject *(std::shared_ptr< arrow::DataType> const &)PyObject *(std::shared_ptr< arrow::Field> const &)PyObject *(std::shared_ptr< arrow::Schema> const &)PyObject *(std::shared_ptr< arrow::Scalar> const &)PyObject *(std::shared_ptr< arrow::Array> const &)PyObject *(std::shared_ptr< arrow::ChunkedArray> const &)PyObject *(std::shared_ptr< arrow::SparseCOOTensor> const &)pyarrow_wrap_sparse_coo_tensorPyObject *(std::shared_ptr< arrow::SparseCSCMatrix> const &)pyarrow_wrap_sparse_csc_matrixPyObject *(std::shared_ptr< arrow::SparseCSFTensor> const &)pyarrow_wrap_sparse_csf_tensorPyObject *(std::shared_ptr< arrow::SparseCSRMatrix> const &)pyarrow_wrap_sparse_csr_matrixPyObject *(std::shared_ptr< arrow::Tensor> const &)PyObject *(std::shared_ptr< arrow::RecordBatch> const &)PyObject *(std::shared_ptr< arrow::Table> const &)std::shared_ptr< arrow::Buffer> (PyObject *)std::shared_ptr< arrow::DataType> (PyObject *)std::shared_ptr< arrow::Field> (PyObject *)std::shared_ptr< arrow::Schema> (PyObject *)std::shared_ptr< arrow::Scalar> (PyObject *)std::shared_ptr< arrow::Array> (PyObject *)std::shared_ptr< arrow::ChunkedArray> (PyObject *)std::shared_ptr< arrow::SparseCOOTensor> (PyObject *)pyarrow_unwrap_sparse_coo_tensorstd::shared_ptr< arrow::SparseCSCMatrix> (PyObject *)pyarrow_unwrap_sparse_csc_matrixstd::shared_ptr< arrow::SparseCSFTensor> (PyObject *)pyarrow_unwrap_sparse_csf_tensorstd::shared_ptr< arrow::SparseCSRMatrix> (PyObject *)pyarrow_unwrap_sparse_csr_matrixstd::shared_ptr< arrow::Tensor> (PyObject *)std::shared_ptr< arrow::RecordBatch> (PyObject *)std::shared_ptr< arrow::Table> (PyObject *)pyarrow_internal_convert_statusbase class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typeInterpreter change detected - this module can only be loaded into one interpreter per process.if _cython_generator_type is not None: try: Generator = _module.Generator except AttributeError: pass else: Generator.register(_cython_generator_type) if _cython_coroutine_type is not None: try: Coroutine = _module.Coroutine except AttributeError: pass else: Coroutine.register(_cython_coroutine_type) Cython module failed to patch module with custom typeCython module failed to register with collections.abc module%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectinvalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'Shared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__annotations__ must be set to a dict object__name__ must be set to a string object__qualname__ must be set to a string object__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function callsfunction's dictionary may not be deletedsetting function's dictionary to a non-dicthasattr(): attribute name must be stringCannot convert %.200s to %.200sjoin() result is too long for a Python stringArgument '%.200s' has incorrect type (expected %.200s, got %.200s)can't send non-None value to a just-started generator__int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)'NoneType' object has no attribute '%.30s'pyarrow.lib.SignalStopHandler.__dealloc__pyarrow.lib.pycapsule_array_deleterpyarrow.lib.pycapsule_schema_deleterpyarrow.lib.pycapsule_stream_deletervalue too large to convert to int8_tunbound method %.200S() needs an argument'%.200s' object is unsliceableUnable to initialize pickling for %.200svalue too large to convert to intvalue too large to convert to int32_tvalue too large to convert to enum arrow::Type::typevalue too large to convert to enum __pyx_t_7pyarrow_3lib_MetadataVersionvalue too large to convert to enum arrow::TimeUnit::typegenerator raised StopIteration%s() got multiple values for keyword argument '%U'too many values to unpack (expected %zd)'NoneType' object is not iterabledictionary changed size during iterationcannot fit '%.200s' into an index-sized integerraise: arg 3 must be a traceback or Noneinstance exception may not have a separate valueraise: exception class must be a subclass of BaseExceptioncalling %R should have returned an instance of BaseException, not %Rexception causes must derive from BaseExceptionpyarrow.lib.dlpack_pycapsule_deleterpyarrow.lib.default_memory_poolpyarrow.lib.system_memory_poolpyarrow.lib.total_allocated_bytespyarrow.lib.ChunkedArray.__init__pyarrow.lib._Tabular.num_columns.__get__pyarrow.lib._Tabular.num_rows.__get__pyarrow.lib._Tabular.schema.__get__pyarrow.lib.SparseCOOTensor.__init__pyarrow.lib.SparseCSRMatrix.__init__pyarrow.lib.SparseCSCMatrix.__init__pyarrow.lib.SparseCSFTensor.__init__pyarrow.lib._RecordBatchFileReader.__exit__pyarrow.lib.ChunkedArray.__cinit__pyarrow.lib.pyarrow_wrap_chunked_arraypyarrow.lib.SparseCOOTensor.equalspyarrow.lib.StringViewBuilder.__len__pyarrow.lib.StringViewBuilder.null_count.__get__pyarrow.lib.StringBuilder.__len__pyarrow.lib.StringBuilder.null_count.__get__pyarrow.lib.SparseCSRMatrix.equalspyarrow.lib.pyarrow_wrap_schemapyarrow.lib.SparseCSCMatrix.equalspyarrow.lib.IpcReadOptions.__init__string.from_py.__pyx_convert_string_from_py_6libcpp_6string_std__in_stringpyarrow.lib.SparseCSFTensor.equalspyarrow.lib.pyarrow_wrap_fieldpyarrow.lib.NativeFile._assert_openpyarrow.lib.pyarrow_wrap_data_typepyarrow.lib.ListType.value_type.__get__pyarrow.lib.LargeListType.value_type.__get__pyarrow.lib.ListViewType.value_type.__get__pyarrow.lib.LargeListViewType.value_type.__get__pyarrow.lib.FixedSizeListType.value_type.__get__pyarrow.lib.DictionaryType.index_type.__get__pyarrow.lib.DictionaryType.value_type.__get__pyarrow.lib.RunEndEncodedType.run_end_type.__get__pyarrow.lib.RunEndEncodedType.value_type.__get__pyarrow.lib.BaseExtensionType.storage_type.__get__pyarrow.lib.Scalar.type.__get__pyarrow.lib.ChunkedArray.type.__get__pyarrow.lib.DataType.num_buffers.__get__pyarrow.lib.KeyValueMetadata.equalspyarrow.lib.SparseCSRMatrix.non_zero_length.__get__pyarrow.lib.SparseCSCMatrix.non_zero_length.__get__pyarrow.lib.SparseCOOTensor.non_zero_length.__get__pyarrow.lib.SparseCSFTensor.non_zero_length.__get__pyarrow.lib.StructScalar.__iter__pyarrow.lib.Table.num_columns.__get__pyarrow.lib.Array.offset.__get__pyarrow.lib.BinaryArray.total_values_length.__get__pyarrow.lib.LargeBinaryArray.total_values_length.__get__pyarrow.lib.Tensor.is_mutable.__get__pyarrow.lib.SparseCSRMatrix.is_mutable.__get__pyarrow.lib.SparseCSCMatrix.is_mutable.__get__pyarrow.lib.SparseCOOTensor.is_mutable.__get__pyarrow.lib.SparseCSFTensor.is_mutable.__get__pyarrow.lib.pyarrow_wrap_resizable_bufferpyarrow.lib.IpcWriteOptions.allow_64bit.__get__pyarrow.lib.IpcWriteOptions.allow_64bit.__set__pyarrow.lib.IpcWriteOptions.use_legacy_format.__get__pyarrow.lib.IpcWriteOptions.use_legacy_format.__set__pyarrow.lib.IpcWriteOptions.use_threads.__get__pyarrow.lib.IpcWriteOptions.use_threads.__set__pyarrow.lib.IpcWriteOptions.emit_dictionary_deltas.__get__pyarrow.lib.IpcWriteOptions.emit_dictionary_deltas.__set__pyarrow.lib.IpcWriteOptions.unify_dictionaries.__get__pyarrow.lib.IpcWriteOptions.unify_dictionaries.__set__pyarrow.lib.IpcReadOptions.ensure_native_endian.__get__pyarrow.lib.IpcReadOptions.ensure_native_endian.__set__pyarrow.lib.IpcReadOptions.use_threads.__get__pyarrow.lib.IpcReadOptions.use_threads.__set__pyarrow.lib.DataType.id.__get__pyarrow.lib.DataType.bit_width.__get__pyarrow.lib.DataType.num_fields.__get__pyarrow.lib.ListType.value_field.__get__pyarrow.lib.LargeListType.value_field.__get__pyarrow.lib.ListViewType.value_field.__get__pyarrow.lib.LargeListViewType.value_field.__get__pyarrow.lib.MapType.keys_sorted.__get__pyarrow.lib.FixedSizeListType.value_field.__get__pyarrow.lib.FixedSizeListType.list_size.__get__pyarrow.lib.StructType.__len__pyarrow.lib.StructType.__getitem__'NoneType' object is not subscriptablepyarrow.lib.StructType.__iter__pyarrow.lib.DictionaryType.ordered.__get__pyarrow.lib.TimestampType.unit.__get__pyarrow.lib.Time32Type.unit.__get__pyarrow.lib.Time64Type.unit.__get__pyarrow.lib.DurationType.unit.__get__pyarrow.lib.Decimal128Type.precision.__get__pyarrow.lib.Decimal128Type.scale.__get__pyarrow.lib.Decimal256Type.precision.__get__pyarrow.lib.Decimal256Type.scale.__get__pyarrow.lib.Field.nullable.__get__pyarrow.lib.Field.type.__get__pyarrow.lib.Scalar.is_valid.__get__pyarrow.lib.Array.null_count.__get__pyarrow.lib.Array.type.__get__pyarrow.lib.Array._name.__get__pyarrow.lib.Tensor.is_contiguous.__get__pyarrow.lib.Tensor.ndim.__get__pyarrow.lib.Tensor.size.__get__pyarrow.lib.Tensor.type.__get__pyarrow.lib.Tensor._ssize_t_shape.__get__pyarrow.lib.Tensor._ssize_t_strides.__get__pyarrow.lib.SparseCSRMatrix.ndim.__get__pyarrow.lib.SparseCSRMatrix.size.__get__pyarrow.lib.SparseCSRMatrix.type.__get__pyarrow.lib.SparseCSCMatrix.ndim.__get__pyarrow.lib.SparseCSCMatrix.size.__get__pyarrow.lib.SparseCSCMatrix.type.__get__pyarrow.lib.SparseCOOTensor.ndim.__get__pyarrow.lib.SparseCOOTensor.size.__get__pyarrow.lib.SparseCOOTensor.has_canonical_format.__get__pyarrow.lib.SparseCOOTensor.type.__get__pyarrow.lib.SparseCSFTensor.ndim.__get__vector.to_py.__pyx_convert_vector_to_py_int64_tpyarrow.lib.SparseCSFTensor.shape.__get__pyarrow.lib.SparseCOOTensor.shape.__get__pyarrow.lib.SparseCSCMatrix.shape.__get__pyarrow.lib.SparseCSRMatrix.shape.__get__pyarrow.lib.Tensor.strides.__get__pyarrow.lib.Tensor.shape.__get__pyarrow.lib.SparseCSFTensor.size.__get__pyarrow.lib.SparseCSFTensor.type.__get__pyarrow.lib.BooleanArray.false_count.__get__pyarrow.lib.BooleanArray.true_count.__get__pyarrow.lib.ChunkedArray.__iter__pyarrow.lib.ChunkedArray.null_count.__get__pyarrow.lib.ChunkedArray.num_chunks.__get__pyarrow.lib.ChunkedArray._name.__get__pyarrow.lib.pyarrow_wrap_bufferpyarrow.lib._Tabular.shape.__get__pyarrow.lib.Table.schema.__get__pyarrow.lib.Table.num_rows.__get__pyarrow.lib.RecordBatch.num_columns.__get__pyarrow.lib.RecordBatch.num_rows.__get__pyarrow.lib.RecordBatch.schema.__get__PyObject_GetBuffer: view==NULL argument is obsoletepyarrow.lib.Buffer.__getbuffer__pyarrow.lib.Buffer.size.__get__pyarrow.lib.Buffer.address.__get__pyarrow.lib.Buffer.is_mutable.__get__pyarrow.lib.Buffer.is_cpu.__get__pyarrow.lib.NativeFile.__repr__pyarrow.lib.NativeFile.closed.__get__pyarrow.lib.CacheOptions.unwrappyarrow.lib.CacheOptions.hole_size_limit.__get__pyarrow.lib.CacheOptions.range_size_limit.__get__pyarrow.lib.CacheOptions.lazy.__get__pyarrow.lib.CacheOptions.lazy.__set__pyarrow.lib.CacheOptions.prefetch_limit.__get__pyarrow.lib.CacheOptions.__init__pyarrow.lib.Codec.compression_level.__get__pyarrow.lib.SignalStopHandler.stop_token.__get__pyarrow.lib._PandasAPIShim._loose_version.__get__pyarrow.lib._PandasAPIShim._version.__get__pyarrow.lib._PandasAPIShim._pd.__get__pyarrow.lib._PandasAPIShim._types_api.__get__pyarrow.lib._PandasAPIShim._compat_module.__get__pyarrow.lib._PandasAPIShim._data_frame.__get__pyarrow.lib._PandasAPIShim._index.__get__pyarrow.lib._PandasAPIShim._series.__get__pyarrow.lib._PandasAPIShim._categorical_type.__get__pyarrow.lib._PandasAPIShim._datetimetz_type.__get__pyarrow.lib._PandasAPIShim._extension_array.__get__pyarrow.lib._PandasAPIShim._extension_dtype.__get__pyarrow.lib._PandasAPIShim._array_like_types.__get__pyarrow.lib._PandasAPIShim._is_extension_array_dtype.__get__pyarrow.lib._PandasAPIShim._lock.__get__pyarrow.lib._PandasAPIShim.has_sparse.__get__pyarrow.lib._PandasAPIShim._pd024.__get__pyarrow.lib._PandasAPIShim._is_v1.__get__pyarrow.lib._PandasAPIShim._is_ge_v21.__get__pyarrow.lib._PandasAPIShim._is_ge_v3.__get__pyarrow.lib.UnionType.__getitem__pyarrow.lib.UnionType.__iter__pyarrow.lib.UnionType.mode.__get__vector.to_py.__pyx_convert_vector_to_py_int8_tpyarrow.lib.UnionType.type_codes.__get__pyarrow.lib.ExtensionType.__cinit__pyarrow.lib.PyExtensionType.__cinit__pyarrow.lib.Date32Scalar.value.__get__pyarrow.lib.Date64Scalar.value.__get__pyarrow.lib.Time32Scalar.value.__get__pyarrow.lib.Time64Scalar.value.__get__pyarrow.lib.TimestampScalar.value.__get__pyarrow.lib.DurationScalar.value.__get__pyarrow.lib.ListScalar.__len__pyarrow.lib.ListScalar.__iter__pyarrow.lib.MapScalar.__iter__pyarrow.lib.UnionScalar.type_code.__get__pyarrow.lib.BufferReader.__init__pyarrow.lib.NativeFile.__cinit__pyarrow.lib._RecordBatchStreamWriter._use_legacy_format.__get__pyarrow.lib._RecordBatchFileReader.num_record_batches.__get__pyarrow.lib._RecordBatchFileReader.schema.__get__EnumBase.__Pyx_EnumMeta.__reduce_cython__EnumBase.__Pyx_EnumBase.__repr__EnumBase.__Pyx_EnumBase.__str__EnumBase.__Pyx_FlagBase.__repr__EnumBase.__Pyx_FlagBase.__str__pyarrow.lib.FixedSizeBufferWriter.set_memcopy_threadspyarrow.lib.RecordBatch.__cinit__pyarrow.lib.pyarrow_wrap_batchpyarrow.lib.MemoryPool.bytes_allocatedpyarrow.lib.MemoryPool.max_memorypyarrow.lib.MockOutputStream.sizeiter_batches_with_custom_metadatapyarrow.lib.RecordBatchReader.iter_batches_with_custom_metadatapyarrow.lib.DataType.__reduce__pyarrow.lib.pyarrow_wrap_tablepyarrow.lib.DictionaryType.__reduce__pyarrow.lib.ListType.__reduce__pyarrow.lib.LargeListType.__reduce__pyarrow.lib.ListViewType.__reduce__pyarrow.lib.LargeListViewType.__reduce__pyarrow.lib.MapType.__reduce__pyarrow.lib.FixedSizeListType.__reduce__pyarrow.lib.StructType.__reduce__pyarrow.lib.UnionType.__reduce__pyarrow.lib.TimestampType.__reduce__pyarrow.lib.FixedSizeBinaryType.__reduce__pyarrow.lib.Decimal128Type.__reduce__pyarrow.lib.Decimal256Type.__reduce__pyarrow.lib.RunEndEncodedType.__reduce__pyarrow.lib.ExtensionType.__arrow_ext_deserialize__pyarrow.lib.FixedShapeTensorType.__reduce__pyarrow.lib.PyExtensionType.set_auto_loadpyarrow.lib.pyarrow_wrap_sparse_csf_tensorpyarrow.lib.KeyValueMetadata.__repr__pyarrow.lib.KeyValueMetadata.__len__pyarrow.lib.KeyValueMetadata.keyspyarrow.lib.KeyValueMetadata.valuespyarrow.lib.KeyValueMetadata.itemspyarrow.lib.pyarrow_wrap_sparse_csc_matrixvector.to_py.__pyx_convert_vector_to_py_intpyarrow.lib.IpcReadOptions.included_fields.__get__pyarrow.lib.pyarrow_wrap_sparse_csr_matrixpyarrow.lib.pyarrow_wrap_sparse_coo_tensorpyarrow.lib.pyarrow_wrap_tensorpyarrow.lib.BooleanScalar.as_pypyarrow.lib.UInt16Scalar.as_pypyarrow.lib.UInt32Scalar.as_pypyarrow.lib.UInt64Scalar.as_pypyarrow.lib.HalfFloatScalar.as_pypyarrow.lib.DoubleScalar.as_pypyarrow.lib.Date32Scalar.as_pypyarrow.lib.Date64Scalar.as_pypyarrow.lib.BinaryScalar.as_bufferpyarrow.lib.StructScalar.__contains__pyarrow.lib.StructScalar.__len__pyarrow.lib.StructScalar.items.genexprpyarrow.lib.StructScalar.itemspyarrow.lib.DictionaryScalar.__reduce__pyarrow.lib._PandasConvertible.__reduce_cython__pyarrow.lib.Array.get_total_buffer_sizepyarrow.lib.RunEndEncodedArray.find_physical_offsetpyarrow.lib.RunEndEncodedArray.find_physical_lengthpyarrow.lib.CacheOptions.__reduce__pyarrow.lib.ChunkedArray.__reduce__pyarrow.lib.ChunkedArray.lengthpyarrow.lib.ChunkedArray.get_total_buffer_sizepyarrow.lib.ChunkedArray.equalspyarrow.lib.ChunkedArray.iterchunkspyarrow.lib._Tabular.from_pydictpyarrow.lib._Tabular.from_pylistpyarrow.lib._Tabular.itercolumnspyarrow.lib._Tabular.remove_columnpyarrow.lib.KeyValueMetadata.wrappyarrow.lib.pyarrow_wrap_metadatapyarrow.lib._Tabular.__reduce_cython__pyarrow.lib.RecordBatch._is_initializedpyarrow.lib.RecordBatch.__reduce__pyarrow.lib.RecordBatch.get_total_buffer_sizepyarrow.lib.Table._is_initializedpyarrow.lib.ListScalar.__getitem__pyarrow.lib.Table.get_total_buffer_sizestring.to_py.__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_stringvector.to_py.__pyx_convert_vector_to_py_std_3a__3a_stringpyarrow.lib.SparseCSFTensor.dim_names.__get__pyarrow.lib.SparseCSFTensor.dim_names.__get__.genexprpyarrow.lib.SparseCOOTensor.dim_names.__get__pyarrow.lib.SparseCOOTensor.dim_names.__get__.genexprpyarrow.lib.SparseCSCMatrix.dim_names.__get__pyarrow.lib.SparseCSCMatrix.dim_names.__get__.genexprpyarrow.lib.SparseCSRMatrix.dim_names.__get__pyarrow.lib.SparseCSRMatrix.dim_names.__get__.genexprpyarrow.lib.KeyValueMetadata.__str__pyarrow.lib.DataType.byte_width.__get__'%.200s' object has no attribute '%U'pyarrow.lib.NativeFile.__setstate_cython__pyarrow.lib.Codec.__setstate_cython__pyarrow.lib.SparseCOOTensor.__setstate_cython__pyarrow.lib.FixedSizeBufferWriter.__setstate_cython__pyarrow.lib.Message.__setstate_cython__pyarrow.lib.BufferedInputStream.__setstate_cython__pyarrow.lib.BufferedOutputStream.__setstate_cython__pyarrow.lib._RecordBatchFileReader.__setstate_cython__pyarrow.lib.MessageReader.__setstate_cython__pyarrow.lib._RecordBatchFileWriter.__setstate_cython__pyarrow.lib._ExtensionRegistryNanny.__setstate_cython__pyarrow.lib.RecordBatchReader.__setstate_cython__pyarrow.lib.BufferReader.__setstate_cython__pyarrow.lib.SparseCSFTensor.__setstate_cython__pyarrow.lib.Tensor.__setstate_cython__pyarrow.lib.SparseCSCMatrix.__setstate_cython__pyarrow.lib.StopToken.__setstate_cython__pyarrow.lib.MockOutputStream.__setstate_cython__pyarrow.lib.OSFile.__setstate_cython__pyarrow.lib.StringViewBuilder.__setstate_cython__pyarrow.lib.IpcReadOptions.__setstate_cython__pyarrow.lib.PythonFile.__setstate_cython__pyarrow.lib.CompressedInputStream.__setstate_cython__pyarrow.lib.MemoryPool.__setstate_cython__pyarrow.lib._RecordBatchStreamReader.__setstate_cython__pyarrow.lib.MemoryMappedFile.__setstate_cython__pyarrow.lib.DictionaryMemo.__setstate_cython__pyarrow.lib._RecordBatchStreamWriter.__setstate_cython__pyarrow.lib.LoggingMemoryPool.__setstate_cython__pyarrow.lib.StringBuilder.__setstate_cython__pyarrow.lib.SignalStopHandler.__setstate_cython__pyarrow.lib.TransformInputStream.__setstate_cython__pyarrow.lib.BufferOutputStream.__setstate_cython__pyarrow.lib._CRecordBatchWriter.__setstate_cython__pyarrow.lib.ProxyMemoryPool.__setstate_cython__pyarrow.lib.IpcWriteOptions.__setstate_cython__pyarrow.lib.SparseCSRMatrix.__setstate_cython__pyarrow.lib.CompressedOutputStream.__setstate_cython__pyarrow.lib._PandasConvertible.to_pandaspyarrow.lib.Schema.types.__get__pyarrow.lib.Transcoder.__init__pyarrow.lib.logging_memory_poolpyarrow.lib.enable_signal_handlerspyarrow.lib._reconstruct_record_batchpyarrow.lib._reconstruct_tablepyarrow.lib.Tensor._make_shape_or_strides_bufferpyarrow.lib._Tabular._is_initialized__mro_entries__ must return a tuplepyarrow.lib.string_to_timeunitobject of type 'NoneType' has no len()pyarrow.lib.__pyx_unpickle__Tabular__set_statepyarrow.lib.__pyx_unpickle__PandasConvertible__set_statepyarrow.lib.RecordBatchReader.from_streampyarrow.lib._ensure_compressionpyarrow.lib.MemoryMappedFile.filenopyarrow.lib.PythonFile.readlinespyarrow.lib.PythonFile.readlinepyarrow.lib.PythonFile.truncatefree variable '%s' referenced before assignment in enclosing scopepyarrow.lib.NativeFile.download.cleanuppyarrow.lib.NativeFile.writelinespyarrow.lib.NativeFile.readallpyarrow.lib.NativeFile._assert_seekablepyarrow.lib.NativeFile._assert_writablepyarrow.lib.NativeFile._assert_readablepyarrow.lib.NativeFile.seekablepyarrow.lib.NativeFile.writablepyarrow.lib.NativeFile.readablepyarrow.lib.SparseCSFTensor.from_dense_numpypyarrow.lib.SparseCSCMatrix.from_dense_numpypyarrow.lib.SparseCSRMatrix.from_dense_numpypyarrow.lib.SparseCOOTensor.from_dense_numpypyarrow.lib.Table.__arrow_c_stream__pyarrow.lib.Table.from_struct_arraypyarrow.lib.RecordBatch.__arrow_c_stream__pyarrow.lib.RecordBatch.filterpyarrow.lib.RecordBatch.__sizeof__pyarrow.lib._Tabular.append_columnpyarrow.lib._Tabular.drop_nullpyarrow.lib._Tabular._ensure_integer_indexpyarrow.lib._Tabular.__dataframe__pyarrow.lib.ChunkedArray.drop_nullpyarrow.lib.ChunkedArray.indexpyarrow.lib.ChunkedArray.filterpyarrow.lib.ChunkedArray.value_countspyarrow.lib.ChunkedArray.uniquepyarrow.lib.ChunkedArray.dictionary_encodepyarrow.lib.ChunkedArray.__array__pyarrow.lib.ChunkedArray.fill_nullpyarrow.lib.ChunkedArray.is_validpyarrow.lib.ChunkedArray.is_nanpyarrow.lib.ChunkedArray.is_nullpyarrow.lib.ChunkedArray.__sizeof__pyarrow.lib.FixedShapeTensorArray.to_numpy_ndarraypyarrow.lib.DictionaryArray.dictionary_decodepyarrow.lib.BaseListArray.value_lengthspyarrow.lib.BaseListArray.value_parent_indicespyarrow.lib.BaseListArray.flattenpyarrow.lib.Array.value_countspyarrow.lib.Array.dictionary_encodepyarrow.lib.FixedShapeTensorScalar.to_numpypyarrow.lib.ExtensionScalar.as_pypyarrow.lib.RunEndEncodedScalar.as_pypyarrow.lib.DictionaryScalar.as_pypyarrow.lib.StructScalar.__str__pyarrow.lib.StructScalar.__repr__pyarrow.lib.StringScalar.as_pypyarrow.lib.BinaryScalar.as_pypyarrow.lib.DurationScalar.as_pypyarrow.lib.Time64Scalar.as_pypyarrow.lib.Time32Scalar.as_pypyarrow.lib.Schema.add_metadatapyarrow.lib.Schema.empty_tablepyarrow.lib.KeyValueMetadata.to_dictpyarrow.lib.KeyValueMetadata.get_allpyarrow.lib.KeyValueMetadata.__reduce__local variable '%s' referenced before assignmentpyarrow.lib.KeyValueMetadata.__iter__pyarrow.lib.PyExtensionType.__arrow_ext_serialize__pyarrow.lib.ExtensionType.__reduce__pyarrow.lib.DataType.to_pandas_dtypeEnumBase.__pyx_unpickle___Pyx_EnumMeta__set_stateEnumBase.__Pyx_EnumMeta.__init__EnumBase.__Pyx_EnumMeta.__iter__pyarrow.lib._RecordBatchFileReader.stats.__get__pyarrow.lib._RecordBatchStreamReader.stats.__get__pyarrow.lib._wrap_metadata_versionpyarrow.lib._RecordBatchStreamWriter._metadata_version.__get__pyarrow.lib.IpcWriteOptions.metadata_version.__get__pyarrow.lib.Message.metadata_version.__get__pyarrow.lib.MessageReader.__next__pyarrow.lib.MonthDayNanoIntervalScalar.value.__get__pyarrow.lib.UnknownExtensionType.__init__pyarrow.lib._PandasAPIShim.__init__pyarrow.lib.Codec.name.__get__pyarrow.lib.RecordBatchReader.__next__pyarrow.lib.NativeFile.__next__pyarrow.lib.NativeFile.__iter__pyarrow.lib.Buffer.__getitem__pyarrow.lib._Tabular.__getitem__pyarrow.lib.ChunkedArray.chunks.__get__pyarrow.lib.ChunkedArray.__str__pyarrow.lib.ChunkedArray.__getitem__pyarrow.lib.ChunkedArray.__len__pyarrow.lib.ChunkedArray.__repr__pyarrow.lib.SparseCSFTensor.__eq__pyarrow.lib.SparseCSFTensor.__repr__pyarrow.lib.SparseCOOTensor.__eq__pyarrow.lib.SparseCOOTensor.__repr__pyarrow.lib.SparseCSCMatrix.__eq__pyarrow.lib.SparseCSCMatrix.__repr__pyarrow.lib.SparseCSRMatrix.__eq__pyarrow.lib.SparseCSRMatrix.__repr__pyarrow.lib.Schema.metadata.__get__pyarrow.lib.Schema.names.__get__pyarrow.lib.Schema.__getitem__pyarrow.lib.PyExtensionType.__init__pyarrow.lib.MemoryPool.__repr__pyarrow.lib.UnionArray.offsets.__get__pyarrow.lib.UnionArray.type_codes.__get__pyarrow.lib.IpcWriteOptions.compression.__get__pyarrow.lib.get_scalar_class_from_typepyarrow.lib.pyarrow_wrap_arraypyarrow.lib.get_array_class_from_typepyarrow.lib.ListArray.values.__get__pyarrow.lib.LargeListArray.values.__get__pyarrow.lib.ListViewArray.values.__get__pyarrow.lib.LargeListViewArray.values.__get__pyarrow.lib.MapArray.keys.__get__pyarrow.lib.MapArray.items.__get__pyarrow.lib.FixedSizeListArray.values.__get__pyarrow.lib.DictionaryArray.dictionary.__get__pyarrow.lib.DictionaryArray.indices.__get__pyarrow.lib.ExtensionArray.storage.__get__pyarrow.lib.ListScalar.values.__get__pyarrow.lib.DictionaryScalar.dictionary.__get__pyarrow.lib.RunEndEncodedArray.run_ends.__get__pyarrow.lib.RunEndEncodedArray.values.__get__pyarrow.lib.ChunkedArray.chunkpyarrow.lib.Decimal256Scalar.as_pypyarrow.lib.Decimal128Scalar.as_pypyarrow.lib.BaseExtensionType.extension_name.__get__pyarrow.lib.MemoryPool.backend_name.__get__pyarrow.lib.Schema.get_field_indexpyarrow.lib.KeyValueMetadata.__contains__pyarrow.lib.StructType.get_field_indexpyarrow.lib.Message.type.__get__pyarrow.lib.Transcoder.__call__pyarrow.lib.NativeFile.truncatepyarrow.lib.NativeFile.readlinespyarrow.lib.NativeFile.readlinepyarrow.lib._unregister_py_extension_typespyarrow.lib.Schema.from_pandaspyarrow.lib._get_pandas_tz_typepyarrow.lib.log_memory_allocationspyarrow.lib.ArrowKeyError.__str__pyarrow.lib._CRecordBatchWriter.stats.__get__generator ignored GeneratorExit'%.200s' object is not subscriptableEnumBase.__Pyx_EnumMeta.__getitem__pyarrow.lib.StructScalar._as_py_tuplepyarrow.lib.StructScalar.as_pypyarrow.lib.PyExtensionType.__reduce__pyarrow.lib.ProxyMemoryPool.__init__pyarrow.lib.LoggingMemoryPool.__init__pyarrow.lib.MessageReader.__init__pyarrow.lib.MemoryPool.__init__pyarrow.lib._Tabular.column_names.__get__pyarrow.lib._Tabular.columns.__get__pyarrow.lib.ChunkedArray.to_pylistpyarrow.lib._Tabular.to_pydictpyarrow.lib.StringViewBuilder.append_valuespyarrow.lib.StringBuilder.append_valuespyarrow.lib._Tabular.to_pylistpyarrow.lib._Tabular.__array__argument after ** must be a mapping, not NoneTypepyarrow.lib.CacheOptions._reconstructpyarrow.lib._CRecordBatchWriter.writepyarrow.lib.benchmark_PandasObjectIsNullpyarrow.lib.StopToken.__reduce_cython__pyarrow.lib.SparseCSFTensor.__reduce_cython__pyarrow.lib.IpcReadOptions.__reduce_cython__pyarrow.lib._RecordBatchFileWriter.__reduce_cython__pyarrow.lib.Tensor.__reduce_cython__pyarrow.lib.MemoryMappedFile.__reduce_cython__pyarrow.lib._CRecordBatchWriter.__reduce_cython__pyarrow.lib.CompressedOutputStream.__reduce_cython__pyarrow.lib.SignalStopHandler.__reduce_cython__pyarrow.lib.OSFile.__reduce_cython__pyarrow.lib._RecordBatchFileReader.__reduce_cython__pyarrow.lib.BufferOutputStream.__reduce_cython__pyarrow.lib.FixedSizeBufferWriter.__reduce_cython__pyarrow.lib._RecordBatchStreamWriter.__reduce_cython__pyarrow.lib.RecordBatchReader.__reduce_cython__pyarrow.lib.SparseCOOTensor.__reduce_cython__pyarrow.lib.SparseCSCMatrix.__reduce_cython__pyarrow.lib.SparseCSRMatrix.__reduce_cython__pyarrow.lib.IpcWriteOptions.__reduce_cython__pyarrow.lib.PythonFile.__reduce_cython__pyarrow.lib.NativeFile.__reduce_cython__pyarrow.lib.MessageReader.__reduce_cython__pyarrow.lib._RecordBatchStreamReader.__reduce_cython__pyarrow.lib.StringBuilder.__reduce_cython__pyarrow.lib.BufferedOutputStream.__reduce_cython__pyarrow.lib.BufferedInputStream.__reduce_cython__pyarrow.lib._ExtensionRegistryNanny.__reduce_cython__pyarrow.lib.StringViewBuilder.__reduce_cython__pyarrow.lib.MemoryPool.__reduce_cython__pyarrow.lib.BufferReader.__reduce_cython__pyarrow.lib.Codec.__reduce_cython__pyarrow.lib.LoggingMemoryPool.__reduce_cython__pyarrow.lib.TransformInputStream.__reduce_cython__pyarrow.lib.DictionaryMemo.__reduce_cython__pyarrow.lib.ProxyMemoryPool.__reduce_cython__pyarrow.lib.CompressedInputStream.__reduce_cython__pyarrow.lib.Message.__reduce_cython__pyarrow.lib.MockOutputStream.__reduce_cython__pyarrow.lib.MapScalar.__getitem__pyarrow.lib.TimestampScalar.__repr__pyarrow.lib.TableGroupBy.__init__pyarrow.lib.ChunkedArray.__eq__pyarrow.lib.ExtensionType.__repr__pyarrow.lib.StringBuilder.__cinit__pyarrow.lib.TimestampScalar.as_pypyarrow.lib.RecordBatch._to_pandaspyarrow.lib._ReadPandasMixin.read_pandaspyarrow.lib.StringViewBuilder.__cinit__pyarrow.lib.Table.to_struct_arraypyarrow.lib.NativeFile.__exit__pyarrow.lib.RecordBatchReader.__exit__pyarrow.lib._CRecordBatchWriter.__exit__EnumTypeToPy.__Pyx_Enum_7pyarrow_3lib_enum__dunderpyx_t_7pyarrow_3lib_MetadataVersion_to_pypyarrow.lib._unwrap_metadata_versionpyarrow.lib.IpcWriteOptions.metadata_version.__set__pyarrow.lib._detect_compressionpyarrow.lib.__pyx_unpickle__Tabular__pyx_unpickle__PandasConvertiblepyarrow.lib.__pyx_unpickle__PandasConvertibleEnumBase.__pyx_unpickle___Pyx_EnumMetapyarrow.lib.CacheOptions.prefetch_limit.__set__pyarrow.lib.CacheOptions.range_size_limit.__set__pyarrow.lib.CacheOptions.hole_size_limit.__set__pyarrow.lib.CacheOptions.from_network_metricspyarrow.lib.FixedSizeBufferWriter.set_memcopy_thresholdpyarrow.lib.FixedSizeBufferWriter.set_memcopy_blocksizepyarrow.lib._Tabular.to_stringpyarrow.lib.UnknownExtensionType.__arrow_ext_serialize__pyarrow.lib._datetime_from_intpyarrow.lib.ChunkedArray.data.__get__Module 'lib' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%dbest base '%.200s' must be equal to first base '%.200s'pyarrow.lib.Buffer.__reduce_ex__pyarrow.lib.Schema.pandas_metadata.__get__pyarrow.lib._Tabular.__setstate_cython__pyarrow.lib._PandasConvertible.__setstate_cython__EnumBase.__Pyx_EnumMeta.__setstate_cython__pyarrow.lib.NativeFile.upload.bg_writepyarrow.lib.SparseCSRMatrix.dim_namepyarrow.lib.SparseCOOTensor.dim_namepyarrow.lib.SparseCSCMatrix.dim_namepyarrow.lib.SparseCSFTensor.dim_namepyarrow.lib._Tabular.drop_columnscan't convert negative value to size_tpyarrow.lib.Field.name.__get__pyarrow.lib.CacheOptions.__eq__pyarrow.lib.RunEndEncodedArray.from_arrayspyarrow.lib.ChunkedArray.combine_chunkspyarrow.lib.LargeStringArray.from_bufferspyarrow.lib.StringArray.from_bufferscan't convert negative value to uint64_tpyarrow.lib.RunEndEncodedArray.from_buffersEnumBase.__Pyx_EnumBase.__new__EnumBase.__Pyx_FlagBase.__new__pyarrow.lib.ArrowCancelled.__init__pyarrow.lib._handle_arrow_array_protocolpyarrow.lib.pyarrow_wrap_scalarpyarrow.lib.NativeFile.download.bg_writepyarrow.lib.KeyValueMetadata.__eq__pyarrow.lib._PandasAPIShim._import_pandaspyarrow.lib.Codec.is_availablepyarrow.lib.Codec.supports_compression_levelpyarrow.lib.ExtensionType.__eq__pyarrow.lib.transcoding_input_streampyarrow.lib._Tabular.add_columnpyarrow.lib._PandasAPIShim._check_importpyarrow.lib._PandasAPIShim._have_pandas_internalpyarrow.lib._PandasAPIShim.get_rangeindex_attributepyarrow.lib._PandasAPIShim.get_valuespyarrow.lib._PandasAPIShim.is_extension_array_dtypepyarrow.lib._PandasAPIShim.is_array_likepyarrow.lib._PandasAPIShim.is_ge_v3pyarrow.lib._PandasAPIShim.is_ge_v21pyarrow.lib._PandasAPIShim.is_v1pyarrow.lib._PandasAPIShim.pandas_dtypepyarrow.lib._PandasAPIShim.infer_dtypepyarrow.lib._PandasAPIShim.data_framepyarrow.lib._PandasAPIShim.seriespyarrow.lib._PandasAPIShim.extension_dtype.__get__pyarrow.lib._PandasAPIShim.datetimetz_type.__get__pyarrow.lib._PandasAPIShim.categorical_type.__get__pyarrow.lib._PandasAPIShim.version.__get__pyarrow.lib._PandasAPIShim.loose_version.__get__pyarrow.lib._PandasAPIShim.pd.__get__pyarrow.lib._PandasAPIShim.compat.__get__pyarrow.lib._PandasAPIShim.have_pandas.__get__pyarrow.lib._PandasAPIShim.is_indexpyarrow.lib._PandasAPIShim.is_data_framepyarrow.lib._PandasAPIShim.is_seriespyarrow.lib._PandasAPIShim.is_categoricalpyarrow.lib._PandasAPIShim.is_datetimetzpyarrow.lib._PandasAPIShim.is_sparsepyarrow.lib.NativeFile.mode.__get__pyarrow.lib.ChunkedArray.formatpyarrow.lib.TimestampType.tz.__get__pyarrow.lib.TableGroupBy.aggregatepyarrow.lib.__pyx_unpickle__PandasAPIShim__set_statepyarrow.lib._PandasAPIShim.__setstate_cython__pyarrow.lib.__pyx_unpickle__PandasAPIShimpyarrow.lib.PyExtensionType.__arrow_ext_deserialize__pyarrow.lib.FixedShapeTensorArray.from_numpy_ndarraypyarrow.lib.KeyValueMetadata.keypyarrow.lib.KeyValueMetadata.valuepyarrow.lib.Tensor.dim_names.__get__pyarrow.lib._PandasAPIShim.__reduce_cython__pyarrow.lib.month_day_nano_intervalpyarrow.lib.RecordBatch._columnpyarrow.lib.BufferReader.__cinit__pyarrow.lib.PythonFile.__cinit__pyarrow.lib.Tensor.__getbuffer__pyarrow.lib.make_streamwrap_funcpyarrow.lib.ExtensionArray.from_storagepyarrow.lib.IpcWriteOptions.__init__pyarrow.lib.ExtensionScalar.value.__get__pyarrow.lib.UnionScalar.value.__get__pyarrow.lib.RunEndEncodedScalar.value.__get__pyarrow.lib.DictionaryScalar.index.__get__pyarrow.lib.NativeFile.get_random_access_filepyarrow.lib.pyarrow_internal_check_statuspyarrow.lib.pyarrow_internal_convert_statuspyarrow.lib.BaseExtensionType.wrap_arraypyarrow.lib.ListViewArray.sizes.__get__pyarrow.lib.Message.metadata.__get__pyarrow.lib.ListArray.offsets.__get__pyarrow.lib.LargeListArray.offsets.__get__pyarrow.lib.LargeListViewArray.offsets.__get__pyarrow.lib.LargeListViewArray.sizes.__get__pyarrow.lib.ListViewArray.offsets.__get__pyarrow.lib.FixedShapeTensorType.value_type.__get__pyarrow.lib.ResizableBuffer.init_rzpyarrow.lib._ExtensionRegistryNanny.release_registrypyarrow.lib._reduce_array_datapyarrow.lib.RecordBatchReader.__arrow_c_stream__pyarrow.lib.RecordBatchReader.closepyarrow.lib._CRecordBatchWriter.closepyarrow.lib._CRecordBatchWriter.write_tablepyarrow.lib.ResizableBuffer.resizepyarrow.lib.MemoryMappedFile.resizepyarrow.lib.SparseCSFTensor.to_numpypyarrow.lib.SparseCSCMatrix.to_scipypyarrow.lib.SparseCSCMatrix.to_numpypyarrow.lib.SparseCSRMatrix.to_scipypyarrow.lib.SparseCSRMatrix.to_numpypyarrow.lib.SparseCOOTensor.to_pydata_sparsepyarrow.lib.SparseCOOTensor.to_scipypyarrow.lib.SparseCOOTensor.to_numpypyarrow.lib.RecordBatch._export_to_cpyarrow.lib.RecordBatch.validatepyarrow.lib.ChunkedArray.__arrow_c_stream__pyarrow.lib.ChunkedArray.validatepyarrow.lib.Array._export_to_cpyarrow.lib.Array._debug_printpyarrow.lib.Schema.__arrow_c_schema__pyarrow.lib.Schema._export_to_cpyarrow.lib.Field.__arrow_c_schema__pyarrow.lib.Field._export_to_cpyarrow.lib.DataType.__arrow_c_schema__pyarrow.lib.DataType._export_to_cpyarrow.lib.SignalStopHandler.__enter__pyarrow.lib.ChunkedArray.to_stringpyarrow.lib.ExtensionType.__init__pyarrow.lib.SignalStopHandler.__cinit__pyarrow.lib.RecordBatch.nbytes.__get__pyarrow.lib.Table.nbytes.__get__pyarrow.lib.ChunkedArray.nbytes.__get__pyarrow.lib.Array.nbytes.__get__pyarrow.lib.MonthDayNanoIntervalScalar.as_pypyarrow.lib.MonthDayNanoIntervalArray.to_pylistpyarrow.lib.NativeFile.downloadpyarrow.lib.NativeFile.readintopyarrow.lib.Codec.maximum_compression_levelpyarrow.lib.Codec.minimum_compression_levelpyarrow.lib.Codec.default_compression_levelpyarrow.lib.Array.__dlpack_device__pyarrow.lib.get_record_batch_sizepyarrow.lib.set_io_thread_countpyarrow.lib._register_py_extension_typepyarrow.lib.unregister_extension_typepyarrow.lib.register_extension_typepyarrow.lib.jemalloc_set_decay_mspyarrow.lib.mimalloc_memory_poolpyarrow.lib.jemalloc_memory_poolpyarrow.lib.set_timezone_db_pathpyarrow.lib.SignalStopHandler.__exit__pyarrow.lib.MapType.item_field.__get__pyarrow.lib.MapType.key_field.__get__pyarrow.lib.Schema.init_schemapyarrow.lib.KeyValueMetadata.initpyarrow.lib.NativeFile.set_output_streampyarrow.lib.RecordBatchReader.read_next_batchpyarrow.lib.KeyValueMetadata.unwrappyarrow.lib.StringViewBuilder.finishpyarrow.lib.StringBuilder.finishpyarrow.lib.DictionaryMemo.__cinit__pyarrow.lib.RecordBatchReader._export_to_cpyarrow.lib.MapType.item_type.__get__pyarrow.lib.MapType.key_type.__get__pyarrow.lib.Array._export_to_c_devicepyarrow.lib.RecordBatch._export_to_c_devicepyarrow.lib.NativeFile.__dealloc__pyarrow.lib.BufferOutputStream.getvaluepyarrow.lib.NativeFile.set_input_streampyarrow.lib._datatype_to_pep3118pyarrow.lib.BaseExtensionType.initpyarrow.lib.FixedShapeTensorType.initpyarrow.lib.ExtensionType.initpyarrow.lib.RunEndEncodedType.initpyarrow.lib.FixedSizeBinaryType.initpyarrow.lib.Decimal256Type.initpyarrow.lib.Decimal128Type.initpyarrow.lib.TimestampType.initpyarrow.lib.DictionaryType.initpyarrow.lib.FixedSizeListType.initpyarrow.lib.LargeListViewType.initpyarrow.lib.LargeListType.initpyarrow.lib.MockOutputStream.__cinit__pyarrow.lib.Message.body.__get__pyarrow.lib._ExtensionRegistryNanny.__cinit__pyarrow.lib.RecordBatchReader.schema.__get__pyarrow.lib.Buffer.parent.__get__pyarrow.lib.FixedSizeBufferWriter.__cinit__pyarrow.lib.Field.metadata.__get__pyarrow.lib._append_array_bufferspyarrow.lib.Schema.remove_metadatapyarrow.lib.Field.remove_metadatapyarrow.lib.pyarrow_unwrap_metadatapyarrow.lib.NativeFile.set_random_access_filepyarrow.lib.SparseCOOTensor.initpyarrow.lib.SparseCSCMatrix.initpyarrow.lib.SparseCSRMatrix.initpyarrow.lib.SparseCSFTensor.initpyarrow.lib._wrap_record_batch_with_metadatapyarrow.lib.Schema.with_metadatapyarrow.lib.Field.with_metadatapyarrow.lib.pyarrow_unwrap_sparse_csr_matrixpyarrow.lib.pyarrow_unwrap_bufferpyarrow.lib.c_mask_inverted_from_objpyarrow.lib.pyarrow_unwrap_sparse_csc_matrixpyarrow.lib.pyarrow_unwrap_tablepyarrow.lib.pyarrow_unwrap_tensorpyarrow.lib.pyarrow_unwrap_fieldpyarrow.lib.pyarrow_unwrap_chunked_arraypyarrow.lib.pyarrow_unwrap_data_typepyarrow.lib.pyarrow_unwrap_arraypyarrow.lib.Array.__arrow_c_array__pyarrow.lib.pyarrow_unwrap_batchpyarrow.lib.RecordBatch.__arrow_c_array__pyarrow.lib.pyarrow_unwrap_sparse_csf_tensorpyarrow.lib.pyarrow_unwrap_sparse_coo_tensorpyarrow.lib.pyarrow_unwrap_schemapyarrow.lib.Message.serialize_topyarrow.lib.SparseCOOTensor.from_tensorpyarrow.lib.SparseCSFTensor.from_tensorpyarrow.lib.SparseCSRMatrix.from_tensorpyarrow.lib.SparseCSCMatrix.from_tensorcannot create std::vector larger than max_size()pyarrow.lib.Field.with_nullablepyarrow.lib._CRecordBatchWriter.write_batchpyarrow.lib.TransformInputStream.__init__pyarrow.lib.Table.replace_schema_metadatapyarrow.lib.RecordBatch.replace_schema_metadatapyarrow.lib.ChunkedArray.slicepyarrow.lib.RecordBatch.equalspyarrow.lib.NativeFile.read_atpyarrow.lib.pyarrow_unwrap_scalarpyarrow.lib.ExtensionScalar.from_storagepyarrow.lib.NullScalar.__cinit__pyarrow.lib.NativeFile.get_input_streampyarrow.lib.NativeFile.get_output_streampyarrow.lib.Schema.field_by_namepyarrow.lib.StructType.field_by_namepyarrow.lib._ndarray_to_arrow_typepyarrow.lib.DataType._import_from_c_capsulepyarrow.lib.DataType._import_from_cpyarrow.lib.Schema.get_all_field_indicespyarrow.lib.StructType.get_all_field_indicespyarrow.lib.StructArray._flattened_fieldpyarrow.lib.LargeListViewArray.flattenpyarrow.lib.ListViewArray.flattenpyarrow.lib.Array._import_from_c_capsulepyarrow.lib.supported_memory_backendspyarrow.lib._RecordBatchFileReader.get_batchpyarrow.lib.RecordBatch._import_from_c_devicepyarrow.lib.RecordBatch._import_from_c_capsulepyarrow.lib.RecordBatch._import_from_cpyarrow.lib.RecordBatch.from_struct_arraypyarrow.lib.RecordBatch.set_columnpyarrow.lib.RecordBatch.remove_columnpyarrow.lib.RecordBatch.add_columnarray cannot contain more than pyarrow.lib.StringBuilder.appendpyarrow.lib.BufferOutputStream.__cinit__pyarrow.lib.NativeFile.read_bufferpyarrow.lib.RecordBatch.serializepyarrow.lib.SparseCSFTensor.to_tensorpyarrow.lib.SparseCSCMatrix.to_tensorpyarrow.lib.SparseCSRMatrix.to_tensorpyarrow.lib.SparseCOOTensor.to_tensorpyarrow.lib.RecordBatch.to_tensorpyarrow.lib.FixedShapeTensorScalar.to_tensorpyarrow.lib.FixedShapeTensorArray.to_tensorpyarrow.lib.FixedShapeTensorType.shape.__get__pyarrow.lib.FixedShapeTensorType.permutation.__get__pyarrow.lib.KeyValueMetadata.__getitem__pyarrow.lib.Field._import_from_c_capsulepyarrow.lib.Field._import_from_cpyarrow.lib.Schema._import_from_c_capsulepyarrow.lib.Schema._import_from_cpyarrow.lib.DictionaryScalar.value.__get__pyarrow.lib.ChunkedArray.getitempyarrow.lib.ChunkedArray._import_from_c_capsulepyarrow.lib.ChunkedArray.unify_dictionariespyarrow.lib._sequence_to_arraypyarrow.lib.Array._import_from_c_devicepyarrow.lib.Array._import_from_cpyarrow.lib.DictionaryArray.from_arrayspyarrow.lib.StructArray.flattenpyarrow.lib.RecordBatch.to_struct_arraypyarrow.lib.ChunkedArray.flattenpyarrow.lib.RecordBatchReader.read_allpyarrow.lib.Table.remove_columnpyarrow.lib.Table.unify_dictionariespyarrow.lib.Table.combine_chunkspyarrow.lib.NativeFile.metadatapyarrow.lib.BufferedInputStream.detachpyarrow.lib.NativeFile.get_streampyarrow.lib.MemoryMappedFile._openpyarrow.lib.MemoryMappedFile.createpyarrow.lib.OSFile._open_readablepyarrow.lib.BufferedOutputStream.detachpyarrow.lib.OSFile._open_writablepyarrow.lib.MessageReader.open_streampyarrow.lib.CompressedInputStream.__init__pyarrow.lib.CompressedOutputStream.__init__pyarrow.lib.BufferedInputStream.__init__pyarrow.lib.BufferedOutputStream.__init__pyarrow.lib.IpcWriteOptions.compression.__set__pyarrow.lib.MessageReader.read_next_messagepyarrow.lib._RecordBatchFileWriter._openpyarrow.lib._RecordBatchStreamWriter._openget_batch_with_custom_metadatapyarrow.lib._RecordBatchFileReader.get_batch_with_custom_metadatapyarrow.lib.RecordBatchReader.read_next_batch_with_custom_metadataread_next_batch_with_custom_metadatapyarrow.lib.RecordBatchReader.from_batchespyarrow.lib.RecordBatchReader._import_from_c_capsulepyarrow.lib.RecordBatchReader._import_from_cpyarrow.lib.RecordBatchReader.castpyarrow.lib._RecordBatchStreamReader._openpyarrow.lib._RecordBatchFileReader._openpyarrow.lib.MapArray.from_arrayspyarrow.lib.FixedSizeListArray.from_arrayspyarrow.lib.native_transcoding_input_streampyarrow.lib.ListArray.from_arrayspyarrow.lib.LargeListArray.from_arrayspyarrow.lib.ListViewArray.from_arrayspyarrow.lib.LargeListViewArray.from_arrayspyarrow.lib.RunEndEncodedArray._from_arrayspyarrow.lib.RecordBatch.from_pandaspyarrow.lib.DictionaryScalar._reconstructpyarrow.lib.StringViewBuilder.appendpyarrow.lib.ChunkedArray.to_numpypyarrow.lib.KeyValueMetadata.__init__.genexprpyarrow.lib.KeyValueMetadata.__init__pyarrow.lib.RecordBatch.rename_columnspyarrow.lib.Table.rename_columnsvector.from_py.__pyx_convert_vector_from_py_int8_tpyarrow.lib._extract_union_paramspyarrow.lib.SparseCOOTensor.from_numpypyarrow.lib.SparseCSCMatrix.from_numpypyarrow.lib.SparseCSRMatrix.from_numpypyarrow.lib.SparseCOOTensor.from_pydata_sparsepyarrow.lib.fixed_shape_tensorpyarrow.lib.SparseCSRMatrix.from_scipypyarrow.lib.SparseCSCMatrix.from_scipypyarrow.lib.SparseCSFTensor.from_numpypyarrow.lib.SparseCOOTensor.from_scipypyarrow.lib.DictionaryArray.from_bufferspyarrow.lib.Array.from_bufferspyarrow.lib.StructArray.from_arrayspyarrow.lib.Table.from_batchespyarrow.lib._RecordBatchFileReader.read_allvector.from_py.__pyx_convert_vector_from_py_intpyarrow.lib.SignalStopHandler._init_signalspyarrow.lib.IpcReadOptions.included_fields.__set__pyarrow.lib.RecordBatch.selectpyarrow.lib._convert_pandas_optionspyarrow.lib._array_like_to_pandaspyarrow.lib.ChunkedArray._to_pandaspyarrow.lib._reconstruct_array_datapyarrow.lib.FixedShapeTensorType.dim_names.__get__pyarrow.lib.UnionArray.from_densepyarrow.lib.UnionArray.from_sparseset.from_py.__pyx_convert_unordered_set_from_py_std_3a__3a_stringpyarrow.lib.StructScalar.__getitem__pyarrow.lib._schema_from_arrayspyarrow.lib.RecordBatch.from_arraysqualified name of the generatorobject being iterated by 'yield from', or Nonesend(arg) -> send 'arg' into generator, return next yielded value or raise StopIteration.throw(typ[,val[,tb]]) -> raise exception in generator, return next yielded value or raise StopIteration.close() -> raise GeneratorExit inside generator._cython_3_0_10.cython_function_or_methodpyarrow.lib.__pyx_scope_struct_21_iter_batches_with_custom_metadatapyarrow.lib.__pyx_scope_struct_20_uploadpyarrow.lib.__pyx_scope_struct_19_downloadpyarrow.lib.__pyx_scope_struct_18_genexprpyarrow.lib.__pyx_scope_struct_17_genexprpyarrow.lib.__pyx_scope_struct_16_genexprpyarrow.lib.__pyx_scope_struct_15_genexprpyarrow.lib.__pyx_scope_struct_14_itercolumnspyarrow.lib.__pyx_scope_struct_13_iterchunkspyarrow.lib.__pyx_scope_struct_12___iter__pyarrow.lib.__pyx_scope_struct_11___iter__pyarrow.lib.__pyx_scope_struct_10___iter__pyarrow.lib.__pyx_scope_struct_9_genexprpyarrow.lib.__pyx_scope_struct_8_itemspyarrow.lib.__pyx_scope_struct_7___iter__pyarrow.lib.__pyx_scope_struct_6___iter__pyarrow.lib.__pyx_scope_struct_5_itemspyarrow.lib.__pyx_scope_struct_4_valuespyarrow.lib.__pyx_scope_struct_3_keyspyarrow.lib.__pyx_scope_struct_2_genexprpyarrow.lib.__pyx_scope_struct_1___iter__pyarrow.lib.__pyx_scope_struct____iter__pyarrow.lib._RecordBatchFileReader The number of record batches in the IPC file. Current IPC read statistics. pyarrow.lib._RecordBatchFileWriterpyarrow.lib._RecordBatchStreamReaderpyarrow.lib._RecordBatchStreamWriterMessageReader() Interface for reading Message objects from some source (like an InputStream) pyarrow.lib.TransformInputStreamTransformInputStream(NativeFile stream, transform_func) Transform an input stream. Parameters ---------- stream : NativeFile The stream to transform. transform_func : callable The transformation to apply. BufferReader(obj) Zero-copy reader from objects convertible to Arrow buffer. Parameters ---------- obj : Python bytes or pyarrow.Buffer Examples -------- Create an Arrow input stream and inspect it: >>> import pyarrow as pa >>> data = b'reader data' >>> buf = memoryview(data) >>> with pa.input_stream(buf) as stream: ... stream.size() ... stream.read(6) ... stream.seek(7) ... stream.read(15) ... 11 b'reader' 7 b'data' pyarrow.lib.BufferOutputStream An output stream that writes to a resizable buffer. The buffer is produced as a result when ``getvalue()`` is called. Examples -------- Create an output stream, write data to it and finalize it with ``getvalue()``: >>> import pyarrow as pa >>> f = pa.BufferOutputStream() >>> f.write(b'pyarrow.Buffer') 14 >>> f.closed False >>> f.getvalue() >>> f.closed True pyarrow.lib.FixedSizeBufferWriter A stream writing to a Arrow buffer. Examples -------- Create a stream to write to ``pyarrow.Buffer``: >>> import pyarrow as pa >>> buf = pa.allocate_buffer(5) >>> with pa.output_stream(buf) as stream: ... stream.write(b'abcde') ... stream ... 5 Inspect the buffer: >>> buf.to_pybytes() b'abcde' >>> buf A stream backed by a regular file descriptor. Examples -------- Create a new file to write to: >>> import pyarrow as pa >>> with pa.OSFile('example_osfile.arrow', mode='w') as f: ... f.writable() ... f.write(b'OSFile') ... f.seekable() ... True 6 False Open the file to read: >>> with pa.OSFile('example_osfile.arrow', mode='r') as f: ... f.mode ... f.read() ... 'rb' b'OSFile' Open the file to append: >>> with pa.OSFile('example_osfile.arrow', mode='ab') as f: ... f.mode ... f.write(b' is super!') ... 'ab' 10 >>> with pa.OSFile('example_osfile.arrow') as f: ... f.read() ... b'OSFile is super!' Inspect created OSFile: >>> pa.OSFile('example_osfile.arrow') A stream that represents a memory-mapped file. Supports 'r', 'r+', 'w' modes. Examples -------- Create a new file with memory map: >>> import pyarrow as pa >>> mmap = pa.create_memory_map('example_mmap.dat', 10) >>> mmap >>> mmap.close() Open an existing file with memory map: >>> with pa.memory_map('example_mmap.dat') as mmap: ... mmap ... A stream backed by a Python file object. This class allows using Python file objects with arbitrary Arrow functions, including functions written in another language than Python. As a downside, there is a non-zero redirection cost in translating Arrow stream calls to Python method calls. Furthermore, Python's Global Interpreter Lock may limit parallelism in some situations. Examples -------- >>> import io >>> import pyarrow as pa >>> pa.PythonFile(io.BytesIO()) Create a stream for writing: >>> buf = io.BytesIO() >>> f = pa.PythonFile(buf, mode = 'w') >>> f.writable() True >>> f.write(b'PythonFile') 10 >>> buf.getvalue() b'PythonFile' >>> f.close() >>> f Create a stream for reading: >>> buf = io.BytesIO(b'PythonFile') >>> f = pa.PythonFile(buf, mode = 'r') >>> f.mode 'rb' >>> f.read() b'PythonFile' >>> f >>> f.close() >>> f Builder class for UTF8 string views. This class exposes facilities for incrementally adding string values and building the null bitmap for a pyarrow.Array (type='string_view'). Builder class for UTF8 strings. This class exposes facilities for incrementally adding string values and building the null bitmap for a pyarrow.Array (type='string'). pyarrow.lib.FixedShapeTensorArray Concrete class for fixed shape tensor extension arrays. Examples -------- Define the extension type for tensor array >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) Create an extension array >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> pa.ExtensionArray.from_storage(tensor_type, storage) [ [ 1, 2, 3, 4 ], [ 10, 20, 30, 40 ], [ 100, 200, 300, 400 ] ] pyarrow.lib.RunEndEncodedArray Concrete class for Arrow run-end encoded arrays. An array holding the logical indexes of each run-end. The physical offset to the array is applied. An array holding the values of each run. The physical offset to the array is applied. Concrete class for Arrow arrays of large variable-sized binary data type. The number of bytes from beginning to end of the data buffer addressed by the offsets of this LargeBinaryArray. Concrete class for Arrow arrays of large string (or utf8) data type. Concrete class for Arrow arrays of duration data type. Concrete class for Arrow arrays of time64 data type. Concrete class for Arrow arrays of time32 data type. Concrete class for Arrow arrays of timestamp data type. Concrete class for Arrow arrays of date64 data type. Concrete class for Arrow arrays of date32 data type. pyarrow.lib.FixedShapeTensorScalar Concrete class for fixed shape tensor extension scalar. Concrete class for Extension scalars. Return storage value as a scalar. Concrete class for Union scalars. Return underlying value as a scalar. Return the union type code for this scalar. pyarrow.lib.RunEndEncodedScalar Concrete class for RunEndEncoded scalars. Concrete class for dictionary-encoded scalars. Return this value's underlying index as a scalar. Return the encoded value as a scalar. Concrete class for map scalars. Concrete class for struct scalars. pyarrow.lib.LargeListViewScalarpyarrow.lib.FixedSizeListScalar Concrete class for list-like scalars. Concrete class for string-like (utf8) scalars. pyarrow.lib.FixedSizeBinaryScalar Concrete class for binary-like scalars. pyarrow.lib.MonthDayNanoIntervalScalar Concrete class for month, day, nanosecond interval scalars. Same as self.as_py() Concrete class for duration scalars. Concrete class for timestamp scalars. Concrete class for time64 scalars. Concrete class for time32 scalars. Concrete class for date64 scalars. Concrete class for date32 scalars. Concrete class for decimal256 scalars. Concrete class for decimal128 scalars. Concrete class for double scalars. Concrete class for float scalars. Concrete class for int64 scalars. Concrete class for uint64 scalars. Concrete class for int32 scalars. Concrete class for uint32 scalars. Concrete class for int16 scalars. Concrete class for uint16 scalars. Concrete class for int8 scalars. Concrete class for uint8 scalars. Concrete class for boolean scalars. NullScalar() Concrete class for null scalars. pyarrow.lib._ExtensionRegistryNannypyarrow.lib.UnknownExtensionTypeUnknownExtensionType(DataType storage_type, serialized) A concrete class for Python-defined extension types that refer to an unknown Python implementation. Parameters ---------- storage_type : DataType The storage type for which the extension is built. serialized : bytes The serialised output. Concrete class for dense union types. Examples -------- Create an instance of a dense UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_DENSE), (DenseUnionType(dense_union),) Create an instance of a dense UnionType using ``pa.dense_union``: >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) DenseUnionType(dense_union) Concrete class for sparse union types. Examples -------- Create an instance of a sparse UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_SPARSE), (SparseUnionType(sparse_union),) Create an instance of a sparse UnionType using ``pa.sparse_union``: >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) SparseUnionType(sparse_union) Base class for union data types. Examples -------- Create an instance of a dense UnionType using ``pa.union``: >>> import pyarrow as pa >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_DENSE), (DenseUnionType(dense_union),) Create an instance of a dense UnionType using ``pa.dense_union``: >>> pa.dense_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) DenseUnionType(dense_union) Create an instance of a sparse UnionType using ``pa.union``: >>> pa.union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())], ... mode=pa.lib.UnionMode_SPARSE), (SparseUnionType(sparse_union),) Create an instance of a sparse UnionType using ``pa.sparse_union``: >>> pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) SparseUnionType(sparse_union) The mode of the union ("dense" or "sparse"). Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.mode 'sparse' The type code to indicate each data type in this union. Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.type_codes [0, 1] ProxyMemoryPool() Memory pool implementation that tracks the number of bytes and maximum memory allocated through its direct calls, while redirecting to another memory pool. _PandasAPIShim() Lazy pandas importer that isolates usages of pandas APIs and avoids importing pandas until it's actually needed Codec(unicode compression, compression_level=None) Compression codec. Parameters ---------- compression : str Type of compression codec to initialize, valid values are: 'gzip', 'bz2', 'brotli', 'lz4' (or 'lz4_frame'), 'lz4_raw', 'zstd' and 'snappy'. compression_level : int, None Optional parameter specifying how aggressively to compress. The possible ranges and effect of this parameter depend on the specific codec chosen. Higher values compress more but typically use more resources (CPU/RAM). Some codecs support negative values. gzip The compression_level maps to the memlevel parameter of deflateInit2. Higher levels use more RAM but are faster and should have higher compression ratios. bz2 The compression level maps to the blockSize100k parameter of the BZ2_bzCompressInit function. Higher levels use more RAM but are faster and should have higher compression ratios. brotli The compression level maps to the BROTLI_PARAM_QUALITY parameter. Higher values are slower and should have higher compression ratios. lz4/lz4_frame/lz4_raw The compression level parameter is not supported and must be None zstd The compression level maps to the compressionLevel parameter of ZSTD_initCStream. Negative values are supported. Higher values are slower and should have higher compression ratios. snappy The compression level parameter is not supported and must be None Raises ------ ValueError If invalid compression value is passed. Examples -------- >>> import pyarrow as pa >>> pa.Codec.is_available('gzip') True >>> codec = pa.Codec('gzip') >>> codec.name 'gzip' >>> codec.compression_level 9 Returns the compression level parameter of the codecCacheOptions(hole_size_limit=None, *, range_size_limit=None, lazy=None, prefetch_limit=None) Cache options for a pre-buffered fragment scan. Parameters ---------- hole_size_limit : int, default 8KiB The maximum distance in bytes between two consecutive ranges; beyond this value, ranges are not combined. range_size_limit : int, default 32MiB The maximum size in bytes of a combined range; if combining two consecutive ranges would produce a range of a size greater than this, they are not combined lazy : bool, default True lazy = false: request all byte ranges when PreBuffer or WillNeed is called. lazy = True, prefetch_limit = 0: request merged byte ranges only after the reader needs them. lazy = True, prefetch_limit = k: prefetch up to k merged byte ranges ahead of the range that is currently being read. prefetch_limit : int, default 0 The maximum number of ranges to be prefetched. This is only used for lazy cache to asynchronously read some ranges after reading the target range. Base class for reading stream of record batches. Record batch readers function as iterators of record batches that also provide the schema (without the need to get any batches). Warnings -------- Do not call this class's constructor directly, use one of the ``RecordBatchReader.from_*`` functions instead. Notes ----- To import and export using the Arrow C stream interface, use the ``_import_from_c`` and ``_export_to_c`` methods. However, keep in mind this interface is intended for expert users. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([('x', pa.int64())]) >>> def iter_record_batches(): ... for i in range(2): ... yield pa.RecordBatch.from_arrays([pa.array([1, 2, 3])], schema=schema) >>> reader = pa.RecordBatchReader.from_batches(schema, iter_record_batches()) >>> print(reader.schema) x: int64 >>> for batch in reader: ... print(batch) pyarrow.RecordBatch x: int64 ---- x: [1,2,3] pyarrow.RecordBatch x: int64 ---- x: [1,2,3] Shared schema of the record batches in the stream. Returns ------- Schema pyarrow.lib._CRecordBatchWriterThe base RecordBatchWriter wrapper. Provides common implementations of convenience methods. Should not be instantiated directly by user code. Current IPC write statistics. pyarrow.lib.CompressedOutputStreamCompressedOutputStream(stream, unicode compression) An output stream wrapper which compresses data on the fly. Parameters ---------- stream : string, path, pyarrow.NativeFile, or file-like object Input stream object to wrap with the compression. compression : str The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd"). Examples -------- Create an output stream wich compresses the data: >>> import pyarrow as pa >>> data = b"Compressed stream" >>> raw = pa.BufferOutputStream() >>> with pa.CompressedOutputStream(raw, "gzip") as compressed: ... compressed.write(data) ... 17 pyarrow.lib.CompressedInputStreamCompressedInputStream(stream, unicode compression) An input stream wrapper which decompresses data on the fly. Parameters ---------- stream : string, path, pyarrow.NativeFile, or file-like object Input stream object to wrap with the compression. compression : str The compression type ("bz2", "brotli", "gzip", "lz4" or "zstd"). Examples -------- Create an output stream wich compresses the data: >>> import pyarrow as pa >>> data = b"Compressed stream" >>> raw = pa.BufferOutputStream() >>> with pa.CompressedOutputStream(raw, "gzip") as compressed: ... compressed.write(data) ... 17 Create an input stream with decompression referencing the buffer with compressed data: >>> cdata = raw.getvalue() >>> with pa.input_stream(cdata, compression="gzip") as compressed: ... compressed.read() ... b'Compressed stream' which actually translates to the use of ``BufferReader``and ``CompressedInputStream``: >>> raw = pa.BufferReader(cdata) >>> with pa.CompressedInputStream(raw, "gzip") as compressed: ... compressed.read() ... b'Compressed stream' pyarrow.lib.BufferedOutputStreamBufferedOutputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None) An output stream that performs buffered reads from an unbuffered output stream, which can mitigate the overhead of many small writes in some cases. Parameters ---------- stream : NativeFile The writable output stream to wrap with the buffer buffer_size : int Size of the buffer that should be added. memory_pool : MemoryPool The memory pool used to allocate the buffer. pyarrow.lib.BufferedInputStreamBufferedInputStream(NativeFile stream, int buffer_size, MemoryPool memory_pool=None) An input stream that performs buffered reads from an unbuffered input stream, which can mitigate the overhead of many small reads in some cases. Parameters ---------- stream : NativeFile The input stream to wrap with the buffer buffer_size : int Size of the temporary read buffer. memory_pool : MemoryPool The memory pool used to allocate the buffer. The base class for all Arrow streams. Streams are either readable, writable, or both. They optionally support seeking. While this class exposes methods to read or write data from Python, the primary intent of using a Arrow stream is to pass it to other Arrow facilities that will make use of it, such as Arrow IPC routines. Be aware that there are subtle differences with regular Python files, e.g. destroying a writable Arrow stream without closing it explicitly will not flush any pending data. The file mode. Currently instances of NativeFile may support: * rb: binary read * wb: binary write * rb+: binary read and write * ab: binary append A base class for buffers that can be resized. Buffer() The base class for all Arrow buffers. A buffer represents a contiguous memory area. Many buffers will own their memory, though not all of them do. The buffer size in bytes. The buffer's address, as an integer. The returned address may point to CPU or device memory. Use `is_cpu()` to disambiguate. Whether the buffer is mutable. Whether the buffer is CPU-accessible. Batch of rows of columns of equal length Warnings -------- Do not call this class's constructor directly, use one of the ``RecordBatch.from_*`` functions instead. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Constructing a RecordBatch from arrays: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Constructing a RecordBatch from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_pandas(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Constructing a RecordBatch from pylist: >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] >>> pa.RecordBatch.from_pylist(pylist).to_pandas() n_legs animals 0 2 Flamingo 1 4 Dog You can also construct a RecordBatch using :func:`pyarrow.record_batch`: >>> pa.record_batch([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Number of columns Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_columns 2 Number of rows Due to the definition of a RecordBatch, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_rows 6 Schema of the RecordBatch and its columns Returns ------- pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.schema n_legs: int64 animals: string Total number of bytes consumed by the elements of the record batch. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.nbytes 116 A collection of top-level named, equal length Arrow arrays. Warnings -------- Do not call this class's constructor directly, use one of the ``from_*`` methods instead. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a RecordBatch: >>> batch = pa.record_batch([n_legs, animals], names=names) >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a dictionary of arrays: >>> pydict = {'n_legs': n_legs, 'animals': animals} >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows: >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, {'year': 2021, 'animals': 'Centipede'}] >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,null]] animals: [["Flamingo","Centipede"]] Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('year', pa.int64()), ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"year": "Year of entry"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema year: int64 n_legs: int64 animals: string -- schema metadata -- year: 'Year of entry' Construct a Table with :func:`pyarrow.table`: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Schema of the table and its columns. Returns ------- Schema Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ... Number of columns in this table. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_columns 2 Number of rows in this table. Due to the definition of a table, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_rows 4 Total number of bytes consumed by the elements of the table. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.nbytes 72 _Tabular() Internal: An interface for common operations on tabular objects. Names of the Table or RecordBatch columns. Returns ------- list of str Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> table = pa.Table.from_arrays([[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]], ... names=['n_legs', 'animals']) >>> table.column_names ['n_legs', 'animals'] List of all columns in numerical order. Returns ------- columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.columns [ [ [ null, 4, 5, null ] ], [ [ "Flamingo", "Horse", null, "Centipede" ] ]] Dimensions of the table or record batch: (#rows, #columns). Returns ------- (int, int) Number of rows and number of columns. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table.shape (4, 2) ChunkedArray() An array-like composed from a (possibly empty) collection of pyarrow.Arrays Warnings -------- Do not call this class's constructor directly. Examples -------- To construct a ChunkedArray object use :func:`pyarrow.chunked_array`: >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> isinstance(pa.chunked_array([[2, 2, 4], [4, 5, 100]]), pa.ChunkedArray) True Return data type of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Number of null entries Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.null_count 1 Total number of bytes consumed by the elements of the chunked array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.nbytes 49 Number of underlying chunks. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.num_chunks 2 Convert to a list of single-chunked arrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.chunks [ [ 2, 2, null ], [ 4, 5, 100 ]] pyarrow.lib.MonthDayNanoIntervalArray Concrete class for Arrow arrays of interval[MonthDayNano] type. Concrete class for Arrow extension arrays. Concrete class for dictionary-encoded Arrow arrays. Concrete class for Arrow arrays of variable-sized binary view data type. Concrete class for Arrow arrays of string (or utf8) view data type. Concrete class for Arrow arrays of variable-sized binary data type. The number of bytes from beginning to end of the data buffer addressed by the offsets of this BinaryArray. Concrete class for Arrow arrays of string (or utf8) data type. Concrete class for Arrow arrays of a Union data type. Get the value offsets array (dense arrays only). Does not account for any slice offset. pyarrow.lib.FixedSizeListArray Concrete class for Arrow arrays of a fixed size list data type. Return the underlying array of values which backs the FixedSizeListArray. Note even null elements are included. Compare with :meth:`flatten`, which returns only the non-null sub-list values. Returns ------- values : Array See Also -------- FixedSizeListArray.flatten : ... Examples -------- >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, None]], ... type=pa.list_(pa.int32(), 2) ... ) >>> array.values [ 1, 2, null, null, 3, null ] Concrete class for Arrow arrays of a map data type. Flattened array of keys across all maps in arrayFlattened array of items across all maps in arraypyarrow.lib.LargeListViewArray Concrete class for Arrow arrays of a large list view data type. Identical to ListViewArray, but with 64-bit offsets. Return the underlying array of values which backs the LargeListArray ignoring the array's offset. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the list view offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list view sizes as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Concrete class for Arrow arrays of a list view data type. Return the underlying array of values which backs the ListViewArray ignoring the array's offset and sizes. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list sizes as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Concrete class for Arrow arrays of a large list data type. Identical to ListArray, but 64-bit offsets. Return the underlying array of values which backs the LargeListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from the sub-lists: >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, 4, None, 6]], ... type=pa.large_list(pa.int32()), ... ) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return the list offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Concrete class for Arrow arrays of a list data type. Return the underlying array of values which backs the ListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- ListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, None, 6]]) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, 5]]) >>> array.offsets [ 0, 2, 2, 5 ] Concrete class for Arrow arrays of a struct data type. Concrete class for Arrow arrays of decimal256 data type. Concrete class for Arrow arrays of decimal128 data type. pyarrow.lib.FixedSizeBinaryArray Concrete class for Arrow arrays of a fixed-size binary data type. Concrete class for Arrow arrays of float64 data type. Concrete class for Arrow arrays of float32 data type. Concrete class for Arrow arrays of float16 data type. Concrete class for Arrow arrays of uint64 data type. Concrete class for Arrow arrays of int64 data type. Concrete class for Arrow arrays of uint32 data type. Concrete class for Arrow arrays of int32 data type. Concrete class for Arrow arrays of uint16 data type. Concrete class for Arrow arrays of int16 data type. Concrete class for Arrow arrays of uint8 data type. Concrete class for Arrow arrays of int8 data type. pyarrow.lib.FloatingPointArray A base class for Arrow floating-point arrays. A base class for Arrow integer arrays. A base class for Arrow numeric arrays. Concrete class for Arrow arrays of boolean data type. Concrete class for Arrow arrays of null data type. SparseCSFTensor() A sparse CSF tensor. CSF is a generalization of compressed sparse row (CSR) index. CSF index recursively compresses each dimension of a tensor into a set of prefix trees. Each path from a root to leaf forms one tensor non-zero index. CSF is implemented with two arrays of buffers and one arrays of integers. SparseCOOTensor() A sparse COO tensor. SparseCSCMatrix() A sparse CSC matrix. SparseCSRMatrix() A sparse CSR matrix. Tensor() A n-dimensional array a.k.a Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Names of this tensor dimensions. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_names ['dim1', 'dim2'] Is this tensor mutable or immutable. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_mutable True Is this tensor contiguous in memory. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_contiguous True The dimension (n) of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.ndim 2 The size of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.size 6 The shape of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.shape (2, 3) Strides of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.strides (12, 4) Array() The base class for all Arrow arrays. Total number of bytes consumed by the elements of the array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. A relative position into another array's data. The purpose is to enable zero-copy slicing. This value defaults to zero but must be applied on all operations with the physical storage buffers. pyarrow.lib._PandasConvertibleScalar() The base class for scalars. Data type of the Scalar object. Holds a valid (non-null) value. Schema() A named collection of types a.k.a schema. A schema defines the column names and types in a record batch or table data structure. They also contain metadata about the columns. For example, schemas converted from Pandas contain metadata about their original Pandas types so they can be converted back to the same types. Warnings -------- Do not call this class's constructor directly. Instead use :func:`pyarrow.schema` factory function which makes a new Arrow Schema object. Examples -------- Create a new Arrow Schema object: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) some_int: int32 some_string: string Create Arrow Schema with metadata: >>> pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Return deserialized-from-JSON pandas metadata field (if it exists) Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> schema = pa.Table.from_pandas(df).schema Select pandas metadata field from Arrow Schema: >>> schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ... The schema's field names. Returns ------- list of str Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the names of the schema's fields: >>> schema.names ['n_legs', 'animals'] The schema's field types. Returns ------- list of DataType Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the types of the schema's fields: >>> schema.types [DataType(int64), DataType(string)] The schema's metadata. Returns ------- metadata: dict Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) Get the metadata of the schema's fields: >>> schema.metadata {b'n_legs': b'Number of legs per animal'} Field() A named field, with a data type, nullability, and optional metadata. Notes ----- Do not use this class's constructor directly; use pyarrow.field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) The field nullability. Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.nullable True >>> f2.nullable False The field name. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field.name 'key' The field metadata. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} KeyValueMetadata(__arg0__=None, **kwargs) KeyValueMetadata Parameters ---------- __arg0__ : dict A dict of the key-value metadata **kwargs : optional additional key-value metadata PyExtensionType(DataType storage_type) Concrete base class for Python-defined extension types based on pickle for (de)serialization. .. warning:: This class is deprecated and its deserialization is disabled by default. :class:`ExtensionType` is recommended instead. Parameters ---------- storage_type : DataType The storage type for which the extension is built. pyarrow.lib.FixedShapeTensorType Concrete class for fixed shape tensor extension type. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> pa.fixed_shape_tensor(pa.int32(), [2, 2]) FixedShapeTensorType(extension) Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Data type of an individual tensor. Shape of the tensors. Explicit names of the dimensions. Indices of the dimensions ordering. ExtensionType(DataType storage_type, extension_name) Concrete base class for Python-defined extension types. Parameters ---------- storage_type : DataType The underlying storage type for the extension type. extension_name : str A unique name distinguishing this extension type. The name will be used when deserializing IPC data. Examples -------- Define a UuidType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class UuidType(pa.ExtensionType): ... def __init__(self): ... pa.ExtensionType.__init__(self, pa.binary(16), "my_package.uuid") ... def __arrow_ext_serialize__(self): ... # since we don't have a parameterized type, we don't need extra ... # metadata to be deserialized ... return b'' ... @classmethod ... def __arrow_ext_deserialize__(self, storage_type, serialized): ... # return an instance of this subclass given the serialized ... # metadata. ... return UuidType() ... Register the extension type: >>> pa.register_extension_type(UuidType()) Create an instance of UuidType extension type: >>> uuid_type = UuidType() Inspect the extension type: >>> uuid_type.extension_name 'my_package.uuid' >>> uuid_type.storage_type FixedSizeBinaryType(fixed_size_binary[16]) Wrap an array as an extension array: >>> import uuid >>> storage_array = pa.array([uuid.uuid4().bytes for _ in range(4)], pa.binary(16)) >>> uuid_type.wrap_array(storage_array) [ ... ] Or do the same with creating an ExtensionArray: >>> pa.ExtensionArray.from_storage(uuid_type, storage_array) [ ... ] Unregister the extension type: >>> pa.unregister_extension_type("my_package.uuid") Concrete base class for extension types. The extension type name. The underlying storage type. Concrete class for run-end encoded types. Concrete class for decimal256 data types. Examples -------- Create an instance of decimal256 type: >>> import pyarrow as pa >>> pa.decimal256(76, 38) Decimal256Type(decimal256(76, 38)) The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.precision 76 The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.scale 38 Concrete class for decimal128 data types. Examples -------- Create an instance of decimal128 type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.precision 5 The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.scale 2 pyarrow.lib.FixedSizeBinaryType Concrete class for fixed-size binary data types. Examples -------- Create an instance of fixed-size binary type: >>> import pyarrow as pa >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) Concrete class for duration data types. Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('s') DurationType(duration[s]) The duration unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.duration('s') >>> t.unit 's' Concrete class for time64 data types. Supported time unit resolutions are 'us' [microsecond] and 'ns' [nanosecond]. Examples -------- Create an instance of time64 type: >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) The time unit ('us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.time64('us') >>> t.unit 'us' Concrete class for time32 data types. Supported time unit resolutions are 's' [second] and 'ms' [millisecond]. Examples -------- Create an instance of time32 type: >>> import pyarrow as pa >>> pa.time32('ms') Time32Type(time32[ms]) The time unit ('s' or 'ms'). Examples -------- >>> import pyarrow as pa >>> t = pa.time32('ms') >>> t.unit 'ms' Concrete class for timestamp data types. Examples -------- >>> import pyarrow as pa Create an instance of timestamp type: >>> pa.timestamp('us') TimestampType(timestamp[us]) Create an instance of timestamp type with timezone: >>> pa.timestamp('s', tz='UTC') TimestampType(timestamp[s, tz=UTC]) The timestamp unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('us') >>> t.unit 'us' The timestamp time zone, if any, or None. Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('s', tz='UTC') >>> t.tz 'UTC' Concrete class for dictionary data types. Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()).ordered False The data type of dictionary indices (a signed integer type). Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).index_type DataType(int16) The dictionary value type. The dictionary values are found in an instance of DictionaryArray. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).value_type DataType(string) Tracking container for dictionary-encoded fields. Concrete class for struct data types. ``StructType`` supports direct indexing using ``[...]`` (implemented via ``__getitem__``) to access its fields. It will return the struct field with the given index or name. Examples -------- >>> import pyarrow as pa Accessing fields using direct indexing: >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type[0] pyarrow.Field >>> struct_type['y'] pyarrow.Field Accessing fields using ``field()``: >>> struct_type.field(1) pyarrow.Field >>> struct_type.field('x') pyarrow.Field # Creating a schema from the struct type's fields: >>> pa.schema(list(struct_type)) x: int32 y: string Concrete class for fixed size list data types. Examples -------- Create an instance of FixedSizeListType: >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_field pyarrow.Field The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_type DataType(int32) The size of the fixed size lists. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).list_size 2 Concrete class for map data types. Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) The field for keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_field pyarrow.Field The data type of keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_type DataType(string) The field for items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_field pyarrow.Field The data type of items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_type DataType(int32) Should the entries be sorted according to keys. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted True Concrete class for large list view data types (like ListViewType, but with 64-bit offsets). Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.string()) LargeListViewType(large_list_view) The field for large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_field pyarrow.Field The data type of large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_type DataType(string) Concrete class for list view data types. Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) The field for list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_field pyarrow.Field The data type of list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_type DataType(string) Concrete class for large list data types (like ListType, but with 64-bit offsets). Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.string()) LargeListType(large_list) The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.large_list(pa.string()).value_type DataType(string) Concrete class for list data types. Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_field pyarrow.Field The data type of list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_type DataType(string) DataType() Base class of all Arrow data types. Each data type is an *instance* of this class. Examples -------- Instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) Bit width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64 Byte width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().byte_width 8 The number of child fields. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().num_fields 0 >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.string()).num_fields 1 >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct.num_fields 2 Number of data buffers required to construct Array type excluding children. Examples -------- >>> import pyarrow as pa >>> pa.int64().num_buffers 2 >>> pa.string().num_buffers 3 MemoryPool() Base class for memory allocation. Besides tracking its number of allocated bytes, a memory pool also takes care of the required 64-byte alignment for Arrow data. The name of the backend used by this MemoryPool (e.g. "jemalloc"). Message() Container for an Arrow IPC message with metadata and optional body IpcReadOptions(bool ensure_native_endian=True, *, bool use_threads=True, list included_fields=None) Serialization options for reading IPC format. Parameters ---------- ensure_native_endian : bool, default True Whether to convert incoming data to platform-native endianness. use_threads : bool Whether to use the global CPU thread pool to parallelize any computational tasks like decompression included_fields : list If empty (the default), return all deserialized fields. If non-empty, the values are the indices of fields to read on the top-level schema IpcWriteOptions(metadata_version=MetadataVersion.V5, *, bool allow_64bit=False, use_legacy_format=False, compression=None, bool use_threads=True, bool emit_dictionary_deltas=False, bool unify_dictionaries=False) Serialization options for the IPC format. Parameters ---------- metadata_version : MetadataVersion, default MetadataVersion.V5 The metadata version to write. V5 is the current and latest, V4 is the pre-1.0 metadata version (with incompatible Union layout). allow_64bit : bool, default False If true, allow field lengths that don't fit in a signed 32-bit int. use_legacy_format : bool, default False Whether to use the pre-Arrow 0.15 IPC format. compression : str, Codec, or None compression codec to use for record batch buffers. If None then batch buffers will be uncompressed. Must be "lz4", "zstd" or None. To specify a compression_level use `pyarrow.Codec` use_threads : bool Whether to use the global CPU thread pool to parallelize any computational tasks like compression. emit_dictionary_deltas : bool Whether to emit dictionary deltas. Default is false for maximum stream compatibility. unify_dictionaries : bool If true then calls to write_table will attempt to unify dictionaries across all batches in the table. This can help avoid the need for replacement dictionaries (which the file format does not support) but requires computing the unified dictionary and then remapping the indices arrays. This parameter is ignored when writing to the IPC stream format as the IPC stream format can support replacement dictionaries. _unregister_py_extension_types r6rZr~rrrrs>sess$TT\TTDԼTT4TTT\TT TLd??@PFKHL(MHMl$Dld\}[@__`@\^lc`x`H````CpGGHHG4WLZZ$[ZIOP`PH~HsHhHHIOZPPPPQ3SV,WVVrQgQ\QT8oS4t))`>P?pDDDOTV4WtVUV`dg h4hgp`@ȕxph5Lܼtld1H0PH@p 4,d4L Gf^ t ##"#s6< T 23M4Y66y tylydyyyz$h0( PySKIJx(3 hp `( A$BDB,CTC(v{||H|щ͐&00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899__pyx_unpickle__Tabular(__pyx_type, long __pyx_checksum, __pyx_state)__pyx_unpickle__PandasConvertible(__pyx_type, long __pyx_checksum, __pyx_state)__pyx_unpickle__PandasAPIShim(__pyx_type, long __pyx_checksum, __pyx_state)benchmark_PandasObjectIsNull(list obj)read_record_batch(obj, Schema schema, DictionaryMemo dictionary_memo=None) Read RecordBatch from message, given a known schema. If reading data from a complete IPC stream, use ipc.open_stream instead Parameters ---------- obj : Message or Buffer-like schema : Schema dictionary_memo : DictionaryMemo, optional If message contains dictionaries, must pass a populated DictionaryMemo Returns ------- batch : RecordBatch read_schema(obj, DictionaryMemo dictionary_memo=None) Read Schema from message or buffer Parameters ---------- obj : buffer or Message dictionary_memo : DictionaryMemo, optional Needed to be able to reconstruct dictionary-encoded fields with read_record_batch Returns ------- schema : Schema read_message(source) Read length-prefixed message from file or buffer-like object Parameters ---------- source : pyarrow.NativeFile, file-like object, or buffer-like object Returns ------- message : Message read_tensor(source) Read pyarrow.Tensor from pyarrow.NativeFile object from current position. If the file source supports zero copy (e.g. a memory map), then this operation does not allocate any memory. This function not assume that the stream is aligned Parameters ---------- source : pyarrow.NativeFile Returns ------- tensor : Tensor write_tensor(Tensor tensor, NativeFile dest) Write pyarrow.Tensor to pyarrow.NativeFile object its current position. Parameters ---------- tensor : pyarrow.Tensor dest : pyarrow.NativeFile Returns ------- bytes_written : int Total number of bytes written to the file get_record_batch_size(RecordBatch batch) Return total size of serialized RecordBatch including metadata and padding. Parameters ---------- batch : RecordBatch The recordbatch for which we want to know the size. get_tensor_size(Tensor tensor) Return total size of serialized Tensor including metadata and padding. Parameters ---------- tensor : Tensor The tensor for which we want to known the size. _RecordBatchFileReader.__setstate_cython__(self, __pyx_state)_RecordBatchFileReader.__reduce_cython__(self)_RecordBatchFileReader.__exit__(self, exc_type, exc_value, traceback)_RecordBatchFileReader.__enter__(self)_RecordBatchFileReader.read_all(self) Read all record batches as a pyarrow.Table _RecordBatchFileReader.get_batch_with_custom_metadata(self, int i) Read the record batch with the given index along with its custom metadata Parameters ---------- i : int The index of the record batch in the IPC file. Returns ------- batch : RecordBatch custom_metadata : KeyValueMetadata _RecordBatchFileReader.get_batch(self, int i) Read the record batch with the given index. Parameters ---------- i : int The index of the record batch in the IPC file. Returns ------- batch : RecordBatch _RecordBatchFileReader._open(self, source, footer_offset=None, IpcReadOptions options=IpcReadOptions(), MemoryPool memory_pool=None)_RecordBatchFileWriter.__setstate_cython__(self, __pyx_state)_RecordBatchFileWriter.__reduce_cython__(self)_RecordBatchFileWriter._open(self, sink, Schema schema, IpcWriteOptions options=IpcWriteOptions())_RecordBatchStreamReader.__setstate_cython__(self, __pyx_state)_RecordBatchStreamReader.__reduce_cython__(self)_RecordBatchStreamReader._open(self, source, IpcReadOptions options=IpcReadOptions(), MemoryPool memory_pool=None)RecordBatchReader.__setstate_cython__(self, __pyx_state)RecordBatchReader.__reduce_cython__(self)RecordBatchReader.from_batches(Schema schema, batches) Create RecordBatchReader from an iterable of batches. Parameters ---------- schema : Schema The shared schema of the record batches batches : Iterable[RecordBatch] The batches that this reader will return. Returns ------- reader : RecordBatchReader RecordBatchReader.from_stream(data, schema=None) Create RecordBatchReader from a Arrow-compatible stream object. This accepts objects implementing the Arrow PyCapsule Protocol for streams, i.e. objects that have a ``__arrow_c_stream__`` method. Parameters ---------- data : Arrow-compatible stream object Any object that implements the Arrow PyCapsule Protocol for streams. schema : Schema, default None The schema to which the stream should be casted, if supported by the stream object. Returns ------- RecordBatchReader RecordBatchReader._import_from_c_capsule(stream) Import RecordBatchReader from a C ArrowArrayStream PyCapsule. Parameters ---------- stream: PyCapsule A capsule containing a C ArrowArrayStream PyCapsule. Returns ------- RecordBatchReader RecordBatchReader.__arrow_c_stream__(self, requested_schema=None) Export to a C ArrowArrayStream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Returns ------- PyCapsule A capsule containing a C ArrowArrayStream struct. RecordBatchReader._import_from_c(in_ptr) Import RecordBatchReader from a C ArrowArrayStream struct, given its pointer. Parameters ---------- in_ptr: int The raw pointer to a C ArrowArrayStream struct. This is a low-level function intended for expert users. RecordBatchReader._export_to_c(self, out_ptr) Export to a C ArrowArrayStream struct, given its pointer. Parameters ---------- out_ptr: int The raw pointer to a C ArrowArrayStream struct. Be careful: if you don't pass the ArrowArrayStream struct to a consumer, array memory will leak. This is a low-level function intended for expert users. RecordBatchReader.cast(self, target_schema) Wrap this reader with one that casts each batch lazily as it is pulled. Currently only a safe cast to target_schema is implemented. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. Returns ------- RecordBatchReader RecordBatchReader.__exit__(self, exc_type, exc_val, exc_tb)RecordBatchReader.__enter__(self)RecordBatchReader.close(self) Release any resources associated with the reader. RecordBatchReader.read_all(self) Read all record batches as a pyarrow.Table. Returns ------- Table RecordBatchReader.iter_batches_with_custom_metadata(self) Iterate over record batches from the stream along with their custom metadata. Yields ------ RecordBatchWithMetadata RecordBatchReader.read_next_batch_with_custom_metadata(self) Read next RecordBatch from the stream along with its custom metadata. Raises ------ StopIteration: At end of stream. Returns ------- batch : RecordBatch custom_metadata : KeyValueMetadata RecordBatchReader.read_next_batch(self) Read next RecordBatch from the stream. Raises ------ StopIteration: At end of stream. Returns ------- RecordBatch _ReadPandasMixin.read_pandas(self, **options) Read contents of stream to a pandas.DataFrame. Read all record batches as a pyarrow.Table then convert it to a pandas.DataFrame using Table.to_pandas. Parameters ---------- **options Arguments to forward to :meth:`Table.to_pandas`. Returns ------- df : pandas.DataFrame _RecordBatchStreamWriter.__setstate_cython__(self, __pyx_state)_RecordBatchStreamWriter.__reduce_cython__(self)_RecordBatchStreamWriter._open(self, sink, Schema schema, IpcWriteOptions options=IpcWriteOptions())_CRecordBatchWriter.__setstate_cython__(self, __pyx_state)_CRecordBatchWriter.__reduce_cython__(self)_CRecordBatchWriter.__exit__(self, exc_type, exc_val, exc_tb)_CRecordBatchWriter.__enter__(self)_CRecordBatchWriter.close(self) Close stream and write end-of-stream 0 marker. _CRecordBatchWriter.write_table(self, Table table, max_chunksize=None) Write Table to stream in (contiguous) RecordBatch objects. Parameters ---------- table : Table max_chunksize : int, default None Maximum number of rows for RecordBatch chunks. Individual chunks may be smaller depending on the chunk layout of individual columns. _CRecordBatchWriter.write_batch(self, RecordBatch batch, custom_metadata=None) Write RecordBatch to stream. Parameters ---------- batch : RecordBatch custom_metadata : mapping or KeyValueMetadata Keys and values must be string-like / coercible to bytes _CRecordBatchWriter.write(self, table_or_batch) Write RecordBatch or Table to stream. Parameters ---------- table_or_batch : {RecordBatch, Table} MessageReader.__setstate_cython__(self, __pyx_state)MessageReader.__reduce_cython__(self)MessageReader.read_next_message(self) Read next Message from the stream. Raises ------ StopIteration At end of stream MessageReader.open_stream(source) Open stream from source, if you want to use memory map use MemoryMappedFile as source. Parameters ---------- source : bytes/buffer-like, pyarrow.NativeFile, or file-like Python object A readable source, like an InputStream Message.__setstate_cython__(self, __pyx_state)Message.__reduce_cython__(self)Message.serialize(self, alignment=8, memory_pool=None) Write message as encapsulated IPC message Parameters ---------- alignment : int, default 8 Byte alignment for metadata and body memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Message.serialize_to(self, NativeFile sink, alignment=8, memory_pool=None) Write message to generic OutputStream Parameters ---------- sink : NativeFile alignment : int, default 8 Byte alignment for metadata and body memory_pool : MemoryPool, default None Uses default memory pool if not specified Message.equals(self, Message other) Returns True if the message contents (metadata and body) are identical Parameters ---------- other : Message Returns ------- are_equal : bool IpcWriteOptions.__setstate_cython__(self, __pyx_state)IpcWriteOptions.__reduce_cython__(self)IpcReadOptions.__setstate_cython__(self, __pyx_state)IpcReadOptions.__reduce_cython__(self)output_stream(source, compression=u'detect', buffer_size=None) Create an Arrow output stream. Parameters ---------- source : str, Path, buffer, file-like object The source to open for writing. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly compression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary write buffer. Examples -------- Create a writable NativeFile from a pyarrow Buffer: >>> import pyarrow as pa >>> data = b"buffer data" >>> empty_obj = bytearray(11) >>> buf = pa.py_buffer(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read(6) ... b'buffer' or from a memoryview object: >>> buf = memoryview(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read() ... b'buffer data' Create a writable NativeFile from a string or file path: >>> with pa.output_stream('example_second.txt') as stream: ... stream.write(b'Write some data') ... 15 >>> with pa.input_stream('example_second.txt') as stream: ... stream.read() ... b'Write some data' input_stream(source, compression=u'detect', buffer_size=None) Create an Arrow input stream. Parameters ---------- source : str, Path, buffer, or file-like object The source to open for reading. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly decompression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary read buffer. Examples -------- Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object: >>> import pyarrow as pa >>> buf = memoryview(b"some data") >>> with pa.input_stream(buf) as stream: ... stream.read(4) ... b'some' Create a readable OSFile (NativeFile) from a string or file path: >>> import gzip >>> with gzip.open('example.gz', 'wb') as f: ... f.write(b'some data') ... 9 >>> with pa.input_stream('example.gz') as stream: ... stream.read() ... b'some data' Create a readable PythonFile (NativeFile) from a a Python file object: >>> with open('example.txt', mode='w') as f: ... f.write('some text') ... 9 >>> with pa.input_stream('example.txt') as stream: ... stream.read(6) ... b'some t' decompress(buf, decompressed_size=None, codec=u'lz4', asbytes=False, memory_pool=None) Decompress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or memoryview-compatible object Input object to decompress data from. decompressed_size : int, default None Size of the decompressed result codec : str, default 'lz4' Compression codec. Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'} asbytes : bool, default False Return result as Python bytes object, otherwise Buffer. memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- uncompressed : pyarrow.Buffer or bytes (if asbytes=True) compress(buf, codec=u'lz4', asbytes=False, memory_pool=None) Compress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol codec : str, default 'lz4' Compression codec. Supported types: {'brotli, 'gzip', 'lz4', 'lz4_raw', 'snappy', 'zstd'} asbytes : bool, default False Return result as Python bytes object, otherwise Buffer. memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- compressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.__setstate_cython__(self, __pyx_state)Codec.__reduce_cython__(self)Codec.decompress(self, buf, decompressed_size=None, asbytes=False, memory_pool=None) Decompress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or memoryview-compatible object decompressed_size : int, default None Size of the decompressed result asbytes : boolean, default False Return result as Python bytes object, otherwise Buffer memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any. Returns ------- uncompressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.compress(self, buf, asbytes=False, memory_pool=None) Compress data from buffer-like object. Parameters ---------- buf : pyarrow.Buffer, bytes, or other object supporting buffer protocol asbytes : bool, default False Return result as Python bytes object, otherwise Buffer memory_pool : MemoryPool, default None Memory pool to use for buffer allocations, if any Returns ------- compressed : pyarrow.Buffer or bytes (if asbytes=True) Codec.maximum_compression_level(unicode compression) Returns the largest valid value for the compression level Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.minimum_compression_level(unicode compression) Returns the smallest valid value for the compression level Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.default_compression_level(unicode compression) Returns the compression level that Arrow will use for the codec if None is specified. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.supports_compression_level(unicode compression) Returns true if the compression level parameter is supported for the given codec. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Codec.is_available(unicode compression) Returns whether the compression support has been built and enabled. Parameters ---------- compression : str Type of compression codec, refer to Codec docstring for a list of supported ones. Returns ------- bool Codec.detect(path) Detect and instantiate compression codec based on file extension. Parameters ---------- path : str, path-like File-path to detect compression from. Raises ------ TypeError If the passed value is not path-like. ValueError If the compression can't be detected from the path. Returns ------- Codec CacheOptions.__reduce__(self)CacheOptions._reconstruct(kwargs)CacheOptions.from_network_metrics(time_to_first_byte_millis, transfer_bandwidth_mib_per_sec, ideal_bandwidth_utilization_frac=0.9, max_ideal_request_size_mib=64) Create suiteable CacheOptions based on provided network metrics. Typically this will be used with object storage solutions like Amazon S3, Google Cloud Storage and Azure Blob Storage. Parameters ---------- time_to_first_byte_millis : int Seek-time or Time-To-First-Byte (TTFB) in milliseconds, also called call setup latency of a new read request. The value is a positive integer. transfer_bandwidth_mib_per_sec : int Data transfer Bandwidth (BW) in MiB/sec (per connection). The value is a positive integer. ideal_bandwidth_utilization_frac : int, default 0.9 Transfer bandwidth utilization fraction (per connection) to maximize the net data load. The value is a positive float less than 1. max_ideal_request_size_mib : int, default 64 The maximum single data request size (in MiB) to maximize the net data load. Returns ------- CacheOptions _detect_compression(path)as_buffer(o)foreign_buffer(address, size, base=None) Construct an Arrow buffer with the given *address* and *size*. The buffer will be optionally backed by the Python *base* object, if given. The *base* object will be kept alive as long as this buffer is alive, including across language boundaries (for example if the buffer is referenced by C++ code). Parameters ---------- address : int The starting address of the buffer. The address can refer to both device or host memory but it must be accessible from device after mapping it with `get_device_address` method. size : int The size of device buffer in bytes. base : {None, object} Object that owns the referenced memory. py_buffer(obj) Construct an Arrow buffer from a Python bytes-like or buffer-like object Parameters ---------- obj : object the object from which the buffer should be constructed. transcoding_input_stream(stream, src_encoding, dest_encoding) Add a transcoding transformation to the stream. Incoming data will be decoded according to ``src_encoding`` and then re-encoded according to ``dest_encoding``. Parameters ---------- stream : NativeFile The stream to which the transformation should be applied. src_encoding : str The codec to use when reading data. dest_encoding : str The codec to use for emitted data. Transcoder.__call__(self, buf)Transcoder.__init__(self, decoder, encoder)TransformInputStream.__setstate_cython__(self, __pyx_state)TransformInputStream.__reduce_cython__(self)BufferedOutputStream.__setstate_cython__(self, __pyx_state)BufferedOutputStream.__reduce_cython__(self)BufferedOutputStream.detach(self) Flush any buffered writes and release the raw OutputStream. Further operations on this stream are invalid. Returns ------- raw : NativeFile The underlying raw output stream. BufferedInputStream.__setstate_cython__(self, __pyx_state)BufferedInputStream.__reduce_cython__(self)BufferedInputStream.detach(self) Release the raw InputStream. Further operations on this stream are invalid. Returns ------- raw : NativeFile The underlying raw input stream CompressedOutputStream.__setstate_cython__(self, __pyx_state)CompressedOutputStream.__reduce_cython__(self)CompressedInputStream.__setstate_cython__(self, __pyx_state)CompressedInputStream.__reduce_cython__(self)BufferReader.__setstate_cython__(self, __pyx_state)BufferReader.__reduce_cython__(self)MockOutputStream.__setstate_cython__(self, __pyx_state)MockOutputStream.__reduce_cython__(self)MockOutputStream.size(self)BufferOutputStream.__setstate_cython__(self, __pyx_state)BufferOutputStream.__reduce_cython__(self)BufferOutputStream.getvalue(self) Finalize output stream and return result as pyarrow.Buffer. Returns ------- value : Buffer allocate_buffer(int64_t size, MemoryPool memory_pool=None, resizable=False) Allocate a mutable buffer. Parameters ---------- size : int Number of bytes to allocate (plus internal padding) memory_pool : MemoryPool, optional The pool to allocate memory from. If not given, the default memory pool is used. resizable : bool, default False If true, the returned buffer is resizable. Returns ------- buffer : Buffer or ResizableBuffer ResizableBuffer.resize(self, int64_t new_size, shrink_to_fit=False) Resize buffer to indicated size. Parameters ---------- new_size : int New size of buffer (padding may be added internally). shrink_to_fit : bool, default False If this is true, the buffer is shrunk when new_size is less than the current size. If this is false, the buffer is never shrunk. Buffer.to_pybytes(self) Return this buffer as a Python bytes object. Memory is copied. Buffer.__reduce_ex__(self, protocol)Buffer.equals(self, Buffer other) Determine if two buffers contain exactly the same data. Parameters ---------- other : Buffer Returns ------- are_equal : bool True if buffer contents and size are equal Buffer.slice(self, offset=0, length=None) Slice this buffer. Memory is not copied. You can also use the Python slice notation ``buffer[start:stop]``. Parameters ---------- offset : int, default 0 Offset from start of buffer to slice. length : int, default None Length of slice (default is until end of Buffer starting from offset). Returns ------- sliced : Buffer A logical view over this buffer. Buffer.hex(self) Compute hexadecimal representation of the buffer. Returns ------- : bytes FixedSizeBufferWriter.__setstate_cython__(self, __pyx_state)FixedSizeBufferWriter.__reduce_cython__(self)FixedSizeBufferWriter.set_memcopy_threshold(self, int64_t threshold) Parameters ---------- threshold : int64 FixedSizeBufferWriter.set_memcopy_blocksize(self, int64_t blocksize) Parameters ---------- blocksize : int64 FixedSizeBufferWriter.set_memcopy_threads(self, int num_threads) Parameters ---------- num_threads : int OSFile.__setstate_cython__(self, __pyx_state)OSFile.__reduce_cython__(self)OSFile.fileno(self)create_memory_map(path, size) Create a file of the given size and memory-map it. Parameters ---------- path : str The file path to create, on the local filesystem. size : int The file size to create. Returns ------- mmap : MemoryMappedFile Examples -------- Create a file with a memory map: >>> import pyarrow as pa >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap: ... mmap.write(b'Create a memory-mapped file') ... mmap.read_at(10, 9) ... 27 b'memory-map' memory_map(path, mode=u'r') Open memory map at file path. Size of the memory map cannot change. Parameters ---------- path : str mode : {'r', 'r+', 'w'}, default 'r' Whether the file is opened for reading ('r'), writing ('w') or both ('r+'). Returns ------- mmap : MemoryMappedFile Examples -------- Reading from a memory map without any memory allocation or copying: >>> import pyarrow as pa >>> with pa.output_stream('example_mmap.txt') as stream: ... stream.write(b'Constructing a buffer referencing the mapped memory') ... 51 >>> with pa.memory_map('example_mmap.txt') as mmap: ... mmap.read_at(6,45) ... b'memory' MemoryMappedFile.__setstate_cython__(self, __pyx_state)MemoryMappedFile.__reduce_cython__(self)MemoryMappedFile.fileno(self)MemoryMappedFile.resize(self, new_size) Resize the map and underlying file. Parameters ---------- new_size : new size in bytes MemoryMappedFile._open(self, path, mode=u'r')MemoryMappedFile.create(path, size) Create a MemoryMappedFile Parameters ---------- path : str Where to create the file. size : int Size of the memory mapped file. PythonFile.__setstate_cython__(self, __pyx_state)PythonFile.__reduce_cython__(self)PythonFile.readlines(self, hint=None) Read lines of the file. Parameters ---------- hint : int Maximum number of bytes read until we stop PythonFile.readline(self, size=None) Read and return a line of bytes from the file. If size is specified, read at most size bytes. Parameters ---------- size : int Maximum number of bytes read PythonFile.truncate(self, pos=None) Parameters ---------- pos : int, optional NativeFile.__setstate_cython__(self, __pyx_state)NativeFile.__reduce_cython__(self)NativeFile.upload(self, stream, buffer_size=None) Write from a source stream to this file. Parameters ---------- stream : file-like object Source stream to pipe to this file. buffer_size : int, optional The buffer size to use for data transfers. NativeFile.download(self, stream_or_path, buffer_size=None) Read this file completely to a local path or destination stream. This method first seeks to the beginning of the file. Parameters ---------- stream_or_path : str or file-like object If a string, a local file path to write to; otherwise, should be a writable stream. buffer_size : int, optional The buffer size to use for data transfers. NativeFile.writelines(self, lines) Write lines to the file. Parameters ---------- lines : iterable Iterable of bytes-like objects or exporters of buffer protocol NativeFile.truncate(self) NOT IMPLEMENTED NativeFile.read_buffer(self, nbytes=None) Read from buffer. Parameters ---------- nbytes : int, optional maximum number of bytes read NativeFile.readlines(self, hint=None) NOT IMPLEMENTED. Read lines of the file Parameters ---------- hint : int maximum number of bytes read until we stop NativeFile.readline(self, size=None) NOT IMPLEMENTED. Read and return a line of bytes from the file. If size is specified, read at most size bytes. Line terminator is always b"\n". Parameters ---------- size : int maximum number of bytes read NativeFile.readinto(self, b) Read into the supplied buffer Parameters ---------- b : buffer-like object A writable buffer object (such as a bytearray). Returns ------- written : int number of bytes written NativeFile.readall(self)NativeFile.read1(self, nbytes=None) Read and return up to n bytes. Unlike read(), if *nbytes* is None then a chunk is read, not the entire file. Parameters ---------- nbytes : int, default None The maximum number of bytes to read. Returns ------- data : bytes NativeFile.read_at(self, nbytes, offset) Read indicated number of bytes at offset from the file Parameters ---------- nbytes : int offset : int Returns ------- data : bytes NativeFile.get_stream(self, file_offset, nbytes) Return an input stream that reads a file segment independent of the state of the file. Allows reading portions of a random access file as an input stream without interfering with each other. Parameters ---------- file_offset : int nbytes : int Returns ------- stream : NativeFile NativeFile.read(self, nbytes=None) Read and return up to n bytes. If *nbytes* is None, then the entire remaining file contents are read. Parameters ---------- nbytes : int, default None Returns ------- data : bytes NativeFile.write(self, data) Write data to the file. Parameters ---------- data : bytes-like object or exporter of buffer protocol Returns ------- int nbytes: number of bytes written NativeFile.flush(self) Flush the stream, if applicable. An error is raised if stream is not writable. NativeFile.seek(self, int64_t position, int whence=0) Change current file stream position Parameters ---------- position : int Byte offset, interpreted relative to value of whence argument whence : int, default 0 Point of reference for seek offset Notes ----- Values of whence: * 0 -- start of stream (the default); offset should be zero or positive * 1 -- current stream position; offset may be negative * 2 -- end of stream; offset is usually negative Returns ------- int The new absolute stream position. NativeFile.tell(self) Return current stream position NativeFile.metadata(self) Return file metadata NativeFile.size(self) Return file size NativeFile._assert_seekable(self)NativeFile._assert_writable(self)NativeFile._assert_readable(self)NativeFile._assert_open(self)NativeFile.close(self)NativeFile.fileno(self) NOT IMPLEMENTED NativeFile.isatty(self)NativeFile.seekable(self)NativeFile.writable(self)NativeFile.readable(self)NativeFile.__exit__(self, exc_type, exc_value, tb)NativeFile.__enter__(self)set_io_thread_count(int count) Set the number of threads to use for I/O operations. Many operations, such as scanning a dataset, will implicitly make use of this pool. Parameters ---------- count : int The max number of threads that may be used for I/O. Must be positive. See Also -------- io_thread_count : Get the size of this pool. set_cpu_count : The analogous function for the CPU thread pool. io_thread_count() Return the number of threads to use for I/O operations. Many operations, such as scanning a dataset, will implicitly make use of this pool. The number of threads is set to a fixed value at startup. It can be modified at runtime by calling :func:`set_io_thread_count()`. See Also -------- set_io_thread_count : Modify the size of this pool. cpu_count : The analogous function for the CPU thread pool. have_libhdfs() Return true if HDFS (HadoopFileSystem) library is set up correctly. SparseCSFTensor.__setstate_cython__(self, __pyx_state)SparseCSFTensor.__reduce_cython__(self)SparseCSFTensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSFTensor.equals(self, SparseCSFTensor other) Return true if sparse tensors contains exactly equal data Parameters ---------- other : SparseCSFTensor The other tensor to compare for equality. SparseCSFTensor.to_tensor(self) Convert arrow::SparseCSFTensor to arrow::Tensor SparseCSFTensor.to_numpy(self) Convert arrow::SparseCSFTensor to numpy.ndarrays with zero copy SparseCSFTensor.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSFTensor Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSFTensor.from_numpy(data, indptr, indices, shape, axis_order=None, dim_names=None) Create arrow::SparseCSFTensor from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the sparse tensor. indptr : numpy.ndarray The sparsity structure. Each two consecutive dimensions in a tensor correspond to a buffer in indices. A pair of consecutive values at `indptr[dim][i]` `indptr[dim][i + 1]` signify a range of nodes in `indices[dim + 1]` who are children of `indices[dim][i]` node. indices : numpy.ndarray Stores values of nodes. Each tensor dimension corresponds to a buffer in indptr. shape : tuple Shape of the matrix. axis_order : list, optional the sequence in which dimensions were traversed to produce the prefix tree. dim_names : list, optional Names of the dimensions. SparseCSFTensor.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSFTensor Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCSFTensor SparseCSCMatrix.__setstate_cython__(self, __pyx_state)SparseCSCMatrix.__reduce_cython__(self)SparseCSCMatrix.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSCMatrix.equals(self, SparseCSCMatrix other) Return true if sparse tensors contains exactly equal data Parameters ---------- other : SparseCSCMatrix The other tensor to compare for equality. SparseCSCMatrix.to_tensor(self) Convert arrow::SparseCSCMatrix to arrow::Tensor SparseCSCMatrix.to_scipy(self) Convert arrow::SparseCSCMatrix to scipy.sparse.csc_matrix SparseCSCMatrix.to_numpy(self) Convert arrow::SparseCSCMatrix to numpy.ndarrays with zero copy SparseCSCMatrix.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSCMatrix Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSCMatrix.from_scipy(obj, dim_names=None) Convert scipy.sparse.csc_matrix to arrow::SparseCSCMatrix Parameters ---------- obj : scipy.sparse.csc_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCSCMatrix.from_numpy(data, indptr, indices, shape, dim_names=None) Create arrow::SparseCSCMatrix from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the sparse matrix. indptr : numpy.ndarray Range of the rows, The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data. indices : numpy.ndarray Column indices of the corresponding non-zero values. shape : tuple Shape of the matrix. dim_names : list, optional Names of the dimensions. SparseCSCMatrix.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSCMatrix Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCSCMatrix SparseCSRMatrix.__setstate_cython__(self, __pyx_state)SparseCSRMatrix.__reduce_cython__(self)SparseCSRMatrix.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCSRMatrix.equals(self, SparseCSRMatrix other) Return true if sparse tensors contains exactly equal data. Parameters ---------- other : SparseCSRMatrix The other tensor to compare for equality. SparseCSRMatrix.to_tensor(self) Convert arrow::SparseCSRMatrix to arrow::Tensor. SparseCSRMatrix.to_scipy(self) Convert arrow::SparseCSRMatrix to scipy.sparse.csr_matrix. SparseCSRMatrix.to_numpy(self) Convert arrow::SparseCSRMatrix to numpy.ndarrays with zero copy. SparseCSRMatrix.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCSRMatrix. Parameters ---------- obj : Tensor The dense tensor that should be converted. SparseCSRMatrix.from_scipy(obj, dim_names=None) Convert scipy.sparse.csr_matrix to arrow::SparseCSRMatrix. Parameters ---------- obj : scipy.sparse.csr_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCSRMatrix.from_numpy(data, indptr, indices, shape, dim_names=None) Create arrow::SparseCSRMatrix from numpy.ndarrays. Parameters ---------- data : numpy.ndarray Data used to populate the sparse matrix. indptr : numpy.ndarray Range of the rows, The i-th row spans from `indptr[i]` to `indptr[i+1]` in the data. indices : numpy.ndarray Column indices of the corresponding non-zero values. shape : tuple Shape of the matrix. dim_names : list, optional Names of the dimensions. SparseCSRMatrix.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCSRMatrix Parameters ---------- obj : numpy.ndarray The dense numpy array that should be converted. dim_names : list, optional The names of the dimensions. Returns ------- pyarrow.SparseCSRMatrix SparseCOOTensor.__setstate_cython__(self, __pyx_state)SparseCOOTensor.__reduce_cython__(self)SparseCOOTensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Returns ------- str SparseCOOTensor.equals(self, SparseCOOTensor other) Return true if sparse tensors contains exactly equal data. Parameters ---------- other : SparseCOOTensor The other tensor to compare for equality. SparseCOOTensor.to_tensor(self) Convert arrow::SparseCOOTensor to arrow::Tensor. SparseCOOTensor.to_pydata_sparse(self) Convert arrow::SparseCOOTensor to pydata/sparse.COO. SparseCOOTensor.to_scipy(self) Convert arrow::SparseCOOTensor to scipy.sparse.coo_matrix. SparseCOOTensor.to_numpy(self) Convert arrow::SparseCOOTensor to numpy.ndarrays with zero copy. SparseCOOTensor.from_tensor(obj) Convert arrow::Tensor to arrow::SparseCOOTensor. Parameters ---------- obj : Tensor The tensor that should be converted. SparseCOOTensor.from_pydata_sparse(obj, dim_names=None) Convert pydata/sparse.COO to arrow::SparseCOOTensor. Parameters ---------- obj : pydata.sparse.COO The sparse multidimensional array that should be converted. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_scipy(obj, dim_names=None) Convert scipy.sparse.coo_matrix to arrow::SparseCOOTensor Parameters ---------- obj : scipy.sparse.csr_matrix The scipy matrix that should be converted. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_numpy(data, coords, shape, dim_names=None) Create arrow::SparseCOOTensor from numpy.ndarrays Parameters ---------- data : numpy.ndarray Data used to populate the rows. coords : numpy.ndarray Coordinates of the data. shape : tuple Shape of the tensor. dim_names : list, optional Names of the dimensions. SparseCOOTensor.from_dense_numpy(cls, obj, dim_names=None) Convert numpy.ndarray to arrow::SparseCOOTensor Parameters ---------- obj : numpy.ndarray Data used to populate the rows. dim_names : list[str], optional Names of the dimensions. Returns ------- pyarrow.SparseCOOTensor Tensor.__setstate_cython__(self, __pyx_state)Tensor.__reduce_cython__(self)Tensor.dim_name(self, i) Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_name(0) 'dim1' >>> tensor.dim_name(1) 'dim2' Tensor.equals(self, Tensor other) Return true if the tensors contains exactly equal data. Parameters ---------- other : Tensor The other tensor to compare for equality. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32) >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"]) >>> tensor.equals(tensor) True >>> tensor.equals(tensor2) False Tensor.to_numpy(self) Convert arrow::Tensor to numpy.ndarray with zero copy Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.to_numpy() array([[ 2, 2, 4], [ 4, 5, 100]], dtype=int32) Tensor.from_numpy(obj, dim_names=None) Create a Tensor from a numpy array. Parameters ---------- obj : numpy.ndarray The source numpy array dim_names : list, optional Names of each dimension of the Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Tensor._make_shape_or_strides_buffer(self, values) Make a bytes object holding an array of `values` cast to `Py_ssize_t`. TableGroupBy.aggregate(self, aggregations) Perform an aggregation over the grouped columns of the table. Parameters ---------- aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)] List of tuples, where each tuple is one aggregation specification and consists of: aggregation column name followed by function name and optionally aggregation function option. Pass empty list to get a single row for each group. The column name can be a string, an empty list or a list of column names, for unary, nullary and n-ary aggregation functions respectively. For the list of function names and respective aggregation function options see :ref:`py-grouped-aggrs`. Returns ------- Table Results of the aggregation functions. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Sum the column "values" over the grouped column "keys": >>> t.group_by("keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] Count the rows over the grouped column "keys": >>> t.group_by("keys").aggregate([([], "count_all")]) pyarrow.Table keys: string count_all: int64 ---- keys: [["a","b","c"]] count_all: [[2,2,1]] Do multiple aggregations: >>> t.group_by("keys").aggregate([ ... ("values", "sum"), ... ("keys", "count") ... ]) pyarrow.Table keys: string values_sum: int64 keys_count: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] keys_count: [[2,2,1]] Count the number of non-null values for column "values" over the grouped column "keys": >>> import pyarrow.compute as pc >>> t.group_by(["keys"]).aggregate([ ... ("values", "count", pc.CountOptions(mode="only_valid")) ... ]) pyarrow.Table keys: string values_count: int64 ---- keys: [["a","b","c"]] values_count: [[2,2,1]] Get a single row for each group in column "keys": >>> t.group_by("keys").aggregate([]) pyarrow.Table keys: string ---- keys: [["a","b","c"]] TableGroupBy.__init__(self, table, keys, use_threads=True)_from_pylist(cls, mapping, schema, metadata) Construct a Table/RecordBatch from list of rows / dictionaries. Parameters ---------- cls : Class Table/RecordBatch mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table/RecordBatch _from_pydict(cls, mapping, schema, metadata) Construct a Table/RecordBatch from Arrow arrays or columns. Parameters ---------- cls : Class Table/RecordBatch mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table/RecordBatch concat_tables(tables, MemoryPool memory_pool=None, unicode promote_options=u'none', **kwargs) Concatenate pyarrow.Table objects. If promote_options="none", a zero-copy concatenation will be performed. The schemas of all the Tables must be the same (except the metadata), otherwise an exception will be raised. The result Table will share the metadata with the first table. If promote_options="default", any null type arrays will be casted to the type of other arrays in the column of the same name. If a table is missing a particular field, null values of the appropriate type will be generated to take the place of the missing field. The new schema will share the metadata with the first table. Each field in the new schema will share the metadata with the first table which has the field defined. Note that type promotions may involve additional allocations on the given ``memory_pool``. If promote_options="permissive", the behavior of default plus types will be promoted to the common denominator that fits all the fields. Parameters ---------- tables : iterable of pyarrow.Table objects Pyarrow tables to concatenate into a single Table. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. promote_options : str, default none Accepts strings "none", "default" and "permissive". **kwargs : dict, optional Examples -------- >>> import pyarrow as pa >>> t1 = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> t2 = pa.table([ ... pa.array([2, 4]), ... pa.array(["Parrot", "Dog"]) ... ], names=['n_legs', 'animals']) >>> pa.concat_tables([t1,t2]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]] table(data, names=None, schema=None, metadata=None, nthreads=None) Create a pyarrow.Table from a Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of arrays or chunked arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the Arrow Table. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. If passed, the output will have exactly this schema (raising an error when columns are not found in the data and ignoring additional data not specified in the schema, when data is a dict or DataFrame). metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). nthreads : int, default None For pandas.DataFrame inputs: if greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). Returns ------- Table See Also -------- Table.from_arrays, Table.from_pandas, Table.from_pydict Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from a python dictionary: >>> pa.table({"n_legs": n_legs, "animals": animals}) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.table(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.table(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: '{"index_columns": [], "column_indexes": [{"name": null, ... Construct a Table from chunked arrays: >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] record_batch(data, names=None, schema=None, metadata=None) Create a pyarrow.RecordBatch from another Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of Arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the RecordBatch. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). Returns ------- RecordBatch See Also -------- RecordBatch.from_arrays, RecordBatch.from_pandas, table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from a python dictionary: >>> pa.record_batch({"n_legs": n_legs, "animals": animals}) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Creating a RecordBatch from a list of arrays with names: >>> pa.record_batch([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Creating a RecordBatch from a list of arrays with names and metadata: >>> my_metadata={"n_legs": "How many legs does an animal have?"} >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'How many legs does an animal have?' Creating a RecordBatch from a pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.record_batch(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Creating a RecordBatch from a pandas DataFrame with schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.record_batch(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... >>> pa.record_batch(df, my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede _reconstruct_table(arrays, schema) Internal: reconstruct pa.Table from pickled components. Table.__arrow_c_stream__(self, requested_schema=None) Export the table as an Arrow C stream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Currently, this is not supported and will raise a NotImplementedError if the schema doesn't match the current schema. Returns ------- PyCapsule Table.join_asof(self, right_table, on, by, tolerance, right_on=None, right_by=None) Perform an asof join between this table and another one. This is similar to a left-join except that we match on nearest key rather than equal keys. Both tables must be sorted by the key. This type of join is most useful for time series data that are not perfectly aligned. Optionally match on equivalent keys with "by" before searching with "on". Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. on : str The column from current table that should be used as the "on" key of the join operation left side. An inexact match is used on the "on" key, i.e. a row is considered a match if and only if left_on - tolerance <= right_on <= left_on. The input dataset must be sorted by the "on" key. Must be a single field of a common type. Currently, the "on" key must be an integer, date, or timestamp type. by : str or list[str] The columns from current table that should be used as the keys of the join operation left side. The join operation is then done only for the matches in these columns. tolerance : int The tolerance for inexact "on" key matching. A right row is considered a match with the left row ``right.on - left.on <= tolerance``. The ``tolerance`` may be: - negative, in which case a past-as-of-join occurs; - or positive, in which case a future-as-of-join occurs; - or zero, in which case an exact-as-of-join occurs. The tolerance is interpreted in the same units as the "on" key. right_on : str or list[str], default None The columns from the right_table that should be used as the on key on the join operation right side. When ``None`` use the same key name as the left table. right_by : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. Returns ------- Table Example -------- >>> import pyarrow as pa >>> t1 = pa.table({'id': [1, 3, 2, 3, 3], ... 'year': [2020, 2021, 2022, 2022, 2023]}) >>> t2 = pa.table({'id': [3, 4], ... 'year': [2020, 2021], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1.join_asof(t2, on='year', by='id', tolerance=-2) pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[1,3,2,3,3]] year: [[2020,2021,2022,2022,2023]] n_legs: [[null,5,null,5,null]] animal: [[null,"Brittle stars",null,"Brittle stars",null]] Table.join(self, right_table, keys, right_keys=None, join_type=u'left outer', left_suffix=None, right_suffix=None, coalesce_keys=True, use_threads=True) Perform a join between this table and another one. Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. keys : str or list[str] The columns from current table that should be used as keys of the join operation left side. right_keys : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. join_type : str, default "left outer" The kind of join that should be performed, one of ("left semi", "right semi", "left anti", "right anti", "inner", "left outer", "right outer", "full outer") left_suffix : str, default None Which suffix to add to left column names. This prevents confusion when the columns in left and right tables have colliding names. right_suffix : str, default None Which suffix to add to the right column names. This prevents confusion when the columns in left and right tables have colliding names. coalesce_keys : bool, default True If the duplicated keys should be omitted from one of the sides in the join result. use_threads : bool, default True Whether to use multithreading or not. Returns ------- Table Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df1 = pd.DataFrame({'id': [1, 2, 3], ... 'year': [2020, 2022, 2019]}) >>> df2 = pd.DataFrame({'id': [3, 4], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1 = pa.Table.from_pandas(df1) >>> t2 = pa.Table.from_pandas(df2) Left outer join: >>> t1.join(t2, 'id').combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2]] year: [[2019,2020,2022]] n_legs: [[5,null,null]] animal: [["Brittle stars",null,null]] Full outer join: >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2,4]] year: [[2019,2020,2022,null]] n_legs: [[5,null,null,100]] animal: [["Brittle stars",null,null,"Centipede"]] Right outer join: >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year') pyarrow.Table year: int64 id: int64 n_legs: int64 animal: string ---- year: [[2019,null]] id: [[3,4]] n_legs: [[5,100]] animal: [["Brittle stars","Centipede"]] Right anti join >>> t1.join(t2, 'id', join_type="right anti") pyarrow.Table id: int64 n_legs: int64 animal: string ---- id: [[4]] n_legs: [[100]] animal: [["Centipede"]] Table.group_by(self, keys, use_threads=True) Declare a grouping over the columns of the table. Resulting grouping can then be used to perform aggregations with a subsequent ``aggregate()`` method. Parameters ---------- keys : str or list[str] Name of the columns that should be used as the grouping key. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Returns ------- TableGroupBy See Also -------- TableGroupBy.aggregate Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.group_by('year').aggregate([('n_legs', 'sum')]) pyarrow.Table year: int64 n_legs_sum: int64 ---- year: [[2020,2022,2021,2019]] n_legs_sum: [[2,6,104,5]] Table.drop(self, columns) Drop one or more columns and return a new table. Alias of Table.drop_columns, but kept for backwards compatibility. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Returns ------- Table New table without the column(s). Table.rename_columns(self, names) Create new table with columns renamed to provided names. Parameters ---------- names : list of str List of new column names. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> new_names = ["n", "name"] >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.set_column(self, int i, field_, column) Replace column in Table at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> table.set_column(1,'year', [year]) pyarrow.Table n_legs: int64 year: int64 ---- n_legs: [[2,4,5,100]] year: [[2021,2022,2019,2021]] Table.remove_column(self, int i) Create new Table with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New table without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.remove_column(1) pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Table.add_column(self, int i, field_, column) Add column to Table at position. A new table is returned with the column added, the original table object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> table.add_column(0,"year", [year]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2021,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Original table is left unchanged: >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.__sizeof__(self)Table.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the table. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.get_total_buffer_size() 76 Table._column(self, int i) Select a column by its numeric index. Parameters ---------- i : int The index of the column to retrieve. Returns ------- ChunkedArray Table._to_pandas(self, options, categories=None, ignore_metadata=False, types_mapper=None)Table.to_reader(self, max_chunksize=None) Convert the Table to a RecordBatchReader. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- RecordBatchReader Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatchReader: >>> table.to_reader() >>> reader = table.to_reader() >>> reader.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... >>> reader.read_all() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.to_batches(self, max_chunksize=None) Convert Table to a list of RecordBatch objects. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- list[RecordBatch] Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatch: >>> table.to_batches()[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Convert a Table to a list of RecordBatches: >>> table.to_batches(max_chunksize=2)[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse >>> table.to_batches(max_chunksize=2)[1].to_pandas() n_legs animals 0 5 Brittle stars 1 100 Centipede Table.from_batches(batches, Schema schema=None) Construct a Table from a sequence or iterator of Arrow RecordBatches. Parameters ---------- batches : sequence or iterator of RecordBatch Sequence of RecordBatch to be converted, all schemas must be equal. schema : Schema, default None If not passed, will be inferred from the first RecordBatch. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] >>> batch = pa.record_batch([n_legs, animals], names=names) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Construct a Table from a RecordBatch: >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a sequence of RecordBatches: >>> pa.Table.from_batches([batch, batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]] Table.to_struct_array(self, max_chunksize=None) Convert to a chunked array of struct type. Parameters ---------- max_chunksize : int, default None Maximum number of rows for ChunkedArray chunks. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- ChunkedArray Table.from_struct_array(struct_array) Construct a Table from a StructArray. Each field in the StructArray will become a column in the resulting ``Table``. Parameters ---------- struct_array : StructArray or ChunkedArray Array to construct the table from. Returns ------- pyarrow.Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.Table.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Table.from_arrays(arrays, names=None, schema=None, metadata=None) Construct a Table from Arrow arrays. Parameters ---------- arrays : list of pyarrow.Array or pyarrow.ChunkedArray Equal-length arrays that should form the table. names : list of str, optional Names for the table columns. If not passed, schema must be passed. schema : Schema, default None Schema for the created table. If not passed, names must be passed. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"animals": "Name of the animal species"}) >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Name of the animal species' Table.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None, bool safe=True) Convert pandas.DataFrame to an Arrow Table. The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of `object`, we need to guess the datatype by looking at the Python objects in this Series. Be aware that Series of the `object` dtype don't carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function. Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the Arrow Table. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``Table``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. safe : bool, default True Check for overflows or other unsafe conversions. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.cast(self, Schema target_schema, safe=None, options=None) Cast table values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast table values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> table.cast(target_schema=my_schema) pyarrow.Table n_legs: duration[s] animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Table.equals(self, Table other, bool check_metadata=False) Check if contents of two tables are equal. Parameters ---------- other : pyarrow.Table Table to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names=["n_legs", "animals"] >>> table = pa.Table.from_arrays([n_legs, animals], names=names) >>> table_0 = pa.Table.from_arrays([]) >>> table_1 = pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata={"n_legs": "Number of legs per animal"}) >>> table.equals(table) True >>> table.equals(table_0) False >>> table.equals(table_1) True >>> table.equals(table_1, check_metadata=True) False Table.unify_dictionaries(self, MemoryPool memory_pool=None) Unify dictionaries across all chunks. This method returns an equivalent table, but where all chunks of each column share the same dictionary values. Dictionary indices are transposed accordingly. Columns without dictionaries are returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> table = pa.table([c_arr], names=["animals"]) >>> table pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog"] -- indices: [0,1,2], -- dictionary: ["Horse","Brittle stars","Centipede"] -- indices: [0,1,2]] Unify dictionaries across both chunks: >>> table.unify_dictionaries() pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [0,1,2], -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [3,4,5]] Table.combine_chunks(self, MemoryPool memory_pool=None) Make a new table by combining the chunks this table has. All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> names = ["n_legs", "animals"] >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] >>> table.combine_chunks() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4,4,5,100]] animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] Table.flatten(self, MemoryPool memory_pool=None) Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> month = pa.array([4, 6]) >>> table = pa.Table.from_arrays([struct,month], ... names = ["a", "month"]) >>> table pyarrow.Table a: struct child 0, animals: string child 1, n_legs: int64 child 2, year: int64 month: int64 ---- a: [ -- is_valid: all not null -- child 0 type: string ["Parrot",null] -- child 1 type: int64 [2,4] -- child 2 type: int64 [null,2022]] month: [[4,6]] Flatten the columns with struct field: >>> table.flatten() pyarrow.Table a.animals: string a.n_legs: int64 a.year: int64 month: int64 ---- a.animals: [["Parrot",null]] a.n_legs: [[2,4]] a.year: [[null,2022]] month: [[4,6]] Table.replace_schema_metadata(self, metadata=None) Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata. Parameters ---------- metadata : dict, default None Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Constructing a Table with pyarrow schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> table= pa.table(df, my_schema) >>> table.schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... Create a shallow copy of a Table with deleted schema metadata: >>> table.replace_schema_metadata().schema n_legs: int64 animals: string Create a shallow copy of a Table with new schema metadata: >>> metadata={"animals": "Which animal"} >>> table.replace_schema_metadata(metadata = metadata).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Which animal' Table.select(self, columns) Select columns of the Table. Returns a new Table with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.select([0,1]) pyarrow.Table year: int64 n_legs: int64 ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] >>> table.select(["year"]) pyarrow.Table year: int64 ---- year: [[2020,2022,2019,2021]] Table.filter(self, mask, null_selection_behavior=u'drop') Select rows from the table. The Table can be filtered based on a mask, which will be passed to :func:`pyarrow.compute.filter` to perform the filtering, or it can be filtered through a boolean :class:`.Expression` Parameters ---------- mask : Array or array-like or .Expression The boolean mask or the :class:`.Expression` to filter the table with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled, does nothing if an :class:`.Expression` is used. Returns ------- filtered : Table A table of the same schema, with only the rows selected by applied filtering Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Define an expression and select rows: >>> import pyarrow.compute as pc >>> expr = pc.field("year") <= 2020 >>> table.filter(expr) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2019]] n_legs: [[2,5]] animals: [["Flamingo","Brittle stars"]] Define a mask and select rows: >>> mask=[True, True, False, None] >>> table.filter(mask) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022]] n_legs: [[2,4]] animals: [["Flamingo","Horse"]] >>> table.filter(mask, null_selection_behavior='emit_null') pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,null]] n_legs: [[2,4,null]] animals: [["Flamingo","Horse",null]] Table.slice(self, offset=0, length=None) Compute zero-copy slice of this Table. Parameters ---------- offset : int, default 0 Offset from start of table to slice. length : int, default None Length of slice (default is until end of table starting from offset). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.slice(length=3) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019]] n_legs: [[2,4,5]] animals: [["Flamingo","Horse","Brittle stars"]] >>> table.slice(offset=2) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019,2021]] n_legs: [[5,100]] animals: [["Brittle stars","Centipede"]] >>> table.slice(offset=2, length=1) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019]] n_legs: [[5]] animals: [["Brittle stars"]] Table.__reduce__(self)Table.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Table._is_initialized(self)table_to_blocks(options, Table table, categories, extension_columns)_reconstruct_record_batch(columns, schema) Internal: reconstruct RecordBatch from pickled components. RecordBatch._import_from_c_device(in_ptr, schema) Import RecordBatch from a C ArrowDeviceArray struct, given its pointer and the imported schema. Parameters ---------- in_ptr: int The raw pointer to a C ArrowDeviceArray struct. type: Schema or int Either a Schema object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. RecordBatch._export_to_c_device(self, out_ptr, out_schema_ptr=0) Export to a C ArrowDeviceArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the record batch schema is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowDeviceArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowDeviceArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. RecordBatch._import_from_c_capsule(schema_capsule, array_capsule) Import RecordBatch from a pair of PyCapsules containing a C ArrowArray and ArrowSchema, respectively. Parameters ---------- schema_capsule : PyCapsule A PyCapsule containing a C ArrowSchema representation of the schema. array_capsule : PyCapsule A PyCapsule containing a C ArrowArray representation of the array. Returns ------- pyarrow.RecordBatch RecordBatch.__arrow_c_stream__(self, requested_schema=None) Export the batch as an Arrow C stream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Currently, this is not supported and will raise a NotImplementedError if the schema doesn't match the current schema. Returns ------- PyCapsule RecordBatch.__arrow_c_array__(self, requested_schema=None) Get a pair of PyCapsules containing a C ArrowArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the batch to this schema. If None, the schema will be returned as-is, with a schema matching the one returned by :meth:`__arrow_c_schema__()`. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. RecordBatch._import_from_c(in_ptr, schema) Import RecordBatch from a C ArrowArray struct, given its pointer and the imported schema. Parameters ---------- in_ptr: int The raw pointer to a C ArrowArray struct. type: Schema or int Either a Schema object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. RecordBatch._export_to_c(self, out_ptr, out_schema_ptr=0) Export to a C ArrowArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the record batch schema is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. RecordBatch.to_tensor(self, bool null_to_nan=False, bool row_major=True, MemoryPool memory_pool=None) Convert to a :class:`~pyarrow.Tensor`. RecordBatches that can be converted have fields of type signed or unsigned integer or float, including all bit-widths. ``null_to_nan`` is ``False`` by default and this method will raise an error in case any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan`` set to ``True``. In this case null values are converted to ``NaN`` and integer type arrays are promoted to the appropriate float type. Parameters ---------- null_to_nan : bool, default False Whether to write null values in the result as ``NaN``. row_major : bool, default True Whether resulting Tensor is row-major or column-major memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Examples -------- >>> import pyarrow as pa >>> batch = pa.record_batch( ... [ ... pa.array([1, 2, 3, 4, None], type=pa.int32()), ... pa.array([10, 20, 30, 40, None], type=pa.float32()), ... ], names = ["a", "b"] ... ) >>> batch pyarrow.RecordBatch a: int32 b: float ---- a: [1,2,3,4,null] b: [10,20,30,40,null] Convert a RecordBatch to row-major Tensor with null values written as ``NaN``s >>> batch.to_tensor(null_to_nan=True) type: double shape: (5, 2) strides: (16, 8) >>> batch.to_tensor(null_to_nan=True).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert a RecordBatch to column-major Tensor >>> batch.to_tensor(null_to_nan=True, row_major=False) type: double shape: (5, 2) strides: (8, 40) >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) RecordBatch.to_struct_array(self) Convert to a struct array. RecordBatch.from_struct_array(StructArray struct_array) Construct a RecordBatch from a StructArray. Each field in the StructArray will become a column in the resulting ``RecordBatch``. Parameters ---------- struct_array : StructArray Array to construct the record batch from. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.RecordBatch.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 RecordBatch.from_arrays(list arrays, names=None, schema=None, metadata=None) Construct a RecordBatch from multiple pyarrow.Arrays Parameters ---------- arrays : list of pyarrow.Array One for each field in RecordBatch names : list of str, optional Names for the batch fields. If not passed, schema must be passed schema : Schema, default None Schema for the created batch. If not passed, names must be passed metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from pyarrow Arrays using names: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Construct a RecordBatch from pyarrow Arrays using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' RecordBatch.from_pandas(cls, df, Schema schema=None, preserve_index=None, nthreads=None, columns=None) Convert pandas.DataFrame to an Arrow RecordBatch Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the RecordBatch. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``RecordBatch``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Convert pandas DataFrame to RecordBatch: >>> import pyarrow as pa >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_pandas(df, schema=my_schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch specifying columns: >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] RecordBatch._to_pandas(self, options, **kwargs)RecordBatch.cast(self, Schema target_schema, safe=None, options=None) Cast record batch values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast batch values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> batch.cast(target_schema=my_schema) pyarrow.RecordBatch n_legs: duration[s] animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.select(self, columns) Select columns of the RecordBatch. Returns a new RecordBatch with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) Select columns my indices: >>> batch.select([1]) pyarrow.RecordBatch animals: string ---- animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Select columns by names: >>> batch.select(["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,2,4,4,5,100] RecordBatch.equals(self, other, bool check_metadata=False) Check if contents of two record batches are equal. Parameters ---------- other : pyarrow.RecordBatch RecordBatch to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch_0 = pa.record_batch([]) >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch.equals(batch) True >>> batch.equals(batch_0) False >>> batch.equals(batch_1) True >>> batch.equals(batch_1, check_metadata=True) False RecordBatch.filter(self, mask, null_selection_behavior=u'drop') Select rows from the record batch. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the record batch with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : RecordBatch A record batch of the same schema, with only the rows selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Define a mask and select rows: >>> mask=[True, True, False, True, False, None] >>> batch.filter(mask).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Horse >>> batch.filter(mask, null_selection_behavior='emit_null').to_pandas() n_legs animals 0 2.0 Flamingo 1 2.0 Parrot 2 4.0 Horse 3 NaN None RecordBatch.slice(self, offset=0, length=None) Compute zero-copy slice of this RecordBatch Parameters ---------- offset : int, default 0 Offset from start of record batch to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> batch.slice(offset=3).to_pandas() n_legs animals 0 4 Horse 1 5 Brittle stars 2 100 Centipede >>> batch.slice(length=2).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot >>> batch.slice(offset=3, length=1).to_pandas() n_legs animals 0 4 Horse RecordBatch.serialize(self, memory_pool=None) Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema. To reconstruct a RecordBatch from the encapsulated IPC message Buffer returned by this function, a Schema must be passed separately. See Examples. Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> buf = batch.serialize() >>> buf Reconstruct RecordBatch from IPC message Buffer and original Schema >>> pa.ipc.read_record_batch(buf, batch.schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] RecordBatch.rename_columns(self, names) Create new record batch with columns renamed to provided names. Parameters ---------- names : list of str List of new column names. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> new_names = ["n", "name"] >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.set_column(self, int i, field_, column) Replace column in RecordBatch at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> batch.set_column(1,'year', year) pyarrow.RecordBatch n_legs: int64 year: int64 ---- n_legs: [2,4,5,100] year: [2021,2022,2019,2021] RecordBatch.remove_column(self, int i) Create new RecordBatch with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New record batch without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.remove_column(1) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] RecordBatch.add_column(self, int i, field_, column) Add column to RecordBatch at position i. A new record batch is returned with the column added, the original record batch object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> batch.add_column(0,"year", year) pyarrow.RecordBatch year: int64 n_legs: int64 animals: string ---- year: [2021,2022,2019,2021] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Original record batch is left unchanged: >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] RecordBatch.__sizeof__(self)RecordBatch.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the record batch An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.get_total_buffer_size() 120 RecordBatch._column(self, int i) Select single column from record batch by its numeric index. Parameters ---------- i : int The index of the column to retrieve. Returns ------- column : pyarrow.Array RecordBatch.replace_schema_metadata(self, metadata=None) Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata Parameters ---------- metadata : dict, default None Returns ------- shallow_copy : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) Constructing a RecordBatch with schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64())], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema) >>> batch.schema n_legs: int64 -- schema metadata -- n_legs: 'Number of legs per animal' Shallow copy of a RecordBatch with deleted schema metadata: >>> batch.replace_schema_metadata().schema n_legs: int64 RecordBatch.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid RecordBatch.__reduce__(self)RecordBatch._is_initialized(self)_Tabular.__setstate_cython__(self, __pyx_state)_Tabular.__reduce_cython__(self)_Tabular.append_column(self, field_, column) Append column at end of columns. Parameters ---------- field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- Table or RecordBatch New table or record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Append column at the end: >>> year = [2021, 2022, 2019, 2021] >>> table.append_column('year', [year]) pyarrow.Table n_legs: int64 animals: string year: int64 ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] year: [[2021,2022,2019,2021]] _Tabular.add_column(self, int i, field_, column)_Tabular.drop_columns(self, columns) Drop one or more columns and return a new Table or RecordBatch. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Raises ------ KeyError If any of the passed column names do not exist. Returns ------- Table or RecordBatch A tabular object without the column(s). Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Drop one column: >>> table.drop_columns("animals") pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Drop one or more columns: >>> table.drop_columns(["n_legs", "animals"]) pyarrow.Table ... ---- _Tabular.remove_column(self, int i)_Tabular.to_string(self, *, show_metadata=False, preview_cols=0) Return human-readable string representation of Table or RecordBatch. Parameters ---------- show_metadata : bool, default False Display Field-level and Schema-level KeyValueMetadata. preview_cols : int, default 0 Display values of the columns for the first N columns. Returns ------- str _Tabular.to_pylist(self) Convert the Table or RecordBatch to a list of rows / dictionaries. Returns ------- list Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> data = [[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]] >>> table = pa.table(data, names=["n_legs", "animals"]) >>> table.to_pylist() [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ... _Tabular.to_pydict(self) Convert the Table or RecordBatch to a dict or OrderedDict. Returns ------- dict Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"]) >>> table.to_pydict() {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']} _Tabular.take(self, indices) Select rows from a Table or RecordBatch. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the tabular object whose rows will be returned. Returns ------- Table or RecordBatch A tabular object with the same schema, containing the taken rows. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.take([1,3]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] _Tabular.sort_by(self, sorting, **kwargs) Sort the Table or RecordBatch by one or multiple columns. Parameters ---------- sorting : str or list[tuple(name, order)] Name of the column to use to sort (ascending), or a list of multiple sorting conditions where each entry is a tuple with column name and sorting order ("ascending" or "descending") **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- Table or RecordBatch A new tabular object sorted according to the sort keys. Examples -------- Table (works similarly for RecordBatch) >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.sort_by('animal') pyarrow.Table year: int64 n_legs: int64 animal: string ---- year: [[2019,2021,2021,2020,2022,2022]] n_legs: [[5,100,4,2,4,2]] animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]] _Tabular.itercolumns(self) Iterator over all columns in their numerical order. Yields ------ Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> for i in table.itercolumns(): ... print(i.null_count) ... 2 1 _Tabular.from_pylist(cls, mapping, schema=None, metadata=None) Construct a Table or RecordBatch from list of rows / dictionaries. Parameters ---------- mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] Construct a Table from a list of rows: >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4]] animals: [["Flamingo","Dog"]] Construct a Table from a list of rows with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' _Tabular.from_pydict(cls, mapping, schema=None, metadata=None) Construct a Table or RecordBatch from Arrow arrays or columns. Parameters ---------- mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> pydict = {'n_legs': n_legs, 'animals': animals} Construct a Table from a dictionary of arrays: >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a dictionary of arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pydict(pydict, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' _Tabular.field(self, i) Select a schema field by its column name or numeric index. Parameters ---------- i : int or string The index or name of the field to retrieve. Returns ------- Field Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.field(0) pyarrow.Field >>> table.field(1) pyarrow.Field _Tabular.drop_null(self) Remove rows that contain missing values from a Table or RecordBatch. See :func:`pyarrow.compute.drop_null` for full usage. Returns ------- Table or RecordBatch A tabular object with the same schema, with rows containing no missing values. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.drop_null() pyarrow.Table year: double n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] _Tabular.column(self, i) Select single column from Table or RecordBatch. Parameters ---------- i : int or string The index or name of the column to retrieve. Returns ------- column : Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Select a column by numeric index: >>> table.column(0) [ [ 2, 4, 5, 100 ] ] Select a column by its name: >>> table.column("animals") [ [ "Flamingo", "Horse", "Brittle stars", "Centipede" ] ] _Tabular._is_initialized(self)_Tabular._ensure_integer_index(self, i) Ensure integer index (convert string column name to integer if needed). _Tabular._column(self, int i) Slice or return column at given index or column name Parameters ---------- key : integer, str, or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- Array (from RecordBatch) or ChunkedArray (from Table) for column input. RecordBatch or Table for slice input. _Tabular.__dataframe__(self, nan_as_null: bool = False, allow_copy: bool = True) Return the dataframe interchange object implementing the interchange protocol. Parameters ---------- nan_as_null : bool, default False Whether to tell the DataFrame to overwrite null values in the data with ``NaN`` (or ``NaT``). allow_copy : bool, default True Whether to allow memory copying when exporting. If set to False it would cause non-zero-copy exports to fail. Returns ------- DataFrame interchange object The object which consuming library can use to ingress the dataframe. Notes ----- Details on the interchange protocol: https://data-apis.org/dataframe-protocol/latest/index.html `nan_as_null` currently has no effect; once support for nullable extension dtypes is added, this value should be propagated to columns. _Tabular.__array__(self, dtype=None, copy=None)chunked_array(arrays, type=None) Construct chunked array from list of array-like objects Parameters ---------- arrays : Array, list of Array, or array-like Must all be the same data type. Can be empty only if type also passed. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be passed as well. type : DataType or string coercible to DataType Returns ------- ChunkedArray Examples -------- >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] ChunkedArray._import_from_c_capsule(stream) Import ChunkedArray from a C ArrowArrayStream PyCapsule. Parameters ---------- stream: PyCapsule A capsule containing a C ArrowArrayStream PyCapsule. Returns ------- ChunkedArray ChunkedArray.__arrow_c_stream__(self, requested_schema=None) Export to a C ArrowArrayStream PyCapsule. Parameters ---------- requested_schema : PyCapsule, default None The schema to which the stream should be casted, passed as a PyCapsule containing a C ArrowSchema representation of the requested schema. Returns ------- PyCapsule A capsule containing a C ArrowArrayStream struct. ChunkedArray.to_pylist(self) Convert to a list of native Python objects. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.to_pylist() [2, 2, 4, 4, None, 100] ChunkedArray.iterchunks(self) Convert to an iterator of ChunkArrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> for i in n_legs.iterchunks(): ... print(i.null_count) ... 0 1 ChunkedArray.chunk(self, i) Select a chunk by its index. Parameters ---------- i : int Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.chunk(1) [ 4, 5, 100 ] ChunkedArray.unify_dictionaries(self, MemoryPool memory_pool=None) Unify dictionaries across all chunks. This method returns an equivalent chunked array, but where all chunks share the same dictionary values. Dictionary indices are transposed accordingly. If there are no dictionaries in the chunked array, it is returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : ChunkedArray Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> c_arr [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ] ] >>> c_arr.unify_dictionaries() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] ChunkedArray.sort(self, order=u'ascending', **kwargs) Sort the ChunkedArray Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : ChunkedArray ChunkedArray.drop_null(self) Remove missing values from a chunked array. See :func:`pyarrow.compute.drop_null` for full description. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.drop_null() [ [ 2, 2 ], [ 4, 5, 100 ] ] ChunkedArray.take(self, indices) Select values from the chunked array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array or ChunkedArray An array with the same datatype, containing the taken values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.take([1,4,5]) [ [ 2, 5, 100 ] ] ChunkedArray.index(self, value, start=None, end=None, *, memory_pool=None) Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.index(4) >>> n_legs.index(4, start=3) ChunkedArray.filter(self, mask, null_selection_behavior=u'drop') Select values from the chunked array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the chunked array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array or ChunkedArray An array of the same type, with only the elements selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> mask = pa.array([True, False, None, True, False, True]) >>> n_legs.filter(mask) [ [ 2 ], [ 4, 100 ] ] >>> n_legs.filter(mask, null_selection_behavior="emit_null") [ [ 2, null ], [ 4, 100 ] ] ChunkedArray.slice(self, offset=0, length=None) Compute zero-copy slice of this ChunkedArray Parameters ---------- offset : int, default 0 Offset from start of array to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.slice(2,2) [ [ 4 ], [ 4 ] ] ChunkedArray.value_counts(self) Compute counts of unique elements in array. Returns ------- An array of structs Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.value_counts() -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ChunkedArray.unique(self) Compute distinct elements in array Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.unique() [ 2, 4, 5, 100 ] ChunkedArray.combine_chunks(self, MemoryPool memory_pool=None) Flatten this ChunkedArray into a single non-chunked array. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.combine_chunks() [ 2, 2, 4, 4, 5, 100 ] ChunkedArray.flatten(self, MemoryPool memory_pool=None) Flatten this ChunkedArray. If it has a struct type, the column is flattened into one array per struct field. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : list of ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> c_arr = pa.chunked_array(n_legs.value_counts()) >>> c_arr [ -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ] >>> c_arr.flatten() [ [ [ 2, 4, 5, 100 ] ], [ [ 2, 2, 1, 1 ] ]] >>> c_arr.type StructType(struct) >>> n_legs.type DataType(int64) ChunkedArray.dictionary_encode(self, null_encoding=u'mask') Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : ChunkedArray A dictionary-encoded version of this array. Examples -------- >>> import pyarrow as pa >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> animals.dictionary_encode() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] ChunkedArray.cast(self, target_type=None, safe=None, options=None) Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- cast : Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Change the data type of an array: >>> n_legs_seconds = n_legs.cast(pa.duration('s')) >>> n_legs_seconds.type DurationType(duration[s]) ChunkedArray.__array__(self, dtype=None, copy=None)ChunkedArray.to_numpy(self, zero_copy_only=False) Return a NumPy copy of this array (experimental). Parameters ---------- zero_copy_only : bool, default False Introduced for signature consistence with pyarrow.Array.to_numpy. This must be False here since NumPy arrays' buffer must be contiguous. Returns ------- array : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_numpy() array([ 2, 2, 4, 4, 5, 100]) ChunkedArray._to_pandas(self, options, types_mapper=None, **kwargs)ChunkedArray.equals(self, ChunkedArray other) Return whether the contents of two chunked arrays are equal. Parameters ---------- other : pyarrow.ChunkedArray Chunked array to compare against. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> n_legs.equals(n_legs) True >>> n_legs.equals(animals) False ChunkedArray.fill_null(self, fill_value) Replace each null element in values with fill_value. See :func:`pyarrow.compute.fill_null` for full usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array or ChunkedArray A new array with nulls replaced by the given value. Examples -------- >>> import pyarrow as pa >>> fill_value = pa.scalar(5, type=pa.int8()) >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.fill_null(fill_value) [ [ 2, 2, 4, 4, 5, 100 ] ] ChunkedArray.is_valid(self) Return boolean array indicating the non-null values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_valid() [ [ true, true, true ], [ true, false, true ] ] ChunkedArray.is_nan(self) Return boolean array indicating the NaN values. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]]) >>> arr.is_nan() [ [ false, true, false, false, null, false ] ] ChunkedArray.is_null(self, *, nan_is_null=False) Return boolean array indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_null() [ [ false, false, false, false, true, false ] ] Slice or return value at given index Parameters ---------- key : integer or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- value : Scalar (index) or ChunkedArray (slice) ChunkedArray.__sizeof__(self)ChunkedArray.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the chunked array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.get_total_buffer_size() 49 ChunkedArray.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid ChunkedArray.format(self, **kwargs) DEPRECATED, use pyarrow.ChunkedArray.to_string Parameters ---------- **kwargs : dict Returns ------- str ChunkedArray.to_string(self, *, int indent=0, int window=5, int container_window=2, bool skip_new_lines=False) Render a "pretty-printed" string representation of the ChunkedArray Parameters ---------- indent : int How much to indent right the content of the array, by default ``0``. window : int How many items to preview within each chunk at the begin and end of the chunk when the chunk is bigger than the window. The other elements will be ellipsed. container_window : int How many chunks to preview at the begin and end of the array when the array is bigger than the window. The other elements will be ellipsed. This setting also applies to list columns. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_string(skip_new_lines=True) '[[2,2,4],[4,5,100]]' ChunkedArray.length(self) Return length of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.length() 6 ChunkedArray.__reduce__(self)StringViewBuilder.__setstate_cython__(self, __pyx_state)StringViewBuilder.__reduce_cython__(self)StringViewBuilder.finish(self) Return result of builder as an Array object; also resets the builder. Returns ------- array : pyarrow.Array StringViewBuilder.append_values(self, values) Append all the values from an iterable. Parameters ---------- values : iterable of string/bytes or np.nan/None values The values to append to the string array builder. StringViewBuilder.append(self, value) Append a single value to the builder. The value can either be a string/bytes object or a null value (np.nan or None). Parameters ---------- value : string/bytes or np.nan/None The value to append to the string array builder. StringBuilder.__setstate_cython__(self, __pyx_state)StringBuilder.__reduce_cython__(self)StringBuilder.finish(self) Return result of builder as an Array object; also resets the builder. Returns ------- array : pyarrow.Array StringBuilder.append_values(self, values) Append all the values from an iterable. Parameters ---------- values : iterable of string/bytes or np.nan/None values The values to append to the string array builder. StringBuilder.append(self, value) Append a single value to the builder. The value can either be a string/bytes object or a null value (np.nan or None). Parameters ---------- value : string/bytes or np.nan/None The value to append to the string array builder. _empty_array(DataType type) Create empty array of the given type. concat_arrays(arrays, MemoryPool memory_pool=None) Concatenate the given arrays. The contents of the input arrays are copied into the returned array. Raises ------ ArrowInvalid If not all of the arrays have the same type. Parameters ---------- arrays : iterable of pyarrow.Array Arrays to concatenate, must be identically typed. memory_pool : MemoryPool, default None For memory allocations. If None, the default pool is used. Examples -------- >>> import pyarrow as pa >>> arr1 = pa.array([2, 4, 5, 100]) >>> arr2 = pa.array([2, 4]) >>> pa.concat_arrays([arr1, arr2]) [ 2, 4, 5, 100, 2, 4 ] FixedShapeTensorArray.from_numpy_ndarray(obj) Convert numpy tensors (ndarrays) to a fixed shape tensor extension array. The first dimension of ndarray will become the length of the fixed shape tensor array. If input array data is not contiguous a copy will be made. Parameters ---------- obj : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array( ... [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]], ... dtype=np.float32) >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr) [ [ 1, 2, 3, 4, 5, 6 ], [ 1, 2, 3, 4, 5, 6 ] ] FixedShapeTensorArray.to_tensor(self) Convert fixed shape tensor extension array to a pyarrow.Tensor. The resulting Tensor will have (ndim + 1) dimensions. The size of the first dimension will be the length of the fixed shape tensor array and the rest of the dimensions will match the permuted shape of the fixed shape tensor. The conversion is zero-copy. Returns ------- pyarrow.Tensor Tensor representing tensors in the fixed shape tensor array concatenated along the first dimension. FixedShapeTensorArray.to_numpy_ndarray(self) Convert fixed shape tensor extension array to a multi-dimensional numpy.ndarray. The resulting ndarray will have (ndim + 1) dimensions. The size of the first dimension will be the length of the fixed shape tensor array and the rest of the dimensions will match the permuted shape of the fixed shape tensor. The conversion is zero-copy. Returns ------- numpy.ndarray Ndarray representing tensors in the fixed shape tensor array concatenated along the first dimension. ExtensionArray.from_storage(BaseExtensionType typ, Array storage) Construct ExtensionArray from type and storage array. Parameters ---------- typ : DataType The extension type for the result array. storage : Array The underlying storage for the result array. Returns ------- ext_array : ExtensionArray RunEndEncodedArray.find_physical_length(self) Find the physical length of this REE array. The physical length of an REE is the number of physical values (and run-ends) necessary to represent the logical range of values from offset to length. This function uses binary-search, so it has a O(log N) cost. RunEndEncodedArray.find_physical_offset(self) Find the physical offset of this REE array. This is the offset of the run that contains the value of the first logical element of this array considering its offset. This function uses binary-search, so it has a O(log N) cost. RunEndEncodedArray.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None) Construct a RunEndEncodedArray from all the parameters that make up an Array. RunEndEncodedArrays do not have buffers, only children arrays, but this implementation is needed to satisfy the Array interface. Parameters ---------- type : DataType The run_end_encoded(run_end_type, value_type) type. length : int The logical length of the run-end encoded array. Expected to match the last value of the run_ends array (children[0]) minus the offset. buffers : List[Buffer] Empty List or [None]. null_count : int, default -1 The number of null entries in the array. Run-end encoded arrays are specified to not have valid bits and null_count always equals 0. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. children : List[Array] Nested type children containing the run_ends and values arrays. Returns ------- RunEndEncodedArray RunEndEncodedArray.from_arrays(run_ends, values, type=None) Construct RunEndEncodedArray from run_ends and values arrays. Parameters ---------- run_ends : Array (int16, int32, or int64 type) The run_ends array. values : Array (any type) The values array. type : pyarrow.DataType, optional The run_end_encoded(run_end_type, value_type) array type. Returns ------- RunEndEncodedArray RunEndEncodedArray._from_arrays(type, allow_none_for_type, logical_length, run_ends, values, logical_offset)StructArray.sort(self, order=u'ascending', by=None, **kwargs) Sort the StructArray Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". by : str or None, default None If to sort the array by one of its fields or by the whole array. **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : StructArray StructArray.from_arrays(arrays, names=None, fields=None, mask=None, memory_pool=None) Construct StructArray from collection of arrays representing each field in the struct. Either field names or field instances must be passed. Parameters ---------- arrays : sequence of Array names : List[str] (optional) Field names for each struct child. fields : List[Field] (optional) Field instances for each struct child. mask : pyarrow.Array[bool] (optional) Indicate which values are null (True) or not null (False). memory_pool : MemoryPool (optional) For memory allocations, if required, otherwise uses default pool. Returns ------- result : StructArray StructArray.flatten(self, MemoryPool memory_pool=None) Return one individual array for each field in the struct. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- result : List[Array] StructArray._flattened_field(self, index, MemoryPool memory_pool=None) Retrieves the child array belonging to field, accounting for the parent array null bitmap. Parameters ---------- index : Union[int, str] Index / position or name of the field. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- result : Array StructArray.field(self, index) Retrieves the child array belonging to field. Parameters ---------- index : Union[int, str] Index / position or name of the field. Returns ------- result : Array DictionaryArray.from_arrays(indices, dictionary, mask=None, bool ordered=False, bool from_pandas=False, bool safe=True, MemoryPool memory_pool=None) Construct a DictionaryArray from indices and values. Parameters ---------- indices : pyarrow.Array, numpy.ndarray or pandas.Series, int type Non-negative integers referencing the dictionary values by zero based index. dictionary : pyarrow.Array, ndarray or pandas.Series The array of values referenced by the indices. mask : ndarray or pandas.Series, bool type True values indicate that indices are actually null. ordered : bool, default False Set to True if the category values are ordered. from_pandas : bool, default False If True, the indices should be treated as though they originated in a pandas.Categorical (null encoded as -1). safe : bool, default True If True, check that the dictionary indices are in range. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise uses default pool. Returns ------- dict_array : DictionaryArray DictionaryArray.from_buffers(DataType type, int64_t length, buffers, Array dictionary, int64_t null_count=-1, int64_t offset=0) Construct a DictionaryArray from buffers. Parameters ---------- type : pyarrow.DataType length : int The number of values in the array. buffers : List[Buffer] The buffers backing the indices array. dictionary : pyarrow.Array, ndarray or pandas.Series The array of values referenced by the indices. null_count : int, default -1 The number of null entries in the indices array. Negative value means that the null count is not known. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. Returns ------- dict_array : DictionaryArray DictionaryArray.dictionary_decode(self) Decodes the DictionaryArray to an Array. DictionaryArray.dictionary_encode(self)LargeStringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0) Construct a LargeStringArray from value_offsets and data buffers. If there are nulls in the data, also a null_bitmap and the matching null_count must be passed. Parameters ---------- length : int value_offsets : Buffer data : Buffer null_bitmap : Buffer, optional null_count : int, default 0 offset : int, default 0 Returns ------- string_array : StringArray StringArray.from_buffers(int length, Buffer value_offsets, Buffer data, Buffer null_bitmap=None, int null_count=-1, int offset=0) Construct a StringArray from value_offsets and data buffers. If there are nulls in the data, also a null_bitmap and the matching null_count must be passed. Parameters ---------- length : int value_offsets : Buffer data : Buffer null_bitmap : Buffer, optional null_count : int, default 0 offset : int, default 0 Returns ------- string_array : StringArray UnionArray.from_sparse(Array types, list children, list field_names=None, list type_codes=None) Construct sparse UnionArray from arrays of int8 types and children arrays Parameters ---------- types : Array (int8 type) children : list field_names : list type_codes : list Returns ------- union_array : UnionArray UnionArray.from_dense(Array types, Array value_offsets, list children, list field_names=None, list type_codes=None) Construct dense UnionArray from arrays of int8 types, int32 offsets and children arrays Parameters ---------- types : Array (int8 type) value_offsets : Array (int32 type) children : list field_names : list type_codes : list Returns ------- union_array : UnionArray UnionArray.field(self, int pos) Return the given child field as an individual array. For sparse unions, the returned array has its offset, length, and null count adjusted. For dense unions, the returned array is unchanged. Parameters ---------- pos : int The physical index of the union child field (not its type code). Returns ------- field : Array The given child field. UnionArray.child(self, int pos) DEPRECATED, use field() instead. Parameters ---------- pos : int The physical index of the union child field (not its type code). Returns ------- field : pyarrow.Field The given child field. FixedSizeListArray.from_arrays(values, list_size=None, DataType type=None, mask=None) Construct FixedSizeListArray from array of values and a list length. Parameters ---------- values : Array (any type) list_size : int The fixed length of the lists. type : DataType, optional If not specified, a default ListType with the values' type and `list_size` length is used. mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- FixedSizeListArray Examples -------- Create from a values array and a list size: >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> arr = pa.FixedSizeListArray.from_arrays(values, 2) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Or create from a values array, list size and matching type: >>> typ = pa.list_(pa.field("values", pa.int64()), 2) >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] MapArray.from_arrays(offsets, keys, items, DataType type=None, MemoryPool pool=None) Construct MapArray from arrays of int32 offsets and key, item arrays. Parameters ---------- offsets : array-like or sequence (int32 type) keys : array-like or sequence (any type) items : array-like or sequence (any type) type : DataType, optional If not specified, a default MapArray with the keys' and items' type is used. pool : MemoryPool Returns ------- map_array : MapArray Examples -------- First, let's understand the structure of our dataset when viewed in a rectangular data model. The total of 5 respondents answered the question "How much did you like the movie x?". The value -1 in the integer array means that the value is missing. The boolean array represents the null bitmask corresponding to the missing values in the integer array. >>> import pyarrow as pa >>> movies_rectangular = np.ma.masked_array([ ... [10, -1, -1], ... [8, 4, 5], ... [-1, 10, 3], ... [-1, -1, -1], ... [-1, -1, -1] ... ], ... [ ... [False, True, True], ... [False, False, False], ... [True, False, False], ... [True, True, True], ... [True, True, True], ... ]) To represent the same data with the MapArray and from_arrays, the data is formed like this: >>> offsets = [ ... 0, # -- row 1 start ... 1, # -- row 2 start ... 4, # -- row 3 start ... 6, # -- row 4 start ... 6, # -- row 5 start ... 6, # -- row 5 end ... ] >>> movies = [ ... "Dark Knight", # ---------------------------------- row 1 ... "Dark Knight", "Meet the Parents", "Superman", # -- row 2 ... "Meet the Parents", "Superman", # ----------------- row 3 ... ] >>> likings = [ ... 10, # -------- row 1 ... 8, 4, 5, # --- row 2 ... 10, 3 # ------ row 3 ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 [] 4 [] dtype: object If the data in the empty rows needs to be marked as missing, it's possible to do so by modifying the offsets argument, so that we specify `None` as the starting positions of the rows we want marked as missing. The end row offset still has to refer to the existing value from keys (and values): >>> offsets = [ ... 0, # ----- row 1 start ... 1, # ----- row 2 start ... 4, # ----- row 3 start ... None, # -- row 4 start ... None, # -- row 5 start ... 6, # ----- row 5 end ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 None 4 None dtype: object LargeListViewArray.flatten(self, memory_pool=None) Unnest this LargeListViewArray by one level. The returned Array is logically a concatenation of all the sub-lists in this Array. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- memory_pool : MemoryPool, optional Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] LargeListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None) Construct LargeListViewArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) sizes : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : LargeListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] ListViewArray.flatten(self, memory_pool=None) Unnest this ListViewArray by one level. The returned Array is logically a concatenation of all the sub-lists in this Array. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- memory_pool : MemoryPool, optional Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] ListViewArray.from_arrays(offsets, sizes, values, DataType type=None, MemoryPool pool=None, mask=None) Construct ListViewArray from arrays of int32 offsets, sizes, and values. Parameters ---------- offsets : Array (int32 type) sizes : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : ListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] LargeListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None) Construct LargeListArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : LargeListArray ListArray.from_arrays(offsets, values, DataType type=None, MemoryPool pool=None, mask=None) Construct ListArray from arrays of int32 offsets and values. Parameters ---------- offsets : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : ListArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], [ 3, 4 ] ] >>> # nulls in the offsets array become null lists >>> offsets = pa.array([0, None, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], null, [ 3, 4 ] ] BaseListArray.value_lengths(self) Return integers array with values equal to the respective length of each list element. Null list values are null in the output. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_lengths() [ 3, 0, null, 1 ] BaseListArray.value_parent_indices(self) Return array of same length as list child values array where each output value is the index of the parent list array slot containing each child value. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_parent_indices() [ 0, 0, 0, 3 ] BaseListArray.flatten(self) Unnest this ListArray/LargeListArray by one level. The returned Array is logically a concatenation of all the sub-lists in this Array. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Returns ------- result : Array MonthDayNanoIntervalArray.to_pylist(self) Convert to a list of native Python objects. pyarrow.MonthDayNano is used as the native representation. Returns ------- lst : list Array.__dlpack_device__(self) Return the DLPack device tuple this arrays resides on. Returns ------- tuple : Tuple[int, int] Tuple with index specifying the type of the device (where CPU = 1, see cpp/src/arrow/c/dpack_abi.h) and index of the device which is 0 by default for CPU. Array.__dlpack__(self, stream=None) Export a primitive array as a DLPack capsule. Parameters ---------- stream : int, optional A Python integer representing a pointer to a stream. Currently not supported. Stream is provided by the consumer to the producer to instruct the producer to ensure that operations can safely be performed on the array. Returns ------- capsule : PyCapsule A DLPack capsule for the array, pointing to a DLManagedTensor. Array._import_from_c_device(in_ptr, type) Import Array from a C ArrowDeviceArray struct, given its pointer and the imported array type. Parameters ---------- in_ptr: int The raw pointer to a C ArrowDeviceArray struct. type: DataType or int Either a DataType object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. Array._export_to_c_device(self, out_ptr, out_schema_ptr=0) Export to a C ArrowDeviceArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the array type is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowDeviceArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowDeviceArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. Array._import_from_c_capsule(schema_capsule, array_capsule)Array.__arrow_c_array__(self, requested_schema=None) Get a pair of PyCapsules containing a C ArrowArray representation of the object. Parameters ---------- requested_schema : PyCapsule | None A PyCapsule containing a C ArrowSchema representation of a requested schema. PyArrow will attempt to cast the array to this data type. If None, the array will be returned as-is, with a type matching the one returned by :meth:`__arrow_c_schema__()`. Returns ------- Tuple[PyCapsule, PyCapsule] A pair of PyCapsules containing a C ArrowSchema and ArrowArray, respectively. Array._import_from_c(in_ptr, type) Import Array from a C ArrowArray struct, given its pointer and the imported array type. Parameters ---------- in_ptr: int The raw pointer to a C ArrowArray struct. type: DataType or int Either a DataType object, or the raw pointer to a C ArrowSchema struct. This is a low-level function intended for expert users. Array._export_to_c(self, out_ptr, out_schema_ptr=0) Export to a C ArrowArray struct, given its pointer. If a C ArrowSchema struct pointer is also given, the array type is exported to it at the same time. Parameters ---------- out_ptr: int The raw pointer to a C ArrowArray struct. out_schema_ptr: int (optional) The raw pointer to a C ArrowSchema struct. Be careful: if you don't pass the ArrowArray struct to a consumer, array memory will leak. This is a low-level function intended for expert users. Array.buffers(self) Return a list of Buffer objects pointing to this array's physical storage. To correctly interpret these buffers, you need to also apply the offset multiplied with the size of the stored data type. Array.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Array.tolist(self) Alias of to_pylist for compatibility with NumPy. Array.to_pylist(self) Convert to a list of native Python objects. Returns ------- lst : list Array.to_numpy(self, zero_copy_only=True, writable=False) Return a NumPy view or copy of this array (experimental). By default, tries to return a view of this array. This is only supported for primitive arrays with the same memory layout as NumPy (i.e. integers, floating point, ..) and without any nulls. For the extension arrays, this method simply delegates to the underlying storage array. Parameters ---------- zero_copy_only : bool, default True If True, an exception will be raised if the conversion to a numpy array would require copying the underlying data (e.g. in presence of nulls, or for non-primitive types). writable : bool, default False For numpy arrays created with zero copy (view on the Arrow data), the resulting array is not writable (Arrow data is immutable). By setting this to True, a copy of the array is made to ensure it is writable. Returns ------- array : numpy.ndarray Array.__array__(self, dtype=None, copy=None)Array._to_pandas(self, options, types_mapper=None, **kwargs)Array.sort(self, order=u'ascending', **kwargs) Sort the Array Parameters ---------- order : str, default "ascending" Which order to sort values in. Accepted values are "ascending", "descending". **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- result : Array Array.index(self, value, start=None, end=None, *, memory_pool=None) Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Array.filter(self, Array mask, *, null_selection_behavior=u'drop') Select values from an array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array An array of the same type, with only the elements selected by the boolean mask. Array.drop_null(self) Remove missing values from an array. Array.take(self, indices) Select values from an array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array An array with the same datatype, containing the taken values. Array.slice(self, offset=0, length=None) Compute zero-copy slice of this array. Parameters ---------- offset : int, default 0 Offset from start of array to slice. length : int, default None Length of slice (default is until end of Array starting from offset). Returns ------- sliced : RecordBatch Slice or return value at given index Parameters ---------- key : integer or slice Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view Returns ------- value : Scalar (index) or Array (slice) Array.fill_null(self, fill_value) See :func:`pyarrow.compute.fill_null` for usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array A new array with nulls replaced by the given value. Array.is_valid(self) Return BooleanArray indicating the non-null values. Array.is_nan(self) Return BooleanArray indicating the NaN values. Returns ------- array : boolean Array Array.is_null(self, *, nan_is_null=False) Return BooleanArray indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array Array.equals(self, Array other) Parameters ---------- other : pyarrow.Array Returns ------- bool Array.format(self, **kwargs) DEPRECATED, use pyarrow.Array.to_string Parameters ---------- **kwargs : dict Returns ------- str Array.to_string(self, *, int indent=2, int top_level_indent=0, int window=10, int container_window=2, bool skip_new_lines=False) Render a "pretty-printed" string representation of the Array. Parameters ---------- indent : int, default 2 How much to indent the internal items in the string to the right, by default ``2``. top_level_indent : int, default 0 How much to indent right the entire content of the array, by default ``0``. window : int How many primitive items to preview at the begin and end of the array when the array is bigger than the window. The other items will be ellipsed. container_window : int How many container items (such as a list in a list array) to preview at the begin and end of the array when the array is bigger than the window. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Array.__sizeof__(self)Array.get_total_buffer_size(self) The sum of bytes in each buffer referenced by the array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Array.from_buffers(DataType type, length, buffers, null_count=-1, offset=0, children=None) Construct an Array from a sequence of buffers. The concrete type returned depends on the datatype. Parameters ---------- type : DataType The value type of the array. length : int The number of values in the array. buffers : List[Buffer] The buffers backing this array. null_count : int, default -1 The number of null entries in the array. Negative value means that the null count is not known. offset : int, default 0 The array's logical offset (in values, not in bytes) from the start of each buffer. children : List[Array], default None Nested type children with length matching type.num_fields. Returns ------- array : Array Array.__reduce__(self)Array.from_pandas(obj, mask=None, type=None, bool safe=True, MemoryPool memory_pool=None) Convert pandas.Series to an Arrow Array. This method uses Pandas semantics about what values indicate nulls. See pyarrow.array for more general conversion from arrays or sequences to Arrow arrays. Parameters ---------- obj : ndarray, pandas.Series, array-like mask : array (boolean), optional Indicate which values are null (True) or not null (False). type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Notes ----- Localized timestamps will currently be returned as UTC (pandas's native representation). Timezone-naive data will be implicitly interpreted as UTC. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray ChunkedArray is returned if object data overflows binary buffer. Array.value_counts(self) Compute counts of unique elements in array. Returns ------- StructArray An array of structs Array.dictionary_encode(self, null_encoding=u'mask') Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : DictionaryArray A dictionary-encoded version of this array. Array.unique(self) Compute distinct elements in array. Returns ------- unique : Array An array of the same data type, with deduplicated elements. Array.sum(self, **kwargs) Sum the values in a numerical array. See :func:`pyarrow.compute.sum` for full usage. Parameters ---------- **kwargs : dict, optional Options to pass to :func:`pyarrow.compute.sum`. Returns ------- sum : Scalar A scalar containing the sum value. Array.view(self, target_type) Return zero-copy "view" of array as another data type. The data types must have compatible columnar buffer layouts Parameters ---------- target_type : DataType Type to construct view as. Returns ------- view : Array Array.cast(self, target_type=None, safe=None, options=None, memory_pool=None) Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, default None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions memory_pool : MemoryPool, optional memory pool to use for allocations during function execution. Returns ------- cast : Array Array.diff(self, Array other) Compare contents of this array against another one. Return a string containing the result of diffing this array (on the left side) against the other array (on the right side). Parameters ---------- other : Array The other array to compare this array with. Returns ------- diff : str A human-readable printout of the differences. Examples -------- >>> import pyarrow as pa >>> left = pa.array(["one", "two", "three"]) >>> right = pa.array(["two", None, "two-and-a-half", "three"]) >>> print(left.diff(right)) # doctest: +SKIP @@ -0, +0 @@ -"one" @@ -2, +1 @@ +null +"two-and-a-half" Array._debug_print(self)_PandasConvertible.__setstate_cython__(self, __pyx_state)_PandasConvertible.__reduce_cython__(self)_PandasConvertible.to_pandas(self, memory_pool=None, categories=None, bool strings_to_categorical=False, bool zero_copy_only=False, bool integer_object_nulls=False, bool date_as_object=True, bool timestamp_as_object=False, bool use_threads=True, bool deduplicate_objects=True, bool ignore_metadata=False, bool safe=True, bool split_blocks=False, bool self_destruct=False, unicode maps_as_pydicts=None, types_mapper=None, bool coerce_temporal_nanoseconds=False) Convert to a pandas-compatible NumPy array or DataFrame, as appropriate Parameters ---------- memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. categories : list, default empty List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures. strings_to_categorical : bool, default False Encode string (UTF8) and binary types to pandas.Categorical. zero_copy_only : bool, default False Raise an ArrowException if this function call would require copying the underlying data. integer_object_nulls : bool, default False Cast integers with nulls to objects date_as_object : bool, default True Cast dates to objects. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Note: in pandas version < 2.0, only datetime64[ns] conversion is supported. timestamp_as_object : bool, default False Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). Non-nanosecond timestamps are supported in pandas version 2.0. If False, all timestamps are converted to datetime64 dtype. use_threads : bool, default True Whether to parallelize the conversion using multiple threads. deduplicate_objects : bool, default True Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower. ignore_metadata : bool, default False If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present safe : bool, default True For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not. split_blocks : bool, default False If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use. self_destruct : bool, default False EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program. Note that you may not see always memory usage improvements. For example, if multiple columns share an underlying allocation, memory can't be freed until all columns are converted. maps_as_pydicts : str, optional, default `None` Valid values are `None`, 'lossy', or 'strict'. The default behavior (`None`), is to convert Arrow Map arrays to Python association lists (list-of-tuples) in the same order as the Arrow Map, as in [(key1, value1), (key2, value2), ...]. If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts. This can change the ordering of (key, value) pairs, and will deduplicate multiple keys, resulting in a possible loss of data. If 'lossy', this key deduplication results in a warning printed when detected. If 'strict', this instead results in an exception being raised when detected. types_mapper : function, default None A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or ``None`` if the default conversion should be used for that type. If you have a dictionary mapping, you can pass ``dict.get`` as function. coerce_temporal_nanoseconds : bool, default False Only applicable to pandas version >= 2.0. A legacy option to coerce date32, date64, duration, and timestamp time units to nanoseconds when converting to pandas. This is the default behavior in pandas version 1.x. Set this option to True if you'd like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise). Returns ------- pandas.Series or pandas.DataFrame depending on type of object Examples -------- >>> import pyarrow as pa >>> import pandas as pd Convert a Table to pandas DataFrame: >>> table = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> table.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(table.to_pandas(), pd.DataFrame) True Convert a RecordBatch to pandas DataFrame: >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(batch.to_pandas(), pd.DataFrame) True Convert a Chunked Array to pandas Series: >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_pandas() 0 2 1 2 2 4 3 4 4 5 5 100 dtype: int64 >>> isinstance(n_legs.to_pandas(), pd.Series) True _restore_array(data) Reconstruct an Array from pickled ArrayData. _normalize_slice(arrow_obj, slice key) Slices with step not equal to 1 (or None) will produce a copy rather than a zero-copy view infer_type(values, mask=None, from_pandas=False) Attempt to infer Arrow data type that can hold the passed Python sequence type in an Array object Parameters ---------- values : array-like Sequence to infer type from. mask : ndarray (bool type), optional Optional exclusion mask where True marks null, False non-null. from_pandas : bool, default False Use pandas's NA/null sentinel values for type inference. Returns ------- type : DataType repeat(value, size, MemoryPool memory_pool=None) Create an Array instance whose slots are the given scalar. Parameters ---------- value : Scalar-like object Either a pyarrow.Scalar or any python object coercible to a Scalar. size : int Number of times to repeat the scalar in the output Array. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.repeat(10, 3) [ 10, 10, 10 ] >>> pa.repeat([1, 2], 2) [ [ 1, 2 ], [ 1, 2 ] ] >>> pa.repeat("string", 3) [ "string", "string", "string" ] >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2) -- is_valid: all not null -- child 0 type: int64 [ 1, 1 ] -- child 1 type: list [ [ 1, 2 ], [ 1, 2 ] ] nulls(size, type=None, MemoryPool memory_pool=None) Create a strongly-typed Array instance with all elements null. Parameters ---------- size : int Array length. type : pyarrow.DataType, default None Explicit type for the array. By default use NullType. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.nulls(10) 10 nulls >>> pa.nulls(3, pa.uint32()) [ null, null, null ] asarray(values, type=None) Convert to pyarrow.Array, inferring type if not provided. Parameters ---------- values : array-like This can be a sequence, numpy.ndarray, pyarrow.Array or pyarrow.ChunkedArray. If a ChunkedArray is passed, the output will be a ChunkedArray, otherwise the output will be a Array. type : string or DataType Explicitly construct the array with this type. Attempt to cast if indicated type is different. Returns ------- arr : Array or ChunkedArray array(obj, type=None, mask=None, size=None, from_pandas=None, bool safe=True, MemoryPool memory_pool=None) Create pyarrow.Array instance from a Python object. Parameters ---------- obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array If both type and size are specified may be a single use iterable. If not strongly-typed, Arrow type will be inferred for resulting array. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` method) can be passed as well. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. mask : array[bool], optional Indicate which values are null (True) or not null (False). size : int64, optional Size of the elements. If the input is larger than size bail at this length. For iterators, if size is larger than the input iterator this will be treated as a "max size", but will involve an initial allocation of size followed by a resize to the actual size (so if you know the exact size specifying it correctly will give you better performance). from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. If passed, the mask tasks precedence, but if a value is unmasked (not-null), but still null according to pandas semantics, then it is null. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray A ChunkedArray instead of an Array is returned if: - the object data overflowed binary storage. - the object's ``__arrow_array__`` protocol method returned a chunked array. Notes ----- Timezone will be preserved in the returned array for timezone-aware data, else no timezone will be returned for naive timestamps. Internally, UTC values are stored for timezone-aware data with the timezone set in the data type. Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by default converted as MonthDayNanoIntervalArray. relativedelta leapdays are ignored as are all absolute fields on both objects. datetime.timedelta can also be converted to MonthDayNanoIntervalArray but this requires passing MonthDayNanoIntervalType explicitly. Converting to dictionary array will promote to a wider integer type for indices if the number of distinct values cannot be represented, even if the index type was explicitly set. This means that if there are more than 127 values the returned dictionary array's index type will be at least pa.int16() even if pa.int8() was passed to the function. Note that an explicit index type will not be demoted even if it is wider than required. Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> pa.array(pd.Series([1, 2])) [ 1, 2 ] >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string())) ... -- dictionary: [ "a", "b" ] -- indices: [ 0, 1, 0 ] >>> import numpy as np >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool)) [ 1, null ] >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64())) >>> arr.type.index_type DataType(int16) _handle_arrow_array_protocol(obj, type, mask, size)_ndarray_to_arrow_type(values, DataType type)scalar(value, type=None, *, from_pandas=None, MemoryPool memory_pool=None) Create a pyarrow.Scalar instance from a Python object. Parameters ---------- value : Any Python object coercible to arrow's type system. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the value. from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- scalar : pyarrow.Scalar Examples -------- >>> import pyarrow as pa >>> pa.scalar(42) >>> pa.scalar("string") >>> pa.scalar([1, 2]) >>> pa.scalar([1, 2], type=pa.list_(pa.int16())) FixedShapeTensorScalar.to_tensor(self) Convert fixed shape tensor extension scalar to a pyarrow.Tensor, using shape and strides derived from corresponding FixedShapeTensorType. The conversion is zero-copy. Returns ------- pyarrow.Tensor Tensor represented stored in FixedShapeTensorScalar. FixedShapeTensorScalar.to_numpy(self) Convert fixed shape tensor scalar to a numpy.ndarray. The resulting ndarray's shape matches the permuted shape of the fixed shape tensor scalar. The conversion is zero-copy. Returns ------- numpy.ndarray ExtensionScalar.from_storage(BaseExtensionType typ, value) Construct ExtensionScalar from type and storage value. Parameters ---------- typ : DataType The extension type for the result scalar. value : object The storage value for the result scalar. Returns ------- ext_scalar : ExtensionScalar ExtensionScalar.as_py(self) Return this scalar as a Python object. UnionScalar.as_py(self) Return underlying value as a Python object. RunEndEncodedScalar.as_py(self) Return underlying value as a Python object. DictionaryScalar.as_py(self) Return this encoded value as a Python object. DictionaryScalar.__reduce__(self)DictionaryScalar._reconstruct(type, is_valid, index, dictionary)MapScalar.as_py(self) Return this value as a Python list. Iterate over this element's values. Return the value at the given index. StructScalar._as_py_tuple(self)StructScalar.as_py(self) Return this value as a Python dict. Return the child value for the given field. Parameters ---------- index : Union[int, str] Index / position or name of the field. Returns ------- result : Scalar StructScalar.items(self)ListScalar.as_py(self) Return this value as a Python list. Iterate over this element's values. Return the value at the given index. Return the number of values. StringScalar.as_py(self) Return this value as a Python string. BinaryScalar.as_py(self) Return this value as a Python bytes. BinaryScalar.as_buffer(self) Return a view over this value as a Buffer object. MonthDayNanoIntervalScalar.as_py(self) Return this value as a pyarrow.MonthDayNano. DurationScalar.as_py(self) Return this value as a Pandas Timedelta instance (if units are nanoseconds and pandas is available), otherwise as a Python datetime.timedelta instance. Return the representation of TimestampScalar using `strftime` to avoid original repr datetime values being out of range. TimestampScalar.as_py(self) Return this value as a Pandas Timestamp instance (if units are nanoseconds and pandas is available), otherwise as a Python datetime.datetime instance. Time64Scalar.as_py(self) Return this value as a Python datetime.timedelta instance. Time32Scalar.as_py(self) Return this value as a Python datetime.timedelta instance. _datetime_from_int(int64_t value, TimeUnit unit, tzinfo=None)Date64Scalar.as_py(self) Return this value as a Python datetime.datetime instance. Date32Scalar.as_py(self) Return this value as a Python datetime.datetime instance. Decimal256Scalar.as_py(self) Return this value as a Python Decimal. Decimal128Scalar.as_py(self) Return this value as a Python Decimal. DoubleScalar.as_py(self) Return this value as a Python float. FloatScalar.as_py(self) Return this value as a Python float. HalfFloatScalar.as_py(self) Return this value as a Python float. Int64Scalar.as_py(self) Return this value as a Python int. UInt64Scalar.as_py(self) Return this value as a Python int. Int32Scalar.as_py(self) Return this value as a Python int. UInt32Scalar.as_py(self) Return this value as a Python int. Int16Scalar.as_py(self) Return this value as a Python int. UInt16Scalar.as_py(self) Return this value as a Python int. Int8Scalar.as_py(self) Return this value as a Python int. UInt8Scalar.as_py(self) Return this value as a Python int. BooleanScalar.as_py(self) Return this value as a Python bool. NullScalar.as_py(self) Return this value as a Python None. Scalar.as_py(self)Scalar.__reduce__(self)Scalar.equals(self, Scalar other) Parameters ---------- other : pyarrow.Scalar Returns ------- bool Scalar.validate(self, *, full=False) Perform validation checks. An exception is raised if validation fails. By default only cheap validation checks are run. Pass `full=True` for thorough validation checks (potentially O(n)). Parameters ---------- full : bool, default False If True, run expensive checks, otherwise cheap checks only. Raises ------ ArrowInvalid Scalar.cast(self, target_type=None, safe=None, options=None, memory_pool=None) Cast scalar value to another data type. See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, default None Type to cast scalar to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions memory_pool : MemoryPool, optional memory pool to use for allocations during function execution. Returns ------- scalar : A Scalar of the given target data type. _unregister_py_extension_types()_register_py_extension_type()_ExtensionRegistryNanny.__setstate_cython__(self, __pyx_state)_ExtensionRegistryNanny.__reduce_cython__(self)_ExtensionRegistryNanny.release_registry(self)is_float_value(obj) Check if the object is a float. Parameters ---------- obj : object The object to check is_integer_value(obj) Check if the object is an integer. Parameters ---------- obj : object The object to check is_boolean_value(obj) Check if the object is a boolean. Parameters ---------- obj : object The object to check from_numpy_dtype(dtype) Convert NumPy dtype to pyarrow.DataType. Parameters ---------- dtype : the numpy dtype to convert Examples -------- Create a pyarrow DataType from NumPy dtype: >>> import pyarrow as pa >>> import numpy as np >>> pa.from_numpy_dtype(np.dtype('float16')) DataType(halffloat) >>> pa.from_numpy_dtype('U') DataType(string) >>> pa.from_numpy_dtype(bool) DataType(bool) >>> pa.from_numpy_dtype(np.str_) DataType(string) schema(fields, metadata=None) Construct pyarrow.Schema from collection of fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). metadata : dict, default None Keys and values must be coercible to bytes. Examples -------- Create a Schema from iterable of tuples: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()), ... pa.field('some_required_string', pa.string(), nullable=False) ... ]) some_int: int32 some_string: string some_required_string: string not null Create a Schema from iterable of Fields: >>> pa.schema([ ... pa.field('some_int', pa.int32()), ... pa.field('some_string', pa.string()) ... ]) some_int: int32 some_string: string Returns ------- schema : pyarrow.Schema ensure_type(ty, bool allow_none=False) -> DataTypetype_for_alias(name) Return DataType given a string alias if one exists. Parameters ---------- name : str The alias of the DataType that should be retrieved. Returns ------- type : DataType fixed_shape_tensor(DataType value_type, shape, dim_names=None, permutation=None) Create instance of fixed shape tensor extension type with shape and optional names of tensor dimensions and indices of the desired logical ordering of dimensions. Parameters ---------- value_type : DataType Data type of individual tensor elements. shape : tuple or list of integers The physical shape of the contained tensors. dim_names : tuple or list of strings, default None Explicit names to tensor dimensions. permutation : tuple or list integers, default None Indices of the desired ordering of the original dimensions. The indices contain a permutation of the values ``[0, 1, .., N-1]`` where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor. For more information on this parameter see :ref:`fixed_shape_tensor_extension`. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) >>> tensor_type FixedShapeTensorType(extension) Inspect the data type: >>> tensor_type.value_type DataType(int32) >>> tensor_type.shape [2, 2] Create a table with fixed shape tensor extension array: >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage) >>> pa.table([tensor], names=["tensor_array"]) pyarrow.Table tensor_array: extension ---- tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]] Create an instance of fixed shape tensor extension type with names of tensor dimensions: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... dim_names=['C', 'H', 'W']) >>> tensor_type.dim_names ['C', 'H', 'W'] Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Returns ------- type : FixedShapeTensorType run_end_encoded(run_end_type, value_type) Create RunEndEncodedType from run-end and value types. Parameters ---------- run_end_type : pyarrow.DataType The integer type of the run_ends array. Must be 'int16', 'int32', or 'int64'. value_type : pyarrow.DataType The type of the values array. Returns ------- type : RunEndEncodedType union(child_fields, mode, type_codes=None) Create UnionType from child fields. A union is a nested type where each logical value is taken from a single child. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from. Unions come in two flavors: sparse and dense (see also `pyarrow.sparse_union` and `pyarrow.dense_union`). Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. mode : str Must be 'sparse' or 'dense' type_codes : list of integers, default None Returns ------- type : UnionType dense_union(child_fields, type_codes=None) Create DenseUnionType from child fields. A dense union is a nested type where each logical value is taken from a single child, at a specific offset. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from, and a buffer of 32-bit offsets indicates at which physical position in the given child array the logical value is to be taken from. Unlike a sparse union, a dense union allows encoding only the child array values which are actually referred to by the union array. This is counterbalanced by the additional footprint of the offsets buffer, and the additional indirection cost when looking up values. Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. type_codes : list of integers, default None Returns ------- type : DenseUnionType sparse_union(child_fields, type_codes=None) Create SparseUnionType from child fields. A sparse union is a nested type where each logical value is taken from a single child. A buffer of 8-bit type ids indicates which child a given logical value is to be taken from. In a sparse union, each child array should have the same length as the union array, regardless of the actual number of union values that refer to it. Parameters ---------- child_fields : sequence of Field values Each field must have a UTF8-encoded name, and these field names are part of the type metadata. type_codes : list of integers, default None Returns ------- type : SparseUnionType struct(fields) Create StructType instance from fields. A struct is a nested type parameterized by an ordered sequence of types (which can all be distinct), called its fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Each field must have a UTF8-encoded name, and these field names are part of the type metadata. Examples -------- Create an instance of StructType from an iterable of tuples: >>> import pyarrow as pa >>> fields = [ ... ('f1', pa.int32()), ... ('f2', pa.string()), ... ] >>> struct_type = pa.struct(fields) >>> struct_type StructType(struct) Retrieve a field from a StructType: >>> struct_type[0] pyarrow.Field >>> struct_type['f1'] pyarrow.Field Create an instance of StructType from an iterable of Fields: >>> fields = [ ... pa.field('f1', pa.int32()), ... pa.field('f2', pa.string(), nullable=False), ... ] >>> pa.struct(fields) StructType(struct) Returns ------- type : DataType dictionary(index_type, value_type, bool ordered=False) -> DictionaryType Dictionary (categorical, or simply encoded) type. Parameters ---------- index_type : DataType value_type : DataType ordered : bool Returns ------- type : DictionaryType Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Use dictionary type to create an array: >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8())) ... -- dictionary: [ "a", "b", "d" ] -- indices: [ 0, 1, null, 2 ] map_(key_type, item_type, keys_sorted=False) -> MapType Create MapType instance from key and item data types or fields. Parameters ---------- key_type : DataType or Field item_type : DataType or Field keys_sorted : bool Returns ------- map_type : DataType Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) Use MapType to create an array: >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]] >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True)) [ keys: [ "a", "b" ] values: [ 1, 2 ], keys: [ "c" ] values: [ 3 ] ] large_list_view(value_type) -> LargeListViewType Create LargeListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.int8()) LargeListViewType(large_list_view) list_view(value_type) -> ListViewType Create ListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) large_list(value_type) -> LargeListType Create LargeListType instance from child data type or field. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2**31 elements, you should prefer list_(). Parameters ---------- value_type : DataType or Field Returns ------- list_type : DataType Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.int8()) LargeListType(large_list) Use the LargeListType to create an array: >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8())) [ [ -1, 3 ], [ -1, 3 ], ... list_(value_type, int list_size=-1) Create ListType instance from child data type or field. Parameters ---------- value_type : DataType or Field list_size : int, optional, default -1 If length == -1 then return a variable length list type. If length is greater than or equal to 0 then return a fixed size list type. Returns ------- list_type : DataType Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) Use the ListType to create a scalar: >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2)) or an array: >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2)) [ [ 1, 2 ], [ 3, 4 ] ] string_view() Create UTF8 variable-length string view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string_view() DataType(string_view) binary_view() Create a variable-length binary view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.binary_view() DataType(binary_view) large_utf8() Alias for large_string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_utf8() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8()) [ "foo", "bar", ... "foo", "bar" ] large_string() Create large UTF8 variable-length string type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_string() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string()) [ "foo", "bar", ... "foo", "bar" ] large_binary() Create large variable-length binary type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer binary(). Examples -------- Create an instance of large variable-length binary type: >>> import pyarrow as pa >>> pa.large_binary() DataType(large_binary) and use the type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary()) [ 666F6F, 626172, 62617A ] binary(int length=-1) Create variable-length or fixed size binary type. Parameters ---------- length : int, optional, default -1 If length == -1 then return a variable length binary type. If length is greater than or equal to 0 then return a fixed size binary type of width `length`. Examples -------- Create an instance of a variable-length binary type: >>> import pyarrow as pa >>> pa.binary() DataType(binary) and use the variable-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary()) [ 666F6F, 626172, 62617A ] Create an instance of a fixed-size binary type: >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) and use the fixed-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3)) [ 666F6F, 626172, 62617A ] utf8() Alias for string(). Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.utf8() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8()) [ "foo", "bar", "baz" ] string() Create UTF8 variable-length string type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.string()) [ "foo", "bar", "baz" ] decimal256(int precision, int scale=0) -> DataType Create decimal type with precision and scale and 256-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). For most use cases, the maximum precision offered by ``decimal128`` is sufficient, and it will result in a more compact and more efficient encoding. ``decimal256`` is useful if you need a precision higher than 38 significant digits. Parameters ---------- precision : int Must be between 1 and 76 scale : int Returns ------- decimal_type : Decimal256Type decimal128(int precision, int scale=0) -> DataType Create decimal type with precision and scale and 128-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). As an example, ``decimal128(7, 3)`` can exactly represent the numbers 1234.567 and -1234.567 (encoded internally as the 128-bit integers 1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567. ``decimal128(5, -3)`` can exactly represent the number 12345000 (encoded internally as the 128-bit integer 12345), but neither 123450000 nor 1234500. If you need a precision higher than 38 significant digits, consider using ``decimal256``. Parameters ---------- precision : int Must be between 1 and 38 scale : int Returns ------- decimal_type : Decimal128Type Examples -------- Create an instance of decimal type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) Create an array with decimal type: >>> import decimal >>> a = decimal.Decimal('123.45') >>> pa.array([a], pa.decimal128(5, 2)) [ 123.45 ] float64() Create double-precision floating point type. Examples -------- Create an instance of float64 type: >>> import pyarrow as pa >>> pa.float64() DataType(double) >>> print(pa.float64()) double Create an array with float64 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float64()) [ 0, 1, 2 ] float32() Create single-precision floating point type. Examples -------- Create an instance of float32 type: >>> import pyarrow as pa >>> pa.float32() DataType(float) >>> print(pa.float32()) float Create an array with float32 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float32()) [ 0, 1, 2 ] float16() Create half-precision floating point type. Examples -------- Create an instance of float16 type: >>> import pyarrow as pa >>> pa.float16() DataType(halffloat) >>> print(pa.float16()) halffloat Create an array with float16 type: >>> arr = np.array([1.5, np.nan], dtype=np.float16) >>> a = pa.array(arr, type=pa.float16()) >>> a [ 15872, 32256 ] >>> a.to_pylist() [1.5, nan] date64() Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01). Examples -------- Create an instance of 64-bit date type: >>> import pyarrow as pa >>> pa.date64() DataType(date64[ms]) Create a scalar with 64-bit date type: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64()) date32() Create instance of 32-bit date (days since UNIX epoch 1970-01-01). Examples -------- Create an instance of 32-bit date type: >>> import pyarrow as pa >>> pa.date32() DataType(date32[day]) Create a scalar with 32-bit date type: >>> from datetime import date >>> pa.scalar(date(2012, 1, 1), type=pa.date32()) month_day_nano_interval() Create instance of an interval type representing months, days and nanoseconds between two dates. Examples -------- Create an instance of an month_day_nano_interval type: >>> import pyarrow as pa >>> pa.month_day_nano_interval() DataType(month_day_nano_interval) Create a scalar with month_day_nano_interval type: >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval()) duration(unit) Create instance of a duration type with unit resolution. Parameters ---------- unit : str One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.DurationType Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('us') DurationType(duration[us]) >>> pa.duration('s') DurationType(duration[s]) Create an array with duration type: >>> pa.array([0, 1, 2], type=pa.duration('s')) [ 0, 1, 2 ] time64(unit) Create instance of 64-bit time (time of day) type with unit resolution. Parameters ---------- unit : str One of 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.Time64Type Examples -------- >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) >>> pa.time64('ns') Time64Type(time64[ns]) time32(unit) Create instance of 32-bit time (time of day) type with unit resolution. Parameters ---------- unit : str one of 's' [second], or 'ms' [millisecond] Returns ------- type : pyarrow.Time32Type Examples -------- >>> import pyarrow as pa >>> pa.time32('s') Time32Type(time32[s]) >>> pa.time32('ms') Time32Type(time32[ms]) timestamp(unit, tz=None) Create instance of timestamp type with resolution and optional time zone. Parameters ---------- unit : str one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond] tz : str, default None Time zone name. None indicates time zone naive Examples -------- Create an instance of timestamp type: >>> import pyarrow as pa >>> pa.timestamp('us') TimestampType(timestamp[us]) >>> pa.timestamp('s', tz='America/New_York') TimestampType(timestamp[s, tz=America/New_York]) >>> pa.timestamp('s', tz='+07:30') TimestampType(timestamp[s, tz=+07:30]) Use timestamp type when creating a scalar object: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC')) >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us')) Returns ------- timestamp_type : TimestampType string_to_tzinfo(name) Convert a time zone name into a time zone object. Supported input strings are: * As used in the Olson time zone database (the "tz database" or "tzdata"), such as "America/New_York" * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30 Parameters ---------- name: str Time zone name. Returns ------- tz : datetime.tzinfo Time zone object tzinfo_to_string(tz) Converts a time zone object into a string indicating the name of a time zone, one of: * As used in the Olson time zone database (the "tz database" or "tzdata"), such as "America/New_York" * An absolute time zone offset of the form +XX:XX or -XX:XX, such as +07:30 Parameters ---------- tz : datetime.tzinfo Time zone object Returns ------- name : str Time zone name int64() Create instance of signed int64 type. Examples -------- Create an instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> print(pa.int64()) int64 Create an array with int64 type: >>> pa.array([0, 1, 2], type=pa.int64()) [ 0, 1, 2 ] uint64() Create instance of unsigned uint64 type. Examples -------- Create an instance of unsigned int64 type: >>> import pyarrow as pa >>> pa.uint64() DataType(uint64) >>> print(pa.uint64()) uint64 Create an array with unsigned uint64 type: >>> pa.array([0, 1, 2], type=pa.uint64()) [ 0, 1, 2 ] int32() Create instance of signed int32 type. Examples -------- Create an instance of int32 type: >>> import pyarrow as pa >>> pa.int32() DataType(int32) >>> print(pa.int32()) int32 Create an array with int32 type: >>> pa.array([0, 1, 2], type=pa.int32()) [ 0, 1, 2 ] uint32() Create instance of unsigned uint32 type. Examples -------- Create an instance of unsigned int32 type: >>> import pyarrow as pa >>> pa.uint32() DataType(uint32) >>> print(pa.uint32()) uint32 Create an array with unsigned int32 type: >>> pa.array([0, 1, 2], type=pa.uint32()) [ 0, 1, 2 ] int16() Create instance of signed int16 type. Examples -------- Create an instance of int16 type: >>> import pyarrow as pa >>> pa.int16() DataType(int16) >>> print(pa.int16()) int16 Create an array with int16 type: >>> pa.array([0, 1, 2], type=pa.int16()) [ 0, 1, 2 ] uint16() Create instance of unsigned uint16 type. Examples -------- Create an instance of unsigned int16 type: >>> import pyarrow as pa >>> pa.uint16() DataType(uint16) >>> print(pa.uint16()) uint16 Create an array with unsigned int16 type: >>> pa.array([0, 1, 2], type=pa.uint16()) [ 0, 1, 2 ] int8() Create instance of signed int8 type. Examples -------- Create an instance of int8 type: >>> import pyarrow as pa >>> pa.int8() DataType(int8) >>> print(pa.int8()) int8 Create an array with int8 type: >>> pa.array([0, 1, 2], type=pa.int8()) [ 0, 1, 2 ] uint8() Create instance of unsigned int8 type. Examples -------- Create an instance of unsigned int8 type: >>> import pyarrow as pa >>> pa.uint8() DataType(uint8) >>> print(pa.uint8()) uint8 Create an array with unsigned int8 type: >>> pa.array([0, 1, 2], type=pa.uint8()) [ 0, 1, 2 ] bool_() Create instance of boolean type. Examples -------- Create an instance of a boolean type: >>> import pyarrow as pa >>> pa.bool_() DataType(bool) >>> print(pa.bool_()) bool Create a ``Field`` type with a boolean type and a name: >>> pa.field('bool_field', pa.bool_()) pyarrow.Field null() Create instance of null type. Examples -------- Create an instance of a null type: >>> import pyarrow as pa >>> pa.null() DataType(null) >>> print(pa.null()) null Create a ``Field`` type with a null type and a name: >>> pa.field('null_field', pa.null()) pyarrow.Field field(name, type=None, nullable=None, metadata=None) Create a pyarrow.Field instance. Parameters ---------- name : str or bytes Name of the field. Alternatively, you can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). type : pyarrow.DataType Arrow datatype of the field. nullable : bool, default True Whether the field's values are nullable. metadata : dict, default None Optional field metadata, the keys and values must be coercible to bytes. Returns ------- field : pyarrow.Field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) unify_schemas(schemas, *, promote_options=u'default') Unify schemas by merging fields by name. The resulting schema will contain the union of fields from all schemas. Fields with the same name will be merged. Note that two fields with different types will fail merging by default. - The unified field will inherit the metadata from the schema where that field is first defined. - The first N fields in the schema will be ordered the same as the N fields in the first schema. The resulting schema will inherit its metadata from the first input schema. Parameters ---------- schemas : list of Schema Schemas to merge into a single one. promote_options : str, default default Accepts strings "default" and "permissive". Default: null and only null can be unified with another type. Permissive: types are promoted to the greater common denominator. Returns ------- Schema Raises ------ ArrowInvalid : If any input schema contains fields with duplicate names. If Fields of the same name are not mergeable. Schema._import_from_c_capsule(schema) Import a Schema from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. Schema.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. Schema._import_from_c(in_ptr) Import Schema from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. Schema._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. Schema.to_string(self, truncate_metadata=True, show_field_metadata=True, show_schema_metadata=True) Return human-readable representation of Schema Parameters ---------- truncate_metadata : boolean, default True Limit metadata key/value display to a single line of ~80 characters or less show_field_metadata : boolean, default True Display Field-level KeyValueMetadata show_schema_metadata : boolean, default True Display Schema-level KeyValueMetadata Returns ------- str : the formatted output Schema.remove_metadata(self) Create new schema without metadata, if any Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Create a new schema with removing the metadata from the original: >>> schema.remove_metadata() n_legs: int64 animals: string Schema.serialize(self, memory_pool=None) Write Schema to Buffer as encapsulated IPC message Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Write schema to Buffer: >>> schema.serialize() Schema.with_metadata(self, metadata) Add metadata as dict of string keys and values to Schema Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Add metadata to existing schema field: >>> schema.with_metadata({"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Schema.add_metadata(self, metadata) DEPRECATED Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Schema.set(self, int i, Field field) Replace a field at position i in the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Replace the second field of the schema with a new field 'extra': >>> schema.set(1, pa.field('replaced', pa.bool_())) n_legs: int64 replaced: bool Schema.remove(self, int i) Remove the field at index i from the schema. Parameters ---------- i : int Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Remove the second field of the schema: >>> schema.remove(1) n_legs: int64 Schema.insert(self, int i, Field field) Add a field at position i to the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Insert a new field on the second position: >>> schema.insert(1, pa.field('extra', pa.bool_())) n_legs: int64 extra: bool animals: string Schema.append(self, Field field) Append a field at the end of the schema. In contrast to Python's ``list.append()`` it does return a new object, leaving the original Schema unmodified. Parameters ---------- field : Field Returns ------- schema: Schema New object with appended field. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Append a field 'extra' at the end of the schema: >>> schema_new = schema.append(pa.field('extra', pa.bool_())) >>> schema_new n_legs: int64 animals: string extra: bool Original schema is unmodified: >>> schema n_legs: int64 animals: string Schema.get_all_field_indices(self, name) Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())]) Get the indexes of the fields named 'animals': >>> schema.get_all_field_indices("animals") [1, 2] Schema.get_field_index(self, name) Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the index of the field named 'animals': >>> schema.get_field_index("animals") 1 Index in case of several fields with the given name: >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema.get_field_index("animals") -1 Schema.field_by_name(self, name) DEPRECATED Parameters ---------- name : str Returns ------- field: pyarrow.Field Schema._field(self, int i) Select a field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Schema.field(self, i) Select a field by its column name or numeric index. Parameters ---------- i : int or string Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Select the second field: >>> schema.field(1) pyarrow.Field Select the field of the column named 'n_legs': >>> schema.field('n_legs') pyarrow.Field Schema.from_pandas(cls, df, preserve_index=None) Returns implied schema from dataframe Parameters ---------- df : pandas.DataFrame preserve_index : bool, default True Whether to store the index as an additional column (or columns, for MultiIndex) in the resulting `Table`. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. Returns ------- pyarrow.Schema Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({ ... 'int': [1, 2], ... 'str': ['a', 'b'] ... }) Create an Arrow Schema from the schema of a pandas dataframe: >>> pa.Schema.from_pandas(df) int: int64 str: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, ... Schema.equals(self, Schema other, bool check_metadata=False) Test if this schema is equal to the other Parameters ---------- other : pyarrow.Schema check_metadata : bool, default False Key/value metadata must be equal too Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> schema1 = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema2 = pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) Test two equal schemas: >>> schema1.equals(schema1) True Test two unequal schemas: >>> schema1.equals(schema2) False Schema.empty_table(self) Provide an empty table according to the schema. Returns ------- table: pyarrow.Table Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Create an empty table with schema's fields: >>> schema.empty_table() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[]] animals: [[]] Schema.__sizeof__(self)Schema.__reduce__(self)Field._import_from_c_capsule(schema) Import a Field from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. Field.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. Field._import_from_c(in_ptr) Import Field from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. Field._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. Field.flatten(self) Flatten this field. If a struct field, individual child fields will be returned with their names prefixed by the parent's name. Returns ------- fields : List[pyarrow.Field] Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('bar', pa.float64(), nullable=False) >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"}) >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False) Flatten a struct field: >>> ff pyarrow.Field not null> >>> ff.flatten() [pyarrow.Field, pyarrow.Field] Field.with_nullable(self, nullable) A copy of this field with the replaced nullability Parameters ---------- nullable : bool Returns ------- field: pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field >>> field.nullable True Create new field by replacing the nullability of an existing one: >>> field_new = field.with_nullable(False) >>> field_new pyarrow.Field >>> field_new.nullable False Field.with_name(self, name) A copy of this field with the replaced name Parameters ---------- name : str Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing the name of an existing one: >>> field_new = field.with_name('lock') >>> field_new pyarrow.Field Field.with_type(self, DataType new_type) A copy of this field with the replaced type Parameters ---------- new_type : pyarrow.DataType Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing type of an existing one: >>> field_new = field.with_type(pa.int64()) >>> field_new pyarrow.Field Field.remove_metadata(self) Create new field without metadata, if any Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} Create new field by removing the metadata from the existing one: >>> field_new = field.remove_metadata() >>> field_new.metadata Field.with_metadata(self, metadata) Add metadata as dict of string keys and values to Field Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) Create new field by adding metadata to existing one: >>> field_new = field.with_metadata({"key": "Something important"}) >>> field_new pyarrow.Field >>> field_new.metadata {b'key': b'Something important'} Field.__reduce__(self)Field.equals(self, Field other, bool check_metadata=False) Test if this field is equal to the other Parameters ---------- other : pyarrow.Field check_metadata : bool, default False Whether Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.equals(f2) False >>> f1.equals(f1) True ensure_metadata(meta, bool allow_none=False) -> KeyValueMetadataKeyValueMetadata.to_dict(self) Convert KeyValueMetadata to dict. If a key occurs twice, the value for the first one is returned KeyValueMetadata.get_all(self, key) Parameters ---------- key : str Returns ------- list[byte] KeyValueMetadata.items(self)KeyValueMetadata.values(self)KeyValueMetadata.keys(self)KeyValueMetadata.value(self, i) Parameters ---------- i : int Returns ------- byte KeyValueMetadata.key(self, i) Parameters ---------- i : int Returns ------- byte KeyValueMetadata.__reduce__(self)KeyValueMetadata.equals(self, KeyValueMetadata other) Parameters ---------- other : pyarrow.KeyValueMetadata Returns ------- bool unregister_extension_type(type_name) Unregister a Python extension type. Parameters ---------- type_name : str The name of the ExtensionType subclass to unregister. Examples -------- Define a UuidType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class UuidType(pa.ExtensionType): ... def __init__(self): ... pa.ExtensionType.__init__(self, pa.binary(16), "my_package.uuid") ... def __arrow_ext_serialize__(self): ... # since we don't have a parameterized type, we don't need extra ... # metadata to be deserialized ... return b'' ... @classmethod ... def __arrow_ext_deserialize__(self, storage_type, serialized): ... # return an instance of this subclass given the serialized ... # metadata. ... return UuidType() ... Register the extension type: >>> pa.register_extension_type(UuidType()) Unregister the extension type: >>> pa.unregister_extension_type("my_package.uuid") register_extension_type(ext_type) Register a Python extension type. Registration is based on the extension name (so different registered types need unique extension names). Registration needs an extension type instance, but then works for any instance of the same subclass regardless of parametrization of the type. Parameters ---------- ext_type : BaseExtensionType instance The ExtensionType subclass to register. Examples -------- Define a UuidType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class UuidType(pa.ExtensionType): ... def __init__(self): ... pa.ExtensionType.__init__(self, pa.binary(16), "my_package.uuid") ... def __arrow_ext_serialize__(self): ... # since we don't have a parameterized type, we don't need extra ... # metadata to be deserialized ... return b'' ... @classmethod ... def __arrow_ext_deserialize__(self, storage_type, serialized): ... # return an instance of this subclass given the serialized ... # metadata. ... return UuidType() ... Register the extension type: >>> pa.register_extension_type(UuidType()) Unregister the extension type: >>> pa.unregister_extension_type("my_package.uuid") UnknownExtensionType.__arrow_ext_serialize__(self)PyExtensionType.set_auto_load(cls, value) Enable or disable auto-loading of serialized PyExtensionType instances. Parameters ---------- value : bool Whether to enable auto-loading. PyExtensionType.__arrow_ext_deserialize__(cls, storage_type, serialized)PyExtensionType.__arrow_ext_serialize__(self)PyExtensionType.__reduce__(self)FixedShapeTensorType.__arrow_ext_scalar_class__(self)FixedShapeTensorType.__reduce__(self)FixedShapeTensorType.__arrow_ext_class__(self)ExtensionType.__arrow_ext_scalar_class__(self) Return an extension scalar class for building scalars with this extension type. This method should return subclass of the ExtensionScalar class. By default, if not specialized in the extension implementation, an extension type scalar will be a built-in ExtensionScalar instance. ExtensionType.__arrow_ext_class__(self) Return an extension array class to be used for building or deserializing arrays with this extension type. This method should return a subclass of the ExtensionArray class. By default, if not specialized in the extension implementation, an extension type array will be a built-in ExtensionArray instance. ExtensionType.__reduce__(self)ExtensionType.__arrow_ext_deserialize__(self, storage_type, serialized) Return an extension type instance from the storage type and serialized metadata. This method should return an instance of the ExtensionType subclass that matches the passed storage type and serialized metadata (the return value of ``__arrow_ext_serialize__``). ExtensionType.__arrow_ext_serialize__(self) Serialized representation of metadata to reconstruct the type object. This method should return a bytes object, and those serialized bytes are stored in the custom metadata of the Field holding an extension type in an IPC message. The bytes are passed to ``__arrow_ext_deserialize`` and should hold sufficient information to reconstruct the data type instance. Initialize an extension type instance. This should be called at the end of the subclass' ``__init__`` method. BaseExtensionType.wrap_array(self, storage) Wrap the given storage array as an extension array. Parameters ---------- storage : Array or ChunkedArray Returns ------- array : Array or ChunkedArray Extension array wrapping the storage array BaseExtensionType.__arrow_ext_scalar_class__(self) The associated scalar class BaseExtensionType.__arrow_ext_class__(self) The associated array extension class RunEndEncodedType.__reduce__(self)Decimal256Type.__reduce__(self)Decimal128Type.__reduce__(self)FixedSizeBinaryType.__reduce__(self)TimestampType.__reduce__(self)UnionType.__reduce__(self) Return a child field by its index. Alias of ``field``. UnionType.field(self, i) -> Field Return a child field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union[0] pyarrow.Field Iterate over union members, in order. Like num_fields(). StructType.__reduce__(self) Return the struct field with the given index or name. Alias of ``field``. Iterate over struct fields, in order. Like num_fields(). StructType.get_all_field_indices(self, name) Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type.get_all_field_indices('x') [0] StructType.field(self, i) -> Field Select a field by its column name or numeric index. Parameters ---------- i : int or str Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Select the second field: >>> struct_type.field(1) pyarrow.Field Select the field named 'x': >>> struct_type.field('x') pyarrow.Field StructType.get_field_index(self, name) Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Index of the field with a name 'y': >>> struct_type.get_field_index('y') 1 Index of the field that does not exist: >>> struct_type.get_field_index('z') -1 FixedSizeListType.__reduce__(self)MapType.__reduce__(self)LargeListViewType.__reduce__(self)ListViewType.__reduce__(self)LargeListType.__reduce__(self)ListType.__reduce__(self)DictionaryType.__reduce__(self)DictionaryMemo.__setstate_cython__(self, __pyx_state)DictionaryMemo.__reduce_cython__(self)DataType._import_from_c_capsule(schema) Import a DataType from a ArrowSchema PyCapsule Parameters ---------- schema : PyCapsule A valid PyCapsule with name 'arrow_schema' containing an ArrowSchema pointer. DataType.__arrow_c_schema__(self) Export to a ArrowSchema PyCapsule Unlike _export_to_c, this will not leak memory if the capsule is not used. DataType._import_from_c(in_ptr) Import DataType from a C ArrowSchema struct, given its pointer. This is a low-level function intended for expert users. DataType._export_to_c(self, out_ptr) Export to a C ArrowSchema struct, given its pointer. Be careful: if you don't pass the ArrowSchema struct to a consumer, its memory will leak. This is a low-level function intended for expert users. DataType.to_pandas_dtype(self) Return the equivalent NumPy / Pandas dtype. Examples -------- >>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() DataType.equals(self, other, *, check_metadata=False) Return true if type is equivalent to passed value. Parameters ---------- other : DataType or string convertible to DataType check_metadata : bool Whether nested Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True DataType.__reduce__(self)DataType.field(self, i) -> Field Parameters ---------- i : int Returns ------- pyarrow.Field _to_pandas_dtype(arrow_type, options=None)_get_pandas_tz_type(arrow_type, coerce_to_ns=False)_get_pandas_type(arrow_type, coerce_to_ns=False)_is_primitive(Type type)supported_memory_backends() Return a list of available memory pool backends jemalloc_set_decay_ms(decay_ms) Set arenas.dirty_decay_ms and arenas.muzzy_decay_ms to indicated number of milliseconds. A value of 0 (the default) results in dirty / muzzy memory pages being released right away to the OS, while a higher value will result in a time-based decay. See the jemalloc docs for more information It's best to set this at the start of your application. Parameters ---------- decay_ms : int Number of milliseconds to set for jemalloc decay conf parameters. Note that this change will only affect future memory arenas total_allocated_bytes() Return the currently allocated bytes from the default memory pool. Other memory pools may not be accounted for. log_memory_allocations(enable=True) Enable or disable memory allocator logging for debugging purposes Parameters ---------- enable : bool, default True Pass False to disable logging set_memory_pool(MemoryPool pool) Set the default memory pool. Parameters ---------- pool : MemoryPool The memory pool that should be used by default. mimalloc_memory_pool() Return a memory pool based on the mimalloc heap. NotImplementedError is raised if mimalloc support is not enabled. jemalloc_memory_pool() Return a memory pool based on the jemalloc heap. NotImplementedError is raised if jemalloc support is not enabled. system_memory_pool() Return a memory pool based on the C malloc heap. logging_memory_pool(MemoryPool parent) Create and return a MemoryPool instance that redirects to the *parent*, but also dumps allocation logs on stderr. Parameters ---------- parent : MemoryPool The real memory pool that should be used for allocations. proxy_memory_pool(MemoryPool parent) Create and return a MemoryPool instance that redirects to the *parent*, but with separate allocation statistics. Parameters ---------- parent : MemoryPool The real memory pool that should be used for allocations. default_memory_pool() Return the process-global memory pool. Examples -------- >>> default_memory_pool() ProxyMemoryPool.__setstate_cython__(self, __pyx_state)ProxyMemoryPool.__reduce_cython__(self)LoggingMemoryPool.__setstate_cython__(self, __pyx_state)LoggingMemoryPool.__reduce_cython__(self)MemoryPool.__setstate_cython__(self, __pyx_state)MemoryPool.__reduce_cython__(self)MemoryPool.max_memory(self) Return the peak memory allocation in this memory pool. This can be an approximate number in multi-threaded applications. None is returned if the pool implementation doesn't know how to compute this number. MemoryPool.bytes_allocated(self) Return the number of bytes that are currently allocated from this memory pool. MemoryPool.release_unused(self) Attempt to return to the OS any memory being held onto by the pool. This function should not be called except potentially for benchmarking or debugging as it could be expensive and detrimental to performance. This is best effort and may not have any effect on some memory pools or in some situations (e.g. fragmentation). _PandasAPIShim.__setstate_cython__(self, __pyx_state)_PandasAPIShim.__reduce_cython__(self)_PandasAPIShim.get_rangeindex_attribute(self, level, name)_PandasAPIShim.get_values(self, obj) Get the underlying array values of a pandas Series or Index in the format (np.ndarray or pandas ExtensionArray) as we need them. Assumes obj is a pandas Series or Index. _PandasAPIShim.is_index(self, obj)_PandasAPIShim.is_series(self, obj)_PandasAPIShim.is_data_frame(self, obj)_PandasAPIShim.is_sparse(self, obj)_PandasAPIShim.is_extension_array_dtype(self, obj)_PandasAPIShim.is_datetimetz(self, obj)_PandasAPIShim.is_categorical(self, obj)_PandasAPIShim.is_array_like(self, obj)_PandasAPIShim.is_ge_v3(self)_PandasAPIShim.is_ge_v21(self)_PandasAPIShim.is_v1(self)_PandasAPIShim.pandas_dtype(self, dtype)_PandasAPIShim.infer_dtype(self, obj)_PandasAPIShim.data_frame(self, *args, **kwargs)_PandasAPIShim.series(self, *args, **kwargs)set_timezone_db_path(path) Configure the path to text timezone database on Windows. Parameters ---------- path : str Path to text timezone database. runtime_info() Get runtime information. Returns ------- info : pyarrow.RuntimeInfo SignalStopHandler.__setstate_cython__(self, __pyx_state)SignalStopHandler.__reduce_cython__(self)SignalStopHandler.__exit__(self, exc_type, exc_value, exc_tb)SignalStopHandler.__enter__(self)SignalStopHandler._init_signals(self)enable_signal_handlers(bool enable) Enable or disable interruption of long-running operations. By default, certain long running operations will detect user interruptions, such as by pressing Ctrl-C. This detection relies on setting a signal handler for the duration of the long-running operation, and may therefore interfere with other frameworks or libraries (such as an event loop). Parameters ---------- enable : bool Whether to enable user interruption by setting a temporary signal handler. StopToken.__setstate_cython__(self, __pyx_state)StopToken.__reduce_cython__(self)ArrowCancelled.__init__(self, message, signum=None)ArrowKeyError.__str__(self)frombytes(o, *, safe=False) Decode the given bytestring to unicode. Parameters ---------- o : bytes-like Input object. safe : bool, default False If true, raise on encoding errors. tobytes(o) Encode a unicode or bytes string to bytes. Parameters ---------- o : str or bytes Input string. encode_file_path(path)_gdb_test_session()_pac()_pc()set_cpu_count(int count) Set the number of threads to use in parallel operations. Parameters ---------- count : int The number of concurrent threads that should be used. See Also -------- cpu_count : Get the size of this pool. set_io_thread_count : The analogous function for the I/O thread pool. cpu_count() Return the number of threads to use in parallel operations. The number of threads is determined at startup by inspecting the ``OMP_NUM_THREADS`` and ``OMP_THREAD_LIMIT`` environment variables. If neither is present, it will default to the number of hardware threads on the system. It can be modified at runtime by calling :func:`set_cpu_count()`. See Also -------- set_cpu_count : Modify the size of this pool. io_thread_count : The analogous function for the I/O thread pool. pyarrow requires pandas 1.0.0 or above, pandas {} is installed. Therefore, pandas-specific integration is not used.precision should be between 1 and 76Unable to avoid a copy while creating a numpy array as requested (converting a pyarrow.Unable to avoid a copy while creating a numpy array as requested (converting a pyarrow.ChunkedArray always results in a copy). If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when neededThe 'names' and 'metadata' arguments are not valid when using Arrow PyCapsule InterfaceStructType.get_all_field_indices (line 982)RunEndEncodedType's expected number of buffers ({0}) did not match the passed number ({1}).RunEndEncodedArray.find_physical_lengthRecordBatch.replace_schema_metadata (line 2465)RecordBatch.get_total_buffer_size (line 2633)RecordBatchReader.read_next_batch_with_custom_metadataRecordBatchReader._import_from_c_capsuleMust pass either names or fields, not bothIncompatible checksums (0x%x vs (0xa1b2f29, 0xabbf506, 0x682aee0) = (_array_like_types, _categorical_type, _compat_module, _data_frame, _datetimetz_type, _extension_array, _extension_dtype, _have_pandas, _index, _is_extension_array_dtype, _is_ge_v21, _is_ge_v3, _is_v1, _lock, _loose_version, _pd, _pd024, _series, _tried_importing_pandas, _types_api, _version, has_sparse))FixedSizeBufferWriter.set_memcopy_thresholdFixedSizeBufferWriter.set_memcopy_blocksizeFixedShapeTensorType.__arrow_ext_scalar_class__FixedShapeTensorArray.from_numpy_ndarray (line 4267)Expected a list of 1-dimensional arrays for SparseCSFTensor.indicesExpected 1-dimensional array for SparseCSCMatrix indicesExpected 1-dimensional array for SparseCSCMatrix indptrExpected 1-dimensional array for SparseCSRMatrix indicesDo not call {}'s constructor directly, use `pyarrow.ipc.MessageReader.open_stream` function instead.Do not call {}'s constructor directly, use `pyarrow.ipc.read_message` function instead.Do not call {}'s constructor directly, use one of the `pyarrow.Array.from_*` functions instead.Do not call {}'s constructor directly, use pa.scalar() instead.Do not call {}'s constructor directly, use public functions like pyarrow.int64, pyarrow.list_, etc. instead.Do not call {}'s constructor directly, use pyarrow.proxy_memory_pool instead.Do not call {}'s constructor directly, use pyarrow.logging_memory_pool instead.ChunkedArray.get_total_buffer_size (line 256)BaseListArray.value_parent_indices (line 2161) Unify dictionaries across all chunks. This method returns an equivalent table, but where all chunks of each column share the same dictionary values. Dictionary indices are transposed accordingly. Columns without dictionaries are returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> table = pa.table([c_arr], names=["animals"]) >>> table pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog"] -- indices: [0,1,2], -- dictionary: ["Horse","Brittle stars","Centipede"] -- indices: [0,1,2]] Unify dictionaries across both chunks: >>> table.unify_dictionaries() pyarrow.Table animals: dictionary ---- animals: [ -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [0,1,2], -- dictionary: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] -- indices: [3,4,5]] The sum of bytes in each buffer referenced by the table. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.get_total_buffer_size() 76 The sum of bytes in each buffer referenced by the record batch An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.get_total_buffer_size() 120 The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_field pyarrow.Field The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.scale 38 The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal256(76, 38) >>> t.precision 76 The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).value_type DataType(int32) Target schema's field names are not matching the table's field names: Target schema's field names are not matching the table's field names: {!r}, {!r} Select a field by its column name or numeric index. Parameters ---------- i : int or string Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Select the second field: >>> schema.field(1) pyarrow.Field Select the field of the column named 'n_legs': >>> schema.field('n_legs') pyarrow.Field Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())]) Get the indexes of the fields named 'animals': >>> schema.get_all_field_indices("animals") [1, 2] Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the index of the field named 'animals': >>> schema.get_field_index("animals") 1 Index in case of several fields with the given name: >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string()), ... pa.field('animals', pa.bool_())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema.get_field_index("animals") -1 Return boolean array indicating the non-null values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_valid() [ [ true, true, true ], [ true, false, true ] ] Return boolean array indicating the NaN values. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = pa.chunked_array([[2, np.nan, 4], [4, None, 100]]) >>> arr.is_nan() [ [ false, true, false, false, null, false ] ] _RecordBatchFileReader.get_batch_with_custom_metadataExpected list of {ndim} np.arrays for SparseCSFTensor.indicesExpected an object implementing the Arrow PyCapsule Protocol for schema (i.e. having a `__arrow_c_schema__` method), got Create instance of unsigned uint64 type. Examples -------- Create an instance of unsigned int64 type: >>> import pyarrow as pa >>> pa.uint64() DataType(uint64) >>> print(pa.uint64()) uint64 Create an array with unsigned uint64 type: >>> pa.array([0, 1, 2], type=pa.uint64()) [ 0, 1, 2 ] Create instance of unsigned uint32 type. Examples -------- Create an instance of unsigned int32 type: >>> import pyarrow as pa >>> pa.uint32() DataType(uint32) >>> print(pa.uint32()) uint32 Create an array with unsigned int32 type: >>> pa.array([0, 1, 2], type=pa.uint32()) [ 0, 1, 2 ] Create UTF8 variable-length string view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string_view() DataType(string_view) Convert the Table or RecordBatch to a list of rows / dictionaries. Returns ------- list Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> data = [[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]] >>> table = pa.table(data, names=["n_legs", "animals"]) >>> table.to_pylist() [{'n_legs': 2, 'animals': 'Flamingo'}, {'n_legs': 4, 'animals': 'Horse'}, ... Construct a Table or RecordBatch from list of rows / dictionaries. Parameters ---------- mapping : list of dicts of rows A mapping of strings to row values. schema : Schema, default None If not passed, will be inferred from the first row of the mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> pylist = [{'n_legs': 2, 'animals': 'Flamingo'}, ... {'n_legs': 4, 'animals': 'Dog'}] Construct a Table from a list of rows: >>> pa.Table.from_pylist(pylist) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4]] animals: [["Flamingo","Dog"]] Construct a Table from a list of rows with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pylist(pylist, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a list of rows with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pylist(pylist, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Compute zero-copy slice of this Table. Parameters ---------- offset : int, default 0 Offset from start of table to slice. length : int, default None Length of slice (default is until end of table starting from offset). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.slice(length=3) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019]] n_legs: [[2,4,5]] animals: [["Flamingo","Horse","Brittle stars"]] >>> table.slice(offset=2) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019,2021]] n_legs: [[5,100]] animals: [["Brittle stars","Centipede"]] >>> table.slice(offset=2, length=1) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2019]] n_legs: [[5]] animals: [["Brittle stars"]] Compute zero-copy slice of this RecordBatch Parameters ---------- offset : int, default 0 Offset from start of record batch to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> batch.slice(offset=3).to_pandas() n_legs animals 0 4 Horse 1 5 Brittle stars 2 100 Centipede >>> batch.slice(length=2).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot >>> batch.slice(offset=3, length=1).to_pandas() n_legs animals 0 4 Horse ChunkedArray.unify_dictionaries (line 1116)Can only instantiate subclasses of PyExtensionType A copy of this field with the replaced nullability Parameters ---------- nullable : bool Returns ------- field: pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field >>> field.nullable True Create new field by replacing the nullability of an existing one: >>> field_new = field.with_nullable(False) >>> field_new pyarrow.Field >>> field_new.nullable False A copy of this field with the replaced name Parameters ---------- name : str Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing the name of an existing one: >>> field_new = field.with_name('lock') >>> field_new pyarrow.Field zero_copy_only must be False for pyarrow.ChunkedArray.to_numpyunregister_extension_type (line 1961)trying to write an immutable bufferself.wrapped cannot be converted to a Python object for picklingself.stop_token cannot be converted to a Python object for picklingself.sp_tensor,self.tp cannot be converted to a Python object for picklingself.sp_sparse_tensor,self.stp cannot be converted to a Python object for picklingself.pool,self.proxy_pool cannot be converted to a Python object for picklingself.pool cannot be converted to a Python object for picklingself.logging_pool,self.pool cannot be converted to a Python object for picklingself.c_options cannot be converted to a Python object for picklingregister_extension_type (line 1906)read_next_batch_with_custom_metadatapyarrow requires pandas 1.0.0 or above, pandas {} is installedpyarrow.PyExtensionType is deprecated and will refuse deserialization by default. Instead, please derive from pyarrow.ExtensionType and implement your own serialization mechanism.promote has been superseded by promote_options='default'.precision should be between 1 and 38pa.output_stream() called with instance of '{}'pa.input_stream() called with instance of '{}'only slices with step 1 supportedno default __reduce__ due to non-trivial __cinit__month_day_nano_interval (line 4086)list_size should be a positive integeriter_batches_with_custom_metadataideal_bandwidth_utilization_fracfield or tuple expected, got Nonecould not infer open mode for file-like object cannot specify 'type' when creating a Field from an ArrowSchemabinary file expected, got text fileaccessing nonexistent buffer segmentWritable buffer requested but Arrow buffer was not mutableUnknownExtensionType.__arrow_ext_serialize__UnionType.type_codes.__get__ (line 1082)UnionType.mode.__get__ (line 1062)Unable to write to object of type: {0}Unable to wrap Datum in a Python objectUnable to read message from object with type: {0}Unable to avoid a copy while creating a numpy array as requested. If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when neededType's expected number of children ({0}) did not match the passed number ({1}).Type's expected number of buffers ({0}) did not match the passed number ({1}).TransformInputStream.__reduce_cython__ Total number of bytes consumed by the elements of the table. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.nbytes 72 Total number of bytes consumed by the elements of the record batch. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.nbytes 116 TimestampType.tz.__get__ (line 1223)Time zones are not available from the C-API.Time64Type.unit.__get__ (line 1299)Time32Type.unit.__get__ (line 1264)This object's internal pointer is NULL, do not use any methods or attributes on this objectThe run_end_type should be 'int16', 'int32', or 'int64'The passed mapping doesn't contain the following field(s) of the schema: {}The 'ordered' flag of the passed categorical values does not match the 'ordered' of the specified type. The object's __arrow_array__ method does not return a pyarrow Array or ChunkedArray.The 'names' and 'metadata' arguments are not valid when passing a pandas DataFrameThe 'field_by_name' method is deprecated, use 'field' insteadThe dtype of the 'categories' of the passed categorical values ({0}) does not match the specified type ({1}). For now ignoring the specified type, but in the future this mismatch will raise a TypeErrorThe dictionary index type should be integer.The 'add_metadata' method is deprecated, use 'with_metadata' insteadThe Scalar value passed as index must have identical type to the dictionary type's index_typeThe Array passed as dictionary must have identical type to the dictionary type's value_typeTensor.is_mutable.__get__ (line 183)Tensor.is_contiguous.__get__ (line 199)Tensor.dim_names.__get__ (line 167)Tensor._make_shape_or_strides_bufferTable.unify_dictionaries (line 4293)Table.replace_schema_metadata (line 4118)Table.num_rows.__get__ (line 4974)Table.num_columns.__get__ (line 4953)Table.get_total_buffer_size (line 5032)Table.from_struct_array (line 4645)TableGroupBy.aggregate (line 6102)Struct field name corresponds to more than one fieldStructType.get_all_field_indicesStringViewBuilder only accepts string objectsStringViewBuilder.__setstate_cython__StringViewBuilder.__reduce_cython__StringBuilder only accepts string objectsSparseCSRMatrix.from_dense_numpySparseCSRMatrix.__setstate_cython__SparseCSRMatrix.__get__..genexprSparseCSFTensor.from_dense_numpySparseCSFTensor.__setstate_cython__SparseCSFTensor.__get__..genexprSparseCSCMatrix.from_dense_numpySparseCSCMatrix.__setstate_cython__SparseCSCMatrix.__get__..genexprSparseCOOTensor.to_pydata_sparseSparseCOOTensor.from_pydata_sparseSparseCOOTensor.from_dense_numpySparseCOOTensor.__setstate_cython__SparseCOOTensor.__get__..genexprSignalStopHandler.__setstate_cython__SignalStopHandler.__reduce_cython__ Select values from the chunked array. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the array whose values will be returned. Returns ------- taken : Array or ChunkedArray An array with the same datatype, containing the taken values. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.take([1,4,5]) [ [ 2, 5, 100 ] ] Schema.remove_metadata (line 3252)Schema passed to names= option, please pass schema= explicitly. Will raise exception in futureSchema.pandas_metadata.__get__ (line 2648)Schema must be an instance of pyarrow.SchemaSchema.metadata.__get__ (line 2725)Schema.get_field_index (line 2956)Schema.get_all_field_indices (line 2996)Schema field name corresponds to more than one fieldSchema and number of arrays unequalRunEndEncodedType's expected number of children ({0}) did not match the passed number ({1}).RunEndEncodedType's expected null_count (0) did not match passed number ({0})RunEndEncodedType expects None as validity bitmap, buffers[0] is not NoneRunEndEncodedArray.find_physical_offset Return the underlying array of values which backs the FixedSizeListArray. Note even null elements are included. Compare with :meth:`flatten`, which returns only the non-null sub-list values. Returns ------- values : Array See Also -------- FixedSizeListArray.flatten : ... Examples -------- >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, None]], ... type=pa.list_(pa.int32(), 2) ... ) >>> array.values [ 1, 2, null, null, 3, null ] Return the underlying array of values which backs the LargeListArray ignoring the array's offset. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the underlying array of values which backs the ListViewArray ignoring the array's offset and sizes. The values array may be out of order and/or contain additional values that are not found in the logical representation of the array. The only guarantee is that each non-null value in the ListView Array is contiguous. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's order and offset. Returns ------- values : Array Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 1, 2 ], [], [ 2, null, 3, 4 ] ] >>> array.values [ 1, 2, null, 3, 4 ] Return the underlying array of values which backs the LargeListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- LargeListArray.flatten : ... Examples -------- The values include null elements from the sub-lists: >>> import pyarrow as pa >>> array = pa.array( ... [[1, 2], None, [3, 4, None, 6]], ... type=pa.large_list(pa.int32()), ... ) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] RecordBatch with its custom metadata Parameters ---------- batch : RecordBatch custom_metadata : KeyValueMetadata RecordBatch.set_column (line 2771)RecordBatch.replace_schema_metadataRecordBatch.remove_column (line 2737)RecordBatch.num_rows.__get__ (line 2532)RecordBatch.get_total_buffer_sizeRecordBatch.from_struct_array (line 3390)RecordBatch.from_pandas (line 3196)RecordBatch.from_arrays (line 3291)RecordBatch.add_column (line 2663)RecordBatch._import_from_c_deviceRecordBatch._import_from_c_capsuleRecordBatchReader.read_next_batchRecordBatchReader.iter_batches_with_custom_metadataRecordBatchReader._import_from_cRecordBatchReader.__setstate_cython__RecordBatchReader.__reduce_cython__RecordBatchReader.__arrow_c_stream__PyExtensionType.__arrow_ext_serialize__PyExtensionType.__arrow_ext_deserialize__ProxyMemoryPool.__setstate_cython__Property `compression` must be None, str, or pyarrow.CodecPassing a pointer value as a float is unsafe and only supported for compatibility with older versions of the R Arrow libraryOnly extension types can be registeredNanosecond resolution temporal type {} is not safely convertible to microseconds to convert to datetime.datetime. Install pandas to return as Timestamp with nanosecond support or access the .value attribute.Nanosecond duration {} is not safely convertible to microseconds to convert to datetime.timedelta. Install pandas to return as Timedelta with nanosecond support or access the .value attribute.Must pass schema, or at least one RecordBatchMust pass names or schema when constructing Table or RecordBatch.Must pass either names or fieldsMust pass a DictionaryType instanceMonthDayNanoIntervalScalar.as_pyMonthDayNanoIntervalArray.to_pylistMockOutputStream.__setstate_cython__MockOutputStream.__reduce_cython__MemoryMappedFile.__setstate_cython__MemoryMappedFile.__reduce_cython__Map key field should be non-nullableMapType.keys_sorted.__get__ (line 771)MapType.key_type.__get__ (line 732)MapType.key_field.__get__ (line 719)MapType.item_type.__get__ (line 758)LoggingMemoryPool.__setstate_cython__LoggingMemoryPool.__reduce_cython__ListView requires DataType or FieldListViewArray.values.__get__ (line 2636)ListViewArray.sizes.__get__ (line 2720)ListViewArray.offsets.__get__ (line 2690)ListViewArray.from_arrays (line 2535)ListType.value_type.__get__ (line 549)ListType.value_field.__get__ (line 536)ListArray.values.__get__ (line 2288)ListArray.offsets.__get__ (line 2359)Length of names ({}) does not match length of arrays ({})LargeListViewType.value_type.__get__ (line 683)LargeListViewArray.offsets.__get__ (line 2978)LargeListViewArray.from_arrays (line 2818)LargeListArray.values.__get__ (line 2441)Iterable should contain Array objects, got {0} insteadIpcWriteOptions.__setstate_cython__IpcReadOptions.__setstate_cython__Invalid value for 'maps_as_pydicts': valid values are 'lossy', 'strict' or `None` (default). Index must either be string or integerIncompatible checksums (0x%x vs (0xe3b0c44, 0xda39a3e, 0xd41d8cd) = ())IPC read statistics Parameters ---------- num_messages : int Number of messages. num_record_batches : int Number of record batches. num_dictionary_batches : int Number of dictionary batches. num_dictionary_deltas : int Delta of dictionaries. num_replaced_dictionaries : int Number of replaced dictionaries. IO thread count must be strictly positiveFixedSizeListType.value_type.__get__ (line 818)FixedSizeListType.list_size.__get__ (line 831)FixedSizeListArray.from_arrays (line 3239)FixedSizeBufferWriter.set_memcopy_threadsFixedSizeBufferWriter.__setstate_cython__FixedSizeBufferWriter.__reduce_cython__FixedShapeTensorType.__arrow_ext_class__FixedShapeTensorScalar.to_tensorFixedShapeTensorArray.to_numpy_ndarrayFixedShapeTensorArray.from_numpy_ndarrayFirst stride needs to be largest to ensure that individual tensor data is contiguous in memory.Field.nullable.__get__ (line 2261)Field.metadata.__get__ (line 2292)ExtensionType.__arrow_ext_serialize__ExtensionType.__arrow_ext_scalar_class__ExtensionType.__arrow_ext_deserialize__Expected scipy.sparse.csr_matrix, got {}Expected scipy.sparse.csc_matrix, got {}Expected scipy.sparse.coo_matrix, got {}Expected pandas DataFrame, python dictionary or list of arraysExpected pandas DataFrame or list of arraysExpected integer or string indexExpected file path, but {0} is a directoryExpected a list of 1-dimensional arrays for SparseCSFTensor.indptrExpected 2-dimensional array for SparseCOOTensor indicesExpected 1-dimensional array for SparseCSRMatrix indptrDurationType.unit.__get__ (line 1331)Duplicate key {}, use pass all items as list of tuples if you intend to have duplicate keysDo not call {}'s constructor directly, use pyarrow.*_memory_pool instead.Do not call Tensor's constructor directly, use one of the `pyarrow.Tensor.from_*` functions instead.Do not call SparseCSRMatrix's constructor directly, use one of the `pyarrow.SparseCSRMatrix.from_*` functions instead.Do not call SparseCSFTensor's constructor directly, use one of the `pyarrow.SparseCSFTensor.from_*` functions instead.Do not call SparseCSCMatrix's constructor directly, use one of the `pyarrow.SparseCSCMatrix.from_*` functions instead.Do not call SparseCOOTensor's constructor directly, use one of the `pyarrow.SparseCOOTensor.from_*` functions instead.Do not call Schema's constructor directly, use `pyarrow.schema` instead.Do not call ChunkedArray's constructor directly, use `chunked_array` function instead.Do not call Buffer's constructor directly, use `pyarrow.py_buffer` function instead.DictionaryType.ordered.__get__ (line 473)DictionaryMemo.__setstate_cython__DictionaryArray.dictionary_encodeDictionaryArray.dictionary_decodeDecimal256Type.scale.__get__ (line 1451)Decimal128Type.scale.__get__ (line 1402)DataType.to_pandas_dtype (line 368)DataType.num_fields.__get__ (line 282)DataType.num_buffers.__get__ (line 304)DataType.byte_width.__get__ (line 260) Convert pandas.DataFrame to an Arrow Table. The column types in the resulting Arrow Table are inferred from the dtypes of the pandas.Series in the DataFrame. In the case of non-object Series, the NumPy dtype is translated to its Arrow equivalent. In the case of `object`, we need to guess the datatype by looking at the Python objects in this Series. Be aware that Series of the `object` dtype don't carry enough information to always lead to a meaningful Arrow type. In the case that we cannot infer a type, e.g. because the DataFrame is of length 0 or the Series only contains None/nan objects, the type is set to null. This behavior can be avoided by constructing an explicit schema and passing it to this function. Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the Arrow Table. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``Table``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. safe : bool, default True Check for overflows or other unsafe conversions. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.Table.from_pandas(df) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Compression type must be lz4, zstd or NoneCompressedOutputStream.__setstate_cython__CompressedInputStream.__setstate_cython__CompressedInputStream.__reduce_cython__Codec.supports_compression_levelChunkedArray.value_counts (line 793)ChunkedArray.type.__get__ (line 82)ChunkedArray.to_pylist (line 1322)ChunkedArray.nbytes.__get__ (line 225)ChunkedArray.iterchunks (line 1304)ChunkedArray.get_total_buffer_sizeChunkedArray.format is deprecated, use ChunkedArray.to_stringChunkedArray.drop_null (line 1054)ChunkedArray.dictionary_encode (line 576)ChunkedArray.combine_chunks (line 709)ChunkedArray.chunks.__get__ (line 1266)ChunkedArray._import_from_c_capsuleCasting field {!r} with null values to non-nullableCannot specify both list_size and typeCannot return a writable array if asking for zero-copyCannot pass both schema and namesCannot pass both schema and metadataCannot pass a numpy masked array and specify a mask at the same timeCannot create multiple NullScalar instancesCannot convert 1D array or scalar to fixed shape tensor arrayCan't convert PyCapsule with name 'Can only get value offsets for dense arraysCacheOptions.from_network_metricsCPU count must be strictly positiveBufferedOutputStream.__reduce_cython__BufferedInputStream.__setstate_cython__BufferedInputStream.__reduce_cython__BufferOutputStream.__setstate_cython__BufferOutputStream.__reduce_cython__BaseListArray.value_parent_indicesBaseListArray.value_lengths (line 2183)BaseExtensionType.__arrow_ext_scalar_class__BaseExtensionType.__arrow_ext_class__Arrays were not all the same length: {0} vs {1}Array.format is deprecated, use Array.to_string Add metadata as dict of string keys and values to Schema Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Add metadata to existing schema field: >>> schema.with_metadata({"n_legs": "Number of legs per animal"}) n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' A null type field may not be non-nullabletype_codes should have the same length as fieldsself.writer cannot be converted to a Python object for picklingself.reader cannot be converted to a Python object for pickling_python_extension_types_registry type: {0.type} shape: {0.shape} strides: {0.strides}pyarrow.Message type: {0} metadata length: {1} body length: {2}pickle-based deserialization of pyarrow.PyExtensionType subclasses is disabled by default; if you only ingest trusted data files, you may re-enable this using `pyarrow.PyExtensionType.set_auto_load(True)`. In the future, Python-defined extension subclasses should derive from pyarrow.ExtensionType (not pyarrow.PyExtensionType) and implement their own serialization mechanism. not supported for buffer protocol'max_chunksize' should be strictly positivemask not implemented with Arrow array inputs yet_break_traceback_cycle_from_frame always results in a copy). If using `np.array(obj, copy=False)` replace it with `np.asarray(obj)` to allow a copy when needed Write Schema to Buffer as encapsulated IPC message Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Write schema to Buffer: >>> schema.serialize() Whether the dictionary is ordered, i.e. whether the ordering of values in the dictionary is important. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()).ordered False Unregister a Python extension type. Parameters ---------- type_name : str The name of the ExtensionType subclass to unregister. Examples -------- Define a UuidType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class UuidType(pa.ExtensionType): ... def __init__(self): ... pa.ExtensionType.__init__(self, pa.binary(16), "my_package.uuid") ... def __arrow_ext_serialize__(self): ... # since we don't have a parameterized type, we don't need extra ... # metadata to be deserialized ... return b'' ... @classmethod ... def __arrow_ext_deserialize__(self, storage_type, serialized): ... # return an instance of this subclass given the serialized ... # metadata. ... return UuidType() ... Register the extension type: >>> pa.register_extension_type(UuidType()) Unregister the extension type: >>> pa.unregister_extension_type("my_package.uuid") Unnest this ListViewArray by one level. The returned Array is logically a concatenation of all the sub-lists in this Array. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- memory_pool : MemoryPool, optional Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] Unify dictionaries across all chunks. This method returns an equivalent chunked array, but where all chunks share the same dictionary values. Dictionary indices are transposed accordingly. If there are no dictionaries in the chunked array, it is returned unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : ChunkedArray Examples -------- >>> import pyarrow as pa >>> arr_1 = pa.array(["Flamingo", "Parrot", "Dog"]).dictionary_encode() >>> arr_2 = pa.array(["Horse", "Brittle stars", "Centipede"]).dictionary_encode() >>> c_arr = pa.chunked_array([arr_1, arr_2]) >>> c_arr [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ] ] >>> c_arr.unify_dictionaries() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] TransformInputStream.__setstate_cython__TimestampType.unit.__get__ (line 1209) The timestamp unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('us') >>> t.unit 'us' The time unit ('us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.time64('us') >>> t.unit 'us' The sum of bytes in each buffer referenced by the chunked array. An array may only reference a portion of a buffer. This method will overestimate in this case and return the byte size of the entire buffer. If a buffer is referenced multiple times then it will only be counted once. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.get_total_buffer_size() 49 The size of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.size 6 The size of the fixed size lists. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.int32(), 2).list_size 2 The shape of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.shape (2, 3) The schema's metadata. Returns ------- metadata: dict Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) Get the metadata of the schema's fields: >>> schema.metadata {b'n_legs': b'Number of legs per animal'} The schema's field types. Returns ------- list of DataType Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the types of the schema's fields: >>> schema.types [DataType(int64), DataType(string)] The schema's field names. Returns ------- list of str Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Get the names of the schema's fields: >>> schema.names ['n_legs', 'animals'] The number of child fields. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().num_fields 0 >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.string()).num_fields 1 >>> struct = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct.num_fields 2 The 'names' argument is not valid when passing a dictionary The field metadata. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} The field for list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_field pyarrow.Field The field for large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_field pyarrow.Field The field for keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_field pyarrow.Field The duration unit ('s', 'ms', 'us' or 'ns'). Examples -------- >>> import pyarrow as pa >>> t = pa.duration('s') >>> t.unit 's' The dictionary value type. The dictionary values are found in an instance of DictionaryArray. Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).value_type DataType(string) The decimal scale (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.scale 2 The decimal precision, in number of decimal digits (an integer). Examples -------- >>> import pyarrow as pa >>> t = pa.decimal128(5, 2) >>> t.precision 5 The data type of list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_type DataType(string) The data type of list values. Examples -------- >>> import pyarrow as pa >>> pa.list_(pa.string()).value_type DataType(string) The data type of large list view values. Examples -------- >>> import pyarrow as pa >>> pa.large_list_view(pa.string()).value_type DataType(string) The data type of large list values. Examples -------- >>> import pyarrow as pa >>> pa.large_list(pa.string()).value_type DataType(string) The data type of keys in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).key_type DataType(string) The data type of items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_type DataType(int32) The data type of dictionary indices (a signed integer type). Examples -------- >>> import pyarrow as pa >>> pa.dictionary(pa.int16(), pa.utf8()).index_type DataType(int16) Test if this schema is equal to the other Parameters ---------- other : pyarrow.Schema check_metadata : bool, default False Key/value metadata must be equal too Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> schema1 = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema2 = pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()) ... ]) Test two equal schemas: >>> schema1.equals(schema1) True Test two unequal schemas: >>> schema1.equals(schema2) False Tensor.strides.__get__ (line 264)Target schema's field names are not matching the record batch's field names: {!r}, {!r}_Tabular.shape.__get__ (line 2023)_Tabular.columns.__get__ (line 1756)_Tabular.column_names.__get__ (line 1734)_Tabular.append_column (line 2290)StructType.get_field_index (line 914)StringViewBuilder.append_valuesStringBuilder.__setstate_cython__ Strides of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.strides (12, 4) SparseCSRMatrix.__reduce_cython__SparseCSFTensor.__reduce_cython__SparseCSCMatrix.__reduce_cython__SparseCOOTensor.__reduce_cython__ Sort the Table or RecordBatch by one or multiple columns. Parameters ---------- sorting : str or list[tuple(name, order)] Name of the column to use to sort (ascending), or a list of multiple sorting conditions where each entry is a tuple with column name and sorting order ("ascending" or "descending") **kwargs : dict, optional Additional sorting options. As allowed by :class:`SortOptions` Returns ------- Table or RecordBatch A new tabular object sorted according to the sort keys. Examples -------- Table (works similarly for RecordBatch) >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.sort_by('animal') pyarrow.Table year: int64 n_legs: int64 animal: string ---- year: [[2019,2021,2021,2020,2022,2022]] n_legs: [[5,100,4,2,4,2]] animal: [["Brittle stars","Centipede","Dog","Flamingo","Horse","Parrot"]] SignalStopHandler._init_signals Should the entries be sorted according to keys. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True).keys_sorted True Should specify one of list_size and type Select single column from Table or RecordBatch. Parameters ---------- i : int or string The index or name of the column to retrieve. Returns ------- column : Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Select a column by numeric index: >>> table.column(0) [ [ 2, 4, 5, 100 ] ] Select a column by its name: >>> table.column("animals") [ [ "Flamingo", "Horse", "Brittle stars", "Centipede" ] ] Select rows from the record batch. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the record batch with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : RecordBatch A record batch of the same schema, with only the rows selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Define a mask and select rows: >>> mask=[True, True, False, True, False, None] >>> batch.filter(mask).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Horse >>> batch.filter(mask, null_selection_behavior='emit_null').to_pandas() n_legs animals 0 2.0 Flamingo 1 2.0 Parrot 2 4.0 Horse 3 NaN None Select rows from a Table or RecordBatch. See :func:`pyarrow.compute.take` for full usage. Parameters ---------- indices : Array or array-like The indices in the tabular object whose rows will be returned. Returns ------- Table or RecordBatch A tabular object with the same schema, containing the taken rows. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.take([1,3]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] Select columns of the Table. Returns a new Table with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.select([0,1]) pyarrow.Table year: int64 n_legs: int64 ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] >>> table.select(["year"]) pyarrow.Table year: int64 ---- year: [[2020,2022,2019,2021]] Select columns of the RecordBatch. Returns a new RecordBatch with the specified columns, and metadata preserved. Parameters ---------- columns : list-like The column names or integer indices to select. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) Select columns my indices: >>> batch.select([1]) pyarrow.RecordBatch animals: string ---- animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Select columns by names: >>> batch.select(["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,2,4,4,5,100] Select a schema field by its column name or numeric index. Parameters ---------- i : int or string The index or name of the field to retrieve. Returns ------- Field Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.field(0) pyarrow.Field >>> table.field(1) pyarrow.Field Select a field by its column name or numeric index. Parameters ---------- i : int or str Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Select the second field: >>> struct_type.field(1) pyarrow.Field Select the field named 'x': >>> struct_type.field('x') pyarrow.Field Select a chunk by its index. Parameters ---------- i : int Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.chunk(1) [ 4, 5, 100 ] Schema of the table and its columns. Returns ------- Schema Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' ... Schema of the RecordBatch and its columns Returns ------- pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.schema n_legs: int64 animals: string RunEndEncodedArray.from_buffersRunEndEncodedArray._from_arrays Returns the name of the i-th tensor dimension. Parameters ---------- i : int The physical index of the tensor dimension. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_name(0) 'dim1' >>> tensor.dim_name(1) 'dim2' Returns implied schema from dataframe Parameters ---------- df : pandas.DataFrame preserve_index : bool, default True Whether to store the index as an additional column (or columns, for MultiIndex) in the resulting `Table`. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. Returns ------- pyarrow.Schema Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({ ... 'int': [1, 2], ... 'str': ['a', 'b'] ... }) Create an Arrow Schema from the schema of a pandas dataframe: >>> pa.Schema.from_pandas(df) int: int64 str: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, ... Return true if type is equivalent to passed value. Parameters ---------- other : DataType or string convertible to DataType check_metadata : bool Whether nested Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> pa.int64().equals(pa.string()) False >>> pa.int64().equals(pa.int64()) True Return true if the tensors contains exactly equal data. Parameters ---------- other : Tensor The other tensor to compare for equality. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> y = np.array([[2, 2, 4], [4, 5, 10]], np.int32) >>> tensor2 = pa.Tensor.from_numpy(y, dim_names=["a","b"]) >>> tensor.equals(tensor) True >>> tensor.equals(tensor2) False Return the process-global memory pool. Examples -------- >>> default_memory_pool() Return the list view sizes as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Return the list view offsets as an int64 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `LargeListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int64Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array.offsets [ 0, 0, 1 ] Return the list sizes as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListViewArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- sizes : Int32Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, None, 3, 4] >>> offsets = [0, 0, 1] >>> sizes = [2, 0, 4] >>> array = pa.ListViewArray.from_arrays(offsets, sizes, values) >>> array.sizes [ 2, 0, 4 ] Return the list offsets as an int32 array. The returned array will not have a validity bitmap, so you cannot expect to pass it to `ListArray.from_arrays` and get back the same list array if the original one has nulls. Returns ------- offsets : Int32Array Examples -------- >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, 5]]) >>> array.offsets [ 0, 2, 2, 5 ] Return the equivalent NumPy / Pandas dtype. Examples -------- >>> import pyarrow as pa >>> pa.int64().to_pandas_dtype() Return sorted list of indices for the fields with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- indices : List[int] Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) >>> struct_type.get_all_field_indices('x') [0] Return length of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.length() 6 Return integers array with values equal to the respective length of each list element. Null list values are null in the output. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_lengths() [ 3, 0, null, 1 ] Return index of the unique field with the given name. Parameters ---------- name : str The name of the field to look up. Returns ------- index : int The index of the field with the given name; -1 if the name isn't found or there are several fields with the given name. Examples -------- >>> import pyarrow as pa >>> struct_type = pa.struct({'x': pa.int32(), 'y': pa.string()}) Index of the field with a name 'y': >>> struct_type.get_field_index('y') 1 Index of the field that does not exist: >>> struct_type.get_field_index('z') -1 Return deserialized-from-JSON pandas metadata field (if it exists) Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> schema = pa.Table.from_pandas(df).schema Select pandas metadata field from Arrow Schema: >>> schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, 'stop': 4, 'step': 1}], ... Return data type of a ChunkedArray. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Return boolean array indicating the null values. Parameters ---------- nan_is_null : bool (optional, default False) Whether floating-point NaN values should also be considered null. Returns ------- array : boolean Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.is_null() [ [ false, false, false, false, true, false ] ] Return a child field by its numeric index. Parameters ---------- i : int Returns ------- pyarrow.Field Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union[0] pyarrow.Field Return a NumPy copy of this array (experimental). Parameters ---------- zero_copy_only : bool, default False Introduced for signature consistence with pyarrow.Array.to_numpy. This must be False here since NumPy arrays' buffer must be contiguous. Returns ------- array : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_numpy() array([ 2, 2, 4, 4, 5, 100]) Replace each null element in values with fill_value. See :func:`pyarrow.compute.fill_null` for full usage. Parameters ---------- fill_value : any The replacement value for null entries. Returns ------- result : Array or ChunkedArray A new array with nulls replaced by the given value. Examples -------- >>> import pyarrow as pa >>> fill_value = pa.scalar(5, type=pa.int8()) >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.fill_null(fill_value) [ [ 2, 2, 4, 4, 5, 100 ] ] Replace column in Table at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> table.set_column(1,'year', [year]) pyarrow.Table n_legs: int64 year: int64 ---- n_legs: [[2,4,5,100]] year: [[2021,2022,2019,2021]] Replace column in RecordBatch at position. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column set. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Replace a column: >>> year = [2021, 2022, 2019, 2021] >>> batch.set_column(1,'year', year) pyarrow.RecordBatch n_legs: int64 year: int64 ---- n_legs: [2,4,5,100] year: [2021,2022,2019,2021] Replace a field at position i in the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Replace the second field of the schema with a new field 'extra': >>> schema.set(1, pa.field('replaced', pa.bool_())) n_legs: int64 replaced: bool Remove the field at index i from the schema. Parameters ---------- i : int Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Remove the second field of the schema: >>> schema.remove(1) n_legs: int64 Remove rows that contain missing values from a Table or RecordBatch. See :func:`pyarrow.compute.drop_null` for full usage. Returns ------- Table or RecordBatch A tabular object with the same schema, with rows containing no missing values. Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [None, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.drop_null() pyarrow.Table year: double n_legs: int64 animals: string ---- year: [[2022,2021]] n_legs: [[4,100]] animals: [["Horse","Centipede"]] Remove missing values from a chunked array. See :func:`pyarrow.compute.drop_null` for full description. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.drop_null() [ [ 2, 2 ], [ 4, 5, 100 ] ] Register a Python extension type. Registration is based on the extension name (so different registered types need unique extension names). Registration needs an extension type instance, but then works for any instance of the same subclass regardless of parametrization of the type. Parameters ---------- ext_type : BaseExtensionType instance The ExtensionType subclass to register. Examples -------- Define a UuidType extension type subclassing ExtensionType: >>> import pyarrow as pa >>> class UuidType(pa.ExtensionType): ... def __init__(self): ... pa.ExtensionType.__init__(self, pa.binary(16), "my_package.uuid") ... def __arrow_ext_serialize__(self): ... # since we don't have a parameterized type, we don't need extra ... # metadata to be deserialized ... return b'' ... @classmethod ... def __arrow_ext_deserialize__(self, storage_type, serialized): ... # return an instance of this subclass given the serialized ... # metadata. ... return UuidType() ... Register the extension type: >>> pa.register_extension_type(UuidType()) Unregister the extension type: >>> pa.unregister_extension_type("my_package.uuid") RecordBatch.to_tensor (line 3438)RecordBatch.serialize (line 2871)RecordBatch.schema.__get__ (line 2556)RecordBatch.rename_columns (line 2830)RecordBatch.nbytes.__get__ (line 2599)RecordBatch._export_to_c_device_RecordBatchStreamWriter.__setstate_cython___RecordBatchStreamWriter.__reduce_cython___RecordBatchStreamReader.__setstate_cython___RecordBatchStreamReader.__reduce_cython___RecordBatchFileWriter.__setstate_cython___RecordBatchFileReader.get_batch_RecordBatchFileReader.__setstate_cython__ProxyMemoryPool.__reduce_cython__ Provide an empty table according to the schema. Returns ------- table: pyarrow.Table Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Create an empty table with schema's fields: >>> schema.empty_table() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[]] animals: [[]] Perform an asof join between this table and another one. This is similar to a left-join except that we match on nearest key rather than equal keys. Both tables must be sorted by the key. This type of join is most useful for time series data that are not perfectly aligned. Optionally match on equivalent keys with "by" before searching with "on". Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. on : str The column from current table that should be used as the "on" key of the join operation left side. An inexact match is used on the "on" key, i.e. a row is considered a match if and only if left_on - tolerance <= right_on <= left_on. The input dataset must be sorted by the "on" key. Must be a single field of a common type. Currently, the "on" key must be an integer, date, or timestamp type. by : str or list[str] The columns from current table that should be used as the keys of the join operation left side. The join operation is then done only for the matches in these columns. tolerance : int The tolerance for inexact "on" key matching. A right row is considered a match with the left row ``right.on - left.on <= tolerance``. The ``tolerance`` may be: - negative, in which case a past-as-of-join occurs; - or positive, in which case a future-as-of-join occurs; - or zero, in which case an exact-as-of-join occurs. The tolerance is interpreted in the same units as the "on" key. right_on : str or list[str], default None The columns from the right_table that should be used as the on key on the join operation right side. When ``None`` use the same key name as the left table. right_by : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. Returns ------- Table Example -------- >>> import pyarrow as pa >>> t1 = pa.table({'id': [1, 3, 2, 3, 3], ... 'year': [2020, 2021, 2022, 2022, 2023]}) >>> t2 = pa.table({'id': [3, 4], ... 'year': [2020, 2021], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1.join_asof(t2, on='year', by='id', tolerance=-2) pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[1,3,2,3,3]] year: [[2020,2021,2022,2022,2023]] n_legs: [[null,5,null,5,null]] animal: [[null,"Brittle stars",null,"Brittle stars",null]] Perform an aggregation over the grouped columns of the table. Parameters ---------- aggregations : list[tuple(str, str)] or list[tuple(str, str, FunctionOptions)] List of tuples, where each tuple is one aggregation specification and consists of: aggregation column name followed by function name and optionally aggregation function option. Pass empty list to get a single row for each group. The column name can be a string, an empty list or a list of column names, for unary, nullary and n-ary aggregation functions respectively. For the list of function names and respective aggregation function options see :ref:`py-grouped-aggrs`. Returns ------- Table Results of the aggregation functions. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Sum the column "values" over the grouped column "keys": >>> t.group_by("keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] Count the rows over the grouped column "keys": >>> t.group_by("keys").aggregate([([], "count_all")]) pyarrow.Table keys: string count_all: int64 ---- keys: [["a","b","c"]] count_all: [[2,2,1]] Do multiple aggregations: >>> t.group_by("keys").aggregate([ ... ("values", "sum"), ... ("keys", "count") ... ]) pyarrow.Table keys: string values_sum: int64 keys_count: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] keys_count: [[2,2,1]] Count the number of non-null values for column "values" over the grouped column "keys": >>> import pyarrow.compute as pc >>> t.group_by(["keys"]).aggregate([ ... ("values", "count", pc.CountOptions(mode="only_valid")) ... ]) pyarrow.Table keys: string values_count: int64 ---- keys: [["a","b","c"]] values_count: [[2,2,1]] Get a single row for each group in column "keys": >>> t.group_by("keys").aggregate([]) pyarrow.Table keys: string ---- keys: [["a","b","c"]] Perform a join between this table and another one. Result of the join will be a new Table, where further operations can be applied. Parameters ---------- right_table : Table The table to join to the current one, acting as the right table in the join operation. keys : str or list[str] The columns from current table that should be used as keys of the join operation left side. right_keys : str or list[str], default None The columns from the right_table that should be used as keys on the join operation right side. When ``None`` use the same key names as the left table. join_type : str, default "left outer" The kind of join that should be performed, one of ("left semi", "right semi", "left anti", "right anti", "inner", "left outer", "right outer", "full outer") left_suffix : str, default None Which suffix to add to left column names. This prevents confusion when the columns in left and right tables have colliding names. right_suffix : str, default None Which suffix to add to the right column names. This prevents confusion when the columns in left and right tables have colliding names. coalesce_keys : bool, default True If the duplicated keys should be omitted from one of the sides in the join result. use_threads : bool, default True Whether to use multithreading or not. Returns ------- Table Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df1 = pd.DataFrame({'id': [1, 2, 3], ... 'year': [2020, 2022, 2019]}) >>> df2 = pd.DataFrame({'id': [3, 4], ... 'n_legs': [5, 100], ... 'animal': ["Brittle stars", "Centipede"]}) >>> t1 = pa.Table.from_pandas(df1) >>> t2 = pa.Table.from_pandas(df2) Left outer join: >>> t1.join(t2, 'id').combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2]] year: [[2019,2020,2022]] n_legs: [[5,null,null]] animal: [["Brittle stars",null,null]] Full outer join: >>> t1.join(t2, 'id', join_type="full outer").combine_chunks().sort_by('year') pyarrow.Table id: int64 year: int64 n_legs: int64 animal: string ---- id: [[3,1,2,4]] year: [[2019,2020,2022,null]] n_legs: [[5,null,null,100]] animal: [["Brittle stars",null,null,"Centipede"]] Right outer join: >>> t1.join(t2, 'id', join_type="right outer").combine_chunks().sort_by('year') pyarrow.Table year: int64 id: int64 n_legs: int64 animal: string ---- year: [[2019,null]] id: [[3,4]] n_legs: [[5,100]] animal: [["Brittle stars","Centipede"]] Right anti join >>> t1.join(t2, 'id', join_type="right anti") pyarrow.Table id: int64 n_legs: int64 animal: string ---- id: [[4]] n_legs: [[100]] animal: [["Centipede"]] _PandasConvertible.to_pandas (line 692)_PandasConvertible.__setstate_cython___PandasConvertible.__reduce_cython___PandasAPIShim.is_extension_array_dtype_PandasAPIShim.get_rangeindex_attribute_PandasAPIShim.__setstate_cython__ Open memory map at file path. Size of the memory map cannot change. Parameters ---------- path : str mode : {'r', 'r+', 'w'}, default 'r' Whether the file is opened for reading ('r'), writing ('w') or both ('r+'). Returns ------- mmap : MemoryMappedFile Examples -------- Reading from a memory map without any memory allocation or copying: >>> import pyarrow as pa >>> with pa.output_stream('example_mmap.txt') as stream: ... stream.write(b'Constructing a buffer referencing the mapped memory') ... 51 >>> with pa.memory_map('example_mmap.txt') as mmap: ... mmap.read_at(6,45) ... b'memory' Number of underlying chunks. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs.num_chunks 2 Number of rows in this table. Due to the definition of a table, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_rows 4 Number of rows Due to the definition of a RecordBatch, all columns have the same number of rows. Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_rows 6 Number of data buffers required to construct Array type excluding children. Examples -------- >>> import pyarrow as pa >>> pa.int64().num_buffers 2 >>> pa.string().num_buffers 3 Number of columns in this table. Returns ------- int Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.num_columns 2 Number of columns Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch.num_columns 2 Names of this tensor dimensions. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.dim_names ['dim1', 'dim2'] Names of the Table or RecordBatch columns. Returns ------- list of str Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> table = pa.Table.from_arrays([[2, 4, 5, 100], ... ["Flamingo", "Horse", "Brittle stars", "Centipede"]], ... names=['n_legs', 'animals']) >>> table.column_names ['n_legs', 'animals'] MessageReader.read_next_messageMessageReader.__setstate_cython__Mask must be a pyarrow.Array of type booleanMask must be a numpy array when converting numpy arraysMask is a different length from sequence being convertedMapType.item_field.__get__ (line 745) Make a new table by combining the chunks this table has. All the underlying chunks in the ChunkedArray of each column are concatenated into zero or one chunk. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> names = ["n_legs", "animals"] >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] >>> table.combine_chunks() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4,4,5,100]] animals: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] List requires DataType or Field List of all columns in numerical order. Returns ------- columns : list of Array (for RecordBatch) or list of ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.columns [ [ [ null, 4, 5, null ] ], [ [ "Flamingo", "Horse", null, "Centipede" ] ]] ListViewArray.flatten (line 2750)ListArray.from_arrays (line 2210)LargeListView requires DataType or FieldLargeListViewArray.values.__get__ (line 2919)LargeListViewArray.flatten (line 3040) Iterator over all columns in their numerical order. Yields ------ Array (for RecordBatch) or ChunkedArray (for Table) Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> for i in table.itercolumns(): ... print(i.null_count) ... 2 1 Is this tensor mutable or immutable. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_mutable True Is this tensor contiguous in memory. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.is_contiguous True IpcWriteOptions.__reduce_cython__Invalid value for compression: {!r}Incompatible storage type {0} for extension type {1}IPC write statistics Parameters ---------- num_messages : int Number of messages. num_record_batches : int Number of record batches. num_dictionary_batches : int Number of dictionary batches. num_dictionary_deltas : int Delta of dictionaries. num_replaced_dictionaries : int Number of replaced dictionaries. Flatten this ChunkedArray. If it has a struct type, the column is flattened into one array per struct field. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : list of ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> c_arr = pa.chunked_array(n_legs.value_counts()) >>> c_arr [ -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] ] >>> c_arr.flatten() [ [ [ 2, 4, 5, 100 ] ], [ [ 2, 2, 1, 1 ] ]] >>> c_arr.type StructType(struct) >>> n_legs.type DataType(int64) FixedSizeListArray.values.__get__ (line 3324)FixedShapeTensorScalar.to_numpyFixedShapeTensorArray.to_tensor Find the first index of a value. See :func:`pyarrow.compute.index` for full usage. Parameters ---------- value : Scalar or object The value to look for in the array. start : int, optional The start index where to look for `value`. end : int, optional The end index where to look for `value`. memory_pool : MemoryPool, optional A memory pool for potential memory allocations. Returns ------- index : Int64Scalar The index of the value in the array (-1 if not found). Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.index(4) >>> n_legs.index(4, start=3) File object is malformed, has no modeField.remove_metadata (line 2345)ExtensionType.__arrow_ext_class___ExtensionRegistryNanny.__setstate_cython___ExtensionRegistryNanny.__reduce_cython__Expected storage type {0} but got {1}Expected list of {ndim} np.arrays for SparseCSFTensor.indptrExpected array or chunked array, got Expected an object implementing the Arrow PyCapsule Protocol for streams (i.e. having a `__arrow_c_stream__` method), got Drop one or more columns and return a new Table or RecordBatch. Parameters ---------- columns : str or list[str] Field name(s) referencing existing column(s). Raises ------ KeyError If any of the passed column names do not exist. Returns ------- Table or RecordBatch A tabular object without the column(s). Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Drop one column: >>> table.drop_columns("animals") pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Drop one or more columns: >>> table.drop_columns(["n_legs", "animals"]) pyarrow.Table ... ---- Do not call Field's constructor directly, use `pyarrow.field` instead. Dimensions of the table or record batch: (#rows, #columns). Returns ------- (int, int) Number of rows and number of columns. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [None, 4, 5, None], ... 'animals': ["Flamingo", "Horse", None, "Centipede"]}) >>> table.shape (4, 2) Dictionary (categorical, or simply encoded) type. Parameters ---------- index_type : DataType value_type : DataType ordered : bool Returns ------- type : DictionaryType Examples -------- Create an instance of dictionary type: >>> import pyarrow as pa >>> pa.dictionary(pa.int64(), pa.utf8()) DictionaryType(dictionary) Use dictionary type to create an array: >>> pa.array(["a", "b", None, "d"], pa.dictionary(pa.int64(), pa.utf8())) ... -- dictionary: [ "a", "b", "d" ] -- indices: [ 0, 1, null, 2 ] DictionaryType.value_type.__get__ (line 500)DictionaryType.index_type.__get__ (line 487) Declare a grouping over the columns of the table. Resulting grouping can then be used to perform aggregations with a subsequent ``aggregate()`` method. Parameters ---------- keys : str or list[str] Name of the columns that should be used as the grouping key. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Returns ------- TableGroupBy See Also -------- TableGroupBy.aggregate Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.group_by('year').aggregate([('n_legs', 'sum')]) pyarrow.Table year: int64 n_legs_sum: int64 ---- year: [[2020,2022,2021,2019]] n_legs_sum: [[2,6,104,5]] DataType.bit_width.__get__ (line 241)DataType._import_from_c_capsule Create variable-length or fixed size binary type. Parameters ---------- length : int, optional, default -1 If length == -1 then return a variable length binary type. If length is greater than or equal to 0 then return a fixed size binary type of width `length`. Examples -------- Create an instance of a variable-length binary type: >>> import pyarrow as pa >>> pa.binary() DataType(binary) and use the variable-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary()) [ 666F6F, 626172, 62617A ] Create an instance of a fixed-size binary type: >>> pa.binary(3) FixedSizeBinaryType(fixed_size_binary[3]) and use the fixed-length binary type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.binary(3)) [ 666F6F, 626172, 62617A ] Create single-precision floating point type. Examples -------- Create an instance of float32 type: >>> import pyarrow as pa >>> pa.float32() DataType(float) >>> print(pa.float32()) float Create an array with float32 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float32()) [ 0, 1, 2 ] Create shallow copy of table by replacing schema key-value metadata with the indicated new metadata (which may be None), which deletes any existing metadata. Parameters ---------- metadata : dict, default None Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Constructing a Table with pyarrow schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> table= pa.table(df, my_schema) >>> table.schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... Create a shallow copy of a Table with deleted schema metadata: >>> table.replace_schema_metadata().schema n_legs: int64 animals: string Create a shallow copy of a Table with new schema metadata: >>> metadata={"animals": "Which animal"} >>> table.replace_schema_metadata(metadata = metadata).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Which animal' Create shallow copy of record batch by replacing schema key-value metadata with the indicated new metadata (which may be None, which deletes any existing metadata Parameters ---------- metadata : dict, default None Returns ------- shallow_copy : RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) Constructing a RecordBatch with schema and metadata: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64())], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch = pa.RecordBatch.from_arrays([n_legs], schema=my_schema) >>> batch.schema n_legs: int64 -- schema metadata -- n_legs: 'Number of legs per animal' Shallow copy of a RecordBatch with deleted schema metadata: >>> batch.replace_schema_metadata().schema n_legs: int64 Create pyarrow.Array instance from a Python object. Parameters ---------- obj : sequence, iterable, ndarray, pandas.Series, Arrow-compatible array If both type and size are specified may be a single use iterable. If not strongly-typed, Arrow type will be inferred for resulting array. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` method) can be passed as well. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the data. mask : array[bool], optional Indicate which values are null (True) or not null (False). size : int64, optional Size of the elements. If the input is larger than size bail at this length. For iterators, if size is larger than the input iterator this will be treated as a "max size", but will involve an initial allocation of size followed by a resize to the actual size (so if you know the exact size specifying it correctly will give you better performance). from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. If passed, the mask tasks precedence, but if a value is unmasked (not-null), but still null according to pandas semantics, then it is null. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. safe : bool, default True Check for overflows or other unsafe conversions. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- array : pyarrow.Array or pyarrow.ChunkedArray A ChunkedArray instead of an Array is returned if: - the object data overflowed binary storage. - the object's ``__arrow_array__`` protocol method returned a chunked array. Notes ----- Timezone will be preserved in the returned array for timezone-aware data, else no timezone will be returned for naive timestamps. Internally, UTC values are stored for timezone-aware data with the timezone set in the data type. Pandas's DateOffsets and dateutil.relativedelta.relativedelta are by default converted as MonthDayNanoIntervalArray. relativedelta leapdays are ignored as are all absolute fields on both objects. datetime.timedelta can also be converted to MonthDayNanoIntervalArray but this requires passing MonthDayNanoIntervalType explicitly. Converting to dictionary array will promote to a wider integer type for indices if the number of distinct values cannot be represented, even if the index type was explicitly set. This means that if there are more than 127 values the returned dictionary array's index type will be at least pa.int16() even if pa.int8() was passed to the function. Note that an explicit index type will not be demoted even if it is wider than required. Examples -------- >>> import pandas as pd >>> import pyarrow as pa >>> pa.array(pd.Series([1, 2])) [ 1, 2 ] >>> pa.array(["a", "b", "a"], type=pa.dictionary(pa.int8(), pa.string())) ... -- dictionary: [ "a", "b" ] -- indices: [ 0, 1, 0 ] >>> import numpy as np >>> pa.array(pd.Series([1, 2]), mask=np.array([0, 1], dtype=bool)) [ 1, null ] >>> arr = pa.array(range(1024), type=pa.dictionary(pa.int8(), pa.int64())) >>> arr.type.index_type DataType(int16) Create new table with columns renamed to provided names. Parameters ---------- names : list of str List of new column names. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> new_names = ["n", "name"] >>> table.rename_columns(new_names) pyarrow.Table n: int64 name: string ---- n: [[2,4,5,100]] name: [["Flamingo","Horse","Brittle stars","Centipede"]] Create new schema without metadata, if any Returns ------- schema : pyarrow.Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Create a new schema with removing the metadata from the original: >>> schema.remove_metadata() n_legs: int64 animals: string Create new record batch with columns renamed to provided names. Parameters ---------- names : list of str List of new column names. Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> new_names = ["n", "name"] >>> batch.rename_columns(new_names) pyarrow.RecordBatch n: int64 name: string ---- n: [2,4,5,100] name: ["Flamingo","Horse","Brittle stars","Centipede"] Create new field without metadata, if any Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field.metadata {b'key': b'Something important'} Create new field by removing the metadata from the existing one: >>> field_new = field.remove_metadata() >>> field_new.metadata Create new Table with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New table without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.remove_column(1) pyarrow.Table n_legs: int64 ---- n_legs: [[2,4,5,100]] Create new RecordBatch with the indicated column removed. Parameters ---------- i : int Index of column to remove. Returns ------- Table New record batch without the column. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.remove_column(1) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] Create large variable-length binary type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer binary(). Examples -------- Create an instance of large variable-length binary type: >>> import pyarrow as pa >>> pa.large_binary() DataType(large_binary) and use the type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.large_binary()) [ 666F6F, 626172, 62617A ] Create large UTF8 variable-length string type. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2GB, you should prefer string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_string() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_string()) [ "foo", "bar", ... "foo", "bar" ] Create instance of unsigned uint16 type. Examples -------- Create an instance of unsigned int16 type: >>> import pyarrow as pa >>> pa.uint16() DataType(uint16) >>> print(pa.uint16()) uint16 Create an array with unsigned int16 type: >>> pa.array([0, 1, 2], type=pa.uint16()) [ 0, 1, 2 ] Create instance of unsigned int8 type. Examples -------- Create an instance of unsigned int8 type: >>> import pyarrow as pa >>> pa.uint8() DataType(uint8) >>> print(pa.uint8()) uint8 Create an array with unsigned int8 type: >>> pa.array([0, 1, 2], type=pa.uint8()) [ 0, 1, 2 ] Create instance of timestamp type with resolution and optional time zone. Parameters ---------- unit : str one of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond] tz : str, default None Time zone name. None indicates time zone naive Examples -------- Create an instance of timestamp type: >>> import pyarrow as pa >>> pa.timestamp('us') TimestampType(timestamp[us]) >>> pa.timestamp('s', tz='America/New_York') TimestampType(timestamp[s, tz=America/New_York]) >>> pa.timestamp('s', tz='+07:30') TimestampType(timestamp[s, tz=+07:30]) Use timestamp type when creating a scalar object: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('s', tz='UTC')) >>> pa.scalar(datetime(2012, 1, 1), type=pa.timestamp('us')) Returns ------- timestamp_type : TimestampType Create instance of signed int64 type. Examples -------- Create an instance of int64 type: >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> print(pa.int64()) int64 Create an array with int64 type: >>> pa.array([0, 1, 2], type=pa.int64()) [ 0, 1, 2 ] Create instance of signed int32 type. Examples -------- Create an instance of int32 type: >>> import pyarrow as pa >>> pa.int32() DataType(int32) >>> print(pa.int32()) int32 Create an array with int32 type: >>> pa.array([0, 1, 2], type=pa.int32()) [ 0, 1, 2 ] Create instance of signed int16 type. Examples -------- Create an instance of int16 type: >>> import pyarrow as pa >>> pa.int16() DataType(int16) >>> print(pa.int16()) int16 Create an array with int16 type: >>> pa.array([0, 1, 2], type=pa.int16()) [ 0, 1, 2 ] Create instance of null type. Examples -------- Create an instance of a null type: >>> import pyarrow as pa >>> pa.null() DataType(null) >>> print(pa.null()) null Create a ``Field`` type with a null type and a name: >>> pa.field('null_field', pa.null()) pyarrow.Field Create instance of boolean type. Examples -------- Create an instance of a boolean type: >>> import pyarrow as pa >>> pa.bool_() DataType(bool) >>> print(pa.bool_()) bool Create a ``Field`` type with a boolean type and a name: >>> pa.field('bool_field', pa.bool_()) pyarrow.Field Create instance of a duration type with unit resolution. Parameters ---------- unit : str One of 's' [second], 'ms' [millisecond], 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.DurationType Examples -------- Create an instance of duration type: >>> import pyarrow as pa >>> pa.duration('us') DurationType(duration[us]) >>> pa.duration('s') DurationType(duration[s]) Create an array with duration type: >>> pa.array([0, 1, 2], type=pa.duration('s')) [ 0, 1, 2 ] Create double-precision floating point type. Examples -------- Create an instance of float64 type: >>> import pyarrow as pa >>> pa.float64() DataType(double) >>> print(pa.float64()) double Create an array with float64 type: >>> pa.array([0.0, 1.0, 2.0], type=pa.float64()) [ 0, 1, 2 ] Create decimal type with precision and scale and 128-bit width. Arrow decimals are fixed-point decimal numbers encoded as a scaled integer. The precision is the number of significant digits that the decimal type can represent; the scale is the number of digits after the decimal point (note the scale can be negative). As an example, ``decimal128(7, 3)`` can exactly represent the numbers 1234.567 and -1234.567 (encoded internally as the 128-bit integers 1234567 and -1234567, respectively), but neither 12345.67 nor 123.4567. ``decimal128(5, -3)`` can exactly represent the number 12345000 (encoded internally as the 128-bit integer 12345), but neither 123450000 nor 1234500. If you need a precision higher than 38 significant digits, consider using ``decimal256``. Parameters ---------- precision : int Must be between 1 and 38 scale : int Returns ------- decimal_type : Decimal128Type Examples -------- Create an instance of decimal type: >>> import pyarrow as pa >>> pa.decimal128(5, 2) Decimal128Type(decimal128(5, 2)) Create an array with decimal type: >>> import decimal >>> a = decimal.Decimal('123.45') >>> pa.array([a], pa.decimal128(5, 2)) [ 123.45 ] Create an Arrow output stream. Parameters ---------- source : str, Path, buffer, file-like object The source to open for writing. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly compression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary write buffer. Examples -------- Create a writable NativeFile from a pyarrow Buffer: >>> import pyarrow as pa >>> data = b"buffer data" >>> empty_obj = bytearray(11) >>> buf = pa.py_buffer(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read(6) ... b'buffer' or from a memoryview object: >>> buf = memoryview(empty_obj) >>> with pa.output_stream(buf) as stream: ... stream.write(data) ... 11 >>> with pa.input_stream(buf) as stream: ... stream.read() ... b'buffer data' Create a writable NativeFile from a string or file path: >>> with pa.output_stream('example_second.txt') as stream: ... stream.write(b'Write some data') ... 15 >>> with pa.input_stream('example_second.txt') as stream: ... stream.read() ... b'Write some data' Create an Arrow input stream. Parameters ---------- source : str, Path, buffer, or file-like object The source to open for reading. compression : str optional, default 'detect' The compression algorithm to use for on-the-fly decompression. If "detect" and source is a file path, then compression will be chosen based on the file extension. If None, no compression will be applied. Otherwise, a well-known algorithm name must be supplied (e.g. "gzip"). buffer_size : int, default None If None or 0, no buffering will happen. Otherwise the size of the temporary read buffer. Examples -------- Create a readable BufferReader (NativeFile) from a Buffer or a memoryview object: >>> import pyarrow as pa >>> buf = memoryview(b"some data") >>> with pa.input_stream(buf) as stream: ... stream.read(4) ... b'some' Create a readable OSFile (NativeFile) from a string or file path: >>> import gzip >>> with gzip.open('example.gz', 'wb') as f: ... f.write(b'some data') ... 9 >>> with pa.input_stream('example.gz') as stream: ... stream.read() ... b'some data' Create a readable PythonFile (NativeFile) from a a Python file object: >>> with open('example.txt', mode='w') as f: ... f.write('some text') ... 9 >>> with pa.input_stream('example.txt') as stream: ... stream.read(6) ... b'some t' Create a variable-length binary view type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.binary_view() DataType(binary_view) Create a strongly-typed Array instance with all elements null. Parameters ---------- size : int Array length. type : pyarrow.DataType, default None Explicit type for the array. By default use NullType. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.nulls(10) 10 nulls >>> pa.nulls(3, pa.uint32()) [ null, null, null ] Create a pyarrow.Table from a Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of arrays or chunked arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the Arrow Table. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. If passed, the output will have exactly this schema (raising an error when columns are not found in the data and ignoring additional data not specified in the schema, when data is a dict or DataFrame). metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). nthreads : int, default None For pandas.DataFrame inputs: if greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). Returns ------- Table See Also -------- Table.from_arrays, Table.from_pandas, Table.from_pydict Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from a python dictionary: >>> pa.table({"n_legs": n_legs, "animals": animals}) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays: >>> pa.table([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.table([n_legs, animals], names=names, metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.table(df) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from pandas DataFrame with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.table(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: '{"index_columns": [], "column_indexes": [{"name": null, ... Construct a Table from chunked arrays: >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array([["Flamingo", "Parrot", "Dog"], ["Horse", "Brittle stars", "Centipede"]]) >>> table = pa.table([n_legs, animals], names=names) >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,2,4],[4,5,100]] animals: [["Flamingo","Parrot","Dog"],["Horse","Brittle stars","Centipede"]] Create a pyarrow.Scalar instance from a Python object. Parameters ---------- value : Any Python object coercible to arrow's type system. type : pyarrow.DataType Explicit type to attempt to coerce to, otherwise will be inferred from the value. from_pandas : bool, default None Use pandas's semantics for inferring nulls from values in ndarray-like data. Defaults to False if not passed explicitly by user, or True if a pandas object is passed in. memory_pool : pyarrow.MemoryPool, optional If not passed, will allocate memory from the currently-set default memory pool. Returns ------- scalar : pyarrow.Scalar Examples -------- >>> import pyarrow as pa >>> pa.scalar(42) >>> pa.scalar("string") >>> pa.scalar([1, 2]) >>> pa.scalar([1, 2], type=pa.list_(pa.int16())) Create a pyarrow.RecordBatch from another Python data structure or sequence of arrays. Parameters ---------- data : dict, list, pandas.DataFrame, Arrow-compatible table A mapping of strings to Arrays or Python lists, a list of Arrays, a pandas DataFame, or any tabular object implementing the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` method). names : list, default None Column names if list of arrays passed as data. Mutually exclusive with 'schema' argument. schema : Schema, default None The expected schema of the RecordBatch. If not passed, will be inferred from the data. Mutually exclusive with 'names' argument. metadata : dict or Mapping, default None Optional metadata for the schema (if schema not passed). Returns ------- RecordBatch See Also -------- RecordBatch.from_arrays, RecordBatch.from_pandas, table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from a python dictionary: >>> pa.record_batch({"n_legs": n_legs, "animals": animals}) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch({"n_legs": n_legs, "animals": animals}).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Creating a RecordBatch from a list of arrays with names: >>> pa.record_batch([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Creating a RecordBatch from a list of arrays with names and metadata: >>> my_metadata={"n_legs": "How many legs does an animal have?"} >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.record_batch([n_legs, animals], ... names=names, ... metadata = my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'How many legs does an animal have?' Creating a RecordBatch from a pandas DataFrame: >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> pa.record_batch(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> pa.record_batch(df).to_pandas() year month day n_legs animals 0 2020 3 1 2 Flamingo 1 2022 5 5 4 Horse 2 2021 7 9 5 Brittle stars 3 2022 9 13 100 Centipede Creating a RecordBatch from a pandas DataFrame with schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.record_batch(df, my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' pandas: ... >>> pa.record_batch(df, my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Create a pyarrow.Field instance. Parameters ---------- name : str or bytes Name of the field. Alternatively, you can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). type : pyarrow.DataType Arrow datatype of the field. nullable : bool, default True Whether the field's values are nullable. metadata : dict, default None Optional field metadata, the keys and values must be coercible to bytes. Returns ------- field : pyarrow.Field Examples -------- Create an instance of pyarrow.Field: >>> import pyarrow as pa >>> pa.field('key', pa.int32()) pyarrow.Field >>> pa.field('key', pa.int32(), nullable=False) pyarrow.Field >>> field = pa.field('key', pa.int32(), ... metadata={"key": "Something important"}) >>> field pyarrow.Field >>> field.metadata {b'key': b'Something important'} Use the field to create a struct type: >>> pa.struct([field]) StructType(struct) Create a file of the given size and memory-map it. Parameters ---------- path : str The file path to create, on the local filesystem. size : int The file size to create. Returns ------- mmap : MemoryMappedFile Examples -------- Create a file with a memory map: >>> import pyarrow as pa >>> with pa.create_memory_map('example_mmap_create.dat', 27) as mmap: ... mmap.write(b'Create a memory-mapped file') ... mmap.read_at(10, 9) ... 27 b'memory-map' Create a Tensor from a numpy array. Parameters ---------- obj : numpy.ndarray The source numpy array dim_names : list, optional Names of each dimension of the Tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) type: int32 shape: (2, 3) strides: (12, 4) Create UTF8 variable-length string type. Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.string() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.string()) [ "foo", "bar", "baz" ] Create StructType instance from fields. A struct is a nested type parameterized by an ordered sequence of types (which can all be distinct), called its fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Each field must have a UTF8-encoded name, and these field names are part of the type metadata. Examples -------- Create an instance of StructType from an iterable of tuples: >>> import pyarrow as pa >>> fields = [ ... ('f1', pa.int32()), ... ('f2', pa.string()), ... ] >>> struct_type = pa.struct(fields) >>> struct_type StructType(struct) Retrieve a field from a StructType: >>> struct_type[0] pyarrow.Field >>> struct_type['f1'] pyarrow.Field Create an instance of StructType from an iterable of Fields: >>> fields = [ ... pa.field('f1', pa.int32()), ... pa.field('f2', pa.string(), nullable=False), ... ] >>> pa.struct(fields) StructType(struct) Returns ------- type : DataType Create MapType instance from key and item data types or fields. Parameters ---------- key_type : DataType or Field item_type : DataType or Field keys_sorted : bool Returns ------- map_type : DataType Examples -------- Create an instance of MapType: >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()) MapType(map) >>> pa.map_(pa.string(), pa.int32(), keys_sorted=True) MapType(map) Use MapType to create an array: >>> data = [[{'key': 'a', 'value': 1}, {'key': 'b', 'value': 2}], [{'key': 'c', 'value': 3}]] >>> pa.array(data, type=pa.map_(pa.string(), pa.int32(), keys_sorted=True)) [ keys: [ "a", "b" ] values: [ 1, 2 ], keys: [ "c" ] values: [ 3 ] ] Create ListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of ListViewType: >>> import pyarrow as pa >>> pa.list_view(pa.string()) ListViewType(list_view) Create ListType instance from child data type or field. Parameters ---------- value_type : DataType or Field list_size : int, optional, default -1 If length == -1 then return a variable length list type. If length is greater than or equal to 0 then return a fixed size list type. Returns ------- list_type : DataType Examples -------- Create an instance of ListType: >>> import pyarrow as pa >>> pa.list_(pa.string()) ListType(list) >>> pa.list_(pa.int32(), 2) FixedSizeListType(fixed_size_list[2]) Use the ListType to create a scalar: >>> pa.scalar(['foo', None], type=pa.list_(pa.string(), 2)) or an array: >>> pa.array([[1, 2], [3, 4]], pa.list_(pa.int32(), 2)) [ [ 1, 2 ], [ 3, 4 ] ] Create LargeListViewType instance from child data type or field. This data type may not be supported by all Arrow implementations because it is an alternative to the ListType. Parameters ---------- value_type : DataType or Field Returns ------- list_view_type : DataType Examples -------- Create an instance of LargeListViewType: >>> import pyarrow as pa >>> pa.large_list_view(pa.int8()) LargeListViewType(large_list_view) Create LargeListType instance from child data type or field. This data type may not be supported by all Arrow implementations. Unless you need to represent data larger than 2**31 elements, you should prefer list_(). Parameters ---------- value_type : DataType or Field Returns ------- list_type : DataType Examples -------- Create an instance of LargeListType: >>> import pyarrow as pa >>> pa.large_list(pa.int8()) LargeListType(large_list) Use the LargeListType to create an array: >>> pa.array([[-1, 3]] * 5, type=pa.large_list(pa.int8())) [ [ -1, 3 ], [ -1, 3 ], ... Converting to Python dictionary is not supported when duplicate field names are present Convert to an iterator of ChunkArrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> for i in n_legs.iterchunks(): ... print(i.null_count) ... 0 1 Convert to a list of single-chunked arrays. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, None], [4, 5, 100]]) >>> n_legs [ [ 2, 2, null ], [ 4, 5, 100 ] ] >>> n_legs.chunks [ [ 2, 2, null ], [ 4, 5, 100 ]] Convert to a list of native Python objects. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.to_pylist() [2, 2, 4, 4, None, 100] Convert to a :class:`~pyarrow.Tensor`. RecordBatches that can be converted have fields of type signed or unsigned integer or float, including all bit-widths. ``null_to_nan`` is ``False`` by default and this method will raise an error in case any nulls are present. RecordBatches with nulls can be converted with ``null_to_nan`` set to ``True``. In this case null values are converted to ``NaN`` and integer type arrays are promoted to the appropriate float type. Parameters ---------- null_to_nan : bool, default False Whether to write null values in the result as ``NaN``. row_major : bool, default True Whether resulting Tensor is row-major or column-major memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Examples -------- >>> import pyarrow as pa >>> batch = pa.record_batch( ... [ ... pa.array([1, 2, 3, 4, None], type=pa.int32()), ... pa.array([10, 20, 30, 40, None], type=pa.float32()), ... ], names = ["a", "b"] ... ) >>> batch pyarrow.RecordBatch a: int32 b: float ---- a: [1,2,3,4,null] b: [10,20,30,40,null] Convert a RecordBatch to row-major Tensor with null values written as ``NaN``s >>> batch.to_tensor(null_to_nan=True) type: double shape: (5, 2) strides: (16, 8) >>> batch.to_tensor(null_to_nan=True).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert a RecordBatch to column-major Tensor >>> batch.to_tensor(null_to_nan=True, row_major=False) type: double shape: (5, 2) strides: (8, 40) >>> batch.to_tensor(null_to_nan=True, row_major=False).to_numpy() array([[ 1., 10.], [ 2., 20.], [ 3., 30.], [ 4., 40.], [nan, nan]]) Convert the Table to a RecordBatchReader. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- RecordBatchReader Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatchReader: >>> table.to_reader() >>> reader = table.to_reader() >>> reader.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... >>> reader.read_all() pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Convert the Table or RecordBatch to a dict or OrderedDict. Returns ------- dict Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> table = pa.Table.from_arrays([n_legs, animals], names=["n_legs", "animals"]) >>> table.to_pydict() {'n_legs': [2, 2, 4, 4, 5, 100], 'animals': ['Flamingo', 'Parrot', ..., 'Centipede']} Convert arrow::Tensor to numpy.ndarray with zero copy Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.to_numpy() array([[ 2, 2, 4], [ 4, 5, 100]], dtype=int32) Convert Table to a list of RecordBatch objects. Note that this method is zero-copy, it merely exposes the same data under a different API. Parameters ---------- max_chunksize : int, default None Maximum number of rows for each RecordBatch chunk. Individual chunks may be smaller depending on the chunk layout of individual columns. Returns ------- list[RecordBatch] Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Convert a Table to a RecordBatch: >>> table.to_batches()[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Convert a Table to a list of RecordBatches: >>> table.to_batches(max_chunksize=2)[0].to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse >>> table.to_batches(max_chunksize=2)[1].to_pandas() n_legs animals 0 5 Brittle stars 1 100 Centipede Construct pyarrow.Schema from collection of fields. Parameters ---------- fields : iterable of Fields or tuples, or mapping of strings to DataTypes Can also pass an object that implements the Arrow PyCapsule Protocol for schemas (has an ``__arrow_c_schema__`` method). metadata : dict, default None Keys and values must be coercible to bytes. Examples -------- Create a Schema from iterable of tuples: >>> import pyarrow as pa >>> pa.schema([ ... ('some_int', pa.int32()), ... ('some_string', pa.string()), ... pa.field('some_required_string', pa.string(), nullable=False) ... ]) some_int: int32 some_string: string some_required_string: string not null Create a Schema from iterable of Fields: >>> pa.schema([ ... pa.field('some_int', pa.int32()), ... pa.field('some_string', pa.string()) ... ]) some_int: int32 some_string: string Returns ------- schema : pyarrow.Schema Construct chunked array from list of array-like objects Parameters ---------- arrays : Array, list of Array, or array-like Must all be the same data type. Can be empty only if type also passed. Any Arrow-compatible array that implements the Arrow PyCapsule Protocol (has an ``__arrow_c_array__`` or ``__arrow_c_stream__`` method) can be passed as well. type : DataType or string coercible to DataType Returns ------- ChunkedArray Examples -------- >>> import pyarrow as pa >>> pa.chunked_array([], type=pa.int8()) [ ... ] >>> pa.chunked_array([[2, 2, 4], [4, 5, 100]]) [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] Construct a Table or RecordBatch from Arrow arrays or columns. Parameters ---------- mapping : dict or Mapping A mapping of strings to Arrays or Python lists. schema : Schema, default None If not passed, will be inferred from the Mapping values. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table or RecordBatch Examples -------- Table (works similarly for RecordBatch) >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> pydict = {'n_legs': n_legs, 'animals': animals} Construct a Table from a dictionary of arrays: >>> pa.Table.from_pydict(pydict) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_pydict(pydict).schema n_legs: int64 animals: string Construct a Table from a dictionary of arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_pydict(pydict, metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a dictionary of arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.Table.from_pydict(pydict, schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from a sequence or iterator of Arrow RecordBatches. Parameters ---------- batches : sequence or iterator of RecordBatch Sequence of RecordBatch to be converted, all schemas must be equal. schema : Schema, default None If not passed, will be inferred from the first RecordBatch. Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] >>> batch = pa.record_batch([n_legs, animals], names=names) >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede Construct a Table from a RecordBatch: >>> pa.Table.from_batches([batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a sequence of RecordBatches: >>> pa.Table.from_batches([batch, batch]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from a StructArray. Each field in the StructArray will become a column in the resulting ``Table``. Parameters ---------- struct_array : StructArray or ChunkedArray Array to construct the table from. Returns ------- pyarrow.Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.Table.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Construct a Table from Arrow arrays. Parameters ---------- arrays : list of pyarrow.Array or pyarrow.ChunkedArray Equal-length arrays that should form the table. names : list of str, optional Names for the table columns. If not passed, schema must be passed. schema : Schema, default None Schema for the created table. If not passed, names must be passed. metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- Table Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a Table from arrays: >>> pa.Table.from_arrays([n_legs, animals], names=names) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Construct a Table from arrays with metadata: >>> my_metadata={"n_legs": "Number of legs per animal"} >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata=my_metadata).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct a Table from arrays with pyarrow schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"animals": "Name of the animal species"}) >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] >>> pa.Table.from_arrays([n_legs, animals], ... schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- animals: 'Name of the animal species' Construct a RecordBatch from multiple pyarrow.Arrays Parameters ---------- arrays : list of pyarrow.Array One for each field in RecordBatch names : list of str, optional Names for the batch fields. If not passed, schema must be passed schema : Schema, default None Schema for the created batch. If not passed, names must be passed metadata : dict or Mapping, default None Optional metadata for the schema (if inferred). Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names = ["n_legs", "animals"] Construct a RecordBatch from pyarrow Arrays using names: >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] >>> pa.RecordBatch.from_arrays([n_legs, animals], names=names).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede Construct a RecordBatch from pyarrow Arrays using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).to_pandas() n_legs animals 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede >>> pa.RecordBatch.from_arrays([n_legs, animals], schema=my_schema).schema n_legs: int64 animals: string -- schema metadata -- n_legs: 'Number of legs per animal' Construct ListViewArray from arrays of int32 offsets, sizes, and values. Parameters ---------- offsets : Array (int32 type) sizes : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : ListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.ListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct ListArray from arrays of int32 offsets and values. Parameters ---------- offsets : Array (int32 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_array : ListArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], [ 3, 4 ] ] >>> # nulls in the offsets array become null lists >>> offsets = pa.array([0, None, 2, 4]) >>> pa.ListArray.from_arrays(offsets, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct LargeListViewArray from arrays of int64 offsets and values. Parameters ---------- offsets : Array (int64 type) sizes : Array (int64 type) values : Array (any type) type : DataType, optional If not specified, a default ListType with the values' type is used. pool : MemoryPool, optional mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- list_view_array : LargeListViewArray Examples -------- >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> offsets = pa.array([0, 1, 2]) >>> sizes = pa.array([2, 2, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], [ 2, 3 ], [ 3, 4 ] ] >>> # use a null mask to represent null values >>> mask = pa.array([False, True, False]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values, mask=mask) [ [ 1, 2 ], null, [ 3, 4 ] ] >>> # null values can be defined in either offsets or sizes arrays >>> # WARNING: this will result in a copy of the offsets or sizes arrays >>> offsets = pa.array([0, None, 2]) >>> pa.LargeListViewArray.from_arrays(offsets, sizes, values) [ [ 1, 2 ], null, [ 3, 4 ] ] Construct FixedSizeListArray from array of values and a list length. Parameters ---------- values : Array (any type) list_size : int The fixed length of the lists. type : DataType, optional If not specified, a default ListType with the values' type and `list_size` length is used. mask : Array (boolean type), optional Indicate which values are null (True) or not null (False). Returns ------- FixedSizeListArray Examples -------- Create from a values array and a list size: >>> import pyarrow as pa >>> values = pa.array([1, 2, 3, 4]) >>> arr = pa.FixedSizeListArray.from_arrays(values, 2) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Or create from a values array, list size and matching type: >>> typ = pa.list_(pa.field("values", pa.int64()), 2) >>> arr = pa.FixedSizeListArray.from_arrays(values,type=typ) >>> arr [ [ 1, 2 ], [ 3, 4 ] ] Concatenate the given arrays. The contents of the input arrays are copied into the returned array. Raises ------ ArrowInvalid If not all of the arrays have the same type. Parameters ---------- arrays : iterable of pyarrow.Array Arrays to concatenate, must be identically typed. memory_pool : MemoryPool, default None For memory allocations. If None, the default pool is used. Examples -------- >>> import pyarrow as pa >>> arr1 = pa.array([2, 4, 5, 100]) >>> arr2 = pa.array([2, 4]) >>> pa.concat_arrays([arr1, arr2]) [ 2, 4, 5, 100, 2, 4 ] Concatenate pyarrow.Table objects. If promote_options="none", a zero-copy concatenation will be performed. The schemas of all the Tables must be the same (except the metadata), otherwise an exception will be raised. The result Table will share the metadata with the first table. If promote_options="default", any null type arrays will be casted to the type of other arrays in the column of the same name. If a table is missing a particular field, null values of the appropriate type will be generated to take the place of the missing field. The new schema will share the metadata with the first table. Each field in the new schema will share the metadata with the first table which has the field defined. Note that type promotions may involve additional allocations on the given ``memory_pool``. If promote_options="permissive", the behavior of default plus types will be promoted to the common denominator that fits all the fields. Parameters ---------- tables : iterable of pyarrow.Table objects Pyarrow tables to concatenate into a single Table. memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool. promote_options : str, default none Accepts strings "none", "default" and "permissive". **kwargs : dict, optional Examples -------- >>> import pyarrow as pa >>> t1 = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> t2 = pa.table([ ... pa.array([2, 4]), ... pa.array(["Parrot", "Dog"]) ... ], names=['n_legs', 'animals']) >>> pa.concat_tables([t1,t2]) pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100],[2,4]] animals: [["Flamingo","Horse","Brittle stars","Centipede"],["Parrot","Dog"]] Compute zero-copy slice of this ChunkedArray Parameters ---------- offset : int, default 0 Offset from start of array to slice length : int, default None Length of slice (default is until end of batch starting from offset) Returns ------- sliced : ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.slice(2,2) [ [ 4 ], [ 4 ] ] Compute distinct elements in array Returns ------- pyarrow.Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.unique() [ 2, 4, 5, 100 ] Compute dictionary-encoded representation of array. See :func:`pyarrow.compute.dictionary_encode` for full usage. Parameters ---------- null_encoding : str, default "mask" How to handle null entries. Returns ------- encoded : ChunkedArray A dictionary-encoded version of this array. Examples -------- >>> import pyarrow as pa >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> animals.dictionary_encode() [ ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 0, 1, 2 ], ... -- dictionary: [ "Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede" ] -- indices: [ 3, 4, 5 ] ] Compute counts of unique elements in array. Returns ------- An array of structs Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.value_counts() -- is_valid: all not null -- child 0 type: int64 [ 2, 4, 5, 100 ] -- child 1 type: int64 [ 2, 2, 1, 1 ] CompressedOutputStream.__reduce_cython__Column {} does not exist in schemaCodec.minimum_compression_levelCodec.maximum_compression_levelCodec.default_compression_levelChunkedArray.unify_dictionariesChunkedArray.to_string (line 116)ChunkedArray.fill_null (line 399)ChunkedArray data type was NULL Check if contents of two tables are equal. Parameters ---------- other : pyarrow.Table Table to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> names=["n_legs", "animals"] >>> table = pa.Table.from_arrays([n_legs, animals], names=names) >>> table_0 = pa.Table.from_arrays([]) >>> table_1 = pa.Table.from_arrays([n_legs, animals], ... names=names, ... metadata={"n_legs": "Number of legs per animal"}) >>> table.equals(table) True >>> table.equals(table_0) False >>> table.equals(table_1) True >>> table.equals(table_1, check_metadata=True) False Check if contents of two record batches are equal. Parameters ---------- other : pyarrow.RecordBatch RecordBatch to compare against. check_metadata : bool, default False Whether schema metadata equality should be checked as well. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch_0 = pa.record_batch([]) >>> batch_1 = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"], ... metadata={"n_legs": "Number of legs per animal"}) >>> batch.equals(batch) True >>> batch.equals(batch_0) False >>> batch.equals(batch_1) True >>> batch.equals(batch_1, check_metadata=True) False Cast table values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- Table Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> table.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast table values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> table.cast(target_schema=my_schema) pyarrow.Table n_legs: duration[s] animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Cast record batch values to another schema. Parameters ---------- target_schema : Schema Schema to cast to, the names and order of fields must match. safe : bool, default True Check for overflows or other unsafe conversions. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- RecordBatch Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) >>> batch.schema n_legs: int64 animals: string -- schema metadata -- pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, ... Define new schema and cast batch values: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.duration('s')), ... pa.field('animals', pa.string())] ... ) >>> batch.cast(target_schema=my_schema) pyarrow.RecordBatch n_legs: duration[s] animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Cast array values to another data type See :func:`pyarrow.compute.cast` for usage. Parameters ---------- target_type : DataType, None Type to cast array to. safe : boolean, default True Whether to check for conversion errors such as overflow. options : CastOptions, default None Additional checks pass by CastOptions Returns ------- cast : Array or ChunkedArray Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.type DataType(int64) Change the data type of an array: >>> n_legs_seconds = n_legs.cast(pa.duration('s')) >>> n_legs_seconds.type DurationType(duration[s]) Cannot specify a mask or a size when passing an object that is converted with the __arrow_array__ protocol.Can only instantiate subclasses of ExtensionTypeCalling .data on ChunkedArray is provided for compatibility after Column was removed, simply drop this attribute_CRecordBatchWriter.__setstate_cython___CRecordBatchWriter.__reduce_cython__ Byte width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().byte_width 8 BufferedOutputStream.__setstate_cython__Buffer size must be larger than zero Append column at end of columns. Parameters ---------- field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- Table or RecordBatch New table or record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Append column at the end: >>> year = [2021, 2022, 2019, 2021] >>> table.append_column('year', [year]) pyarrow.Table n_legs: int64 animals: string year: int64 ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] year: [[2021,2022,2019,2021]] Append a field at the end of the schema. In contrast to Python's ``list.append()`` it does return a new object, leaving the original Schema unmodified. Parameters ---------- field : Field Returns ------- schema: Schema New object with appended field. Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Append a field 'extra' at the end of the schema: >>> schema_new = schema.append(pa.field('extra', pa.bool_())) >>> schema_new n_legs: int64 animals: string extra: bool Original schema is unmodified: >>> schema n_legs: int64 animals: string Alias for string(). Examples -------- Create an instance of a string type: >>> import pyarrow as pa >>> pa.utf8() DataType(string) and use the string type to create an array: >>> pa.array(['foo', 'bar', 'baz'], type=pa.utf8()) [ "foo", "bar", "baz" ] Alias for large_string(). Examples -------- Create an instance of large UTF8 variable-length binary type: >>> import pyarrow as pa >>> pa.large_utf8() DataType(large_string) and use the type to create an array: >>> pa.array(['foo', 'bar'] * 50, type=pa.large_utf8()) [ "foo", "bar", ... "foo", "bar" ] Add column to Table at position. A new table is returned with the column added, the original table object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array, list of Array, or values coercible to arrays Column data. Returns ------- Table New table with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> table.add_column(0,"year", [year]) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2021,2022,2019,2021]] n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Original table is left unchanged: >>> table pyarrow.Table n_legs: int64 animals: string ---- n_legs: [[2,4,5,100]] animals: [["Flamingo","Horse","Brittle stars","Centipede"]] Add column to RecordBatch at position i. A new record batch is returned with the column added, the original record batch object is left unchanged. Parameters ---------- i : int Index to place the column at. field_ : str or Field If a string is passed then the type is deduced from the column data. column : Array or value coercible to array Column data. Returns ------- RecordBatch New record batch with the passed column added. Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> batch = pa.RecordBatch.from_pandas(df) Add column: >>> year = [2021, 2022, 2019, 2021] >>> batch.add_column(0,"year", year) pyarrow.RecordBatch year: int64 n_legs: int64 animals: string ---- year: [2021,2022,2019,2021] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Original record batch is left unchanged: >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Add a field at position i to the schema. Parameters ---------- i : int field : Field Returns ------- schema: Schema Examples -------- >>> import pyarrow as pa >>> schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())]) Insert a new field on the second position: >>> schema.insert(1, pa.field('extra', pa.bool_())) n_legs: int64 extra: bool animals: string A grouping of columns in a table on which to perform aggregations. Parameters ---------- table : pyarrow.Table Input table to execute the aggregation on. keys : str or list[str] Name of the grouped columns. use_threads : bool, default True Whether to use multithreading or not. When set to True (the default), no stable ordering of the output is guaranteed. Examples -------- >>> import pyarrow as pa >>> t = pa.table([ ... pa.array(["a", "a", "b", "b", "c"]), ... pa.array([1, 2, 3, 4, 5]), ... ], names=["keys", "values"]) Grouping of columns: >>> pa.TableGroupBy(t,"keys") Perform aggregations: >>> pa.TableGroupBy(t,"keys").aggregate([("values", "sum")]) pyarrow.Table keys: string values_sum: int64 ---- keys: [["a","b","c"]] values_sum: [[3,7,5]] A copy of this field with the replaced type Parameters ---------- new_type : pyarrow.DataType Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field pyarrow.Field Create new field by replacing type of an existing one: >>> field_new = field.with_type(pa.int64()) >>> field_new pyarrow.Field transfer_bandwidth_mib_per_sec's constructor directly, use one of the `__pyx_unpickle__PandasConvertible type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape} type: {0.type} shape: {0.shape}get_batch_with_custom_metadata Write RecordBatch to Buffer as encapsulated IPC message, which does not include a Schema. To reconstruct a RecordBatch from the encapsulated IPC message Buffer returned by this function, a Schema must be passed separately. See Examples. Parameters ---------- memory_pool : MemoryPool, default None Uses default memory pool if not specified Returns ------- serialized : Buffer Examples -------- >>> import pyarrow as pa >>> n_legs = pa.array([2, 2, 4, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Parrot", "Dog", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.RecordBatch.from_arrays([n_legs, animals], ... names=["n_legs", "animals"]) >>> buf = batch.serialize() >>> buf Reconstruct RecordBatch from IPC message Buffer and original Schema >>> pa.ipc.read_record_batch(buf, batch.schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,2,4,4,5,100] animals: ["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"] Unnest this LargeListViewArray by one level. The returned Array is logically a concatenation of all the sub-lists in this Array. Note that this method is different from ``self.values`` in that it takes care of the slicing offset as well as null elements backed by non-empty sub-lists. Parameters ---------- memory_pool : MemoryPool, optional Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> values = [1, 2, 3, 4] >>> offsets = [2, 1, 0] >>> sizes = [2, 2, 2] >>> array = pa.LargeListViewArray.from_arrays(offsets, sizes, values) >>> array [ [ 3, 4 ], [ 2, 3 ], [ 1, 2 ] ] >>> array.flatten() [ 3, 4, 2, 3, 1, 2 ] UnionArray does not have child {} Total number of bytes consumed by the elements of the chunked array. In other words, the sum of bytes from all buffer ranges referenced. Unlike `get_total_buffer_size` this method will account for array offsets. If buffers are shared between arrays then the shared portion will only be counted multiple times. The dictionary of dictionary arrays will always be counted in their entirety even if the array only references a portion of the dictionary. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.nbytes 49 The type code to indicate each data type in this union. Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.type_codes [0, 1] The timestamp time zone, if any, or None. Examples -------- >>> import pyarrow as pa >>> t = pa.timestamp('s', tz='UTC') >>> t.tz 'UTC' The time unit ('s' or 'ms'). Examples -------- >>> import pyarrow as pa >>> t = pa.time32('ms') >>> t.unit 'ms' The mode of the union ("dense" or "sparse"). Examples -------- >>> import pyarrow as pa >>> union = pa.sparse_union([pa.field('a', pa.binary(10)), pa.field('b', pa.string())]) >>> union.mode 'sparse' The field nullability. Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.nullable True >>> f2.nullable False The field name. Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) >>> field.name 'key' The field for list view values. Examples -------- >>> import pyarrow as pa >>> pa.list_view(pa.string()).value_field pyarrow.Field The field for items in the map entries. Examples -------- >>> import pyarrow as pa >>> pa.map_(pa.string(), pa.int32()).item_field pyarrow.Field The dimension (n) of this tensor. Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> x = np.array([[2, 2, 4], [4, 5, 100]], np.int32) >>> tensor = pa.Tensor.from_numpy(x, dim_names=["dim1","dim2"]) >>> tensor.ndim 2 Test if this field is equal to the other Parameters ---------- other : pyarrow.Field check_metadata : bool, default False Whether Field metadata equality should be checked as well. Returns ------- is_equal : bool Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('key', pa.int32()) >>> f2 = pa.field('key', pa.int32(), nullable=False) >>> f1.equals(f2) False >>> f1.equals(f1) True _Tabular.drop_columns (line 2223)Table.schema.__get__ (line 4910)Table.rename_columns (line 5229)Table.nbytes.__get__ (line 4998)Table.combine_chunks (line 4246) Select values from the chunked array. See :func:`pyarrow.compute.filter` for full usage. Parameters ---------- mask : Array or array-like The boolean mask to filter the chunked array with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled. Returns ------- filtered : Array or ChunkedArray An array of the same type, with only the elements selected by the boolean mask. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> mask = pa.array([True, False, None, True, False, True]) >>> n_legs.filter(mask) [ [ 2 ], [ 4, 100 ] ] >>> n_legs.filter(mask, null_selection_behavior="emit_null") [ [ 2, null ], [ 4, 100 ] ] Select rows from the table. The Table can be filtered based on a mask, which will be passed to :func:`pyarrow.compute.filter` to perform the filtering, or it can be filtered through a boolean :class:`.Expression` Parameters ---------- mask : Array or array-like or .Expression The boolean mask or the :class:`.Expression` to filter the table with. null_selection_behavior : str, default "drop" How nulls in the mask should be handled, does nothing if an :class:`.Expression` is used. Returns ------- filtered : Table A table of the same schema, with only the rows selected by applied filtering Examples -------- >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2019, 2021], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) Define an expression and select rows: >>> import pyarrow.compute as pc >>> expr = pc.field("year") <= 2020 >>> table.filter(expr) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2019]] n_legs: [[2,5]] animals: [["Flamingo","Brittle stars"]] Define a mask and select rows: >>> mask=[True, True, False, None] >>> table.filter(mask) pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022]] n_legs: [[2,4]] animals: [["Flamingo","Horse"]] >>> table.filter(mask, null_selection_behavior='emit_null') pyarrow.Table year: int64 n_legs: int64 animals: string ---- year: [[2020,2022,null]] n_legs: [[2,4,null]] animals: [["Flamingo","Horse",null]] Schema.with_metadata (line 3182)Schema.types.__get__ (line 2702)Schema.names.__get__ (line 2674)RunEndEncodedArray.from_arrays Return whether the contents of two chunked arrays are equal. Parameters ---------- other : pyarrow.ChunkedArray Chunked array to compare against. Returns ------- are_equal : bool Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> animals = pa.chunked_array(( ... ["Flamingo", "Parrot", "Dog"], ... ["Horse", "Brittle stars", "Centipede"] ... )) >>> n_legs.equals(n_legs) True >>> n_legs.equals(animals) False Return the underlying array of values which backs the ListArray ignoring the array's offset. If any of the list elements are null, but are backed by a non-empty sub-list, those elements will be included in the output. Compare with :meth:`flatten`, which returns only the non-null values taking into consideration the array's offset. Returns ------- values : Array See Also -------- ListArray.flatten : ... Examples -------- The values include null elements from sub-lists: >>> import pyarrow as pa >>> array = pa.array([[1, 2], None, [3, 4, None, 6]]) >>> array.values [ 1, 2, 3, 4, null, 6 ] If an array is sliced, the slice still uses the same underlying data as the original array, just with an offset. Since values ignores the offset, the values are the same: >>> sliced = array.slice(1, 2) >>> sliced [ null, [ 3, 4, null, 6 ] ] >>> sliced.values [ 1, 2, 3, 4, null, 6 ] Return array of same length as list child values array where each output value is the index of the parent list array slot containing each child value. Examples -------- >>> import pyarrow as pa >>> arr = pa.array([[1, 2, 3], [], None, [4]], ... type=pa.list_(pa.int32())) >>> arr.value_parent_indices() [ 0, 0, 0, 3 ] Render a "pretty-printed" string representation of the ChunkedArray Parameters ---------- indent : int How much to indent right the content of the array, by default ``0``. window : int How many items to preview within each chunk at the begin and end of the chunk when the chunk is bigger than the window. The other elements will be ellipsed. container_window : int How many chunks to preview at the begin and end of the array when the array is bigger than the window. The other elements will be ellipsed. This setting also applies to list columns. skip_new_lines : bool If the array should be rendered as a single line of text or if each element should be on its own line. Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_string(skip_new_lines=True) '[[2,2,4],[4,5,100]]' RecordBatchReader.from_batchesRecordBatchReader._export_to_c_RecordBatchFileWriter.__reduce_cython___RecordBatchFileReader.read_all_RecordBatchFileReader.__reduce_cython____Pyx_EnumMeta.__setstate_cython__ Number of null entries Returns ------- int Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, None, 100]]) >>> n_legs.null_count 1 Null pointer (value before cast = MapArray.from_arrays (line 3107)ListViewType.value_field.__get__ (line 622)LargeListViewArray.sizes.__get__ (line 3009)LargeListViewArray.from_arraysLargeListType.value_type.__get__ (line 588)IpcReadOptions.__reduce_cython__ Flatten this field. If a struct field, individual child fields will be returned with their names prefixed by the parent's name. Returns ------- fields : List[pyarrow.Field] Examples -------- >>> import pyarrow as pa >>> f1 = pa.field('bar', pa.float64(), nullable=False) >>> f2 = pa.field('foo', pa.int32()).with_metadata({"key": "Something important"}) >>> ff = pa.field('ff', pa.struct([f1, f2]), nullable=False) Flatten a struct field: >>> ff pyarrow.Field not null> >>> ff.flatten() [pyarrow.Field, pyarrow.Field] Flatten this Table. Each column with a struct type is flattened into one column per struct field. Other columns are left unchanged. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- Table Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> month = pa.array([4, 6]) >>> table = pa.Table.from_arrays([struct,month], ... names = ["a", "month"]) >>> table pyarrow.Table a: struct child 0, animals: string child 1, n_legs: int64 child 2, year: int64 month: int64 ---- a: [ -- is_valid: all not null -- child 0 type: string ["Parrot",null] -- child 1 type: int64 [2,4] -- child 2 type: int64 [null,2022]] month: [[4,6]] Flatten the columns with struct field: >>> table.flatten() pyarrow.Table a.animals: string a.n_legs: int64 a.year: int64 month: int64 ---- a.animals: [["Parrot",null]] a.n_legs: [[2,4]] a.year: [[null,2022]] month: [[4,6]] Flatten this ChunkedArray into a single non-chunked array. Parameters ---------- memory_pool : MemoryPool, default None For memory allocations, if required, otherwise use default pool Returns ------- result : Array Examples -------- >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs [ [ 2, 2, 4 ], [ 4, 5, 100 ] ] >>> n_legs.combine_chunks() [ 2, 2, 4, 4, 5, 100 ] FixedSizeListArray.from_arraysField "{}" does not exist in schema_ExtensionRegistryNanny.release_registryDictionaryMemo.__reduce_cython__Decimal256Type.precision.__get__ (line 1437)Decimal128Type.precision.__get__ (line 1388) Create instance of signed int8 type. Examples -------- Create an instance of int8 type: >>> import pyarrow as pa >>> pa.int8() DataType(int8) >>> print(pa.int8()) int8 Create an array with int8 type: >>> pa.array([0, 1, 2], type=pa.int8()) [ 0, 1, 2 ] Create instance of fixed shape tensor extension type with shape and optional names of tensor dimensions and indices of the desired logical ordering of dimensions. Parameters ---------- value_type : DataType Data type of individual tensor elements. shape : tuple or list of integers The physical shape of the contained tensors. dim_names : tuple or list of strings, default None Explicit names to tensor dimensions. permutation : tuple or list integers, default None Indices of the desired ordering of the original dimensions. The indices contain a permutation of the values ``[0, 1, .., N-1]`` where N is the number of dimensions. The permutation indicates which dimension of the logical layout corresponds to which dimension of the physical tensor. For more information on this parameter see :ref:`fixed_shape_tensor_extension`. Examples -------- Create an instance of fixed shape tensor extension type: >>> import pyarrow as pa >>> tensor_type = pa.fixed_shape_tensor(pa.int32(), [2, 2]) >>> tensor_type FixedShapeTensorType(extension) Inspect the data type: >>> tensor_type.value_type DataType(int32) >>> tensor_type.shape [2, 2] Create a table with fixed shape tensor extension array: >>> arr = [[1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400]] >>> storage = pa.array(arr, pa.list_(pa.int32(), 4)) >>> tensor = pa.ExtensionArray.from_storage(tensor_type, storage) >>> pa.table([tensor], names=["tensor_array"]) pyarrow.Table tensor_array: extension ---- tensor_array: [[[1,2,3,4],[10,20,30,40],[100,200,300,400]]] Create an instance of fixed shape tensor extension type with names of tensor dimensions: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... dim_names=['C', 'H', 'W']) >>> tensor_type.dim_names ['C', 'H', 'W'] Create an instance of fixed shape tensor extension type with permutation: >>> tensor_type = pa.fixed_shape_tensor(pa.int8(), (2, 2, 3), ... permutation=[0, 2, 1]) >>> tensor_type.permutation [0, 2, 1] Returns ------- type : FixedShapeTensorType Create instance of an interval type representing months, days and nanoseconds between two dates. Examples -------- Create an instance of an month_day_nano_interval type: >>> import pyarrow as pa >>> pa.month_day_nano_interval() DataType(month_day_nano_interval) Create a scalar with month_day_nano_interval type: >>> pa.scalar((1, 15, -30), type=pa.month_day_nano_interval()) Create instance of 64-bit time (time of day) type with unit resolution. Parameters ---------- unit : str One of 'us' [microsecond], or 'ns' [nanosecond]. Returns ------- type : pyarrow.Time64Type Examples -------- >>> import pyarrow as pa >>> pa.time64('us') Time64Type(time64[us]) >>> pa.time64('ns') Time64Type(time64[ns]) Create instance of 64-bit date (milliseconds since UNIX epoch 1970-01-01). Examples -------- Create an instance of 64-bit date type: >>> import pyarrow as pa >>> pa.date64() DataType(date64[ms]) Create a scalar with 64-bit date type: >>> from datetime import datetime >>> pa.scalar(datetime(2012, 1, 1), type=pa.date64()) Create instance of 32-bit time (time of day) type with unit resolution. Parameters ---------- unit : str one of 's' [second], or 'ms' [millisecond] Returns ------- type : pyarrow.Time32Type Examples -------- >>> import pyarrow as pa >>> pa.time32('s') Time32Type(time32[s]) >>> pa.time32('ms') Time32Type(time32[ms]) Create instance of 32-bit date (days since UNIX epoch 1970-01-01). Examples -------- Create an instance of 32-bit date type: >>> import pyarrow as pa >>> pa.date32() DataType(date32[day]) Create a scalar with 32-bit date type: >>> from datetime import date >>> pa.scalar(date(2012, 1, 1), type=pa.date32()) Create half-precision floating point type. Examples -------- Create an instance of float16 type: >>> import pyarrow as pa >>> pa.float16() DataType(halffloat) >>> print(pa.float16()) halffloat Create an array with float16 type: >>> arr = np.array([1.5, np.nan], dtype=np.float16) >>> a = pa.array(arr, type=pa.float16()) >>> a [ 15872, 32256 ] >>> a.to_pylist() [1.5, nan] Create an Array instance whose slots are the given scalar. Parameters ---------- value : Scalar-like object Either a pyarrow.Scalar or any python object coercible to a Scalar. size : int Number of times to repeat the scalar in the output Array. memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. Returns ------- arr : Array Examples -------- >>> import pyarrow as pa >>> pa.repeat(10, 3) [ 10, 10, 10 ] >>> pa.repeat([1, 2], 2) [ [ 1, 2 ], [ 1, 2 ] ] >>> pa.repeat("string", 3) [ "string", "string", "string" ] >>> pa.repeat(pa.scalar({'a': 1, 'b': [1, 2]}), 2) -- is_valid: all not null -- child 0 type: int64 [ 1, 1 ] -- child 1 type: list [ [ 1, 2 ], [ 1, 2 ] ] Convert to a pandas-compatible NumPy array or DataFrame, as appropriate Parameters ---------- memory_pool : MemoryPool, default None Arrow MemoryPool to use for allocations. Uses the default memory pool if not passed. categories : list, default empty List of fields that should be returned as pandas.Categorical. Only applies to table-like data structures. strings_to_categorical : bool, default False Encode string (UTF8) and binary types to pandas.Categorical. zero_copy_only : bool, default False Raise an ArrowException if this function call would require copying the underlying data. integer_object_nulls : bool, default False Cast integers with nulls to objects date_as_object : bool, default True Cast dates to objects. If False, convert to datetime64 dtype with the equivalent time unit (if supported). Note: in pandas version < 2.0, only datetime64[ns] conversion is supported. timestamp_as_object : bool, default False Cast non-nanosecond timestamps (np.datetime64) to objects. This is useful in pandas version 1.x if you have timestamps that don't fit in the normal date range of nanosecond timestamps (1678 CE-2262 CE). Non-nanosecond timestamps are supported in pandas version 2.0. If False, all timestamps are converted to datetime64 dtype. use_threads : bool, default True Whether to parallelize the conversion using multiple threads. deduplicate_objects : bool, default True Do not create multiple copies Python objects when created, to save on memory use. Conversion will be slower. ignore_metadata : bool, default False If True, do not use the 'pandas' metadata to reconstruct the DataFrame index, if present safe : bool, default True For certain data types, a cast is needed in order to store the data in a pandas DataFrame or Series (e.g. timestamps are always stored as nanoseconds in pandas). This option controls whether it is a safe cast or not. split_blocks : bool, default False If True, generate one internal "block" for each column when creating a pandas.DataFrame from a RecordBatch or Table. While this can temporarily reduce memory note that various pandas operations can trigger "consolidation" which may balloon memory use. self_destruct : bool, default False EXPERIMENTAL: If True, attempt to deallocate the originating Arrow memory while converting the Arrow object to pandas. If you use the object after calling to_pandas with this option it will crash your program. Note that you may not see always memory usage improvements. For example, if multiple columns share an underlying allocation, memory can't be freed until all columns are converted. maps_as_pydicts : str, optional, default `None` Valid values are `None`, 'lossy', or 'strict'. The default behavior (`None`), is to convert Arrow Map arrays to Python association lists (list-of-tuples) in the same order as the Arrow Map, as in [(key1, value1), (key2, value2), ...]. If 'lossy' or 'strict', convert Arrow Map arrays to native Python dicts. This can change the ordering of (key, value) pairs, and will deduplicate multiple keys, resulting in a possible loss of data. If 'lossy', this key deduplication results in a warning printed when detected. If 'strict', this instead results in an exception being raised when detected. types_mapper : function, default None A function mapping a pyarrow DataType to a pandas ExtensionDtype. This can be used to override the default pandas type for conversion of built-in pyarrow types or in absence of pandas_metadata in the Table schema. The function receives a pyarrow DataType and is expected to return a pandas ExtensionDtype or ``None`` if the default conversion should be used for that type. If you have a dictionary mapping, you can pass ``dict.get`` as function. coerce_temporal_nanoseconds : bool, default False Only applicable to pandas version >= 2.0. A legacy option to coerce date32, date64, duration, and timestamp time units to nanoseconds when converting to pandas. This is the default behavior in pandas version 1.x. Set this option to True if you'd like to use this coercion when using pandas version >= 2.0 for backwards compatibility (not recommended otherwise). Returns ------- pandas.Series or pandas.DataFrame depending on type of object Examples -------- >>> import pyarrow as pa >>> import pandas as pd Convert a Table to pandas DataFrame: >>> table = pa.table([ ... pa.array([2, 4, 5, 100]), ... pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) ... ], names=['n_legs', 'animals']) >>> table.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(table.to_pandas(), pd.DataFrame) True Convert a RecordBatch to pandas DataFrame: >>> import pyarrow as pa >>> n_legs = pa.array([2, 4, 5, 100]) >>> animals = pa.array(["Flamingo", "Horse", "Brittle stars", "Centipede"]) >>> batch = pa.record_batch([n_legs, animals], ... names=["n_legs", "animals"]) >>> batch pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] >>> batch.to_pandas() n_legs animals 0 2 Flamingo 1 4 Horse 2 5 Brittle stars 3 100 Centipede >>> isinstance(batch.to_pandas(), pd.DataFrame) True Convert a Chunked Array to pandas Series: >>> import pyarrow as pa >>> n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) >>> n_legs.to_pandas() 0 2 1 2 2 4 3 4 4 5 5 100 dtype: int64 >>> isinstance(n_legs.to_pandas(), pd.Series) True Convert pandas.DataFrame to an Arrow RecordBatch Parameters ---------- df : pandas.DataFrame schema : pyarrow.Schema, optional The expected schema of the RecordBatch. This can be used to indicate the type of columns if we cannot infer it automatically. If passed, the output will have exactly this schema. Columns specified in the schema that are not found in the DataFrame columns or its index will raise an error. Additional columns or index levels in the DataFrame which are not specified in the schema will be ignored. preserve_index : bool, optional Whether to store the index as an additional column in the resulting ``RecordBatch``. The default of None will store the index as a column, except for RangeIndex which is stored as metadata only. Use ``preserve_index=True`` to force it to be stored as a column. nthreads : int, default None If greater than 1, convert columns to Arrow in parallel using indicated number of threads. By default, this follows :func:`pyarrow.cpu_count` (may use up to system CPU count threads). columns : list, optional List of column to be converted. If None, use all columns. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022], ... 'month': [3, 5, 7, 9], ... 'day': [1, 5, 9, 13], ... 'n_legs': [2, 4, 5, 100], ... 'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]}) Convert pandas DataFrame to RecordBatch: >>> import pyarrow as pa >>> pa.RecordBatch.from_pandas(df) pyarrow.RecordBatch year: int64 month: int64 day: int64 n_legs: int64 animals: string ---- year: [2020,2022,2021,2022] month: [3,5,7,9] day: [1,5,9,13] n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch using schema: >>> my_schema = pa.schema([ ... pa.field('n_legs', pa.int64()), ... pa.field('animals', pa.string())], ... metadata={"n_legs": "Number of legs per animal"}) >>> pa.RecordBatch.from_pandas(df, schema=my_schema) pyarrow.RecordBatch n_legs: int64 animals: string ---- n_legs: [2,4,5,100] animals: ["Flamingo","Horse","Brittle stars","Centipede"] Convert pandas DataFrame to RecordBatch specifying columns: >>> pa.RecordBatch.from_pandas(df, columns=["n_legs"]) pyarrow.RecordBatch n_legs: int64 ---- n_legs: [2,4,5,100] Convert numpy tensors (ndarrays) to a fixed shape tensor extension array. The first dimension of ndarray will become the length of the fixed shape tensor array. If input array data is not contiguous a copy will be made. Parameters ---------- obj : numpy.ndarray Examples -------- >>> import pyarrow as pa >>> import numpy as np >>> arr = np.array( ... [[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]], ... dtype=np.float32) >>> pa.FixedShapeTensorArray.from_numpy_ndarray(arr) [ [ 1, 2, 3, 4, 5, 6 ], [ 1, 2, 3, 4, 5, 6 ] ] Convert NumPy dtype to pyarrow.DataType. Parameters ---------- dtype : the numpy dtype to convert Examples -------- Create a pyarrow DataType from NumPy dtype: >>> import pyarrow as pa >>> import numpy as np >>> pa.from_numpy_dtype(np.dtype('float16')) DataType(halffloat) >>> pa.from_numpy_dtype('U') DataType(string) >>> pa.from_numpy_dtype(bool) DataType(bool) >>> pa.from_numpy_dtype(np.str_) DataType(string) Construct a RecordBatch from a StructArray. Each field in the StructArray will become a column in the resulting ``RecordBatch``. Parameters ---------- struct_array : StructArray Array to construct the record batch from. Returns ------- pyarrow.RecordBatch Examples -------- >>> import pyarrow as pa >>> struct = pa.array([{'n_legs': 2, 'animals': 'Parrot'}, ... {'year': 2022, 'n_legs': 4}]) >>> pa.RecordBatch.from_struct_array(struct).to_pandas() animals n_legs year 0 Parrot 2 NaN 1 None 4 2022.0 Construct MapArray from arrays of int32 offsets and key, item arrays. Parameters ---------- offsets : array-like or sequence (int32 type) keys : array-like or sequence (any type) items : array-like or sequence (any type) type : DataType, optional If not specified, a default MapArray with the keys' and items' type is used. pool : MemoryPool Returns ------- map_array : MapArray Examples -------- First, let's understand the structure of our dataset when viewed in a rectangular data model. The total of 5 respondents answered the question "How much did you like the movie x?". The value -1 in the integer array means that the value is missing. The boolean array represents the null bitmask corresponding to the missing values in the integer array. >>> import pyarrow as pa >>> movies_rectangular = np.ma.masked_array([ ... [10, -1, -1], ... [8, 4, 5], ... [-1, 10, 3], ... [-1, -1, -1], ... [-1, -1, -1] ... ], ... [ ... [False, True, True], ... [False, False, False], ... [True, False, False], ... [True, True, True], ... [True, True, True], ... ]) To represent the same data with the MapArray and from_arrays, the data is formed like this: >>> offsets = [ ... 0, # -- row 1 start ... 1, # -- row 2 start ... 4, # -- row 3 start ... 6, # -- row 4 start ... 6, # -- row 5 start ... 6, # -- row 5 end ... ] >>> movies = [ ... "Dark Knight", # ---------------------------------- row 1 ... "Dark Knight", "Meet the Parents", "Superman", # -- row 2 ... "Meet the Parents", "Superman", # ----------------- row 3 ... ] >>> likings = [ ... 10, # -------- row 1 ... 8, 4, 5, # --- row 2 ... 10, 3 # ------ row 3 ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 [] 4 [] dtype: object If the data in the empty rows needs to be marked as missing, it's possible to do so by modifying the offsets argument, so that we specify `None` as the starting positions of the rows we want marked as missing. The end row offset still has to refer to the existing value from keys (and values): >>> offsets = [ ... 0, # ----- row 1 start ... 1, # ----- row 2 start ... 4, # ----- row 3 start ... None, # -- row 4 start ... None, # -- row 5 start ... 6, # ----- row 5 end ... ] >>> pa.MapArray.from_arrays(offsets, movies, likings).to_pandas() 0 [(Dark Knight, 10)] 1 [(Dark Knight, 8), (Meet the Parents, 4), (Sup... 2 [(Meet the Parents, 10), (Superman, 3)] 3 None 4 None dtype: object Compare contents of this array against another one. Return a string containing the result of diffing this array (on the left side) against the other array (on the right side). Parameters ---------- other : Array The other array to compare this array with. Returns ------- diff : str A human-readable printout of the differences. Examples -------- >>> import pyarrow as pa >>> left = pa.array(["one", "two", "three"]) >>> right = pa.array(["two", None, "two-and-a-half", "three"]) >>> print(left.diff(right)) # doctest: +SKIP @@ -0, +0 @@ -"one" @@ -2, +1 @@ +null +"two-and-a-half" ChunkedArray.to_numpy (line 477)ChunkedArray.is_valid (line 368)ChunkedArray.dictionary_encode_CRecordBatchWriter.write_table_CRecordBatchWriter.write_batchBufferReader.__setstate_cython__ Bit width for fixed width type. Examples -------- >>> import pyarrow as pa >>> pa.int64() DataType(int64) >>> pa.int64().bit_width 64 Add metadata as dict of string keys and values to Field Parameters ---------- metadata : dict Keys and values must be string-like / coercible to bytes Returns ------- field : pyarrow.Field Examples -------- >>> import pyarrow as pa >>> field = pa.field('key', pa.int32()) Create new field by adding metadata to existing one: >>> field_new = field.with_metadata({"key": "Something important"}) >>> field_new pyarrow.Field >>> field_new.metadata {b'key': b'Something important'} _unregister_py_extension_typespyarrow.interchange.dataframepyarrow.Message(uninitialized)child is deprecated, use fieldTensor.shape.__get__ (line 247)_Tabular.itercolumns (line 1989)_Tabular.from_pylist (line 1925)_Tabular.from_pydict (line 1858)_Tabular._ensure_integer_indexTable.replace_schema_metadataTable.remove_column (line 5136)StringBuilder.__reduce_cython__Schema._import_from_c_capsuleRecordBatch.num_columns.__get__ (line 2511)RecordBatch.from_struct_array_RecordBatchStreamWriter._open_RecordBatchStreamReader._openRecordBatchReader.from_stream_RecordBatchFileReader.__enter__PyExtensionType.set_auto_load_PandasAPIShim.__reduce_cython__Only stream=None is supported.MessageReader.__reduce_cython__ListViewType.value_type.__get__ (line 635)LargeStringArray.from_buffersLargeListViewType.value_field.__get__ (line 670)Incompatible storage type for FixedSizeListType.value_field.__get__ (line 805)FixedShapeTensorType.__reduce__Field.with_nullable (line 2438)Field.with_metadata (line 2311)DictionaryScalar._reconstructChunkedArray.num_chunks.__get__ (line 1219)ChunkedArray.null_count.__get__ (line 207)ChunkedArray.is_null (line 311)ChunkedArray.flatten (line 639)ChunkedArray.__arrow_c_stream__only valid on writable filesonly valid on seekable filesonly valid on readable files_make_shape_or_strides_bufferfixed_shape_tensor (line 5091)default_memory_pool (line 124)benchmark_PandasObjectIsNullType_INTERVAL_MONTH_DAY_NANOTensor.size.__get__ (line 231)Tensor.ndim.__get__ (line 215)Table.from_batches (line 4699)StructArray._flattened_fieldSchema.get_all_field_indicesSchema.from_pandas (line 2826)Schema.empty_table (line 2759)RecordBatch.select (line 3076)RecordBatch.filter (line 2976)RecordBatch.equals (line 3028)RecordBatch.__arrow_c_stream___RecordBatchFileReader.__exit____Pyx_EnumMeta.__reduce_cython__PythonFile.__setstate_cython___PandasAPIShim.is_categoricalNativeFile.__setstate_cython__MemoryPool.__setstate_cython__Invalid time unit for time64: Invalid time unit for time32: Indices must be integer typeI/O operation on closed fileFixedSizeBinaryType.__reduce__Field.name.__get__ (line 2278)Field "{}" exists {} times in schemaField._import_from_c_capsuleExtensionScalar.from_storageExpected a pointer value, got Expected a non-empty ndarrayDictionaryArray.from_buffersChunkedArray.unique (line 756)ChunkedArray.is_nan (line 344)ChunkedArray.filter (line 897)ChunkedArray.equals (line 435)ChunkedArray.chunk (line 1237)BufferReader.__reduce_cython__BaseExtensionType.wrap_arrayArray._import_from_c_capsule__pyx_unpickle___Pyx_EnumMeta__pyx_unpickle__PandasAPIShim_py_extension_type_auto_load_handle_arrow_array_protocolcreate_memory_map (line 1076)coerce_temporal_nanoseconds_Tabular.to_pylist (line 2161)_Tabular.to_pydict (line 2135)_Tabular.drop_null (line 1795)Table.get_total_buffer_sizeTable.from_pandas (line 4462)Table.from_arrays (line 4542)StringBuilder.append_valuesStopToken.__setstate_cython__SparseCSRMatrix.from_tensorSparseCSFTensor.from_tensorSparseCSCMatrix.from_tensorSparseCOOTensor.from_tensorRecordBatch.to_struct_arrayRecordBatch.slice (line 2919)RecordBatch._is_initializedRecordBatch.__arrow_c_array___RecordBatchFileWriter._open_RecordBatchFileReader._open_ReadPandasMixin.read_pandasPlease implement {0}.__reduce___PandasConvertible.to_pandas_PandasAPIShim.is_datetimetz_PandasAPIShim.is_data_frame_PandasAPIShim.is_array_likeOffset must be non-negativeNativeFile._assert_writableNativeFile._assert_seekableNativeFile._assert_readableMust pass decompressed_sizeMask must not contain nullsLength must be non-negativeExtensionArray.from_storageExpected int index, got type 'DictionaryArray.from_arraysChunkedArray.take (line 1008)ChunkedArray.slice (line 839)ChunkedArray.length (line 96)ChunkedArray.index (line 961)ChunkedArray.combine_chunksBufferedOutputStream.detachBufferOutputStream.getvalueBatch number {0} out of rangeBaseListArray.value_lengthsArray.get_total_buffer_sizeArray._import_from_c_devicesupports_compression_level_register_py_extension_typemax_ideal_request_size_mibfrom_numpy_dtype (line 5356)Table.to_batches (line 4772)Table.set_column (line 5170)Table.add_column (line 5062)StructType.get_field_indexSparseCSRMatrix.from_scipySparseCSRMatrix.from_numpySparseCSFTensor.from_numpySparseCSCMatrix.from_scipySparseCSCMatrix.from_numpySparseCOOTensor.from_scipySparseCOOTensor.from_numpySchema.serialize (line 3218)RunEndEncodedType.__reduce__RecordBatch.rename_columnsRecordBatch.cast (line 3130)RecordBatch._import_from_cRecordBatchReader.read_allPythonFile.__reduce_cython___PandasAPIShim.pandas_dtypeOperation on closed writerOperation on closed readerNativeFile.__reduce_cython__MonthDayNanoIntervalScalarMemoryPool.bytes_allocatedMemoryPool.__reduce_cython__Mask must be boolean dtypeLargeListViewType.__reduce__LargeListViewArray.flattenLargeListArray.from_arraysFixedSizeListType.__reduce__Failed to allocate {0} bytesExpected list or tuple, got {}, {}ChunkedArray.cast (line 542)_CRecordBatchWriter.__enter__BufferedInputStream.detachunregister_extension_typetime_to_first_byte_millissupported_memory_backends, please pass it explicitlynum_replaced_dictionariesminimum_compression_levelmaximum_compression_levellarge_list_view (line 4697)default_compression_levelUnionType.field (line 1110)Tensor.from_numpy (line 61)_Tabular.sort_by (line 2047)_Tabular.__setstate_cython__Table.to_reader (line 4842)Table.join_asof (line 5446)StructType.field (line 947)StructScalar._as_py_tupleStopToken.__reduce_cython__SparseCSRMatrix.to_tensorSparseCSFTensor.to_tensorSparseCSCMatrix.to_tensorSparseCOOTensor.to_tensorSignalStopHandler.__enter__Scalar type not supportedScalar data type was NULLRunEndEncodedScalar.as_pyRecordBatch.remove_column_RecordBatchWithMetadataRecordBatchReader.__enter___PandasAPIShim.infer_dtypeNot an ArrowSchema objectMonthDayNanoIntervalArrayMessage.__setstate_cython__MessageReader.open_streamMemoryPool.release_unusedListViewArray.from_arraysKeyValueMetadata.__reduce__Invalid value of whence: {0}Field.with_type (line 2371)Field.with_name (line 2406)DictionaryScalar.__reduce__DataType.__arrow_c_schema__ChunkedArray.value_countsCacheOptions._reconstruct_CRecordBatchWriter.__exit__Array._export_to_c_devicetranscoding_input_stream_reconstruct_record_batchpyarrow.vendored.versionis_extension_array_dtypeget_rangeindex_attributedownload..bg_writeTensor.dim_name (line 145)Tensor.__setstate_cython___Tabular.column (line 1683)Table.unify_dictionariesTable.group_by (line 5288)StringViewBuilder.finishStringViewBuilder.appendStringArray.from_buffersSparseCSRMatrix was NULLSparseCSRMatrix.to_scipySparseCSRMatrix.to_numpySparseCSRMatrix.dim_nameSparseCSFTensor was NULLSparseCSFTensor.to_numpySparseCSFTensor.dim_nameSparseCSCMatrix was NULLSparseCSCMatrix.to_scipySparseCSCMatrix.to_numpySparseCSCMatrix.dim_nameSparseCOOTensor was NULLSparseCOOTensor.to_scipySparseCOOTensor.to_numpySparseCOOTensor.dim_nameSignalStopHandler.__exit__RecordBatch._export_to_cRecordBatchReader.__exit__PyExtensionType.__reduce___PandasAPIShim.get_values_PandasAPIShim.data_frameOSFile.__setstate_cython__KeyValueMetadata.to_dictKeyValueMetadata.get_allDataType.to_pandas_dtypeDataType.equals (line 338)Chunk index out of range._CRecordBatchWriter.write_CRecordBatchWriter.closeArrowNotImplementedErrorArray data type was NULLreplace_schema_metadataregister_extension_typepyarrow/pandas-shim.pxioutput_stream (line 2714)null_selection_behaviormonth_day_nano_interval.from_*` functions instead.download..cleanupconcat_tables (line 5885)concat_arrays (line 4420)chunked_array (line 1411)arrow.py_extension_typeWrapping scalar of type Tensor.to_numpy (line 96)_Tabular.field (line 1829)_Tabular._is_initialized_Tabular.__reduce_cython__Table.from_struct_arrayTable.flatten (line 4181)StructArray.from_arraysSchema.remove (line 3101)Schema.insert (line 3063)Schema.equals (line 2787)Schema.append (line 3024)Schema.__arrow_c_schema__RecordBatch.from_pandasRecordBatch.from_arraysRecordBatchWithMetadata_RecordBatchStreamWriter_RecordBatchStreamReaderRecordBatchReader.close_PandasAPIShim.is_sparse_PandasAPIShim.is_series_PandasAPIShim.is_ge_v21PYARROW_IGNORE_TIMEZONENativeFile._assert_openMessage.__reduce_cython__MemoryMappedFile.resizeMemoryMappedFile.filenoMemoryMappedFile.createKeyValueMetadata.valuesKeyValueMetadata.equalsInvalid promote options: Field.flatten (line 2477)Expected sparse.COO, got {}DictionaryType.__reduce__DictionaryEncodeOptionsDecimal256Type.__reduce__Decimal128Type.__reduce__DataType expected, got {!r}DataType._import_from_cCodec.__setstate_cython__ChunkedArray.iterchunksChunkedArray._to_pandasArrowSerializationErrorArray.dictionary_encodewritable file expectedupload..bg_writestrings_to_categoricalrecord_batch (line 5559)readable file expectednum_dictionary_batcheslog_memory_allocationslarge_string (line 4449)large_binary (line 4421)input_stream (line 2628)fixed_size_binary_typeenable_signal_handlersemit_dictionary_deltas__arrow_ext_scalar_class__UnionArray.from_sparseType_FIXED_SIZE_BINARYTimestampType.__reduce__Tensor.equals (line 115)Tensor.__reduce_cython___Tabular.take (line 2097)Table.select (line 4067)Table.filter (line 3995)Table.equals (line 4351)Table.__arrow_c_stream__TableGroupBy.aggregateSparseCSRMatrix.equalsSparseCSFTensor.equalsSparseCSCMatrix.equalsSparseCOOTensor.equalsSchema.remove_metadataSchema.get_field_indexSchema.field (line 2872)ScalarAggregateOptionsResizableBuffer.resizeRecordBatch.set_columnRecordBatch.add_columnRecordBatch._to_pandasRecordBatchReader.cast_PandasAPIShim.is_index_PandasAPIShim.is_ge_v3OSFile.__reduce_cython__Not a metadata version: NativeFile.read_bufferMemoryMappedFile._openLargeListType.__reduce__KeyValueMetadata.valueKeyValueMetadata.itemsInvalid union mode {0!r}FixedShapeTensorScalarField.equals (line 2216)Field.__arrow_c_schema__ExtensionType.__reduce___ExtensionRegistryNannyDictionaryScalar.as_pyDecimal256Scalar.as_pyDecimal128Scalar.as_pyCompressedOutputStreamChunkedArray.to_stringChunkedArray.to_pylistChunkedArray.fill_nullChunkedArray.drop_nullBinaryScalar.as_buffer{0.__class__.__name__}({1}){0.__class__.__name__}({0})total_allocated_bytesstring_view (line 4521)set_memcopy_thresholdset_memcopy_blocksize__pyx_unpickle__Tabularpyarrow.pandas_compatpyarrow/benchmark.pxinum_dictionary_deltas_ndarray_to_arrow_typejemalloc_set_decay_ms__init__..genexpr_import_from_c_capsuleget_total_buffer_sizeget_record_batch_sizeget_all_field_indicesbinary_view (line 4506)__arrow_ext_deserialize__UnionArray.from_denseTimestampScalar.as_py_Tabular.remove_column_Tabular.append_columnTable.to_struct_arrayTable.slice (line 3930)Table._is_initializedSchema._import_from_cRecordBatch.to_tensorRecordBatch.serialize_RecordBatchFileWriter_RecordBatchFileReaderNativeFile.writelinesNativeFile.get_streamMockOutputStream.sizeMemoryPool.max_memoryMask must be 1D arrayListViewType.__reduce__ListViewArray.flattenListArray.from_arraysKeyValueMetadata.keysHalfFloatScalar.as_pyFixedSizeBufferWriterFixedSizeBinaryScalarFixedShapeTensorArrayField.remove_metadataExtensionScalar.as_pyDataType._export_to_cCompressedInputStreamCodec.__reduce_cython__ChunkedArray was NULLChunkedArray.validateChunkedArray.to_numpyChunkedArray.is_validChunkedArray.__sizeof__ChunkedArray.__reduce__CacheOptions.__reduce__BaseListArray.flattenArrowCancelled.__init__Array.__dlpack_device__Array.__arrow_c_array__value_parent_indicesuse_pandas_sentinelsshow_schema_metadataset_timezone_db_pathmimalloc_memory_poolmemory_map (line 1034)large_utf8 (line 4479)large_list (line 4602)jemalloc_memory_poolitems..genexprinteger_object_nulls_import_from_c_devicehave_signal_refcyclehas_canonical_formatfrom_network_metricsfind_physical_offsetfind_physical_lengthensure_native_endian_ensure_integer_indexdictionary (line 4810)decimal128 (line 4233)UnsupportedOperationUnknown enum value: '%s'UnknownExtensionTypeType_RUN_END_ENCODEDType_LARGE_LIST_VIEWType_FIXED_SIZE_LISTTransformInputStream_Tabular.drop_columnsTable.rename_columnsTable.join (line 5330)Table.combine_chunksTable.cast (line 4399)StringBuilder.finishStringBuilder.appendSchema.with_metadataSchema.set (line 3132)Schema.field_by_nameRecordBatch.validateRecordBatch.__sizeof__RecordBatch.__reduce__PythonFile.readlines_PandasAPIShim.seriesNon-fixed width typeNativeFile.readlinesMessage.serialize_toMapArray.from_arraysKeyValueMetadata.keyFixedSizeBinaryArrayFixedShapeTensorTypeField._import_from_cDurationScalar.as_pyChunkedArray.is_nullChunkedArray.flattenChunkedArray.__array__BufferedOutputStreamArray._import_from_c to requested schema timestamp (line 3891)timestamp_as_objectshow_field_metadataset_memcopy_threadsset_io_thread_countpyarrow/builder.pxilogging_memory_poollist_view (line 4659)list_parent_indicesindex out of boundsget_datetimetz_typedetected_simd_leveldefault_memory_pooldeduplicate_objectsdataframe_to_arrays compression_level=batch_with_metadata__arrow_ext_serialize__allow_none_for_type_Tabular.itercolumns_Tabular.from_pylist_Tabular.from_pydict_Tabular.__dataframe__Table.remove_columnTableGroupBy.__init__StructType.__reduce__StructScalar.__iter__StructArray.flattenSchema.add_metadataSchema._export_to_cRunEndEncodedScalarRecordBatch._column__Pyx_FlagBase.__repr____Pyx_EnumBase.__repr__PythonFile.truncatePythonFile.readline_PandasAPIShim.is_v1NotImplementedErrorNo type alias for {0}NativeFile.writableNativeFile.truncateNativeFile.seekableNativeFile.readlineNativeFile.readintoNativeFile.readableNativeFile.metadataNativeFile.downloadLargeListViewScalarInvalid file mode: {0}FixedSizeListScalarFixedSizeBinaryTypeField.with_nullableField.with_metadataExpected Schema, got {}End of Arrow streamDEFAULT_BUFFER_SIZEChunkedArray.uniqueChunkedArray.lengthChunkedArray.is_nanChunkedArray.formatChunkedArray.filterChunkedArray.equalsChunkedArray.__iter__BufferedInputStreamBooleanScalar.as_pyArrowKeyError.__str__Array.diff (line 928)unify_dictionaries' to pointer addresstable_to_dataframesystem_memory_poolpyarrow/tensor.pxipyarrow/scalar.pxipyarrow/memory.pxipyarrow/config.pxipyarrow/compat.pxinum_record_batchesincrementalencoderincrementaldecoder_get_pandas_tz_typefrom_pydata_sparsefrom_numpy_ndarrayfixed_shape_tensor_export_to_c_deviceduration (line 4036)_detect_compression_default_chunk_sizedataframe_to_typescline_in_tracebackasyncio.coroutinesUnionType.__reduce__UInt64Scalar.as_pyUInt32Scalar.as_pyUInt16Scalar.as_pyTime64Scalar.as_pyTime32Scalar.as_py_Tabular.add_columnTable.from_batchesStructScalar.itemsStructScalar.as_pyStringScalar.as_pySchema.from_pandasSchema.empty_tableRunEndEncodedArrayRecordBatch.selectRecordBatch.filterRecordBatch.equals__Pyx_FlagBase.__str____Pyx_FlagBase.__new____Pyx_EnumBase.__str____Pyx_EnumBase.__new__NativeFile.readallNativeFile.read_atNativeFile.__enter__Less than one byteLargeListViewArrayInvalid merge mode: FloatingPointArrayFixedSizeListArrayField._export_to_cExpected Array, got DoubleScalar.as_pyDate64Scalar.as_pyDate32Scalar.as_pyColumn {!r} not foundCodec.is_availableChunkedArray.sliceChunkedArray.indexChunkedArray.chunk_CRecordBatchWriterBuffer.__reduce_ex__BufferOutputStreamBinaryScalar.as_pyArrowCapacityErrorArray.value_countsArray.from_buffersArray._export_to_cArray._debug_printuse_legacy_formattruncate_metadatatotal_buffer_size to requested type to_pandas_dtype_reconstruct_tableread_record_batchread_next_messagepyarrow/types.pxipyarrow/table.pxipyarrow/error.pxipyarrow/array.pxiproxy_memory_pool_perform_join_asoflist_value_lengthfrom_struct_arrayfloat64 (line 4206)float32 (line 4179)float16 (line 4149)extension_columnsdictionary_encodedictionary_decodedecompressed_size_datetime_from_intcreate_memory_mapcompression_levelc_tensor_ext_typebytes_allocatedUnionScalar.as_pyUInt8Scalar.as_pyType_SPARSE_UNIONType_LARGE_STRINGType_LARGE_BINARYTranscoder.__init__Transcoder.__call__Tensor.from_numpy_Tabular.to_string_Tabular.to_pylist_Tabular.to_pydict_Tabular.drop_nullTable.from_pandasTable.from_arraysStructType.__iter__StructArray.fieldStringViewBuilderSignalStopHandlerRunEndEncodedTypeRecordBatch.sliceRecordBatchReader_PandasConvertibleNativeFile.uploadNativeFile.isattyNativeFile.filenoNativeFile.__exit__Message.serializeLoggingMemoryPoolListType.__reduce__LargeStringScalarLargeListViewTypeLargeBinaryScalarInvalid time unit: Int64Scalar.as_pyInt32Scalar.as_pyInt16Scalar.as_pyFloatScalar.as_pyFixedSizeListTypeDataType.__reduce__ChunkedArray.takeChunkedArray.sortChunkedArray.castBuffer.to_pybytesBaseExtensionTypeArray.from_pandasuint64 (line 3764)uint32 (line 3710)uint16 (line 3656)tzinfo_to_stringtop_level_indentto_pydata_sparseto_numpy_ndarraytime64 (line 3993)time32 (line 3950)struct (line 4869)string_to_tzinfostring (line 4319)schema (line 5286)schema_as_stringscalar (line 1145)requested_schemarelease_registryrange_size_limitmetadata_version_logical_offset_logical_lengthis_pandas_objectis_integer_valueis_boolean_valueget_record_batch_gdb_test_sessionfrom_numpy_dtypefrom_dense_numpyencode_file_pathdate64 (line 4128)date32 (line 4107)cpp_version_infoconverted_arrayscontainer_windowcompiler_versioncategorical_typec_extension_namec_check_metadatabinary (line 4369)UnionType.__iter__UnionMode_SPARSEUnionArray.fieldUnionArray.childType_STRING_VIEWType_DENSE_UNIONType_BINARY_VIEWTable.to_batchesTable.set_columnTable.add_columnTable._to_pandasStructType.fieldStructArray.sortStringViewScalarSchema.to_stringSchema.serializeRecordBatch.cast_PyArrowDataFrameNullScalar.as_pyNativeFile.writeNativeFile.read1NativeFile.flushNativeFile.closeMockOutputStreamMemoryMappedFileMapType.__reduce__MapScalar.__iter__ListScalar.as_pyLargeStringArrayLargeBinaryArrayKeyValueMetadataInt8Scalar.as_pyExpected integerDictionaryScalarDecimal256ScalarDecimal128ScalarCodec.decompressBinaryViewScalarArrowMemoryErrorArray._to_pandasuint8 (line 3602)to_struct_array_to_pandas_dtypetable_to_blockstable (line 5723)__setstate_cython__set_memory_poolrun_end_encodedrepeat (line 438)remove_metadataread_next_batch__pyx_PickleErrorpyarrow/lib.pyxpyarrow/ipc.pxipyarrow.computepyarrow.Field<{0}>promote_options_normalize_slicemetadata_lengthmax_output_sizemaps_as_pydictsmake_datetimetzlist_ (line 4536)large_list_viewio_thread_countint64 (line 3791)int32 (line 3737)int16 (line 3683)included_fieldsignore_metadatahole_size_limitgot null buffergit_descriptionget_tensor_size_get_pandas_typeget_field_indexfull_so_version_flattened_fieldfile_descriptorfield (line 3465)ensure_metadatadictionary_memocustom_metadatacollections.abcc_shrink_to_fitc_max_chunksizec_chunked_array bytes_allocated=bool_ (line 3579)_assert_writable_assert_seekable_assert_readable__arrow_ext_class__allocate_bufferUnionType.fieldUnionMode_DENSEType_LARGE_LISTType_HALF_FLOATType_DICTIONARYType_DECIMAL256Type_DECIMAL128TimestampScalarTensor was NULLTensor.to_numpyTensor.dim_name_Tabular.sort_by_Tabular._column_Tabular.__array__Table.to_readerTable.join_asofStringViewArraySparseUnionTypeSparseCSRMatrixSparseCSFTensorSparseCSCMatrixSparseCOOTensorSchema.__sizeof__Schema.__reduce__Scalar was NULLScalar.validateScalar.__reduce__ResizableBuffer_ReadPandasMixinPyExtensionTypeProxyMemoryPoolNativeFile.tellNativeFile.sizeNativeFile.seekNativeFile.readMetadataVersionMapScalar.as_pyLargeListScalarIpcWriteOptionsHalfFloatScalarField.with_typeField.with_nameExtensionScalarDictionaryArrayDecimal256ArrayDecimal128ArrayDatetimeTZDtypeDataType.equalsBinaryViewArrayArrowIndexErrorArray.to_stringArray.to_pylistArray.fill_nullArray.drop_nullzero_copy_onlyutf8 (line 4344)type_for_aliastransform_functimedelta64[us]timedelta64[ns]timedelta64[ms]table_or_batchsum_duplicates_stringify_pathstream_or_pathstream_capsuleskip_new_linesschema_capsule_run_end_typerun_end_encoderequested_typerename_columnsrelease_unused_registry_nannypyarrow/io.pxipreserve_indexprefetch_limitout_schema_ptrnulls (line 388)null (line 3557)map_ (line 4735)logical_offsetlogical_length_is_initializedis_float_valueis_categoricalint8 (line 3629)group_by_aggrsforeign_bufferextension_typeextension_namedate_as_objectcurrent_threadcsparse_tensorcpp_build_infocontextmanagercompiler_flagscombine_chunkscheck_metadatac_storage_typec_result_tablec_record_batchc_concatenatedbackend_name__arrow_c_stream____arrow_c_schema__array (line 121)%Y-%m-%dT%H:%M:%S%zType_TIMESTAMPType_LIST_VIEWTimestampArray_Tabular.columnTable.validateTable.group_byTable.__sizeof__Table.__reduce__RuntimeWarningNotImplementedMessage.equalsListViewScalarLargeListArrayIpcReadOptionsHalfFloatArrayField.__reduce__ExtensionDtypeExtensionArrayDurationScalarDictionaryTypeDictionaryMemoDenseUnionTypeDecimal256TypeDecimal128TypeDataType.fieldCould not cast Codec.compressBufferedIOBaseAttributeErrorAssertionErrorArrowTypeErrorArrowExceptionArrowCancelledArray was NULLArray.validateArray.to_numpyArray.is_validArray.__sizeof__Array.__reduce__Array.__dlpack__writer_threadwith_nullablewith_metadatavalue_offsetsvalue_lengths_use_threadsunify_schemastimedelta64[s]target_schemashrink_to_fitshow_metadataset_cpu_countset_auto_loadself_destruct_restore_arrayremove_column__reduce_cython__pyarrow.aceropandas_compatoutput_streamoutput_lengthoutput_buffernull_encodingmaybe_py_listmax_chunksizemake_tz_awareis_datetimetzis_data_frameis_array_like__init_subclass___import_from_cfrom_pylistfrom_pydict_from_arraysfooter_offsetfield_indicesfield_by_name__dlpack_device__dest_encodingdatetime64[us]datetime64[ns]datetime64[ms]concat_tablesconcat_arrayscolumn_arrayscoalesce_keys__class_getitem__chunked_arraycall_functionc_permutationc_from_pandasc_file_offsetc_field_namesc_buffer_size__arrow_c_array__array_capsulearg_dict_memoappend_valuesappend_columnType_DURATIONTimestampTypeTensor.equals_Tabular.fieldTable.flattenTable._columnStringBuilderStopIterationSchema.removeSchema.insertSchema.equalsSchema.appendSchema._fieldSchema.__iter__Scalar.equals_PandasAPIShimOSFile.filenoMessageReaderListViewArrayLargeListTypeIntervalDtypeFutureWarningField.flattenExtensionTypeDurationArrayBuffer.equalsBooleanScalarBaseListArrayBaseExceptionArrowKeyErrorArray.is_nullArray.buffersArray.__array__write_tensorversion_info_value_typevalue_countsuse_setstatetypes_mappertimestamp[us]timestamp[ns]timestamp[ms]struct_arraystorage_typestaticmethodsrc_encodingsplit_blockssparse_unionsort_indicesserialize_toscipy.sparseruntime_inforun_end_typeright_suffixrequirementsrecord_batchread_message__pyx_checksumpyarrow.utilpreview_cols_perform_joinpandas_dtypepackage_kindordered_dictnum_messagesmillisecondsmicroseconds_member_names_max_memorylist_flattenlarge_stringlarge_binary_is_primitive_is_path_likeis_mutable_is_coroutineis_availableinput_stream_initializing_init_signalshave_libhdfsfrom_storagefrom_buffersfrom_batches_filter_tableencoded_pathdrop_columns_dictionarydictionary_datetime64[s]cpy_ext_typecoerce_to_nschild_fieldscasted_batchcasted_arrayc_type_codesc_schema_ptrc_child_datac_axis_order backend_name=aggregationsadd_metadata_WriteStatsUInt64ScalarUInt32ScalarUInt16ScalarTime64ScalarTime32Scalar_Tabular.takeTable.selectTable.filterTable.equalsTableGroupByStructScalarStringScalarSchema.fieldScalar.as_pyRuntimeError__Pyx_FlagBase__Pyx_EnumBasePickleBufferNumericArrayMonthDayNanoListViewTypeIntegerArrayField.equalsDurationTypeDoubleScalarDate64ScalarDate32ScalarCodec.detectChunkedArrayCacheOptionsBuffer.sliceBufferReaderBooleanArrayBinaryScalarArrowInvalidArrowIOErrorArray.uniqueArray.tolistArray.is_nanArray.formatArray.filterArray.equalsArray.__iter__write_tablewrite_queuewrite_batchvalue_fielduse_threadstotal_bytesto_pandastimestamp[s]target_typestruct_typestring_viewsource_pathright_table_reconstructread_tensorread_schemaread_pandasread_bufferpyarrow.libpermutationpandas_typeoutput_typeoutput_sizeout_indicesother_tableother_batchopen_streamnum_threadsnum_columnsnum_buffersnull_to_nannull_bitmapnan_is_nullnan_as_null__mro_entries__memory_poolmain_thread_list_sizeleft_suffixkeys_sorteditercolumns is_writable= is_seekable= is_readable=inner_batchinner_arrayinfer_dtypehave_pandasfunc_nohashfrom_tensorfrom_streamfrom_sparse_from_pylist_from_pydictfrom_pandasfrom_arraysfile_offsetfield_namesfield_index_export_to_censure_typeempty_table_empty_arrayduration[us]duration[ns]duration[ms]dense_union_debug_printcpp_versioncompressioncompiler_idcollectionscloudpicklec_type_namec_rz_bufferc_dim_namesbuffer_sizebody_lengthbinary_view_assert_open_as_py_tuple__arrow_array__allow_64bit_WeakrefableVersionInfoUserWarningUnionScalarUInt8ScalarUInt64ArrayUInt32ArrayUInt16ArrayType_UINT64Type_UINT32Type_UINT16Type_TIME64Type_TIME32Type_STRUCTType_STRINGType_DOUBLEType_DATE64Type_DATE32Type_BINARYTime64ArrayTime32ArrayTable.sliceSystemErrorStructArrayStringArraySparseDtypeSortOptionsScalar.castRuntimeInfoRecordBatch_ReadStatsPickleErrorPeriodDtypeOrderedDictNullOptionsMemoryErrorMaskedArrayInt64ScalarInt32ScalarInt16ScalarImportErrorFloatScalarDoubleArrayDo not call Date64ArrayDate32ArrayCategoricalBufferErrorBinaryArrayArray.sliceArray.indexwritelineswrap_arrayvalue_typetype_codesto_pybytesto_batchesthis_tablethis_batchstartswithstacklevelsp_storageso_versionsimd_levelset_columnserialized_run_endsright_keysresult_obj__pyx_vtable____pyx_resultput_nowaitpermissive_pandas_apiout_schemaout_indptrout_coordsother_typenum_fieldsnum_chunksnum_arraysnull_countnew_schemanamedtuplememory_map max_memory=left outerlarge_utf8large_listiterchunksitem_field is_mutable=infer_typeindex_typegroup_byget_valuesget_streamfrom_scipyfrom_numpyfrom_densefrom_codes__from_arrow__fill_valueextensionsext_scalarduration[s]dlm_tensordictionarydest_codecdecompressdecimal256decimal128date32[day]data_framecsr_matrixcsc_matrixcoo_matrixcontextlibcategoriesc_type_ptrc_timezonec_nullablec_metadatac_datatypebytes_readbyte_widthbuild_typeaxis_orderastimezonearrow_typearray_dataallow_noneallow_copyadd_columnWriteStatsValueErrorUnionArrayUInt8ArrayType_UINT8Type_INT64Type_INT32Type_INT16Type_FLOATTranscoderTime64TypeTime32TypeTextIOBaseTable.joinTable.dropTable.castStructTypeSchema.setQueueEmptyPythonFileNullScalarNativeFileMemoryPoolListScalarInt8ScalarInt64ArrayInt32ArrayInt16ArrayIndexErrorFloatArrayExpressionBuffer.hexArray.viewArray.takeArray.sortArray.diffArray.cast3.0.0.dev0writeablewr_handlewith_typewith_name__version__unit_codetype_nametracebacktoleranceto_tensorto_stringto_readerto_pylistto_pydict_to_pandastimestamptimedeltatime64[us]time64[ns]time32[ms]thresholdthreadingtemp_memosrc_codecsp_tensorsp_scalarsort_keysserializerow_majorresizablerequestedree_array__reduce_ex__readlinesrd_handle__pyx_state_pydecimalpyarrow.{} {}pyarrow.py_bufferprecisionowned_bufout_arrayother_arr_offsetsnew_field__metaclass__list_viewlist_typelist_sizelarge_strkey_fieldjoin_typejoin_asofitem_typeisenabledis_sparseis_seriesis_ge_v21int_index_indicesin_streamhalffloatgetsizeofgetsignalget_batchfrombytesflattenedfill_nullext_arrayexc_valueenumerate_encoderdrop_nulldim_names_decoderdate64[ms]__dataframe__cpu_countc_schemasc_orderedc_optionsc_indicesc_buffersc_batchesblocksizebit_widthascendingas_bufferarrow_objalignmentaggregateaggr_nameaddressUnionTypeType_LISTType_INT8Type_BOOLTypeErrorTimestampTimedeltaStopTokenReadStatsNullArrayMapScalarListArrayLZ4_FRAMEInt8ArrayDataFrameBuildInfoArray.sumwritablewarnings_valuesvalidatetruncateto_scipyto_numpytimezonetime32[s]this_arrt_reader_structstrftime__setstate____set_name__seekablerun_endsright_onright_byregisterree_typereadlinereadintoreadableread_allr_extptr__qualname____pyx_typepy_fieldprotocolposition own_file=out_dataobject_num_rowsnullablenthreadsnew_typenew_sizenbatchesmodulemetadatakey_typeis_validis_indexis_ge_v3is_cpuis_alive_group_bygetvalue__getstate___getframeext_type: expected exc_typeexc_infoendswithdurationdownloaddim_namedecay_msdatetimecpp_filecompresscombined_columnclosedchildrenc_tensorc_tablesc_streamc_schemac_scalarc_resultc_readerc_offsetc_nbytesc_fieldsc_bufferc_arraysbufferedbg_writebackendsbBhHiIqQType_MAPReceived '_MetadataMapArrayListTypeKeyErrorEnumTypeEnumBaseEOFErrorDataTypewrappedversiontype_idtobytesto_dicttimeout_tablestridesstoragesortingsort_by_sizessecondsschemasresultsrequirereplacereadallread_atpyarrowpy_listpromotepresent__prepare__parentsout_ptrout_buforderedoptionsoffsetsnewcolsndarraymissingmessage__members__mappinglexsort_itemsis_nullindicesindex_get_allgenexprfloat64float32float16flattenfield__fieldexc_valenvironentriesencoderdisabledefaultdecoderdecimalctensorcomputecolumnscleanupchunkedcapsulec_tablec_shapec_namesc_fieldc_batchc_arraybuffersbuf_lenbooleanbatchesbackendasbytesasarray__array__argsort address=VersionType_NA_TabularSIG_IGNSIG_DFLSIGTERMMessageMappingMapTypeLZ4_RAWIntFlagIntEnumIOErrorDecimalwriterwindowwhencevstackvaluesuploadupdateuniqueuint64uint32uint16tzinfotype__typetype tosorttolisttime64time32tensortargettablesstructstringstrictstreamstablesparsesourcesnappyskipna__sizeof__ size=signumsignalseriesselectschemascalarresultresizerepeatremove__reduce__readerpylistpydictpy_bufpiecespickleparentpandasoutput_openoffsetobjectnomasknbytes name=_name__name__n_rows__module__lookuplengthkwargs_keysisattyis_nan is_cpu=invertinsertindptrindentin_ptr__import__handlegit_idformatfinishfilterfilenofieldsexc_tberrorsequalsencodeenabledouble__dlpack___dictdevicedetectdetachdecodedate64date32createcoordscompatcolumncodecs closed=chunkscastedc_typec_sizec_sinkc_poolc_pathc_modec_metac_maskc_infoc_datac_addrbufobjbufferboolbinaryatexitastypearraysarangeappendThreadTensorSeriesSchemaScalarSNAPPYSIGINTOSFileIOBase. Detail: BufferBROTLIwritevalueutf-8upperunionuint8typestitlethrowtablesuperstrstatestartstack__slots__slicesleepsizesshapescipyscale<%s.%s: %d>read1ravelrangequeueqsizepyobjpybufpatchpac_pacotherordernumpynullsnamesminormajorlz4lowerlossyloadslineslevelitemsisdiris_v1int64int32int16indexidentflushfloatflagsfinalfield__enter__emptydumpsdtypedensedeltacpoolcountcodescodercodecclose__class__chunkchildc_rawc_ptrc_bufc_arrbz2batchas_pyarrayaliasacero?TableQueueIndexFieldFalseEmptyCodecArray2.1.02.0.01.0.0zstdwarnviewutf8unittypetime__test__telltakestopstep__spec__sortsizesinksendselfseeksarrsafe__repr__readrbprodpoolpc_pcpathpackopennullnonendimnamemodemmapmetamask__main__list_linelazykindkeysjsonjoin__iter__itemint8__init__hinthash_gzipfuncfull__exit__enumdropdonediff__dict__destdaysdatedatacopycast__call__bool_bodybaseaxisargs__arg0__aggr, ...{}: {}----ZSTDTrueNone_NULLLockGZIP.zstzipvalutctypsyssum__str__sigset%s.%srowretresrawr+br+pos__pacoutoptobj__new__nanmap_.lz4libkeyidxhex, got getend__doc__dctcolclscat.bz2bufarrapianyabc: '.')=><_LZ4COOBZ2{0} {1}wbusu8u4u2u1tztytbsprerb+pd__pcosonntnsnpnfmsmaioidi8i4i2i1.gzgcf8f4f2dtdfbybsadab*.V5V4V3V2V1NAxwvsrqonkihfedcbaTQMKIHCB @Resize capacity must be positive (requested: Resize cannot downsize (requested: , current length: ReadNext with custom metadatabasic_string::appendvector::_M_realloc_insertBinaryView or StringView elements cannot reference strings larger than 2GBvector::_M_default_appendSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEN5arrow8internal20ArrayBuilderExtraOpsINS_17BaseBinaryBuilderINS_10BinaryTypeEEESt17basic_string_viewIcSt11char_traitsIcEEEEN5arrow4util18EqualityComparableINS_6ScalarEEESt23enable_shared_from_thisIN5arrow6ScalarEEFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_EFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEN5arrow4util18EqualityComparableINS_7compute15FunctionOptionsEEESt19_Sp_make_shared_tagSt14default_deleteIN5arrow6BufferEESt14default_deleteIN5arrow4util5CodecEESt14default_deleteIN5arrow15ResizableBufferEESt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EESt18bad_variant_accessN5arrow5ArrayEN5arrow15DictionaryArrayEN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEEN5arrow13BinaryBuilderEN5arrow13StringBuilderEN5arrow17StringViewBuilderEN5arrow6ScalarEN5arrow10NullScalarEN5arrow8internal19PrimitiveScalarBaseEN5arrow16DictionaryScalarEN5arrow15ExtensionScalarEN5arrow2io12OutputStreamEN5arrow4util12CodecOptionsEN5arrow7compute15FunctionOptionsEN5arrow7compute11CastOptionsEPFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEPFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_ESt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EESt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EESt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE??{Gz?@@h㈵>pA @@ Constructed with a non-error sta[],?;P lP|Pe`f gMPgtg6gpii(jtjjjkkl4ldlllxi|m{m*mmѳnnc|qĖUB77:̦&;E;W; i;`{;0;;h=:>5QGQ YQl lQ ~Q Qt Q Q$ Q Q RR)@CCȂhD߂G H XH$HIMIń J܄dJJ KNlKKLLL6MM\Mm`NPVWW'HX=YTDYkYYZTZLJZއp[\ \#p]I]a ^y`^^_h_G_w ````a׉TaabPb7bObDccNJcDdd5deDe}eeŋDf݋ff Dg%g=gU@hhh%DiqiƍiLjpjŎjݎDk2kl\l>lV mndmm nnΐnDoo*oBHprpp&PqTqzrPrr’rHss4sK0tbdww x\x͓xyRTyxyyhqx{XTh'XK4_T,{8g`p8.nTe|}<D8Kd?pP'dy`X6$x8S $@<8hilSp<4mp>PDPlrlP|P Q Q4QHQ\Q,pQ8t>Lt>t>t,?t?t?uL@4ulAhuAu,BuBu,CuCu,DvDv,E,vE@v,FTvFhv,G|vGv,HvHv,IvIvJwJ>?@ FL|K|LLL\M,O OD\StlB,F KSHXx\aLj|p4|td|x<l$Tl,,Dt\ܹܻ0`l, P@pl ,lX l|\8d#\/38"<@#lBH#Fx#M#LP#V $ \<$_l$i$m$ s$lx,%}\%́%%܎%<&̐$&ܙT&&&&ܯ'̱D'P> >>l>\, ? DL@0PW<^8_ds$L|<||/\MOlaL\c||~ |dL|l\dll4 \ <@L&)+L\< WZ,L^\\^p,`|puXv|~ < Lt  t8\ zRx $FJ w?;*3$"D\p| xtp l h[ ,${AC BEDY H J Th,| {AC BED[ F J p|,$AC BED H J ,T8AC BEDn C J ,AC BEDn C J ,kAC BEDF K J ,DkAC BEDF K J ,(kAC BEDF K J ,XĬkAC BEDF K J ,kAC BEDF K J ,DkAC BEDF K J ,kAC BEDF K J ,ĭ[AC BEDu L J ,H{AC BED[ F J xDP#TC E D`\X(dpAC FED\ S(Z<@r,P{AC BED[ F J  DAI Ar A (40,($ 0DXl   4H\pܰذ$8L` t   ( < P d x         , @ T (h $| 0 , 8 4 @ < H D P0 LD XX Tl P L H T P \ X d10 vAC BEDC F J F 0@ ܱvAC BED@ I J F 0t (vAC BED@ I J F 0 tvAC BED@ I J F 0 vAC BED@ I J F 0 AC BEDS F J F D hAC Ei F 0h vAC BED@ I J F 0 0vAC BED@ I J F 0 |vAC BED@ I J F 0ȴvAC BED@ I J F 08vAC BED@ I J F 0l`vAC BED@ I J F 0vAC BED@ I J F 0vAC BED@ I J F DP0\XAC r J PqAF ] D t2$$AU _ K B F ܷ ط Է з ̷ ȷ (ķ < P d x         , @ T h | | x t p l h d ` 0\ DX XT lP L H D @ < 8 4  0  , 4( H$ \ p         $ 8 $L6AC E_ H E  t/AC Cg  VqC N F M P\h$OAC DED}  L,4X!AC DJED A 4dXAC BG| G k E a G 8йAC BEFI F X H H F s1AC BDf  7QC b @4QC _ <`6P&dzPLRxb ,$AAC BZ F   XAE BGH ()AC FED}  AF E` D (<AC BEGH (h>AC BEEKf 0qAC DH L W I $4lQC ED C (|AC Ax C h H $мtYC [ I s (D(AC Al O h H (p|AC Al O h H (нAC Al O h H $AC Q @AC O ;QC Ae <0P<3AC Ah ,p̋AC BIEEH  _"AX B 4$5AC E $SAC DEE <c4PAF FK F Q F K ,_AC DEDy F N $AC BDX F ({RS E} I( KAT R F N F D  DXTQC Ak H hAC BD ږNJ  A,_)AC DHEK $XAC BH 1(,˙AC BEEF} $X3]bAJ FKb l88HĿFJC FDC F P HH ^ B ,FAr BPIIHE $AHq u  DVDdAJ CU (hDCAC FEH+ $EAC FK $mAC A D d H(A] I LF DAAJ Cr <p Pldhxdp l h 0dAC AD G J F h H 4AG BDN D l D y G (4AF BEDX F `cAC EY (hAC Cx I g A (AC Al O h H @6JC HD FH  tQY I @ laY I  ` 8AC Cp  8AC Cp  8AC Cp $ 0AU ] E z F  !tAAJ Cr  ,!AAJ Cr  P!AAJ Cr  t!AAJ Cr <!$ JC BEF DH !$!MAC Ar I L $"MAC Ar I L $<"dAC C] D (d"HAC As H h H $">AC AU F ] ,"dAC BKm B Y G ""#$#8#L#`#t####4#AC BEGD J [ E L # 1aF I $@1aF I $<$`[AC Az A Z  d$YAC EW H $YAC EW H 0$ H EB G H ` $AqF I (%,cAC BEH| A ,%ps@%sT%Hsh%s|% s%s%s%ds%s%mAC g E e K $,>mAC g E e K 8T>H>AC HH H Q G U K (>L}AC A F h H $>\AJ Ed L W $>\AJ Ed L W $ ? \AJ Ed L W 4?H @AC _ E T?h @AC _ E t? HAC b J $? }AC z B e K $?!}AC z B e K ?h!AC V <@h!aC Ea FP S N F D@!X@"XAC r J x@"XAC r J @#XAC r J @T#AC E{ D <@$AC FEDR K } K ; E A#@\Z I AC K K (y`AAC K K ((zCAC K K ,TzFAG FF G ,z8IAC Ms D ,zLAC HH K ,zOkAC C G P H {PRYC P D a 8{Q]Y] f 0X{DQ@AC G B Y G q G 0{PR@AC G B Y G q G 0{\S@AC G B Y G q G ,{hTAG HD F ,$|HXQAC MD K (T|x[{AC K B ,|]AG FED G ,||akAC C G P H (|b{AC M E }d\AJ Q D { ,0}LdkAC C G P H ,`}ekAC C G P H ,}fAC I C V J }AC HH H ,X@AC HH H ,$HBAC HH H ,T8DAC HH H ,(FAC HH H ,HAC HH H ,JAC FJK) H ,` AC BP I ,DcAG FF A 0|(h AC HH E 4|CXz  0X|k^ AC HH E |X?  ,$(orAC HHK A ,Txq AG HD C ,hv AG HD C ,X{iAC HHw E $AC u G { E 8 AC I D X H _ I ,H AG HD F DxKAN BEED L  J  H  F OKE x A (9VtAC DGD` ( AO CX E g I 8KAS p A (X5WAC DEJ ,AC M I ,(AC M I , AC HD K (AC FH G ,@|,AC M H ,p|AC M G ,ܮ0AF HH E ,Жܱ0AF HH E ,ܴPAF FJ E (0AC K F ,\AC DGHx A ( AC K F ,AC FF B ,AC M  B ,AC M  B ,H$AC M  B (x AC K B ,AC M  B ,ԘAC M  B ,AC M  B ,4AC DGH D ,dhAC DGH D ,AC DGH D ,ęHAC DGH D ,AC FF F ,$xJAC FEH G ,TAC M H ,x AC HH F ,AC FEKF H ,8!KAC FF F ,X&AC FEKF H (D>"AC I H ,pDAC M H ,HAG DGD H ,ЛLMAC HHC I , RAC DGH G ,0UAC Mw H ,`AC K F ,xBAC M J ,ĥ8FAF FED G ,MAC FEH A ,$S:AC FEH  E ,TWAC FEH E ,H`AC M] B ,fAC M D ,k" AC FF E (tAC KO B ,@LwAC FFM C (p~AC K F ( AC K E ,ȧ|AC M E (LAC K F ,$(AC M I ,TAG I F (DAC K E ,YAC M| C ܗ!AC Ey F 1AC E~ I ,(AC HH F Xԛ\AJ Q D {  |\AJ Q D {  LdAJ Y D { (ĩAC K F  =AC EU B ((AC K F (TxAG I D (AC M F ,AC HH D (ܪAC K F d `=AC EU B (@|1AC K B l =AC EU B ,AC HH H ,ԫ(AC HH F (xaC Gn G ,0,AC FEH C ,` AC DEH H (AC K G ,sAC I E (AC K F (4DAC FD G (DXAC K D ,pAC HH G \X(ȭTAC Gn G (HaC Gn G ( AC DJ F Lp`l(thAC Gn G (\aC Gn G (̮AC DJ F  ( |AC Gn G (LpaC Gn G (x$AC DJ F (̯AC Gn G (aC Gn G ($8AC DJ F Pd(xAC Gn G (aC Gn G (аLAC DJ F ($AC K F (P_AC FD H ,|PAC HH G (ԱAC K B ,SAC FJ I 0D(XsAC K B ,(OAC DN F (HAC K F (AC FH E ( AC K F 8L(`|AC K F ,AC FJ F (8AC DJ F $,8 AC HH G h | (9AC M , 8AC M C ,AC M) F (AC M J $H$$AC I D ,p,TAC M A $\$oAJ U H [ E $ȵ$oAJ U H [ E $$oAJ U H [ E $4%oAJ U H [ E @|%dAJ U H {  d%dAJ U H { $&wAJ Z K [ E (l&AC Ed C  A (ܶ'AC Ed C  A $'oAJ U H [ E $0'tAJ S B S $X4(oAJ U H [ E $|(oAJ U H [ E $(oAJ U H [ E ,з )AC FEH A 0-oGAC M G qH4 02oAC M G +H 0<7AC I E pG 0d<AC HH H ȢG 0>AC HH H ZGp 0D4A1TAC HH F xGH 0G ,AC DM E УF 0J1AC HH F (F ,P AC Me J UU0UgAC HH H ؤEQ  pEAW BH ,WAC HHF F ,Ļ[AG FF D (XcIAC K A , |eAC I F ,PiAC I F (lIAC K A ,nRAG HD F ,ܼ}h AC M} A , P AC HK E (<AC K H (hAC K H ,HcAC HD E ,ĽAC HD K ,l AC HD K ,$(rAC I F ,TxAC HHB J ,hAC HD E ,<AC HH G ,\AC HH D ,AC P F $DAS K~ C lpAC E I $LAC E E $AC I D H\?LC FH FH P BBBA I ,P(@L*AC M C $lPAM BDy C $GAC I E =4 AC BHc G Y G g A , AC MO H 48TAC I E  D ,p AC P} G ,AF DH D ,AC M H ,AF FED I ,0AC M B ,`lAC M B ,AC M B ,AC M B ,AC M B , AC M B ,P<$AC M B ,'AC M B \+?AC @ D ,x,-AC HJ# G ,x2AC HJ C ,47AC M E ,d?& AC M E KAH Ev D (LlAC K G ,NAC FEH I ,HSAC FEH I ,DWzAC M D ,t\t AC M I ,Hi( AC HH7 E HrDr,@rUAC I D ,,pwUAC I D (\|lAC I I ,BAC HHy C ,AC FEH A ,AC I K (DfAC FD E (DAC FD K (pAC FD H ,AC FJ H 0XiAC HK G :- ($(}AC BEHE H ,P|JAC HH K , AC FJ D ,AC HH I $\AC z J { E $AC z J { E $0lAC z J { E $XAC z J { E $|AC z J { E $AC z J { E $AC z J { E $AC z J { E $ AC z J { E $H$AC z J { E $pAC z J { E $4AC z J { E $AC z J { E $DAC z J { E $AC z J { E $8TAC z J { E $`AC z J { E $dAC z J { E $AC z J { E $tAC z J { E $AC z J { E $(AC z J { E $P AC z J { E $xAC z J { E $AC z J { E $AC z J { E $,AC z J { E $AC z J { E $@<AC z J { E $hAC z J { E $LAC z J { E $AC z J { E $\AC z J { E $AC z J { E $0lAC z J { E $XAC z J { E $|AC z J { E $AC z J { E ,"AC FEH F , AC M E 0,D(,X$AJ HD B ,AC FJ E (DaC Gn G ,AC FJ E (xaC Gn G ,@,AC FJ E (paC Gn G ,`AC FJ E ( aC Gn G , AC FJ E ((aC Gn G ,TAC FJ E (HaC Gn G $AC CN K ,AC FJ C $ ,0*AC HH D ,`*AC HH D ,*AC HH D ,AC FEH H ,"xAC M, C , )AC HH E 0Pl,3AC BEEEH D ,x2sAC BGF K ,7AC FEH G ,h; AC BJJ F ,8EAG HD J ,DHXAG HD F ,tNXAG HD F ,8SXAG HD F (hXAC FH H (<\AC K8 I ,,0aAC HH I (\hZAC FS C N B $j,j AG FF D ,s AG FF D ,`| AG FF D ,,@AC I F  A ,\AC I F  A ,AC I F  A ,hAJ DO H ,AC FEH E ,0AC FEH E ,LAC Ij A |`AC Eb E , AC HH G ,|.AJ DHK F ,|AC BEL F ,0AC FEH^ K ,`<AE FJ D ,)/AC FM/ ,AC G A  G ,lAC G A  G , AC FEH, E ,HCAC FEH, E ,xHAC FEH, E ,dNAC FEH, E ,SAC FEH, E ,DY AC M G ,8fAF HD G ,hnAC I F ,rAC I F ,duAC I F ,xAC I F ,(| AC DEF G ,Xć AC DEF G ( AC I H ,AF BIK H ,hAF BIK H 4$AC Eo H H H G I E K ,L! AC Is H ,| AC P D , jAC HH H ,`AF BNp I ( aC Gn G ,8tAG FED F ,hAJ HD G ,AC FF F ,h AJ BQ G ,h AJ BQ G 4($AC Eo H H H G I E K ,` AJ DH G ,/ AJ HK F ,;N AJ HHo F ,F= AJ HDB G , SAJ FF J ,P<[F0AG DH A ,\' AG HD E ,\AJ DO C (AC M| C , ?AC P A ,<AC DGH G (laC Gn G ,4lAC FJ I ,tAC HH A ,AC HH A ,(TAF FJ I ,X AC FEHt E (aC Gn G ,XAG HD C ,HAC FJ D (($,< AG FF C ,l" AJ I E ,( AC FJ J (`-aC Gn G ,.( AC FI F (8AC E} J ,L8AG HD C ,|=AC DEJ D ,0E*AC HH D (0GAC DF{ G ,K*AC HH D (8MAC FD K ,d(Q*AC HH D ((SAC EN I O A (TAC EN I O A (UAC EN I O A ,VAC DL K ,H]*AC HH D ,x_ AC I D ,ti*AC HH D ,tkAC FF B ,dmAC FF B $8ToAC El K O $`oAC El K O $$pAC El K O $pAC El K O $pAC El K O $\qAC El K O $(qAC El K O  P,r/AC Eb E (t8s;AC FD C ,Lw*AC HH D (Ly;AC FD C ,`}*AC HH D (,`;AC FD C ,Xt*AC HH D (t;AC FD C ,*AC HH D (;AC FD C ,*AC HH D (@AC FDg K ,l*AC HH D (AC BJG I ,ԛAC Iu F <AC Ef A O A Z F O A  A ,8dAF HDd I ,hAC HH F ,DAC HH F ,AC FJG E ,4AC HH3 I (($AC K E ,TAG HK F ,HAC BID I (AC G G ,AC BGF. G , AG HD J ,@ AC I E ,p AC M G ,(AC HH D , + AG FF D ,3AG HD A 0COAC FEH E A ,JWAC HK E ,hAC FEH G ,tkAC FEH H ,nAC FEH H ,HpAC HJ B ,xDuAC FED C ,ԄAF DO I ,tr AC M D ĥ!NJK C H,Х.AC BJq G 0PAC HDS E Q$8AC A K E $~AC A} N m 0hb AC DZ I >x L> (`lTAC Aj A O I uAC b 08AC BQ D lX>ݬ  ȹBAH O H a  (BAH O H a  L BAH O H a  pLBAH O H a  xBAH O H a  BAH O H a  кBAH O H a  BAH O H a  $(BAH O H a  HTBAH O H a  lBAH O H a  BAH O H a  ػBAH O H a  BAH O H a  0BAH O H a  \BAH O H a  DBAH O H a  hBAH O H a  BAH O H a  BAH O H a  8BAE O K a 0dͩAC FEH^ K #; 0AC FEH^ K :u $4t|YAC E F \:E 0x53AC FEHO J t:o' 0.AC HK K :' (p}AC BEHE H ,AC M3 D 04ϨAJ DHf C :x 0AC HH E :Ԩ 04 AC HH E hp: 0~AC HH E /:` 0d DAC HD G 9Dj 0<$1 NAN Pv K p922 0/ AG FF D 9o ,`,9AG I( G 0A7AC FEH= D P9 R 0tTE6AF BGEH C s9? 8L|#AC M# D  H *9 0,RۧAG HH F `8  $dYtAJ S B S 0 YAJ FD  G  J $TH\oAJ U H [ E $|\oAJ U H [ E 00\AG FEH E d8ͦ 40`AC FJ D @ H ,4aAC E} J ` H X ,d(bAC EK D ` H X ,bAC EK D ` H X $cAN CP N $cAN CP N ,dAG EG D ` H X ,DdAG EG D ` H X ,tdAG EG D ` H X ,heAG EG D ` H X ,eAG EG D ` H X 0HfAF DEF C 5 ,\nAG EG D ` H X , oAG EG D ` H X ,oAG EG D ` H X ,pAN Ci E G I ,pAN Ci E G I $L`qAN Cy M $t(rAN Cy M 4rAC E] J p H q O H 0sAC C^ K d L  A 0tAC C^ K d L  A 0<puAC C^ K d L  A 0pLvAC C^ K d L  A 0(wAC C^ K d L  A 0xAC C^ K d L  A 0 xAC C^ K d L  A 0@yAC C^ K d L  A 0tzAC C^ K d L  A ,t{AC C^ K _ A 0D|,AC C D [ M g 0 @},AC C D [ M g 4@<~-AC C} L M K p H 4x4-AC C} L M K p H 4,-AC C} L M K p H 4$-AC C} L M K p H 4 -AC C} L M K p H 4X-AC C} L M K p H 4 -AC C} L M K p H $AC H` D 0|AC FF* F /$ 0$EAG P^ J /3О ,AC CC F Z F ̧(ا]AC CN K |   :AC O E a 0(  AC FEH; F .5% 0  AC FM H L. 4ؽ$AC Ct E M K g A 0оAC As H N J h H $ B t*& (@AC I B *ڙ (w™AC I A M* 00AC DL H d*i 0 MAC BJ K )) 0 AC FEH K ) 08` ͘AG FF C lQ) ,AC K  D )0 (ǘAC I D c) 00AC M H d')r (VAC I D (9 04 !AC M H ( (,AC I D X{(ȗ 0x`AC M A ?( 0 tAC M J 'T ((@"78AC G B T' (t4$wAC G| I ' ,h%CΖAC FH E ['0 0d(AC DL H H3'y ,l,C]AC FH E &0H 0/,AC HD J && ,BCAC FH E H&0 0l E͕AC DL H y& ,DICAC FH E =&0x ,@L\AC K K H&; ,l|MCAC FH E %0 ,xPAC K H %͔ 0RAC FEH J Hy% ,lTqAC K H =%P 0V4AC FEH J $ ,XAC K H H$ӓ 0l,ZAC FEH J $ (\PwAC GX E I$^ 0]FAG BJ A D $0P 0h@r4AF FF K #,I , -AG P H #tW 04 ;AC P J H#LF 0l*AC HE A #U 0ğAC HE A #U 0ؒAC HE A P#U˒ 0tTAC HE A #U (uAC G A #i ,PWQAC K F H#UR @l\6AC FED J P.w... ~#rj 0NAC FEH E #De 0,L IAC DHN B `#V` 0DAC HH E #% 0 AC HH E B# 04tΑAC HH E h# 0lAC FF G "t ( XAC G B "@ 00(AC FJR J 0dt#AC HH G " 0L#ҐAC HH G ! 0$AJ DH" G H!, 0lsAC HHm G n!T ,48AC M B 2! 0 AG BJP A L  0 0p h AG DIN7 F   0 >ȏAC HH F !! 0 JAC HKc F T  . 0x &uAC HHk A  &p 0 ,TAC HHe G  r 6 (( /AC G K T :  (t 0AC G K   Ȏ 0 82AC HKS F ? 0 :AG HD F L u 0p 8LYAC HHm G t: $ OAC E D  @ $ x 0)LY"xAC HKs F ) #x 0)SiwAG HD G *a Hx 0D*Zi xAG HD G x*Q H+x 0*a`xAC HHC I *A Fx 0x2P1"xAG FEH D 25T x 028xAG BIH H 31gx 0(3WxAF BGF B \3@ x <3wAN G G v B  H 3w 03 wAG HD H 4 x ,<4\EwAC K H l4SVw 04XwAG FEHk J 4Qw 04wAG FEHk J 5w (KHsAM A G N J 0KAC DL P v 05h' wAC HKD E 5ov 05@$QvAC M C ,65 v 0P6H(vAG DP J 6~v D6/&cvAC M H  L ~ B ] E 6ZGv ,M2AC CW B  I 0D7d3 vAG HG J x7T@v 877f%vAG DIEDR.Z.9 E 7uiKv 07</vAG HD A 08v 0T84@uAC HH3 I 8EAQv 08Eb5vAC HH A 8..bv 09J FvAF BGJ A 89Pv 0\9lUrvAC HH E 9v (PZ!09ZivAC HK F 9v 0 :`ijvAC HK F T:qdv (x:fHvAC G A :;)v (:DgvAC G I :u (;guAC G H <;u Qh!0QhAC DL P v 0RiAC DL P v 0LRPjAG BEH F b A 0RkAC DL P v 0@<ktAG HM D ,RnAC CW B  I 0<4o {tAF FED F <=At 0<xtAC FEH A 0=&A)u 0T=y uAC FEH A =Aau 0=EuAG HD G =Au 0>tuAC FFB F 8>Au 0\>ܓ uAJ DH7 B >z#v 0>D vAC BQ J >|mv U|!,U{AC BJP P J 0P?  vAG DW E ?8u V!,0V}AC E F  K ,`V AC CW B  I 0@e auAC FEH D P@8Au 0t@RuAG HD K @!Iv 0@-vAG HD G AAxv 0$A\vAG HD A XAv 0|A|vAC HH+ A A9Aw 0AUvAG HD D B"iw 0,BjmwAC FEHZ G `B3Aw 0BUwAG HD D Bi4x 0B xAJ FMp B C-ix (YtoAC Cf C y Y0tCbxAC HD H (Z0AC DLD F 0C xAC FEK F D+(x 0,D,  xAC FF E (ZAC DLDv B 0D\wAC DGH I D;x (X[oAC Cf C y [0$EowAF FID B 0XEPwAC DIF E 0E wAF BJ I EUw ,X\)AC CW B  I ,F)'wAG G= D DFiw 0hF-< zwAF DEM F Fnw 0Fh9G wAF DGD D Fw 0G`EwAC DGH F LG"x 0pGKxAF DGD H Ga:"x 0GSR xAC HDW I GCtx 0 H^xAC FF F TH_dx 0xHf%xAC FFB F HkAy 0HxlfxAC HHh D IT.y ,_oAC CW B  I 0XI p6xAC MC D IA y 0It6xAC MC D IA2y 0Jw6yAC MC D yAC MC D JAy 0J fyAJ FED2 D Jy 0K/yAC M G DK<y 0hKЍyAC M  D KXy 0KhyAC HH E K;y b!(,LdyAC I F XL}y ,xLeyAG M D Ls?y 0@cAC DL P v 8MX7xAC BK J h H ] E $c\MAC Ar I H 0dMxAC DZ) E M+x 0MJoxAC HK E Mgsx ,dAC CW B  I 0DN'xAC HH; A xNArx 0NVxAC HH A NAx 8N!{xAC BK J h H ] E ,exAC CW B  I 0`O+xAC HH3 I OcA}x 0OaxAC HH A OLAx 0P7 xAG BJ E DP5?x 0hPjxAC FEHZ G PAy 0P7 xAG BJ E P?y ,uhRAC CW B  I ,$vRAC CW B  I 0_SB>yAC M D `V~y 08`XcyAC HD E l`3gy ,w[AC CW B  I 0`x\yAG P F `y^@y ,wcAC I\ G 0Ha gtxAC HH F |aO&y 0aHkxAG DL H aAy ,lxrAC CW B  I 0(brGxAG HH H \bU2y ,xyAC CW B  I 0bzO xAG I I b/y ,|yAC CW B  I 08cO xAG I K lc,y z(zoAC Cf C y 0cxAC M K dMx 0(dVxAC I~ E \d$x (zAC Cw B y 0dqxAC MP G dg:x 0e$xAC HH F 8eI@x ,{AC CW B  I <e|xAC G H  E K M 0euxAJ BGFL J fx 0$f xAJ BGFL J Xfx $|LAC A] F \ 0f xAC FEK D f 3x 0f xAG I I 0gcx (}tAC EU J  I ,}AC CW B  I 0ghIxAG HD D g|gx 0hZKxAC HH E + 0 (SAG FFS I Tuw 40 AG FF H  H 05AC BN D 8$AC DD E R F  F 0 $ AG DS A TX 0ukAK HD A  0" AG DGD} D   0<AF FM F pM  0$AF HK G ȅ 3C 0l3WAC HD I $  A UW } } }}}}}}}}} p%m %>.,; GUm cqB hsB sHa   a   E[  | hsZ hsZ $ c    $ c   b{Z e  6     >t'4iPL      M&o7Y{&  4y%&   .TNA6 8 8 8 788#%4\bne[   Ef4jd} V    6+  ,}b  a       ; 50X5dKF469  6 `  pN      L: 6 p)pk  n   n4444,45scEH  6 ),5,5,5,5,5,5,5 P RtY   1h -     w      +6UvR X:   X:   X:  5;d R N~ ~ % %  ~ ~ /~ 7  7  7 su:O )V)s)  )#"#)*E...!"!6.""#.'-*&V  p -  n-[;2    N % $#@n#!#@n#!#@n#!#@n#! G-P~`2;t555555555  5*5* 5+5+5/CG  da [       %G7$U  ` "4LG0I   ,  -a    ] >&'8:f *V* s*!!*#3$**% I) k    0k    (     ! !/c   g9#' n m f f9, \\ ;) ;)% LXc}!JD\Vh}      #   #1B I!=#%q!$ '-9 Y:    E    #  0 7i6 9y('S^*    O" fh  6 "" fh  F "[     m #  &   =  6 *iMM-MMMMMMM8# # #:####1M-)" ) )")#)1MMMX+_ B   IJ%+#cb   QF?B  .  =7QF?B  .  =7Q   " d        1Q   " d        1Q   " d        1Q   " d        1       0>r8weZb     5JP)T +Sv) q        Q           R   \ d         '&MXg_@DK  ,    8     D"F9-#  &   /z            e    0    $5      @zo    B "         3    'o    B "         3    '-q   "-q   "fXcu    _q    4    7p9   6 `pN. R  L  FLK ! U    +  f  #D9   6 `pO$P}t       Efq D ?U%'D>PLL+d   M,L ) 8? 'BHTeM    1^b y               M4M   -   d              $]    d                  3:3W-0 g    *  k   *  k    2w BG BF Fz       >(4K;$&#  j     3   $&#j     3    ${   -     E       f    $      f    0   (         $ ` E F0.r#  &   o?   2       &  . G  .#   E     H   '1{   -     E       f    $ E     H     '      7      d  `            $%L E>E.OB)M|                  $%L E>E.OB)Ma/ w   L  22QF<<Pk  TsU&s            2 "F4%v     G   -9@E0';![s T(Fe  &/4p.3.i[|    ; d              !nL 6 ;  4*6#o    :  .4gq.B    H     : A &          0     3      $EHt=Wj-4$t=Wj-4$t=Wj-4$t=Wj-4$x*    4j_  -     ,        ,:X        2 $c, / 3   g  E    :    &\_, j  %4lF  j  {   -   d             .o   -  d            $   -   d              $o   -  d            $5   0  +*|                  $5   0  +*+!73  )      A   ]  AY  "Ur#" : = =   -  4 g      m        $z          A   3      $  .                     p K  _Z.To    R E   H5 O E    H       N(( ( ( ( ((G((>j( ( (%-%(&&(''('>RxH3  c31JJ W i     ,     5S-  y31";'r,*0  GVE`\0(E|                  $0  GVE`\0(Ez          A   3      $z          A   3      $z          A   3      $YQ       }  aU    N^Sj/Pb t!      5    9 O   i       OQ  *  S    9W !         a9     2\-dm>p   *       %       4>X-UUZ3  ^      Q{fQ xL  3    `     \   &   m{h        6  y       dy      j)p    "<B@e z 2  3     <\iS*]\9   f         2 # /u -   P  r  F!{ -   P  r   F-bH ]   .#6FE  U c$N          f  &i+   d     M       `h*   d     M       `Y 6 .   k9    U!! 9! ! ! !!!9!!J!!!!>!Q!!!8!!!!&!-!!!!!!!!  !Sqf" " " "&"%F"L""";".""1!!`!!!! ! ! ! !C!!!!!!!%!!(!!!! S                  <(t""" " "P."^"""""" !/"t""" " "P."^"""""" !/".% & & %&%D%%3%&& %U% O 0%!"%|.% & & %&%D%%3%&& %U% O 0%!"%|511{111 0 11110011V1111410111 l!1!"1#Y$1$$1(1*n1,1-1-1-1.01.1/1/10101rO! ! ! !>!!4'!!4?& & &.&& &4++)***)))*))*******)) * * * ) * ) ) ) *+**++++(*))*)(&()))))++*+*)++)++ # *!!+!(!)")#*$+$+$+%*%+')'('('+*+*++*+***u****,**3*+*+*'*^+ *$*&&*q-i:9  *Pc     S*.G' # 3oM.!UM 4"5#4$4$4%5&5&4+5-43534-#:FKYNv0p   ' CTdYIYNi   8{ d _Yqh G  v  &5H <M]RBME e 57 FxK;QIl   *S/}r   $      K   *       )  X3]8Xu{.      m7.      m7} (( ((((((Y( (  (!!(%(&(% ~ 00 00000 0""&0#:$0%+&0&&W0''0/0%  00 00000 0""&0#:$0%+&0&&W0''0/0% yo*********!"4*""*(*(*!HY^/NN/. //////// t!/!#/&'1/'(/+/,/)! 5%:*55qi. . ......."#.%%.-.!Q LLqi. . ......."#.%%.-.!Q LL.______=_ _!x"v_#_$_%%v_&' _45_77A_8 8e_::_[^\e_-% %[nyo< < << <<F<<$&<))<,,<;<<<!:Z_z" # "#""x"""#"""" [!"x. 22322222 2!!2%%2((2)2)2*2*2*2%aSR RRARSRR R$@$R((ER),*S+R.J0R9R9R@RAR1)]R 0;3Fe 2K[Pum6 6 66 6#%!6%&b6((6)637366666 2u7 3   A# Ctyyp< # azfypxl!* 0      O42  |vMC0g   6BxGf    %5*  ,  R& MC0g   6BxGfYLI}%d })!!>754 5 4"5#4'5'5( 49UK          q  -# (}((9   d      >Z^,, ,m{,,,,`,,,,,7,, ,!,!,"f$,%,%,&,)),,,,,1(z+* +y+++f+h+++*+++* + *!"*#+#+***+**1({691sR      .C3>+B&&&&&$&%~&q)qa}jL- =&&%&$&&&&&%%&&&O&&&&&&&&&&!&"%"%%&%`qh''''''' ' ' '#&'''!  C~ w/K% 5+C >(->}>>qg( )((&((()) @(!!(((Fw| 333r33Pr3P333333p3h3`3333B33@3/3)3#3^3j3383Y3333333T333pz33303t333x3333330333Z3[3q3J3N3G3@3393X33S3333`33p3 `@ PSERF P p@ PSERF P *0@ UFTG) в`P `   `` p ` $0 @ P p$  @ $p p   ` Ї  P   @ `  p  0 @ P P p  0 @         `  0 P ` p @ P  0  &` p 0 P' 0 P   P' @'  0   p p  ` PP `p P 0 @@ @   0     i|>  #(Xd6`d6oH ؂8, hw6E ooDoox=oXu66g(x`l@0 0 6@66pg(Ag(g(P@7)g(0g(g(tg(@8) h(`h(Xh(P@8)#J(8)X(9)H9)`0 L (@666@@=(h( $h(0-h(05h(`?g(`?g(@ @?h(DIh(DRh(P _h(P kh(xh(h(` h(` h(.pAh(.pAh(0.Bh(P>h(ph(,PN(863pAh(6`6D  @y6@s ,o<(@(x9)0d@p 9)(Y@ P89)0P[@ 0I :)0_@0 P:)0b@ @:)0`@P P:)0]@ :)8p\@p ;)8^@ `@;)Xg@p p;)8R@P ;)HPi@ P;)Pt@ <)0Q@ P-(<)X`@ `X<)8`P@ <)8O@ <)8N@p <)8M@P =)@0c@ 0=)8 M@ `=)8PL@ =)!D =@66i(б=)F(=)g(@f8(#*\a(`C * .)s# *c(&*$6(`*7(*o<(P*@(@*(>)p6f8(p`#*o<(*@(*P>)6 6F( =)f8(#*o<(*@(`*x>)p 66"i(`5i(`f8(`i#`*o<(` *@(*Gi( !>) @6d(#@*ai(0Dd(PS#*o<(@*@(`*?)h8?)6 =o<(p *@(@ *ji(p0D0@)P p;6o<(`*@(*i(h6@_(p*o<(@*@(*@B)x`B)6 \(  *o<(*@(`*XD)hD)6+<(  *M(*M(*o<(*@(`*i(pDF) <@6A(}*o<(*@(*i( DhJ)P P<`6Zd(" *f8("*JX(@< *A( *o<(*@(*i(ppDL) ;6@'B(*B(*B(*o<(`*@( *i( `6@6XR) 66J  j(AF($`(-K(S@'-[(&-o<(@&-@(&-j( 66 S)`661j(AF(0 ,-K(W+-[(@*-o<(@*-@()-S)HDT)P I6{3D(Ж@7-b(.!4-S(@z1-V)HDV)P I`66p*j(00W) K(W)e(0;$E-pg(C-P(= >-?(V<-?(W`;-3j(HD(X)P I6tPj(=X)dj(HDY)P I60tP(#Y-j(HD`Y)P Izj(HDY)P I0{j(HDY)P I{j(HD(Z)P Izj(HDpZ)P Iyj(HDZ)P IPyZ)0[)`6@CX(&.b()!`%.k(0`[)6`6N([)J().V( (.2k(0[)@66N([)Jk(0\)J( *.p\)0\)7 7N(И[)J(p*.Tk(0\)7`7D(@])N(Н!P])rc(NK(x$+.=(>@+.J(*.qk(0@7 7])7C,J(=+.k(077]): 7`;';,:f=(<@..J(P,.)J(0,.G( D])0Ck(0Bk(0C^)0pCk(077(^)7@7@@, K(@J(0`..k(0I l(0D(l(0HFl(0X^)`7`DJ( /.^)0B_l(00D}l(0^)7 H( :@0.J(/.^)0 _) 7 7N(ph_)J(0.l(0_) #7"7N(J(p@1.l(0_)`%7%7@N(@J(42.G(D 2.l(0_)'7`'7N(J(3.l(0 `)*7)7`N(@J(" 4.l(0P`)@,7+7N(J(44.m(0`).7 .7N(@J(P/`5.1m(0`)`07@J($5.Nm(0`)@27J(@*@6.km(0 a) 47 J(.6.m(0Pa)67 J(-7.m(0Pa)77J(,`7.m(0a)97J(,7.m(0a);7`J(0+ 8.m(0a)=7J(`*8.n(0b)`?7J()8.n(0@b)@A7`J((@9.2n(0pb) C7J('9.Kn(0b)E7@J( ':.bn(0b)F7J(P&`:.zn(0c)H7J(P%:.n(00c)J7 @J( ;.hc)(@L7$X(PB.o<(PA.@(A.c)X c)N7N6(/n(@e)n(@hg)pn(@S7S7i)T7@T7p00p,n(n)g(` o)=($/n(0@p)V7@`o<(@D/@(D/o(0`7o(X7o<(D/@(D/Ko(Dq) @_7Z7 Q(fo( k;(D;(?i(p;(;(P;(C;(`>i(j;( n;(y;(R(@[H(A(;(`;(;( ;(;(;(@;(;( <(p<(<(P#<(A(M/R(M/<(0|L/<({L/$<(yL/XR(@^`I/o<( I/@(pWH/mo(8D -`b7b7&;(f(&@P/$6( P/7(O/o<(O/@( @O/o((Pd7o<(pS/@(`R/o(00pq)@g7f7pL# :h(o(Y(`y)M(`*Q(p*Q(*Z(P9@*Y(6*Y(3*GJ( *O( *G(Do<(*@(*o(@ y)@k7`j7o(o(` p(0p( sM(p*NK(`*=(X0*p((~)m7`m7g('h)ai(D[(@*.)p#*غ(p*c("@*X(/*$6(P*7(a@*7C(#*Y(*`(#*B(@&*`(@#@*A(m*e(#*o<(*@(*)()r7q7F(P)fb(@*U_( 6*>X(2`*X(1*$6(0*7(g`*o<( *@(*Ѓ)h)t7`*#o<(@*@(*)hȆ)v7"#o<(@*@( *x)h)x7<# Hd( #*o<(*@( `*)hȍ)z7p2#@Hd("*o<(@*@(*=p(h)0}7}7n()Tp( $6( +7(P\ +G(D:( +B(  +fB( +B(p` +A(ph +X( I+_8(`#+B(+oB(Р`+RB( +_(`"+3"+_(^ +X(@?+^(+fb( +Y( (+Od(`"*_(y*LB(`*-B(0*Y(P!*B(d*B(`*ai(@Db(` *B(^*"B(P*Y(p*qS(*o<(0`*@(` *[p(@)7JX(P8*wp(@Pw777) 7 7|@{@{`,_()p()p(p`)p()4()G(`wD@(*W( *=(*N(@*<(*p(@DД);77p(xH)p(Py0)g(yx)p()q?(0`,=(@,X(~`,r_(H ,V(p,>(Є{,P(P{,d( ~ u,c?(w r,c(Pi n,}e($@k,b( e,W( `,+P( Y,=(ps`T,f(P ' P,7C(`J,@(> J,4e(0T$=,pg(p'4,C(c `1,M(z"1,b( !',Y( z`%,`([ #,](Щ ,B(P@,`(0U ,[(`,fa(L ,p(8@)77g(x)p(<)p(px)p(0)q?(,X(v,=(p,W(p,+P(@,f(&,r_(PB+-`(`"+P(`"+c(`"+=(\@+7C(+4e(e$+pg(0(+C(0+M(N@+e(0&+](w`+>d("+@(+V(+>(+P( p+d(0"+c?("+c("`+}e($ +B(+@(@+AO(  +R( B`}+B({+p(p<77)%@7 7 \ @t@@tP,p(/P)e(5X)p(@p(m:(v)g(T(0,C(`B,G(<DV(f,C(9`,q?(V0,C(4,D(1,[F(@-,5Q(Pi,kQ(Po,W?(Pu ,'O(`,C(P( ,J(C,D(a`,Y( ,c?(v@,O(u,d(<,gC(`# ,o<(P{,@(, q(@077Dx)> r7`7дд@,u(9(4H)j(s8)p(P@)%q(ps)P(P)9h(s=(@]%-29(`$-G(@DY(p -3S(@-X(Д`->(` -P(-[E(Њ-BE(-'E(P- E(`~`-=(a -@(E' -X($ -T(w` -7C(o-.6( g--`(0"@,P(,E( c,D( _`,W(0k,+P(X,D(PP,C(`K ,D(G ,O(P@,c(0!,2H( ,/?(d@,D(`;,B(,`(0!,)HD)P I7qD(@-0q(HD )P I77qV(V( j9-Kq(XPDX)`pJ@77P|rc(Pa(.6(X-DD(P@X-P(p>&T-pg(pW"O-gq(HD)P Isq(HD)P Irq(HD@)P I7PoPj(`=)q(HD)P I7sP(0[-q(HDh)P I 77psg(q(P() N(@c-[F(Pca-f( m'_-g(']- )HD@)P I7`7pz K()pg(#d-r(HD)P I 77|a=()f=(`)pg(#j-8)HDX)P I77Pr K()P( ()r(P )pg(($}--`( x-r(HD)P I@77pp K()P()r()pg($`--`( -5r(HD)P I7@7pw K()P( 0)pg(`$@-Pr(HDx)P I 77w K(0)P(` )pg(#-fr(HDP I70n-`(-dD(@-VD(@ -r(HD*P I 7o[F(N-a( L--`(p" K-pg(m&G-O(E-r(HD(*P Ixr(HDp*P Ix*HD*P Inr(HD0*P Ipr(HDp*P I~r(HD*P IЀs(HD*P I0~2s(HD0*P IIs(HDp*P IPas(HD*P I}xs(HD*P I~s(HD0*P I}s(HDp*P Ip}s(HD*P I*HD*P Iws(HDH*P I0xs(HD*P Int(HD*P I 70qt(@q+t(q6t(HD*P IpLt(@@D8*0=p 77@p(p@ht(im:(k_(ppmt(pwt(@:9(pG(DB(`+e(%+i^(`+X(0L`+b( !+=(P+O(o+o<( +@(`+t(@pD*>07 7p p(?ht(gm:(`l_(@hmt(wt(9t(h9(0iG(DB(@6+e(`N% 5+&f( &3+f(%`2+i^(1+X(s1+X(pi0+X(b0+b(!/+=(.+O(d-+o<(@-+@(-+t(@pD*9`7 7Pp(p?ht(fm:(0m_(gmt(wt(@99(pgG(DB( !+e(b%+&f( %@+i^( `+X(V+X(N`+b(`!+=(`+O(@j+o<(p+@(@`+t(@0D* :77p(>ht(Pem:(n_(emt(wt(89(0fG(@DB(`++e(z%(+&f(%'+i^(&+X(_ &+X(X%+b(! %+=(p $+O(_ #+o<( "+@("+t(P 7DH*@9p7@7'mt(0 *p(p> *u(b@ *ht(Pc *_(c*m:(o*|:(nH*9(0dd:(ds:(dxA(@NA+G(De( %`?+X(Pu=+=(:+O(`Y8+o<(08+@( `8+u(H77D*P I`@8@7@^^0,j(ap(p*?(<*9(b9h(pb4Y(-S(r-7C(0-2a(! -LS( -E(0@-.6(p-D(p-4e(G-=(0#P-P(R&->(Q -P(`-G(DY(`Y-3S(-=( -[E(p-BE(p -'E(- E(`-W(`-C(л-D(`-+P(/ -D(p`-O(`-@(PI'`-T( -X(@$-D( -D(-X(-m](7-Y( @-`(O"`-](0-`( `-[(-fa(F"-3Z( <->Z(? -*@8 @( -o<(B`-@(p -#u(0@;*P 8` 8#9(04*'E( ^H*7C(0+@>.X(`<.G(PD=(p|;.=(@';.J(p;.6u(8 8` 80Z0*0 @] 8 8`YY ,Iu(*g(*;(1*3`P *=(.P(p.aF(F.=(.4e(.[F(@=.@(@.K(.RH( : .`( .AF(9.Gc(Py!`.-c(r!`.c(i!.!F(3.](j.b(!`.](@H.Y(P.Y(.`(c! .G(D]Y(`.`(^!@.Yu(@0/D@"*:p`889_(X0%*:h(X&*30'*9(`Y=(@/=(/G(D](@r`/](`N /^(`.1^(.4_(@,.-`(.Y(.`(`V!.]Y( .`(P!.ku(8А88Т@(* `e@8$p @!p ,`@=(5@ /G( D=(Z /H(` /N(` /a=(  / K(  /f=(P` /uF(Q /mF(L /u( u(P )*80=(`/N6(f /9=( c/S=(/**P**8 8f(@-*m:(:!x-*mt(e'-*u(*@181(pVP?*v(H@*@381(V A*v(HA*58@581(U8C*R(D*=($/v(HD*@8878pv(@UE*w(2F*f(p2G*=(P1/ w(0еH* :8@o<( `1/@( 1/(w(H@<8 <80I*`T<8PQPRQ+RH(pD,/`((/=( p'/?w(HHL*`?8>80]w(P@M*f(1N*iw(pQN*=(`0/sw(HO*@B8@A8w(P*w(Q*w(@pR*w(0PS*w(P T*=(00/w(HU*D8 D8]w(P0V*f( 1W*=(`0/w(HW*G8F8]w(OX*f(0Y*=(0/w(HPZ*`I8H8]w(Of(@0h[*=(p0/ x(H0\*K8@K8]w(N]*f(/]*=(`0/!x(@K ^*`RN8M8*-E(L6x(Mx_*@x(P`*Kx(N(a*Vx(5b*=(8/G(D=(6`6/Q(0m`5/Y(@@4/`(3/]Y(`2/`(01/bx((c*Q8`Q8&0yx(`7d*L(%@G/<(F/<(E/G(Do<(@E/@(@E/x( !Oe* U8@T8`9(O36i( x(=( * ^( *b( *G(ODo<(0*@(*x(h*`e*@X8W8Px(H@Ix(0JJ68(P 'o<(*@(*x(X g*[8@Z8s x(A`B#i(PCC6i(pp1M( `F#x(D Ey(FFc(pGGo<(@*@(*y( =M(p x*(x*S(Zx*)L(@y*a( y*Zc(!{*d(W#|*b(3!}*"_(%`*PZ(F*rZ(I*@(@*o<(P*7(*$6(`*c(&* .)s# *\a(`C *f8(#*@(*o<(*f8(p`#*@(`*o<(*f8(#*@(*o<(*e(#*A(m*`(@#@*B(@&*`(#*Y(*7C(#*7(a@*$6(P*X(/*c("@*غ(p*.)p#*[(@* M(0E@*@(*o<(` *f8(`i#`*@(*o<( *7(g`*$6(0*X(1*>X(2`*U_( 6*fb(@*@(`*o<(@*d(PS#*d(#@*@(*o<(0*b( * ^( *=( *@(*o<(@*@(*o<(*WP(*>P(*O(`*GJ(`*@(*o<(*O( *GJ( *Y(3*Y(6*Z(P9@*Q(*Q(p*M(`*=(X0*NK(`*sM(p*)M(u`*H(R*Z(L*b( !*Q(@)*H(Vp*7(5*@(@ *o<(p *@( `*o<(*Hd( #*@(*o<(@*Hd("*@(*o<(@*@( *o<(@*@(*o<(`*@(*o<(@*_(p*@(`*o<(*\(  *a( *JX(P8*<(*N(@*=(*W( *@(*@(`*o<(*M(*M(*<(  *@(*o<(*A(}*N(@* L(*@(*o<(*A( *JX(@< *f8("*Zd(" *@( *o<(`*B(*B(*B(*@(` *o<(0`*3y(@qS(*3y(wQ( wQ(`Y(p*"B(P*B(^*b(` *B(`*B(d*Y(P!*-B(0*LB(`*_(y*Od(`"*Y( (+fb( +^(+X(@?+_(^ +3"+_(`"+RB( +oB(Р`+B(+_8(`#+X( I+A(ph +B(p` +fB( +B(  +:( +7(P\ +$6( +Z(R@ +s7(0 +Z(V+@(`+o<( +O(o+=(P+b( !+X(0L`+i^(`+e(%+B(`+@(@`+o<(p+O(@j+=(`+b(`!+X(N`+X(V+i^( `+&f( %@+e(b%+B( !+@("+o<( "+O(_ #+=(p $+b(! %+X(X%+X(_ &+i^(&+&f(%'+e(z%(+B(`++@(-+o<(@-+O(d-+=(.+b(!/+X(b0+X(pi0+X(s1+i^(1+f(%`2+&f( &3+e(`N% 5+B(@6+@( `8+o<(08+O(`Y8+=(:+X(Pu=+e( %`?+xA(@NA+PS(`B+7(L+jQ(@ M+4Q(|@O+qf(&`Q+S(Y+5T(@i+eA( Iz+B({+R( B`}+AO(  +@(@+B(+}e($ +c("`+c?("+d(0"+P( p+>(+V(+@(+>d("+](w`+e(0&+M(N@+C(0+pg(0(+4e(e$+7C(+=(\@+c(`"+P(`"+-`(`"+r_(PB+f(&,+P(@,W(p,=(p,X(v,q?(,1g(P',KA(D ,fa(L ,[(`,`(0U ,B(P@,](Щ ,`([ #,Y( z`%,b( !',M(z"1,C(c `1,pg(p'4,4e(0T$=,@(> J,7C(`J,f(P ' P,=(ps`T,+P( Y,W( `,b( e,}e($@k,c(Pi n,c?(w r,d( ~ u,P(P{,>(Є{,V(p,r_(H ,X(~`,=(@,q?(0`,@(,o<(P{,gC(`# ,d(<,O(u,c?(v@,Y( ,D(a`,J(C,C(P( ,'O(`,W?(Pu ,kQ(Po,5Q(Pi,[F(@-,D(1,C(4,q?(V0,C(9`,V(f,C(`B,T(0,Wf(& ,`(0!,B(,D(`;,/?(d@,2H( ,c(0!,O(P@,D(G ,C(`K ,D(PP,+P(X,W(0k,D( _`,E( c,P(,-`(0"@,.6( g-7C(o-T(w` -X($ -@(E' -=(a - E(`~`-'E(P-BE(-[E(Њ-P(->(` -X(Д`-3S(@-Y(p -29(`$-=(@]%-@(&-o<(@&-[(&-K(S@'-AF($`(-@()-o<(@*-[(@*-K(W+-AF(0 ,-O(5--=f( &--S(@z1-b(.!4-3D(Ж@7-V( j9-?(W`;-?(V<-P(= >-pg(C-e(0;$E-O(E-pg(m&G--`(p" K-a( L-[F(N-pg(pW"O-P(p>&T-DD(P@X-.6(X-P(#Y-P(0[-g(']-f( m'_-[F(Pca- N(@c-pg(#d-pg(#j--`( x-pg(($}--`( -pg($`-pg(`$@-pg(#-VD(@ -dD(@--`(-D(@->Z(? -3Z( <-fa(F"-[(-`( `-](0-`(O"`-Y( @-m](7-X(-D(-D( -X(@$-T( -@(PI'`-O(`-D(p`-+P(/ -D(`-C(л-W(`- E(`-'E(-BE(p -[E(p-=( -3S(-Y(`Y-P(`->(Q -P(R&-=(0#P-4e(G-D(p-.6(p-E(0@-LS( -2a(! -7C(0-S(r-4Y(-@(p -o<(B`-@( -f( _'`.H(j.`(p`.Ca(' .Va(1 .!L(P.c(!.AQ( .`(` .c(09"!.b()!`%.X(&.V( (.J().J( *.J(p*.J(*.=(>@+.NK(x$+.J(=+.)J(0,.J(P,.f=(<@..J(0`..J( /.J(/.H( :@0.J(0.J(p@1.J(42.J(3.J(" 4.M(4.J(44.J(P/`5.J($5.J(@*@6.J(.6.J(-7.J(,`7.J(,7.J(0+ 8.J(`*8.J()8.J((@9.J('9.J( ':.J(P&`:.J(P%:.J( ;.J(p;.=(@';.=(p|;.X(`<.7C(0+@>.n* y A..}V(C`.V(J.V(Q.Z(px.b(H!.YU(p.kU(`.~U( .U(`.U(.U( .U(P.U(`.U(.U(@.[F(p$.e(@%.`(^!@.]Y(`.`(c! .Y(.Y(P.](@H.b(!`.](j.!F(3.c(i!.-c(r!`.Gc(Py!`.AF(9.`( .RH( : .K(.@(@.[F(@=.4e(.=(.aF(F.P(p.=(.`(P!.]Y( .`(`V!.Y(.-`(.4_(@,.1^(.^(`.](`N /](@r`/=(/=(@/J(X /mF(L /uF(Q /f=(P` / K(  /a=(  /N(` /H(` /=(Z /=(5@ /[(~/[(@/N6(/S=(/9=( c/N6(f /=(`/f6(/=(/6(P /f6(P`/6(/=(ip/9=(/N6(P /1(!/f6("/6(P`#/=(P#/=( $/=(@$/=(`$/=($/=($/[F(,@%/=( p'/`((/[F(.*/RH(pD,/=(`0/=(00/=(`0/=(0/=(p0/=(`0/=(P1/@( 1/o<( `1/`(01/]Y(`2/`(3/Y(@@4/Q(0m`5/=(6`6/=(8/[F(*8/Q(`9/,I(9/LI(9/<( :/py(; @:/[(:/y(=/]I(=/L(@<>/y(`@?/y(?/y(@/@(9@/A(=B/y( C/@(D/o<(@D/@(D/o<(D/@(@E/o<(@E/<(E/<(F/L(%@G/@(pWH/o<( I/XR(@^`I/A(kI/A( J/A(J/R( K/R(`K/;(qK/R(K/R(p L/R(w`L/$<(yL/<({L/<(0|L/Q(0M/R(P@M/R(M/A(M/3[(N/NT(N/@( @O/o<(O/7(O/$6( P/f(&@P/4A(AP/@(`R/o<(pS/7(u@S/G(S/I( S/I(T/I(U/y( 0U/`N(DU/qN(KU/T[(P`U/6(@V/UM(G(G(P(\G(0G(P(P@(o<(GCC: (GNU) 4.4.7 20120313 (Red Hat 4.4.7-23)GCC: (conda-forge gcc 12.3.0-7) 12.3.0 X   p [d8    W  {  O { P  p P   U    Z P  p  kg p k k P ks k 0 k# k|  [ p {E     #  @   P '  ` a  p  S  P Z  rZ  0 {  1  v:  P v  v  P v  vR  P  9  vQ v v v, vv v v vA }  X9$ qK 2p  / V @  ` I S 0O  !  > 1n P7 4 6 &5 AN Xs I `8A 8@   qJ 0l  t 4 o   \  @4 `;n p09 3"  E< 0%    P|@ # Q @ V `  #  ^1 6 2 @d S zRc ] P: P2s/ 0sh P6s 1s 3s  4s@ 5s} P1s P5s P7s*  6sk  2s  P3s  P4s"! 7sW! 4{! U! 3S!6 " Pc2" Z" " `_" " P{P#8#8@# UK# T3$ m$ $ܼ8$ ?)% h*% 0^% 1% +% ]b&9:&x9[&p9}& F& XF& o3& P3 & j3 ' 3 $' 37' @3M' 3j' P3' е3 ' X3' P=3' 3' 3( 3#( 3?( 3Z( 3s( 3 ( 3 ( `3 ( V3( 3 ( -3 ) 3) @3 0) 3=) 03N) Pb3r) 0b3) >3) 83) 23) P3 ) 2* / C* @/r* 2* 0** ~2;* x2'+ s2T+ @.3+ 0+ `q2+ o2o, `l2h5, h2:b, ,3u, @3, i3, 3, pi3, `3 - 3(- 3D- 3d- 3- M3- p3- <3- C3 . Д3>. 03Y. @3 q. PS3. PZ3. 03 . 0y3. `3/ P3 ,/ P3I/ @3 c/ 03 }/ 00/ Ё3/ 3/ B30 3 ,0 3 F0 3a0 У3}0 3 0 3 0 P3 0 3 0 30 @31 0331 3 M1 3 g1 31 31 3 1 31 `002 3!2 Pi3D2 3d2 32 3 2 32 3 2 32 y3 3 3+3 0S3Q3 0Z3v3 p33 `33 P33 33 33 +33 34 374 0&e4 0-4 p<34 34 0i34 B3$5 0(R5 `0#5 d//5 B35 3 5 а3 6 b306 p3P6 3m6 36 `-36 3 6 36 x3 7 3!7 3 :7 P3Z7 0%7 0'7 B37 3 7 P<3#8 -3!O8 03o8 Щ38 3 8 g2%8 3 8 Њ39 @3$9 x3E9 0&s9 `0(9 G39 p39 0': g2)F: `B3o: f2: 3: '3: 0$: 3 ; G31; M3X; f2&; @f2(; S3; R3; -3 *< ,3 V< 3 p< i3< pM3< 0"< e2q= `0,C= e21p= /3= 0$= 0>= 0,)> @0EW> 0%> 0"> 07> e2l? @0'E @0#KE c/.{E Ѐ3E B3E 0x3E ;3F 0p31F 63 \F h3F ,3!F Y3F 0$F x3G B3HG 0'vG @63+G 63,G 3G A3H 34H p3SH A3|H h3H @,3!H a3H 0#I a3AI L2"nI @ 0$I L2 I /,I w3J ;3CJ ph3fJ PM3J 0%J Ph3J &3J 0h3K Y39K 03UK p3sK L2 K p3 K 3K `L2 L @L2 4L 0!bL L2#L p3L )3L h3L 0(,M @ 0*ZM pa3~M K2)M 0+M 0+N @H2e4N B2eaN @@2gN <2N /N /O 42FO 22sO ,2dO $2O 2O `2w'P 3 SP @3P `2P 2W P @2Q 13Q `1`Q /Q 1lQ 1 Q @3R @1kBR 1oR 3R 3 R 0 R @1^"S /QS 18~S @1S 1S 1T 21T @1/^T 1XT 3T 1 T 1U 1.U 1[U 1U @1U 1U /V `1->V 1*kV 1V `1V @1V 1lW 1LW 1yW 2W 1W |1`W `w1Z,X u1YX 2X @2X 2X 2 Y @25Y s1bY 2Y q1hY 2 Y @p1XZ n1AZ @m1nZ k1Z 2vZ g1(Z e1![ @d1N[ /}[ `/[ a1g[ `_1K\ \13\ Y1`\ W1\ T1\ Q1\ N1] ?1A] ;1n] @51S] 31] /1 ] w3^ 03 -^ 3 C^ 0M3j^ P3^ g3^ Y3^ `/1 ^ /1&+_ 0'Y_ 3v_ R3_ Y3_ 3_ 0( ` @0'9` R3_` 0$` p3 ` `3 ` P3` `3 ` P3 a 033a 3Pa 3ea 3a P3a Pa3a 3a pY3b 2-1b /)_b М3|b 03b 0a3b 3b PY3b 2-+c /)Yc *1c آ3c 3 c 3c @/"d /"/d A3Xd ;3d 0Y3d Ȣ3d @2!d /#e 3;e M3be 53e a3e 3e Y3e )1-f /*Mf )1-zf &1f $1f P3 f /Ug /WIg `$1Gvg `/Ig /wg `/wh /w.h `/w\h /eh /Jh c/Ph c/Ni b/mHi `b/@xi b/`i a/Xi a/ej @3 !j @3 ;j 3[j 14j /\j 3j 3j p3k 03 )k /&Wk 3 mk 3k w3k 3 k 3 k /8k `/9+l `/8[l ``/9l `/9l 3l w3l /C(m `/DXm p;3m P;3m 1{m /zn `1&5n /+cn A3n 3n /!n 1=o />4o G3#\o @/,o /?o /)o /)p @/)Bp X3gp 1&p 3 p 3p `A3p 3 q `31q 1*^q `1,q 2)q 3q g3q 0;3!r 3y 53 iy o3y pg3y 0w3y :3y Pg3z `/(Jz /*xz /*z @_/,z _/,{ 3&{ 2R{ /+{ 1.{ w3{ 03{ @G3| `//C| @531n| /0| @1w| @2D| @2!} @2M} 3 e} 03 ~} 3} 3} 3} =3} 3} X3~ 31~ 0g3T~ 3b~ 3v~ 3~ /*~ `/}~ 1~ p:3> 3 W /H ]/w 15 53  3  3 8 /'f P:3 3  3  3 3  3  Љ30 3 H 3 a 3 3  3  Г3́ 3 3 3  3, v3M p3m X3 3 0:3ۂ :3 P`3) @1$V `/^ L3 H3ǃ 2! /#! p3> 1"k /$ 1MƄ @1J /7! 1cN 3 d 3 L3 X3΅ pR3 0`3 o3: g3] PR3 `3 pX3̆ #3݆ 3  3  3' p3F 83b G3 `/* `3Շ p3 _3 2,@ P3` G3 1' 2 /+ //= `2-i @ 1. v3 P3։ F3 431) /0W 1) p3 43͊ 03 @A3 `/:C 03c 3 z f3 1"ʋ /& /%& p3 > P3\ 3 r 3 /( `/' `3 f3+ 1"X pL3 /& /*ۍ /(  `/)7 (3S 3 m f3 2, 43+ /$ 1lB 1 o 83 3 /$Ώ /& 3  17 3 M po3o 2! 3  03А P3 3 3  1&M `/%{ /% /$ב /' `/%3 3H 19u f3 `18Œ 1- F3 A3C 3 \ и3 u 3 /# /% _3 PX38 0X3] X3 `3  F3” 93 F3 pf37 PL3^ 3s P3 `43  1" 0L3  1 : W3_ L3 3 Ј3 Po3 x3  @3 В34 /#b `/% Pf3 3 ͗ K3 /$" `F3J /!x /$ A3Ϙ `/! @]/+- /B[ /. 3 23 0ؙ @0Q /3 /a P3 y 3 3 @F3 93  W3/ @3X @3 @3 3ț v3 3 3& 0f3I p3h v3 03 p3Ĝ ~3 _3 pv3) ~3I Pv3j 0v3 0o3 3ʝ v3 3 3% u3F P3e u3 P3 f3Ǟ u3 o3  3& u3G _3k K3 3  3 Ÿ @3 ڟ 03 2#$ @0Q 0~ `0 2נ 0B 01 0 ^ 3 x 3 p_3 0R3֡ @3 @3 /'H 043s 0 F3Ȣ F3 3  W3. 03I 43!t 0# R3ǣ `0( Q3 K3A `@3j 93 @@3 @3 0( W38 P_3\ 0_3 pW3 PW3ʥ pu3 E3 n35 03T 0% 0' @3צ `0( @/}2 0 _ 0?  0h  3 ҧ Я3  3  ?3 . /;\ 0 3 0"Ө /$ 3 3< /*j @/( Q3 33 03  E3) 93S Pu3t n3 0u3 3Ѫ ~3 u3 ~32 `93!\ @2# 3 p~3« t3 P~3 3 3$ 3 < 3Y ?3 3  h3  p3 ʬ e3 33! @93 B 2)n `0+ ?3ĭ 0! `/6  2 L e3o 2) 0+Ȯ p?3 3 /%> K3e Q3 /$ /& 2 @/!A ]/)q _3 0W3 `2 p33 /4? E3g /" \/7ű W3 0+ `0-D P33o V3 0+ 0- 033 pK3@ P?3i 93 n3 n3׳ t3 Q3 0 K E3s /# @/"ϴ 0?3 ^3 ^3@ /#n 3 `E3 0~3Ե 93 ~3 83H V3m /$ V3 /$ 33 @/*G /"u \/. 0'ҷ 23, /)+ PK3R /& @E3 @0'ո @/$ @\/03 0'` }3 83 e3͹ 0" ^3 /#L 3k ?3 >3 e3 0"  pn3/ `/] `0 0 0 0 2<= 0@j 0  `0ļ @0  3 ^32 0H_ @0 2 0 / /C 0p 0G @0ʾ `|/& @0& `0S 0 y/n 0ܿ @0O  w/8 0e 0 0 0 2 /3F /t /E / `0 `0* `2V @0 @0 }3 0  /(+ \/([ 02 0  t3 0K3 Ї3 E3C /Jq /N `/] [/\ 3  33 3G 3\ 3q 3 3 3 p^3 3 3  P3 " K3I 3d J3 3 p3 3 pV3 3 P36 03S t3t 3 pe3 23 pt3 /$, 3G PV3l }3 83 ب3 0V3 3  Pn32 P^3V /5 }3 83 `83 /)& 0^3J `/#x Ȩ3 V3 /$ /- 2!@ }0'm `{0M /+ @/_ 3 U36 ^3Z /# Б3 E3 3  0n3 3& 2!R n3t 2! y0 w0 t/m) t0V p0 l0 h0 b0Y  `26 ]0ic 2 @/ 3 `]0) \0+ 3J J3q pQ3 /$ /& `\0  @3 9 V0f 3 /) V0" @/$  PQ32 ]3V /! D3 /# D3 >3+ 0Q3Q /! Q3 J3 P3 3 @/)= @V0"j /$ P3 ]3 /! D38 D3` >3 P3 P3 J3 pP3" 3? /)m V0" @/$ PP3 ]3 /!@ `D3h >3 0P3 pJ3 P3 3! /)O U0"| /$ O3 ]3 @/!" @D3J D3r p>3 O3 O3 PJ3 O34 03 M 3j 3 3  0J3 P>3 T0= 3 2 pO3X 3s 23  @T0" m3 0>3 m38 /*f p3  p}3 К3 3 /$  /&8 PO3^ T0  0O3 @/. 3 3  @83@ p3_ Pt3 U3 p3 `3  0t3 J3% P}3E 0}3e 3 } P3 t3 P3 I3 /!0 `[/,` D3 S0& /5 3  3  3 P3 7 s3X p]3| /# P]3 3 3 3! Pe3D 03b 3 C3 3  m3 m3 2!0 3 H @3 b 0]3 03  ]3 x3 U3 03# >3L }3l 83 3 =3 U3 @/$1 =3Z /( 3 O3 п3  3 I3& pm3H @2!t /& /# s3 23  Pm3> 2!j P23 @/* 2! 3  \3- 3K C3s 3  0e3 А3 C3 3 I39 e3\ N3 /% 3 3  3 s3 pU3D I3k p3 023 PU3 |3 d3 S0#H 3d N3 `S0* S0% 0m3 2"2 3Q =3z h3 0U3 s3 23! ps3' 13!R Ps3s 13! d3 R0# P3  pI32 3L \3p І3 =3 3 p=3 3 R0XL @t/Q{ s/G 3 \3 N3 /%6 @3S N3y /$ X3 \3 p3 PI3, @/(Z /% 83 `13  73 @R0"4 03Q U3v 3 @2* N0V 3  /\2 @/^` /E M0 M0 L0 @K0B s/q `J0 I0 H0 G0% r/T G0 `q/ /- F0  2>7 /e E0 /> 2 @D0 `C0F B0s p/ @2 @A0$ 2' @2"S 2  /S [/X A0<  @?07 /Ue @/i /L /8 =0 ;0I 90v 80< 70 605 40* m/Y j/ `2 30 2  20: `2f @/\ 3 3  3  |3 3  /$A з3 Z 3 t |3 3  /$ /- 3  3 6 x3R 3o d3 H3 p\3 @/% 20', 2X / @/O 3  P3 03  m3, /'Z `20) (3 3  3  3 3  3  3 0 3M 3j 3 3 3  83 P\3 l3 3 7 Й3T p3 l `3  P3  p3  73 3 3 l3; 3Z `3 q h3 X3  3 l3 Ѕ3 3  p30 p3 I `3 b P3 { X3 @3  03  3  @3  /O( @/PV 3 o 3  |3 3  Ю3  p|3 3  3 / P|3O 03 g 3  3 / Z/ Y/X- @/2[ /( /' )0h `d/U 3 + P3I `2"u 03 d3 0\3 3 3  ж3 .  3M  P3 d  3  0|3  3  0I3  /#  @/)B  l3d  /-  pl3  2  `%0q  @!0:  Pl3\  3 u  3  3  3  3  3  3  3  3  0 8  T3]  /;  3  2#  0c  @3  #3,  3>  3P  3b  3t  !3  3  3  3  3  3  3  3  3 3 30 3A 3O 3_ 3p /% 3 3 3  3  3  H3 31 83 H (3 _ 3 y 3 3  3 3  3  о3  0s3* 0W 3h 3y 3 (3 3 3 3  3 3 x3+ 3< 3O h3 d 3 3  3 3 p3 pd3 \3+ s3L 3 c T3 3  3  3 p3  `3 X3  3 $ `3 = P3Z 03w 3 3  }3 |3 v3 3 3  H3 4 P3I 3c Ƞ3  83  3 r3 H3 73 (3  @/$K Џ3i Pd3 3  3  3 @3  3 3 @3) 0"V 3f 3w 83 c3 03  3  03 \3 3 p3  И3= 3\ `3 t 3 3 3 U3 3 (3 3  p3  3' 3 > 3Q 3 g ȧ3 3  3 `3  3 3  N3 P3 6 3 O 3m 3 3 3  3 3 3  G3 @3( 3E 93Y @3 q 23 3 3  03  3  3  x3  +3  3; $3O 3b 3u 3  3 3  3 3  3  3  P3 8 h3 O 3d 3 3 3 3 x3  3 h3  X3  `3; 3 S 3g @3  3  3  3  x3 /@  3" 34 3H 03 b 3 | 3 p3 3  x3 3 3  0137 H3 M 3` 3u h3 T3 3 3  P3 3  {3,  3 @  3 Y  3j  83  3  3  3  3  3  3  3 ! 33! 3G! 83]! (3x! 3! h3! (3 ! 3! 3 ! 3 " @3!" r3B" 3W" 3r" T3" 3" pT3" 03" 3 # H3)# 3G# 3 _# 3s# 3# /0# 3# 3# 83# 3 $ 3 $$ 3B$ X3 Y$ 3 s$ 3$ 3$ 0=3$ н3 $ (3% 3 % 30% 3L% 3Z% 3l% 3 % H3% r3% {3% 3% 3 & Ў3$& 3B& 83 Y& 3 o& 3& 3& 3& ئ3& 3& 3 ' 3' 3 5' 3 K' 3`' 3 x' 0l3' 3 ' 3' 3' (3 ' p3 ( 3)( 3>( {3^( I3( pr3( 73( 3( |3( 3 ) 3!) `3 9) Ȧ3T) 3h) {3) 3) 3) 3 ) 3) Э3 ) 3 * 3%* l3G* p3d* 3v* 3 * 3 * 3* @3 * 3 * 3* 3+ 3"+ 36+ 3 L+ pN3r+ PT3+ 3 + 3 + 3+ v3+ p3+ 3 , p{34, 3 M, p3 f, 03 ~, `3 , [3, j3, P3 , @3 - [3$- 38- 3V- 3 p- 3- 3 - 3- 3 - k3- `3. k3$. 03 =. `3 P. 3e. 3w. 3 . 3. z3. s3. 3 . l3. 3 / 3 / 3 7/ 3 I/ x3 _/ 3 x/ P{3/ 3 / 3 / h3 / 3/ 30 340 3 L0 3\0 3l0 3|0 Y30 30 30 30 3 0 31 @3%1 3 >1 /"l1 e31 031 3 1 ^31 3 1 3 1 W3 2 3'2 S3:2 k3\2 k3~2 P32 0{32 `732 [33 M33 x3(3 3 ?3 3\3 G3o3 `33 @33 33 A33 x33 ؟34 I3*4 0T3O4 3 h4 h3 4 3 4 p3 4 3 4 м3 4 p35 pk3'5 3 ?5 P3]5 C35 {35 д3 5 z35 3 5 X3 6 3 +6 H3 F6 3 ^6 3 w6 `3 6 3 6 36 3 6 3 6 3 7 37 p3 67 3F7 03d7 X3y7 37 P37 0d37 3 7 28 0r3&8 3C8 З3`8 z38 PN38 38 d38 3 8 39 c3?9 3 W9 X3 m9 x3 9 h3 9 H3 9 83 9 39 39 (3 : 3 #: ȟ3?: 3O: |3a: 3o: X3 : ;3: =3: Pk3: v3: P3 ; `3 *; 0k3L; 3]; q3o; 3; 3; 3; 3; 3; 3; @/! < ;3< 3.< 3K< 03 _< 83z< c3< k3< %3< H3 < p3= z3'= z3G= 3[= 53n= H3= r3= p3 = 83 = 3= P3 > `3 > h3 *> c3M> @3g> 3> 03> @3 > 03 > 3 > [3 ? 3 ? P3=? 03Z? 3w? 3? (3 ? 3? }3? 3? 3 @ 3@ 3 6@ 3T@ 3p@ 3@ 3 @ 3@ 3@ 3@ 0N3A 3,A 3 CA 3 YA 3 oA 3A Ѝ3A P3 A 3 A 3A 3B Ь3-B 3FB 3_B 3 vB 3 B 3B 3 B 3B 3B 3B 3 C @3 %C 3 3D 3D 3 E 93E 3.E p[3RE 3 jE j3E 3E H3E 3 E P[3 F pj3-F 43?F 3 WF г3 pF 3F 3F (3F 3F /3F 3F )3F Ж3G 3 +G q3LG 3 cG 3 |G /'G x3 G 3G h3 G q3H 3+H 0[3OH q3pH h3H 3H X3H 3H 3H 3 I 3I 3/I 3BI z3RI 3 dI 3 }I 3I 3I @73I إ3I 3J @3J 34J 01aJ ȥ3|J `0,J `C3J 3 J 3 K 3 K H3GK 3bK 3|K 3 K 3 K Pj3K 3 K 3K 3 L X3"L 3 8L 3UL p3sL p3 L `3 L 0j3L H3L 3M 3M 3*M 3l /N(m 0@Um /Sm `/Km /Dm /A n @0@:n 3Ln 3 cn `3 }n 3 n 3n l3n 3n 3 n 3o pq3)o b3Lo Pq3mo b3o 3o 03 o j3o 3 o p3p l3+p 0q3Lp ؤ3gp i3p Ȥ3p h3p 3p 3p 3p 3q 3q 3 )q f3| p3_| 3~| 3 | 3 | 3| 3 | 3| 3 } u3} 3=} n3Q} pb3t} p3} 3 } 3 } M3} 2~ 31~ 3 G~ P3f~ `/$~ M3~ S3~ ]3~ h3~ b3~ \3 3  01H 3d 3y 3  3 3  U3 3 G3 D3 A3 >3/ p3M P3k 03 3 Py3ƀ 3 3 3  H32 /&` 03 23 +3 Z3ׁ 3 ;3 03 i3; 3 U 3 p O3 3 3 3 K3ς 3  3  x3 h34 i3V 3 n p3  $3 3 3 Ƀ N3ۃ 3 3 I3 p3 % 835 3I 3] X3x 3  H3 3  p3 ؄ 3  3 `3  pZ3? 3 X 3 q p3  P3  `3  й3 Ӆ 3 83 3 /?> G3O B3` D3r 9(9(݆9( Πd/ 2CU u~ m `( 9=x9mp9h9Ɉ`9X9P9HH9y@989Չ09(9) 9S9j9~99   % d  `  q c ! !+ "6O `#t # 0% &E ' w ) p*d *$ `+>h +d ,ڐ ,  0,= @,u P, `,ۑ p, ,F ,~ , ,ߒ , P-1L -1 -[Ɠ 0.Y .YD . z 0A `0c P8p/ 8se @9n 9nҕ :n  :n> ;nu p;n ;n P<n <nQ 0=n =nΗ >n >nA >~ p?{ ?^ P@^D @^ A^̙ pA B\ B+9 B+,p9\ C+x9כ @C+(09` pC+9 C+1h9f C+X9՝ D+ 9R 0D+9ў `D+9F Dy H+՟9 I+S9 0IϠ I pJx; JZ K PLϡ88@N M@8ڢ`8@ Ma88@ڣ N# 8b@8@ O8&8@c `P`88@/ 0Qw88@ R>88@ 3ߧ W YO 8@8@Ψ Z8 P[<8~8@ p\`8S8@ ]8# 8@c ^88@8 _8Ĭ8@ `O88@Э b@8\`8@ 0cI8&8@e dIʯ8%8@ e f `gj gyk 8@8@ Piy: je `lf lWƲ6  0nU,9[ nU9 nU'9U PoU@9ȴ oU9; pU9 ppUp93 pUw(9 0qU 9 qUk9 qU 9 PrUlh9 rU89 sUe09 psU۹P9 sUMH9z 0tUź9 tUB9t t@8`8@= v{s wU9 pwU+x9[ wU9ܽ 0xU#9Q xU9̾ xU9G PyU(9 yU 9- zUv9 pzUX9' zUo9 0{U9 {UW9 {U9  P|YU(9 |U`9 }U89d p}U9 }U9F 0~UȎ9 ~U9* ~Up9 PU؎9 UT9 UЎ9 pU69c ЀU9 0M& 's  `8  @? {  Ў kH s s sO k k `st k Pk$ s @k k1 n k s6 s s s8 s k m/ `mq К> } \ \Z P\ @ @1 0Hv(9 }h9  }L9t 9  `S X9 X`9# Xnx9 @  @7 X~9 `H9' Hs9 Xh9 `XcH9 XP9 XK9x X`9 X189^ @XX9 X(9B X@9 `X09, Xv 9 X9 XeЌ9 `H9 @`Z09 XȌ9 XjH9 `X9 `^@9 `89  XT89 X9 @XM9 X9 X;9i `X`9 X%9S X9 X 99 XP9 @X،91 Xw@9 X9  LP9t Gx V  }[8BX8pV8R8 8O8 KM~8 Pt 9A fL88I89: 8@G893 08D894 8B89B 08?89< 8 =8 9? 08 b9 :8 @x `(/% (/8 '/e88D8s689 0Mp8{38؏9 Q`8|18Џ9 0FP8q/8ȏ9 ;@8h-89 0:08n+89 \ 8`)89 e8'89 0n8$89 08"89  889'   !/88A9w 80@8^988 pMs y8 1W8}p9  82 `& = 8C`8j = 8B `18@8 pK ! 7 <c  -A5 88\ 7  @| 08 7I  ^ (8 7F  [  8 7C  X 8 @7@  a 8 718^7878@798l7`87@8`7C 8n78 787A8m78@787A`8p7@8`7 87G8| 787 8=7k878`7`8E7m@87 87#8V`7878 7;8f78 7`8 7;@8k7 878' 7U8787#8P M9 !V@7 -H7 ,8<`7`9 M87D9q M8 0h} zt7p8 @k7@80  V= 9  E! @=! 1" " `"`{7#80K#9#@y7#80#؍9$ w7P$80$Ѝ9$ u7$@80(%ȍ9`% s7%p7%k7% 8$& a& p&h7' ^'8' e7' 0y'8( c7C( (``7(8p) lh) @S( * p]t* ` * P}K+ t+ 0;&, ;,Y7, @m8- - P;. ;w. ;. `cI/8{/W7/8/U7 0830 9090 *1 R7R1 @'/%1 '/81 $/R1p82P7G29x2`82N7293P893M7l3x93J73084I7,4p9X484 G7484@E75835`C7\585A7585?7 6p876=7b6P86;76086:778<7 87f787@67787`4788G827r888078p89.729P8_9,79089@*798:(7::8g:%7:8:`#7: 2.);8X; !7;8;7;p8&<7Q<P8<@7<08<7=8J=7u=8= 7=8>77>8h>7>8> 7> @/./? /.7T? ..6?p8?7?P8@ 7L@08{@@ 7@8@ 7A8;A7cA8A ,.7A @,.6A8$B7SB8B6Bp8B 6CP8?C6mC08C6C8D 6/D8[D6D8D6D8 E@67E8cE6Ep8E6EP8F 6GF08xF 6F8F@6 G8AG 6uG6G6G80G؋9(H@6QH80HЋ9H6H@8@ I #Iȋ9I `#J6J80J9K@6QK80K9K 6K80L9LL6{L@80L9L6M886M9lM PKM6N6:N6qN6N`6N6O`6MO6O 6O6O66P@6oP6P6P`6 Q6[Q 6Q6Q6R@6QR6R6R`6 S6ES 6S6S6S@6OT6cT 6T @6T T @sT %"U l&AU9`U9~U9U9U؊9UЊ9UȊ9V96V9VV9oV9V9V9V9V9Wh9&W`9FWX9`WP9wWH9W@9W89W09W(9X .EX .X .X PcX .Y pFpY Y ^Y Y !Z &_Z Z @HZ # [ 5[ S[ }[ p \ P\ \ \ +] m] 0] P] pB^ ^ ^ _ [_ _ 0_ PA` p` ` #a la a b }Hb 0b b P8c c c d _d d d Pe PXe e Pf rf Pf  g Pwg og9g @Bg *h gh Rh kh `k i kdi @2i 2i 2"j k_j pkj kj Pk4k k{k 0Bk kk El l l m fm m um d2n upn dn po `Do `o Po Wp })Mlp Pp @"p @#5q `#q % q /l r @0lPr 0lr 1lr 1l,s 2dts p2ds 2l t P3lTt 3dt 04dt 4du 5\u )u 5u 8t:v @9tv 9tv @:t$w :bw `8~ w ;jw <l6x <lpx `=x =|y p>xPy >xy p?xy ?x+z p@xtz @z Ap{ `Bc{ PCp{ C| DpP| E| Fp| FJ} pGp} G} HpA~ @I~ 0Jp~ J+ Kd Ld M Nl Nt` Ot Ot Pt; Pp| Ptȁ pQd QeM PR\ R `T' @Upl U\ V\ pV\) V\i 0Wd Wd Xd: Xd} Xp `YX Ya' 0Z^_ [ @] ^p* ^x` _ ` al+ bXc pbX b|ވ Pcl clP 0dX dX̉ dX PelT el 0fXڊ fl gl` pgX gl @hl) h| 0iX il j= k `lō 0m  nM n oÎ ppl pXI @ql ql֏ r pss sd psl sX@ @ty u v xd3 pxdo xl Pyd y9 @{o | d d- ppl p P ;  Ε \ `\e p 0 \2 v @!ė p I k X٘ `X% Xk X X X@ @X X͚ X `Xa X X XK X @X X4 p| pp p PpL p 0e̞ \  e  ۟ ` Pe 0 ) @|? | @| | @|F | @Ģ  : >  oף  `  1 K   @8 `p бl @X6 8x8cp8Ҩh8F  0ϩ  X   B  ͫ  W  p@ `g ^ n (p8h8 E P PүP8IH8Ű ` @8s88 p,08(8 d 8׳8O 88 ߵ8G8 `88 I880 v88U p8h8 ޻`8_X8 0P8H8# n@888a P08,(8  Y88f  k'  R p ] @  pl  @ p P@a p  QA88 {S8x8( g `k { P \\ k "k> # P% P&  'L ' ( ) `*Z 0+ , ,( -k . P/W8a88D8 4n8k88N8 :  :P ;e < 6 =v >^ B88,8x8C G 88 Q pRYQ88 V W6 X @]8<8 ` `O a  d@ |U> fP Pi8-8 Po8F8 Pu@ pwUR vP P{8Yx8p8Fh8 0  U`8X8> Є n n H pL88X    : АR U p ` Z     `I s ) 0 ТL< 883 )y L8x8 *  ' Ni   " 88 `X   U   U @ ` b   i    @a `  P   M  @ `W   `    c @  ` o   o   1 S 5 9 *% =g 6* Ar9( D s I  @Ni p8h8 @w*+ Vu `W8x89 \ w `K cO M*t fK# +] Pf jE m  y },  T 0 0 P$ `l P  <  Р &  p j      <  `    2  @    J  L  0    PK"  e  " 8%8 P `#6 P(u88X @- 18G8 4 9H `B5 G8L8 `K 88 PPC88L X"88 _8T8 c88 gNP8H8]@888p o(`82X8 wC `~G8x8F P8 8y   80!8! Њ!8h"8"8e#8# 1$ Ж|$ P$ @)%`8%X8 & t&p8&h8y' '82(x8( ( () pe) 8)8P* *@8*88`+ л+P8,H8s, ,p8"-h8- -89.x8. p.8B/8/ p/8T08083181 `1 p,28283 pJ3838;484805 0n5 85876 0r6P86H8Y7 |7 7 48 p8 t8 9 Sb9 9 9 09: }: : p ;8{;x8;`8h<X8<8S=8=8>>8>8+?8?p8@h8@ @8CA8A "A 8hB8B @'C@8C88C 0+/DP8DH8E 3_E8E8JF 9:F @=F F G@8tG88G L-H8H8&I Q" qI8I8|J ZJ ]6Kp8Kh8?L `eL fL8bM8M i|9N 0mLN`8NX8jO oO @sO @yD:P {Y|P8P81Q !|Q 1Q `R8}R8R `\@S \S dS 3T rT 0=T pU cU UP8VH8V VP87WH8W  X LX =X 1X 0 `@ 88 F@ pD18887 Y@ J ڐ O Oc O8x8x k@ }@ R/8z8ʓ V ^IU8Ĕ88 `88v d848 phI08b(8Ԙ jR8k8ř yh 8l8Κ8-888T8x8  W pP8֝H8$ @T`8X8ߞ c#08(8 8P8  l >888C8 r8<888M  88E  < \ P@88 9 y  p?  *+ R G =   0 5 P z `  B @ Ҫ  U @ #Ϋ & *O @.? /- 5@ `; C& ͭ888 O׮ Pl SN W \zگ at  n( m Pw `w 0l508(8& 8888 B[  Л˴06=86 **6a6Զ ,* 6(6 .*C v88  iK88| CA- } J< P u88 ` p  R  0 J  @ R  P L  ` W  p S   0C   @C   PM   `B   p8   6 0  "  w88l88e   E  0   < ~  P 0f   p0 P  % % &q `* p* 1N 3x 4x8588$888 @< >3 0Ese J N 8lx8 X% P\Xj aX gX pl88 pp u? }Zh }Z ~ ~ p $  w     \  ph Z   Y p8h8/`8{X8 .8}x8@8\8808;(8p8h8`8X8k88F88%P8H8 . @p  pA/668 N8 8 8 8 +`8 _@8  8 8 8:8x88x8.p8nh8`8X8$P8dH8@88808Z8 8 8 8 `8 (@8 Y 8 8 (8 8,8j888.8 e888$8g888&8i888(8k8 8 8 `8 U8888Wx8p8@8  8 V8 8 8 h8^`8X8P8"H8`@88808$(8b 88 8 `8 >@8 y 8 8 8 )8 d8 8 `8 @8 \ 8 8 8 8 [8 8 `8 @8 ^ 8 8 8 8 Q8 8 `8 @8 ] 8 8 8 8 K8 8 `8 @8  8 L888 8N8 8 8 8 +8i888-8k888/8m88818o8883x8qp8h8`85 X8s P8 H8 @87 88u 08 (8  89 8w 8 8 8; 8y 8 8 8= 8{ 8 8 8?8}888A8888Cx8p8h8`8EX8P8H8@8G88088 `8 '@8 [ 8 8 8 8 !8 [8 `8 @8  8 I8 8 8 8 >8 x`8 @8  8 "8 `8 8 8 8 H`8 x@8  8 8 (8 c8 8 8 #`8 q@8  8 8 @8 {8 8 8 T`8 @8  8 "8 k8 8 8 D8 |`8 (8 87 8f@8  8 8  8 M 8 8 8 `8 3!@8 n! 8 !8 !8 !8 1"8 h"8 "`8 "@8 " 8 .#8 b#8 #8 #8 $8 C$`8 v$@8 $ 8 $8 %8 ;%8 j%8 %8 %`8 &@8 E& 8 t&8 &8 &8 '8 :'8 l'`8 '@8 ' 8 (8 B(8 }(8 (8 (8 )`8 5)@8 [) 8 )8 )8 )8 )8 *8 @*`8 g*@8 * 8 *8 *8 +8 @+8 g+8 +`8 +@8 + 8 ,8 ?,8 h,8 ,8 ,8 ,`8 -@8 7- 8 _-8 -8 -8 -8 .8 A.`8 h.@8 . 8 .8 .8 /8 B/8 j/8 /`8 /@8 / 8 08 Q08080818S18181828U28282838W38383848Y48484858[5x85p85h86`8]6X86P86H87@8_7887087(88 8a888888 98c989898":8e:ؿ8:п8:ȿ8$;8g;8;8;8&<8i<8<8<8(=8k=x8=p8=h8*>`8m>X8>P8>H8,?@8o?88?08?(8.@ 8q@8@8@80A8sA8A8A82B8uBؾ8Bо8BȾ84C8wC8C8C86D8yD8D8D8;E8Ex8Ep8Fh8AF`8FX8FP8GH8GG@8G88G08H(8MH 8H8H8I8SI8I8I8J8YJ8Jؽ8Jн8 KȽ8_K8K8K8&L8 VL8 L8 L8 L`8 M@8 AM 8 qM8 M8 N8 SN8 N8 N8O8SO8O8O8P8YP`8 P@8 P 8 P8 Q8 EQ8 wQ8 Q8 Q`8 R@8 ER 8 xR8 R8 R8 S8 FS8 }S`8 S@8 S 8 T8 TT8 T8 T8 T8 $U`8 XU@8 U 8 U8 V8 ?V8 sV8 V8 Vx8Wp8\W7W @-.W`8 W@8 )X 8 eX8 X8 X8 Y8 IY8 Y`8 Y@8 Y 8 ,Z8 mZ8 Z8 Z8 [8 M[`8 t[@8 [ 8 [8 [8 \8 J\8 z\8 \`8 \@8 F] 8 y]8 ]8 ]8 ]8 %^8 S^`8 ^@8 ^ 8 ^8 %_8 Y_8 _8 _8 _`8 '`@8 U` 8 `8 `8 `8 a8 @a8 la`8 a@8 a 8 a8 $b8 Vb8 b8 b8 b`8 c@8 Fc 8 uc8 c8 c8 d8 Vd8 d`8 d@8 e 8 9e8 ~e8 e8 e8 6f8 lf`8 f@8 f 8 g8 Zg8 g8 g8 h8 =h`8 oh@8 h 8 h8 i8 Wi8 i8 i8 j`8 Xj@8 j 8 j8 j8 7k8 ik8 k8 k`8 +l@8 tl 8 l8 l8 Bm8 m8 m8 m`8 *n@8 `n 8 n8 n8 o8 Yo8 o8 o`8 p@8 Tp 8 p8 p8 q8 Cq8 yq8 q`8 q@8 0r 8 gr8 r8 r8 s8 Ds8 ~s`8 s@8 s 8 #t8 dt8 t8 t8 u8 Ku`8 u@8 u 8 u8 v8 Xv8 v8 v8 w`8 =w@8 vw 8 w8 w8 -x8 `x8 x8 x`8 y@8 Cy 8 ty8 y8 y8 z8 Dz8 zz`8 z@8 z 8 {8 C{8 w{8 {8 {8 |`8 S|@8 | 8 |8 }8 B}8 x}8 }8 }`8 7~@8 p~ 8 ~8 ~8 8 [8 8 `8 @8 1 8 f8 8 Ҁ8 8 F8 `8 ā@8  8 78 t8 8 8 :8 |`8 @8  8 ,8 b~8 ~8 „~8 ~8 `~8 J@~8  ~8 ~8 }8 (}8 V}8 }8 `}8 @}8 ! }8 X}8 |8 |8 |8 !|8 P`|8 @|8  |8 |8 &{8 X{8 {8 {8 `{8 @{8 G {8 {8 z8 z8 z8 9z8 g`z8 @z8 ̋ z8 z8 Iy8 {y8 y8 ڌy8 `y8 E@y8  y8 Íy8 x8 ;x8 x8 x8 `x8 4@x8 w x8 x8 w8 'w8 kw8 w8 `w8 /@w8 k w8 w8 v8 v8 [v8 v8 ϒ`v8 @v8 Y v8 v8 דu8 u8 Qu8 u8 ǔ`u8 @u8 < u8 wu8 t8 t8 Ct8 t8 `t8 @t8 3 t8 lt8 s8 s8 1s8 _s8 `s8 Ř@s8  s8 0s8 fr8 r8 ҙr8 r8 :`r8 m@r8  r8 r8 #q8 aq8 q8 ɛq8 `q8 -@q8 ` q8 q8 Ŝp8 p8 2p8 ep8 `p8 Н@p8  p8 =p8 vo8 o8 o8  o8 Nn8 n8 Οh8 `8Qn8 n8 `n8 @n8 / n8 pn8 m8 m8 m8 Sm8 `m8 ޢ@m8  m8 =m8 kl8 l8 ߣl8 *l8 w`l8 Ĥ@l8  l8 Yl8 k8 k8 k8 k8 J`k8 @k8  k8 k8 6j8 ~j8 j8 j8 ?`j8 @j8  j8 j8 Ui8 i8 i8 'i8 n`i8 @i8  i8 <i8 h8 Ϋh8 h8 Mh8 `h8 @h8  h8 h8 Bg8 wg8 g8 g8 .`g8 [@g8  g8 Ϯg8 f8 Pf8 f8 f8 `f8 ,@f8 g f8 f8 e8 e8 X8YP8H8ܱ@8 88_08(8 8&e8 he8 `e8 @e8 4 e8 ce8 d8 ʴd8 d8 Bd8 }`d8 @d8  d8 Ed8 c8 c8 c8 =c8 |`c8 @c8  c8 Mc8 b8 ٸb8 'b8 eb8 `b8 @b8 U b8 8Ѻ8b8 Oa8 a8 ʻa8 a8 D`a8 @a8 μ a8 a8 Y`8 `8 `8 (`8 h``8 @`8  `8 @`8 _8 ֿ88Y88_8 _8 \_8 `_8 838w@_8  _8 _8 G^8 ^8 ^8 ^8 *`^8 W@^8  ^8 ^8 ]8 #]8 b]8 ]8   @8M8808(8 K`8X8P8H8Y b 8Vx8p8Eh8 * *B 0*888l8  8u88f8 & t p   X88Y `+ /j088(8 5 p8Wh8@8:88`8X8P8H8y @`8X8wP8H82 `YsP8H8V _08#(8 d8w8 @jI88R oP8H8 u  `) 88D88_@888z88 ` 8=888 P 8N88K8 @ 8y88\88A8  8H888R88 B888H888W ${ @! 8O8 p p8th8`8KX8 j8Y8 p F `8X8P8H8    88e08(8 #h 8q8 0h < 88/ p<$U = P  \N . g= e@88808S(8 u |F0.888<888O '  @08*(8~ 0 ? 8g88&8 8u8 A@888< p  8e88 8o 8 8  P$   `(  @) 8r 8 89 8 8 8i  `8  p<  <H < B" 8K8 PM ( `Rf @^@6DH6 k*666 q*6Ȑ6 w* y6 { 0|А6Nؐ6 0* 66) P*u   N P  p. y   /FP6X6I *p6x6 *`6rh6 *?66N  p* 6%!6! *!6}"6" *J# }#8#8$8`$8$ $ &%8y%8%8&&8& & ' ?'@8'88(08~((8( ,)8)x8* e*8*x8 + d+@8+88[,08,(8V-o8 - 8.8.8 /8/8/8r0 60 :0 pJ f1 pW1 Z 2 ce2828\3838W4848R5 rO5`85X8V6 7q 6 @zW68W7878,888898q9898H:8:8;8;8; 4< {< `< 0=@8p=88= $>8>8?8?8?8m@8@8VA8A  B8aB8B r C08C(8D 8D8E b UE8E8E8:Fx8Fp8Fh8(G Jq dG6hG G lq H BuT is T CT s &U J]U s U Q U8 Vx8rVp8Vh8W s DTW P W@8W88TX08X(8Y 8UY8Y s 2Y j m8m8m8 5w  @< Mw " @? d ew  I w 06 0L x с N 3x l V Kx  XR cx  _ {x = b x  pi 3 x  s7΅ x  Puw^ x  vC x 0& zq #y  ~C ;y 0R 8*8 ky &  ДCT y 0  y . Cn y 0  z > C~ !z 0Ì  Qz ^  iz  7 z  ʎ z  `` z  @ z E P z  p-88" z 0p P8(8 ){ , ` /8ϔ8t U{ t  08(8 8888  { LZ  | U 0 j| UZ P | U p } U\ u i}  WZ } U p8u88r8o8 2@o8 q8x8pp8h8o`o8  } r P!>883 H~ D~ (  ~ V 3X ~  6 ~ U P9   <l< *  ? ̦ B  F#a Z  I# r 4 Lt  , R  H V   `Y8h8ת  0 n d888y8ڬ  & px>g ! ~J8Qx8 ʀ . uG88 &I   ڰ ` 6 ]  N  %`8zX8Բ f ? j8x8p8h8`85X8Ƕ8R8 3 Pp  @ ؁ 7 v  , ? , bƺ   &q ,  C & #p Z  # q P @ n 8 & 0c;   cO 3  Nݿ J )- `N s ) } 0H | p  -2  / ˃ Q @ ' & e ! $C 'B !  ) 8   +=Y O ? 0K + P3y  7J P8~H8 A^7 6} @H  < `Nc E  \ | |  a  ʅ  M d9`8X8  j[=  ( Pp @r{ 7 (R w _   6 d p 8>8858 " 3 U N m u  `  ! ͇ L }   088 88 7N 4 u @ Щ8u8 L 7R     S ˈ ; Y8L8  # iA ) H i q H `M F `` F? ` E F `. F  0 (@8p88 ъ  508{(8 | [ J ׋ cP8H8q :  ` Z 7T `"k c %k c^ @, W  0 % n 5b 6 @  =*U  PBy m 7I Hy 7 PO3 8x8 ێ ( VC88  ($ ^f + 0 Pc [ 6 0k6{ r  ps1  TT y ݐ g W' D p [    88' z z @E08-(8 g V M Ԓ  ' 8k88Z88K8  QP 888 8 )  G Zs P T( f i `[ ^  0 u AO b . p "8t888x P pr8M8 4  i9886 S  i8_8 m ?   )   (  v A  Af  y ? A  G  A  !   A  ' M 8 82   zo  1 8 8  | |  ;   8R  `C e  0 A  L RO q  0U   AW [   c F A Pi U08d(8 8k8 қ i@ w j ; A ~ U&P8H8+@8884 | i @8b88 iL 0 08 (8 8 8 N + $ y v   U ` '- U b < 08(8 n G @888  h P     :\  ` R 8 !8! t! 6" [ d" !%" A # !fG# .# !6# . A$$ `!6p$ o A$ !6 % A^% !6% A% ! C& 2 & )!/& <9' .!'  X' 3!( w ;_( :!d( ( 8>8?8?8@8@8A8A8 Bx8Bp8Ch8C`8DX8DP8DH8}E8E8~F@8F88yG8G8zH08H(8uI 8I8pJ 09"] J J F"2K  K O"K $ L pW"-bL8L8wM8N8N N p"L 'O z 7qO z"JO P 0"U YP 9 7P "P p ADQ ""QP8QH8qR@8R88`S S "jS 7 A6T 0""xTp8Th8cU`8UX8RV x V `"V A2W `"xW ? AW `"X AFX "X X "&Y 8Y8Y j %AZ "Z Z `"W&[ ^ s[ "[8>\8\  ] "Y \]8]8^ _^ B _  #BM_  _ 3a`  ^` p#$ap8ah8b #t^b s &b "#b Mc `*#Gc  Uc p2#O 9d t d <#O d &e `F#se ~ Me pL#Vf ˸ $Bf PS#qf :f W#)g ) @lg p`#g i h `i# ]h  h s# i 3i |#j cj p#j #Gk  |k @#Zk pXl #Zl pm #G Lm { m # m  ^?n #nP8joH8Lp b pp #gp8q8'r8r8ns8t8t ҽ Vt #Ou8ux8^v ( v88w8w8Ix8x8\y8y  E\z #bzP8B{H8{@8|880} W {} `$b}08m~(8 88m  $$ 88^888a8 ` ($$8]888r8!8Չ n * 0;${@888X08Ƌ(89 8888 ! Q 0T$ @ A e$- Au x$ď8*8 88b  p$8;8 )ʒ $8888. } @$  4 $ Jp8Ɩh8G # 4 $`8X8P8H8%@888808(8K W  $( $ T8ԝ8Y o $B 08d(8ܟ R o' %. i`8X8 hY @%  ) ڡ  %z `8ZX8P8H8@ } .% _  ?%̤  D 0F%L a D `N%#ڥ08<(8 88p  b%% 8r8ܨ8C888 d Ӫ z%%888Ux8p8(h8 0  %':88 8p8ܯ 6 %z 88y ± %U`8jX8˲P8)H8@888M  %U8F888h8ƶ8) z %/Ʒ8.88x8tp8޹h8M`8X8& ` w &_û888޼8A888`8þ  p>&b  R& B m&)88T88!888Rx8p8h888U88" ~ p &  U f &2p8h881x8  0&  Y &688 /  &}& | &   &D, @` & 8288;88F88Q88\88g88r8 M P 'e b  P ' : t  `'p8h8i  +'  q/ E'y PI' N'A [8 _'1x 1 e' 88%  `w m'@8;88 q 4  'Q08(8L = '4  & 'S8 888} q w P' 0 '$ @88808(8 X  'P 'u`8"X8P8H8 @  @'" M@888: N  p' e 6 0(y  3 (8D8883 I p 6@66@6@6P6$8 760R6P{`66`@6 65 *r * *  *cI `*' *F */ *>b6 *c */ *>j6 6P *s *1h *@ 66x) `*eh *1 *@@6/ @*@i * @*& *5.6`d *- *<6`" *%a *46 p*  @*)M *86 * *+G *:6 * *[ * *. `*=:@6b * * *.`63 *j *. * * *)T *86 *p *. *c `*# *2`6P @67 6j6P `(-O @'-  &-O @&-* &-96P6*`6Y6P ,-K +-  @*-. *-&o )-56 @7-m/ 4-To 1-`66x  E-m_ C- >- <-<& `;-cn6P6@ Y-j`6`G &.6 `%.j6``6P) ).U^ (.@6@6x *.V&7@[ 7P *.^7`7- +.Aj @+." *.]@7P 7$7@O +.L~7P7 7 @..,  ,.O`  ,. 7P 7 7@ @7P<  `..Mm `7@  /.Q 7`  @0.a1  /.Pd  7@  7P  0.f  #7@L "7P|  @1. `%7` %7P  2.G '7@u `'7P  3.f *7@)7P1 4.fc@,7@+7P 4.e.7@ .7PM `5.e`07@ 5.V@27@ @6.VO 47@} 6.P67@ 7.O 77@> `7.Ss97@ 7.M;7@ 8.N1=7@^ 8.M`?7@ 8.N@A7@ @9.MM C7@{ 9.NE7@ :.L F7@6 `:.MgH7@ :.PJ7@ ;.M&L7_ B./ A.0 A.?>N7@t /3S7PS7T7@3@T7x^ $/V7` @D/( D/7IX7`| D/* D/9@_7 6Z7f M/- M/1 L/  L/F L/ `I/; I/'  H/6N`b7b7P @P/& P/"0 O/>k O/* @O/9d7`" S/"\ R/1@g7f7x `* *YE *M @*P *- *,D *!s * * *- @k7D `j7r  *  `*"  0*&!m7@Y!`m7P! @*! *Q%" *y" @*" *b" *"+# @*<g# *# *# *I!$ *g$ @*>$ *$ *0% **u% *9%r7 %q7P&& @*_& *<& `*& *a' *$W' `*>' *,' *;%(t7`]( */( *>(v7`() @*.p) *=)x7) *,* *-s* `*<*z7* *,+ @*,r+ *;+}7+}7x, +G, +3{, +, +, +- ` +M- +:- +- +- +"(. `+"e. +". +9. +A/ +G9/ +j/ +/ +/ +$/ *60 *j0 `*\0 0*0 **1 *3:1 *p1 *1 *<1 *2 *I2 *A|2 `*#2 *227@+3 *a37P37373 73 74 *;4 *h4 *4 @*%4 *i47*57W5 `,"5 @,5 `,5 ,0C6 ,x6 {,6 {,6 u,+7 r,f7 n,?7 @k,?7 e,Q8 `,D8 Y,x8 `T,8 P,;8 `J,9 J,0J9 =,# 9 4,K 9 `1,9 1,O8: ', o: `%,{: #,: ,$; @,Hd; , ; ,; ,,<7 S<7z< ,< ,< , = ,8= ,Me= ,= += +!= +3> +l> @+> +> + > + (? +`? @+? +7? `+? +b*@ +[[@ +@ +@ p+@ +'A +[A `+2A +$A +A @+.B +FB `}+ vB {+BB7PB7B@7C 7HC ,0zC ,C ,C `, D 0,YD ,D ,D ,D , E ,UE ,~E `,E ,4E ,wF `,2OF ,F @,$F ,YF ,1#G ,gZG ,!G ,0G7PG7H7`MH`7@{H %-H $-H -I @-TI `-I -<I -J ->J -sJ -J `-IJ -K -DPK -zK ` -4K -K -2L @,hL ,L ,L `,M ,IM ,~M ,M ,M ,N @,PN ,N ,N @,MN ,!6O ,wO ,/O7@O @-:P7@jP7PP 9-P@7Q7x7Q X-(xQ @X-cQ T-Q O-3R7P`R7@R [-`R 7R7xS c-;MS a-~S _-S ]-S7@T`7PST d-T 7@T7xT j-RU7`LU7U }-U x-U@7`(V7WV `-V -V7@V@7x&W @-`W 7@W7xW -KW7X -NX @-0X -X 7X N-+Y L-hY K-dY G-,Y E-RZ 7x3Z 7`dZ7@Z +zZ +[ +M[ `+q[ +b[ +[ +4\ +(w\ `+7\7\ 7h] 6+z_] 5+] 3+H] `2+V^ 1+V^ 1+r^ 0+l^ 0+n _ /+cG_ .+_ -+_ @-+(_ -+7A``7r` 7@` !+z` +ra @+HZa `+a +qa `+k b +bFb +~b +b +(b `+7@c7qc7@c `++c (+sd '+IYd &+d &+rd %+l e %+cEe $+}e #+e "+(e "+7?f 7^f7 f@7f A+f `?+T#g =+Rg :+g 8+ g 8+g `8+.$h7PDh7ch@8`h@7h -h -, i -7i -Oai -ti @-i -i -#j -Uj P-j -j -pj -&k -NVk -k -k -Uk - l -[:l `-Qjl `-l -l `-Ml -<m `-Jm -um `-=m --m -Nn -5n -Vbn -n -n @-ln `-'o -_o `-<o -o -p -'Cp -g{p@8p -p `-+0q -:wq 8q` 8xq @>.q `<.!r ;.Or ;.r p;.r 8Pr` 8r 8 s 8?s .qs .s .4s .t .77t .dt @.t .t .It .;u .iu `.cu `.u .eu .$v .Yv `.v .v .rv .&w .\w `.w @. w`8w8"x @/eNx /~x `/x //x .Fy .)Hy .|y .y .y .z .Kz . z8Pz8z@8`{ @ /G{ /"{ ` /{ /{ /)| /b| ` /| /| /}8?} `/!z} /.} /I ~ /K~8~ 8~ //~ /&? /6 !8 / /M p/ /ʀ `/p $8G#8xz `#/c "/a  !/?I&8@|@&8x #/# )8@(8xK $/ +8@+8x @$/ `-8@R `$/%@/8P@18P@38P58@E@58xt $/@88@܅78  1/ E :8`u `1/' 1/6@<8P <8B<8n ,/ (/ p'/ `?8@S>8 0/#ˆ@B8@@A8 00/ED8@x D8x `0/#G8@F8xC 0/z`I8@H8x؊ 0/K8@:@K8xd 0/N8 M8 8/ `6/6N `5/ @4/ 3/ 2/1 1/qQ8`Q8Pɍ @G/ F/> E/t @E/# E/2 U8@T8D *r *} *׏ *  */M@X8`}W8 *' *61[8`b@Z8@ @*(Ց *7 x*FQ x*P x*Lђ @y*' y*@ {*\l |* }*}œ `*; *( *X @* *” *' * *~C `*w *  *Е * *3 p*g *, *ʖ @*H *' @ +[ + +] `B+j  L+;$ M+Q @O+~ `Q+ Y+Ҙ @i+= z+e2 ,Eb ,p ,ʙ --L --% `.LT . `. .)ך . .(% .sK .4 .. !.^ 4.> A.!X PA. @B.Ü B. C.% D.V @F.+} J.3 J.؝ K.  U.; `W.a @Z. @^. @a. f.B  `i.1 `m.,a o.  q.2 u.ܟ y. z.4 z._ |.v .Z .  .v ., .(W . . `.ҡ . .  .F .} . `.ˢ . .A .J .z . `.ţ . . .5 .[ .} `. .pɤ ._ . .}? /An /3 @/?ޥ @%/  */:> 8/l `9/+ 9/4Ϧ 9/1 :/, @:/Ve :/U =/ϧ =/ >/4 @?/h ?/ @/PΨ @/  B/2 C/e I/ J/#٩ J/$ K/(Q `K/$ K/3Ԫ K/( L/)Q `L/( M/)˫ @M/& N/: N/jf P/- @S/4Ӭ S/ S/1 T/X U/ 0U/ DU/ۭ KU/ `U/Y) V/P T3u Z3 j3 q3ܮ Pr3 r3 pz3> 3] 3z 3 3 p3Я 3 3 `3 ! 3 ; 03 U 03 o 3  3  3  3 հ 3  3  3  35 P3 M 3b x3w 3 83 @3 `3˱ h3 p3 93  @3 G31 N3E 3Y 3m t3 3 3 B3 3Ѳ 3 3 3  3  33 3F #3Y )3l /3 S3 X3 3 3dz 3ٳ 3 3 Y3 ^3! 33 [3D 3U J3e 3s 3 3 3 o   Ӵ8ߴ`d6 Xd615?   ,ȵ !  B 1# Զ  P  P P# P  P X   " TN   !  @ `l"{  Ѻ # P ov  ʻ  Q(  )M O!ۼ ) 0 X8 ", ` "  w Կ  H     p s    `:!!'3: P]!l  p R= ' 0 P _ " 0  ~ Q   m"  @  o  h8 t " P# 'Aj   P ^ P  Pm ]# @ p ! R# F   % A }Y m"  ( P     @ E# (8 !$     $Q n"6   'A R !r P  и$f # k"M6Z   AV  P   O!: 0S `-s @  8 ` `l"M $8   I    P 6 0   #J @@!M "  3  D )#bx8~   ~H8 _ # P o !  "E `; { `   n"< m"v # `  @   @  ` 5 W     o `u   6   A }  cp8{  # p K 0s  S p ]     TK     P'A `% z" 0 H $S  K   Q 0; !0 P  p  / P 0  Я#  B @ t `   0  ' @ I    ` 7 p  " * \  # 0  )#^ @@!M P o S ! p$8 p 6o , 0  Q   m" N p  "  p ]#  &AC   E# #   ;# P  v p Xu68 ^#P  ! L' ` YP8x  p $ 8 `-X  `8C ?!7 $   A >!    ` ^  k"    P, 1 `  Я#08 ] @  @   и$f(   o"  @ h   V $8 `!  @B ~ p!   `; {c P o `! 0@8 g  ;`8R q    M P  P#   =     F  `  @ !  L'-  p    8  ;#|  o"     b    `  F4  =T    P]!  z" "O `  # r U `:!! 0 ( !88 `  h   0 ( GZ \!! '    _ p      M r E   @ !% в? @  p  *M ` y `  E#o& u  R# "Q  ` O @  $!   T 1#   "B #     ` Y B  P" 0 = @$m E#o >! A!  ~  0  "!  m! 3! @ ! 0 C" " 0; !" *# 0 #  # P  K$ $ P#$ ";%  % #% -& "^& ",&hw6& @ 5' 0  ' `' ( @(  ( P"( 8n) P ) Ї<* #*  * P,+ p!=+ ^#+!o68+0,R," P%,- p6-!r68- @--- .<.J.\." L%../`/q//// 880! 3Oe0s000!r680!k6D1U1! 3}112! 3Zw2 `2223K3" "Y3! 33!Hg633 kA4445l555555 6!i6^6m6666 787\777!h68778!l6(8! 3Qr8! 3.88 9)9 O9!f699!pi6:5:!Hr68:!m6 :::! 3Q;" `d'0;" &/<<<I<u<!po68<<!hk6,=H===>5>!k6>!`j6>!Xh6>W???88?! @3L:@@@" p$ZAfAABB-BOOOO P#P" 'CpQQQQ! k6TRwRRRRRSS$SSSS8TTT U! 3BOU\UqUU! 3UU)VV!pg68VW%W`88ZWWWWWW88'X5X`88mXXXX! 3NY$Y4YNY `Y!g6YYY!xj6@Z!r68ZZ[6[`[[ [" `"Y \\9\i\y\ \\:]J]]]]!g6^^3^}^^^!j66__!j6_ `S`` ````8aWapa}aa" @$bb!s68>c!p68cccc" M'edd! 3e^e!t68ee P~ef*f!8n60If\fmfff kMgP9ggggh.hRhshh! 3Mh iKi!n6 miiii+jjj #(pjjj! 03kTkek{kklylllm=m!hq68mmmm/n @#fnn! 39nn!o-o`oqo! p3o89oopSpp0qJqq! 3Cqr @Sr`r! 3Krrr! 3O/sKs sss+t! 3L{tttttu,uOuvuu!k6u!k6Vvjvvvvv"w!f6Swxww!i6w x#x}xx!g6y `kiyyyy!r68zTz" @&z{!0j6P{!h6v{{{!(s68 | |H|r|||!xt68$}! @3c}88}! P3}~~C~@9j~~~4Bi! 3!t68@ uyҀZfuF  KxB!J!(h6c!8o68ʅ! P3+!h6K #" k&_!k6އ! `3SF!p68 L" P&)!8k6  PB!l6^r! `3YȌ>2BbmӍ$6]w!n6 7p̏5Gy`88*@Lf(^v" p'5!f6;M}!o68ϓl" ${> @l`88 $! 3Bz͖FT!p68! 3/͗Pj!ph6m! `3"KDe! 32ɛ!j6>! 3:|Ԝ! @3/)<Uj}! 3() 88`! 3Zatğ9ɠ! @3m:Kѡ! 3'/̢6j!n68ۣ Ќ `9 >rǤ!Xi62ޥ1DRd! 3..AQa} -88b" -%]Ahߪ! 3" %y۫6GN88K&?!q68cq ?! 3[ _q fw! i6  ! u68i|ڲCb۳ @" `'DIZ7Hz>O88!Pp68ҷ/!p68x!j6иF! @3H@ % ׺1A 0z" &Ի! 3$ռ!i6J˽ݽG!g6ݾ I s̿E]q[ T" P&4!xl6f!j6  36Bb!j6r$^!t68! 3A^! 03!f6Rؼ8t!j6Zn!xm66R!i6 ! 3V  "kqkw" F"YKW88! 3H88Id k ! @3O9[/!h6Q[!0g6! 3Xdw! 3Mgz)6!0q68! 3P"dv!g6&" <&%\h! 3L! 3$&Er! 33! 3V3E#!Hj6!@i6^! `3E!8l6 5G V" pr#M!k6 "' 88ar!f6 r!o68Sr88!n6 9!f66{" @' F !Pk6{! @3-!k6&!q68n!hn6 crti.ocall_gmon_startlib.cpp_ZL40__pyx_f_7pyarrow_3lib_timeunit_to_stringN5arrow8TimeUnit4typeE_ZL26__pyx_mstate_global_static_ZL54__pyx_pw_7pyarrow_3lib_10NativeFile_8download_3cleanupP7_objectS0__ZL48__pyx_pw_7pyarrow_3lib_13MessageReader_7__iter__P7_object_ZL49__pyx_tp_traverse_7pyarrow_3lib_SignalStopHandlerP7_objectPFiS0_PvES1__ZL46__pyx_tp_traverse_7pyarrow_3lib__PandasAPIShimP7_objectPFiS0_PvES1__ZL60__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct____iter__P7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_1___iter__P7_objectPFiS0_PvES1__ZL60__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_2_genexprP7_objectPFiS0_PvES1__ZL57__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_3_keysP7_objectPFiS0_PvES1__ZL59__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_4_valuesP7_objectPFiS0_PvES1__ZL58__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_5_itemsP7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_6___iter__P7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_7___iter__P7_objectPFiS0_PvES1__ZL58__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_8_itemsP7_objectPFiS0_PvES1__ZL60__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_9_genexprP7_objectPFiS0_PvES1__ZL62__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_10___iter__P7_objectPFiS0_PvES1__ZL62__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_11___iter__P7_objectPFiS0_PvES1__ZL62__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_12___iter__P7_objectPFiS0_PvES1__ZL64__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_13_iterchunksP7_objectPFiS0_PvES1__ZL65__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_14_itercolumnsP7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_15_genexprP7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_16_genexprP7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_17_genexprP7_objectPFiS0_PvES1__ZL61__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_18_genexprP7_objectPFiS0_PvES1__ZL62__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_19_downloadP7_objectPFiS0_PvES1__ZL60__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_20_uploadP7_objectPFiS0_PvES1__ZL87__pyx_tp_traverse_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadataP7_objectPFiS0_PvES1__ZL32__pyx_tp_traverse___Pyx_EnumMetaP7_objectPFiS0_PvES1__ZL29__pyx_tp_clear___Pyx_EnumMetaP7_object_ZL29__Pyx_CyFunction_get_qualnameP22__pyx_CyFunctionObjectPv_ZL28__Pyx_CyFunction_get_globalsP22__pyx_CyFunctionObjectPv_ZL25__Pyx_CyFunction_get_codeP22__pyx_CyFunctionObjectPv_ZL25__Pyx_CyFunction_traverseP22__pyx_CyFunctionObjectPFiP7_objectPvES3__ZL27__Pyx_call_next_tp_traverseP7_objectPFiS0_PvES1_PFiS0_S3_S1_E_ZL24__Pyx_call_next_tp_clearP7_objectPFiS0_E_ZL25__pyx_bisect_code_objectsP26__Pyx_CodeObjectCacheEntryii_ZL24__Pyx_Coroutine_traverseP21__pyx_CoroutineObjectPFiP7_objectPvES3__ZL39__pyx_tp_new_7pyarrow_3lib__WeakrefableP11_typeobjectP7_objectS2__ZL54__pyx_tp_traverse_7pyarrow_3lib__RecordBatchFileReaderP7_objectPFiS0_PvES1__ZL44__pyx_tp_traverse_7pyarrow_3lib_BufferReaderP7_objectPFiS0_PvES1__ZL38__pyx_tp_traverse_7pyarrow_3lib_OSFileP7_objectPFiS0_PvES1__ZL48__pyx_tp_traverse_7pyarrow_3lib_MemoryMappedFileP7_objectPFiS0_PvES1__ZL42__pyx_tp_traverse_7pyarrow_3lib_PythonFileP7_objectPFiS0_PvES1__ZL37__pyx_tp_traverse_7pyarrow_3lib_ArrayP7_objectPFiS0_PvES1__ZL41__pyx_tp_new_7pyarrow_3lib__PandasAPIShimP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib__PandasAPIShim_ZL43__pyx_tp_traverse_7pyarrow_3lib_RecordBatchP7_objectPFiS0_PvES1__ZL44__pyx_tp_traverse_7pyarrow_3lib_ChunkedArrayP7_objectPFiS0_PvES1__ZL47__pyx_tp_traverse_7pyarrow_3lib_SparseCSFTensorP7_objectPFiS0_PvES1__ZL47__pyx_tp_traverse_7pyarrow_3lib_SparseCOOTensorP7_objectPFiS0_PvES1__ZL47__pyx_tp_traverse_7pyarrow_3lib_SparseCSCMatrixP7_objectPFiS0_PvES1__ZL47__pyx_tp_traverse_7pyarrow_3lib_SparseCSRMatrixP7_objectPFiS0_PvES1__ZL38__pyx_tp_traverse_7pyarrow_3lib_TensorP7_objectPFiS0_PvES1__ZL37__pyx_tp_traverse_7pyarrow_3lib_FieldP7_objectPFiS0_PvES1__ZL28__Pyx_Coroutine_get_qualnameP21__pyx_CoroutineObjectPv_ZL24__Pyx_Coroutine_get_nameP21__pyx_CoroutineObjectPv_ZL36__pyx_tp_new_7pyarrow_3lib_StopTokenP11_typeobjectP7_objectS2__ZL37__pyx_vtabptr_7pyarrow_3lib_StopToken_ZL26__Pyx_RaiseArgtupleInvalidPKcilll_ZL30__Pyx_RaiseNeedMoreValuesErrorl_ZL29__Pyx_CyFunction_Vectorcall_OP7_objectPKS0_mS0__ZL24__Pyx_ErrOccurredWithGILv_ZL29__Pyx_get_object_dict_versionP7_object_ZL25__Pyx_PyObject_SetAttrStrP7_objectS0_S0__ZL25__Pyx_PyObject_GetAttrStrP7_objectS0_Py_DECREF_ZL30__Pyx_CyFunction_InitClassCellP7_objectS0__ZL21__Pyx_CyFunction_reprP22__pyx_CyFunctionObject_ZL25__Pyx_CheckKeywordStringsP7_objectPKci_ZL27__Pyx_CyFunction_CallMethodP7_objectS0_S0_S0__ZL19__Pyx_PyObject_CallP7_objectS0_S0__ZL27__Pyx_PyDict_GetItemDefaultP7_objectS0_S0__ZL32__Pyx_CyFunction_get_annotationsP22__pyx_CyFunctionObjectPv_ZL25__Pyx_CyFunction_get_dictP22__pyx_CyFunctionObjectPv_ZL19__Pyx_PyList_AppendP7_objectS0__ZL21__Pyx_ListComp_AppendP7_objectS0__ZL18__Pyx_CppExn2PyErrv_ZL15__Pyx_SetVtableP11_typeobjectPv_ZL24__Pyx_CalculateMetaclassP11_typeobjectP7_object_ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_7___iter__P11_typeobjectP7_objectS2__ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_7___iter___ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_7___iter___ZL20__Pyx__Coroutine_NewP11_typeobjectPFP7_objectP21__pyx_CoroutineObjectP3_tsS2_ES2_S2_S2_S2_S2__ZL25__Pyx_copy_spec_to_moduleP7_objectS0_PKcS2_i_ZL33__Pyx_PyUnicode_ConcatInPlaceImplPP7_objectS0__ZL25__Pyx_Coroutine_get_frameP21__pyx_CoroutineObjectPv_ZL43__pyx_tp_dealloc_7pyarrow_3lib__WeakrefableP7_object_ZL43__pyx_tp_dealloc_7pyarrow_3lib_CacheOptionsP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_ProxyMemoryPoolP7_object_ZL44__pyx_tp_dealloc_7pyarrow_3lib_StringBuilderP7_object_ZL48__pyx_tp_dealloc_7pyarrow_3lib_StringViewBuilderP7_object_ZL41__pyx_pw_7pyarrow_3lib_9_gdb_test_sessionP7_objectS0__ZL27__Pyx_PyImport_AddModuleRefPKc_ZL18__Pyx_PyMethod_NewP7_objectS0_S0__ZL25__Pyx_CyFunction_get_nameP22__pyx_CyFunctionObjectPv_ZL20__Pyx_ExportFunctionPKcPFvvES0__ZL7__pyx_m_ZL34__Pyx_modinit_function_export_codev_ZL34__pyx_f_7pyarrow_3lib_check_statusRKN5arrow6StatusE_ZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusE_ZL45__pyx_f_7pyarrow_3lib_maybe_unbox_memory_poolP34__pyx_obj_7pyarrow_3lib_MemoryPool_ZL37__pyx_f_7pyarrow_3lib_box_memory_poolPN5arrow10MemoryPoolE_ZL39__pyx_f_7pyarrow_3lib_wrap_array_outputP7_object_ZL32__pyx_f_7pyarrow_3lib_wrap_datumRKN5arrow5DatumE_ZL38__pyx_f_7pyarrow_3lib_get_input_streamP7_objectbPSt10shared_ptrIN5arrow2io11InputStreamEE_ZL32__pyx_f_7pyarrow_3lib_get_readerP7_objectbPSt10shared_ptrIN5arrow2io16RandomAccessFileEE_ZL32__pyx_f_7pyarrow_3lib_get_writerP7_objectPSt10shared_ptrIN5arrow2io12OutputStreamEE_ZL37__pyx_f_7pyarrow_3lib_get_native_fileP7_objectb_ZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5__ZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0__ZL33__pyx_f_7pyarrow_3lib_ensure_typeP7_objectiP40__pyx_opt_args_7pyarrow_3lib_ensure_type_ZL40__pyx_f_7pyarrow_3lib_string_to_timeunitP7_object_ZL45__pyx_f_7pyarrow_3lib_pyarrow_unwrap_metadataP7_object_ZL43__pyx_f_7pyarrow_3lib_pyarrow_wrap_metadataRKSt10shared_ptrIKN5arrow16KeyValueMetadataEE_ZL51__pyx_f_7pyarrow_3lib_pyarrow_internal_check_statusRKN5arrow6StatusE_ZL53__pyx_f_7pyarrow_3lib_pyarrow_internal_convert_statusRKN5arrow6StatusE_ZL39__pyx_f_7pyarrow_3lib_pyarrow_is_bufferP7_object_ZL42__pyx_f_7pyarrow_3lib_pyarrow_is_data_typeP7_object_ZL41__pyx_f_7pyarrow_3lib_pyarrow_is_metadataP7_object_ZL38__pyx_f_7pyarrow_3lib_pyarrow_is_fieldP7_object_ZL39__pyx_f_7pyarrow_3lib_pyarrow_is_schemaP7_object_ZL38__pyx_f_7pyarrow_3lib_pyarrow_is_arrayP7_object_ZL46__pyx_f_7pyarrow_3lib_pyarrow_is_chunked_arrayP7_object_ZL39__pyx_f_7pyarrow_3lib_pyarrow_is_scalarP7_object_ZL39__pyx_f_7pyarrow_3lib_pyarrow_is_tensorP7_object_ZL50__pyx_f_7pyarrow_3lib_pyarrow_is_sparse_coo_tensorP7_object_ZL50__pyx_f_7pyarrow_3lib_pyarrow_is_sparse_csr_matrixP7_object_ZL50__pyx_f_7pyarrow_3lib_pyarrow_is_sparse_csc_matrixP7_object_ZL50__pyx_f_7pyarrow_3lib_pyarrow_is_sparse_csf_tensorP7_object_ZL38__pyx_f_7pyarrow_3lib_pyarrow_is_tableP7_object_ZL38__pyx_f_7pyarrow_3lib_pyarrow_is_batchP7_object_ZL21__Pyx_PyObject_IsTrueP7_object_ZL18__Pyx_PyType_ReadyP11_typeobject_ZL18__pyx_pymod_createP7_objectP11PyModuleDef_ZZL30__Pyx_check_single_interpretervE19main_interpreter_id_ZL15__Pyx_IsSubtypeP11_typeobjectS0__ZL19__Pyx_SetNewInClassP7_objectS0_S0__ZL19__Pyx_IsAnySubtype2P11_typeobjectS0_S0__ZL47__pyx_tp_traverse_7pyarrow_3lib_DictionaryArrayP7_objectPFiS0_PvES1__ZL20__Pyx_PyDict_GetItemP7_objectS0__ZL57__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_10___iter__P11_typeobjectP7_objectS2__ZL60__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_10___iter___ZL59__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_10___iter___ZL24__Pyx_Method_ClassMethodP7_object_ZL24__Pyx_CyFunction_get_docP22__pyx_CyFunctionObjectPv_ZL28__Pyx_Coroutine_patch_moduleP7_objectPKc.constprop.0_ZL15__Pyx_patch_abcv_ZZL15__Pyx_patch_abcvE11abc_patched_ZL23__Pyx_ImportType_3_0_10P7_objectPKcS2_mm33__Pyx_ImportType_CheckSize_3_0_10.constprop.0_ZL30__Pyx_modinit_type_import_codev_ZL19__Pyx_PyInt_AddObjCP7_objectS0_lii.constprop.0_ZL39__pyx_tp_new_7pyarrow_3lib__WeakrefableP11_typeobjectP7_objectS2_.constprop.0_ZL16__Pyx_PyCode_NewiiiiiiP7_objectS0_S0_S0_S0_S0_S0_S0_iS0_.constprop.0_ZL25__Pyx_InitCachedConstantsv_ZL25__pyx_builtin_UserWarning_ZL27__pyx_builtin_FutureWarning_ZL28__pyx_builtin_RuntimeWarning_ZNSt6vectorIaSaIaEEaSERKS1_.isra.0_ZL35__Pyx_CreateStringTabAndInitStringsv_ZL14__pyx_k_binary_ZL19__pyx_k_binary_view_ZL14__pyx_k_bool_2_ZL18__pyx_k_build_type_ZL13__pyx_k_close_ZL16__pyx_k_closed_2_ZL23__pyx_k_custom_metadata_ZL24__pyx_k_compiler_version_ZL19__pyx_k_compiler_id_ZL22__pyx_k_compiler_flags_ZL35__pyx_k_coerce_temporal_nanoseconds_ZL13__pyx_k_batch_ZL9__pyx_k_b_ZL22__pyx_k_arrow_c_stream_ZL22__pyx_k_arrow_c_schema_ZL21__pyx_k_arrow_c_array_ZL19__pyx_k_arrow_array_ZL18__pyx_k_WriteStats_ZL19__pyx_k_VersionInfo_ZL19__pyx_k_RuntimeInfo_ZL31__pyx_k_RecordBatchWithMetadata_ZL17__pyx_k_ReadStats_ZL12__pyx_k_None_ZL9__pyx_k_K_ZL17__pyx_k_BuildInfo_ZL8__pyx_k__ZL11__pyx_k_0_1_ZL30__pyx_k_0___class_____name___0_ZL30__pyx_k_0___class_____name___1_ZL13__pyx_k_1_0_0_ZL13__pyx_k_2_0_0_ZL13__pyx_k_2_1_0_ZL18__pyx_k_3_0_0_dev0_ZL39__pyx_k_A_copy_of_this_field_with_the_r_ZL41__pyx_k_A_copy_of_this_field_with_the_r_2_ZL41__pyx_k_A_copy_of_this_field_with_the_r_3_ZL39__pyx_k_A_grouping_of_columns_in_a_tabl_ZL40__pyx_k_A_null_type_field_may_not_be_non_ZL39__pyx_k_Add_a_field_at_position_i_to_th_ZL39__pyx_k_Add_column_to_RecordBatch_at_po_ZL39__pyx_k_Add_column_to_Table_at_position_ZL38__pyx_k_Add_metadata_as_dict_of_string_ZL40__pyx_k_Add_metadata_as_dict_of_string_2_ZL39__pyx_k_Alias_for_large_string_Examples_ZL39__pyx_k_Alias_for_string_Examples_Creat_ZL39__pyx_k_Append_a_field_at_the_end_of_th_ZL39__pyx_k_Append_column_at_end_of_columns_ZL13__pyx_k_Array_ZL21__pyx_k_Array___array_ZL29__pyx_k_Array___arrow_c_array_ZL22__pyx_k_Array___dlpack_ZL29__pyx_k_Array___dlpack_device_ZL20__pyx_k_Array___iter_ZL22__pyx_k_Array___reduce_ZL22__pyx_k_Array___sizeof_ZL26__pyx_k_Array__debug_print_ZL26__pyx_k_Array__export_to_c_ZL33__pyx_k_Array__export_to_c_device_ZL28__pyx_k_Array__import_from_c_ZL36__pyx_k_Array__import_from_c_capsule_ZL35__pyx_k_Array__import_from_c_device_ZL24__pyx_k_Array__to_pandas_ZL21__pyx_k_Array_buffers_ZL18__pyx_k_Array_cast_ZL32__pyx_k_Array_data_type_was_NULL_ZL31__pyx_k_Array_dictionary_encode_ZL18__pyx_k_Array_diff_ZL27__pyx_k_Array_diff_line_928_ZL23__pyx_k_Array_drop_null_ZL20__pyx_k_Array_equals_ZL23__pyx_k_Array_fill_null_ZL20__pyx_k_Array_filter_ZL20__pyx_k_Array_format_ZL40__pyx_k_Array_format_is_deprecated_use_A_ZL26__pyx_k_Array_from_buffers_ZL25__pyx_k_Array_from_pandas_ZL35__pyx_k_Array_get_total_buffer_size_ZL19__pyx_k_Array_index_ZL20__pyx_k_Array_is_nan_ZL21__pyx_k_Array_is_null_ZL22__pyx_k_Array_is_valid_ZL19__pyx_k_Array_slice_ZL18__pyx_k_Array_sort_ZL17__pyx_k_Array_sum_ZL18__pyx_k_Array_take_ZL22__pyx_k_Array_to_numpy_ZL23__pyx_k_Array_to_pylist_ZL23__pyx_k_Array_to_string_ZL20__pyx_k_Array_tolist_ZL20__pyx_k_Array_unique_ZL22__pyx_k_Array_validate_ZL26__pyx_k_Array_value_counts_ZL18__pyx_k_Array_view_ZL22__pyx_k_Array_was_NULL_ZL40__pyx_k_Arrays_were_not_all_the_same_len_ZL22__pyx_k_ArrowCancelled_ZL29__pyx_k_ArrowCancelled___init_ZL26__pyx_k_ArrowCapacityError_ZL22__pyx_k_ArrowException_ZL20__pyx_k_ArrowIOError_ZL23__pyx_k_ArrowIndexError_ZL20__pyx_k_ArrowInvalid_ZL21__pyx_k_ArrowKeyError_ZL27__pyx_k_ArrowKeyError___str_ZL24__pyx_k_ArrowMemoryError_ZL32__pyx_k_ArrowNotImplementedError_ZL31__pyx_k_ArrowSerializationError_ZL22__pyx_k_ArrowTypeError_ZL22__pyx_k_AssertionError_ZL22__pyx_k_AttributeError_ZL9__pyx_k_B_ZL14__pyx_k_BROTLI_ZL11__pyx_k_BZ2_ZL21__pyx_k_BaseException_ZL25__pyx_k_BaseExtensionType_ZL40__pyx_k_BaseExtensionType___arrow_ext_cl_ZL40__pyx_k_BaseExtensionType___arrow_ext_sc_ZL36__pyx_k_BaseExtensionType_wrap_array_ZL21__pyx_k_BaseListArray_ZL29__pyx_k_BaseListArray_flatten_ZL35__pyx_k_BaseListArray_value_lengths_ZL40__pyx_k_BaseListArray_value_lengths_line_ZL40__pyx_k_BaseListArray_value_parent_indic_ZL42__pyx_k_BaseListArray_value_parent_indic_2_ZL35__pyx_k_Batch_number_0_out_of_range_ZL19__pyx_k_BinaryArray_ZL20__pyx_k_BinaryScalar_ZL30__pyx_k_BinaryScalar_as_buffer_ZL26__pyx_k_BinaryScalar_as_py_ZL23__pyx_k_BinaryViewArray_ZL24__pyx_k_BinaryViewScalar_ZL38__pyx_k_Bit_width_for_fixed_width_type_ZL20__pyx_k_BooleanArray_ZL21__pyx_k_BooleanScalar_ZL27__pyx_k_BooleanScalar_as_py_ZL14__pyx_k_Buffer_ZL19__pyx_k_BufferError_ZL26__pyx_k_BufferOutputStream_ZL40__pyx_k_BufferOutputStream___reduce_cyth_ZL40__pyx_k_BufferOutputStream___setstate_cy_ZL35__pyx_k_BufferOutputStream_getvalue_ZL20__pyx_k_BufferReader_ZL36__pyx_k_BufferReader___reduce_cython_ZL38__pyx_k_BufferReader___setstate_cython_ZL26__pyx_k_Buffer___reduce_ex_ZL21__pyx_k_Buffer_equals_ZL18__pyx_k_Buffer_hex_ZL39__pyx_k_Buffer_size_must_be_larger_than_ZL20__pyx_k_Buffer_slice_ZL25__pyx_k_Buffer_to_pybytes_ZL22__pyx_k_BufferedIOBase_ZL27__pyx_k_BufferedInputStream_ZL40__pyx_k_BufferedInputStream___reduce_cyt_ZL40__pyx_k_BufferedInputStream___setstate_c_ZL34__pyx_k_BufferedInputStream_detach_ZL28__pyx_k_BufferedOutputStream_ZL40__pyx_k_BufferedOutputStream___reduce_cy_ZL39__pyx_k_BufferedOutputStream___setstate_ZL35__pyx_k_BufferedOutputStream_detach_ZL39__pyx_k_Byte_width_for_fixed_width_type_ZL9__pyx_k_C_ZL11__pyx_k_COO_ZL40__pyx_k_CPU_count_must_be_strictly_posit_ZL26__pyx_k_CRecordBatchWriter_ZL34__pyx_k_CRecordBatchWriter___enter_ZL33__pyx_k_CRecordBatchWriter___exit_ZL39__pyx_k_CRecordBatchWriter___reduce_cyt_ZL39__pyx_k_CRecordBatchWriter___setstate_c_ZL32__pyx_k_CRecordBatchWriter_close_ZL32__pyx_k_CRecordBatchWriter_write_ZL38__pyx_k_CRecordBatchWriter_write_batch_ZL38__pyx_k_CRecordBatchWriter_write_table_ZL20__pyx_k_CacheOptions_ZL29__pyx_k_CacheOptions___reduce_ZL33__pyx_k_CacheOptions__reconstruct_ZL40__pyx_k_CacheOptions_from_network_metric_ZL39__pyx_k_Calling_data_on_ChunkedArray_is_ZL40__pyx_k_Can_only_get_value_offsets_for_d_ZL39__pyx_k_Can_only_instantiate_subclasses_ZL41__pyx_k_Can_only_instantiate_subclasses_2_ZL40__pyx_k_Can_t_convert_PyCapsule_with_nam_ZL40__pyx_k_Cannot_convert_1D_array_or_scala_ZL40__pyx_k_Cannot_create_multiple_NullScala_ZL40__pyx_k_Cannot_pass_a_numpy_masked_array_ZL40__pyx_k_Cannot_pass_both_schema_and_meta_ZL40__pyx_k_Cannot_pass_both_schema_and_name_ZL40__pyx_k_Cannot_return_a_writable_array_i_ZL39__pyx_k_Cannot_specify_a_mask_or_a_size_ZL40__pyx_k_Cannot_specify_both_list_size_an_ZL39__pyx_k_Cast_array_values_to_another_da_ZL39__pyx_k_Cast_record_batch_values_to_ano_ZL39__pyx_k_Cast_table_values_to_another_sc_ZL40__pyx_k_Casting_field_r_with_null_values_ZL19__pyx_k_Categorical_ZL39__pyx_k_Check_if_contents_of_two_record_ZL39__pyx_k_Check_if_contents_of_two_tables_ZL32__pyx_k_Chunk_index_out_of_range_ZL20__pyx_k_ChunkedArray_ZL28__pyx_k_ChunkedArray___array_ZL37__pyx_k_ChunkedArray___arrow_c_stream_ZL27__pyx_k_ChunkedArray___iter_ZL29__pyx_k_ChunkedArray___reduce_ZL29__pyx_k_ChunkedArray___sizeof_ZL40__pyx_k_ChunkedArray__import_from_c_caps_ZL31__pyx_k_ChunkedArray__to_pandas_ZL25__pyx_k_ChunkedArray_cast_ZL34__pyx_k_ChunkedArray_cast_line_542_ZL26__pyx_k_ChunkedArray_chunk_ZL36__pyx_k_ChunkedArray_chunk_line_1237_ZL40__pyx_k_ChunkedArray_chunks___get___line_ZL35__pyx_k_ChunkedArray_combine_chunks_ZL40__pyx_k_ChunkedArray_combine_chunks_line_ZL39__pyx_k_ChunkedArray_data_type_was_NULL_ZL38__pyx_k_ChunkedArray_dictionary_encode_ZL40__pyx_k_ChunkedArray_dictionary_encode_l_ZL30__pyx_k_ChunkedArray_drop_null_ZL40__pyx_k_ChunkedArray_drop_null_line_1054_ZL27__pyx_k_ChunkedArray_equals_ZL36__pyx_k_ChunkedArray_equals_line_435_ZL30__pyx_k_ChunkedArray_fill_null_ZL39__pyx_k_ChunkedArray_fill_null_line_399_ZL27__pyx_k_ChunkedArray_filter_ZL36__pyx_k_ChunkedArray_filter_line_897_ZL28__pyx_k_ChunkedArray_flatten_ZL37__pyx_k_ChunkedArray_flatten_line_639_ZL27__pyx_k_ChunkedArray_format_ZL40__pyx_k_ChunkedArray_format_is_deprecate_ZL40__pyx_k_ChunkedArray_get_total_buffer_si_ZL42__pyx_k_ChunkedArray_get_total_buffer_si_2_ZL26__pyx_k_ChunkedArray_index_ZL35__pyx_k_ChunkedArray_index_line_961_ZL27__pyx_k_ChunkedArray_is_nan_ZL36__pyx_k_ChunkedArray_is_nan_line_344_ZL28__pyx_k_ChunkedArray_is_null_ZL37__pyx_k_ChunkedArray_is_null_line_311_ZL29__pyx_k_ChunkedArray_is_valid_ZL38__pyx_k_ChunkedArray_is_valid_line_368_ZL31__pyx_k_ChunkedArray_iterchunks_ZL40__pyx_k_ChunkedArray_iterchunks_line_130_ZL27__pyx_k_ChunkedArray_length_ZL35__pyx_k_ChunkedArray_length_line_96_ZL40__pyx_k_ChunkedArray_nbytes___get___line_ZL37__pyx_k_ChunkedArray_null_count___get_ZL37__pyx_k_ChunkedArray_num_chunks___get_ZL26__pyx_k_ChunkedArray_slice_ZL35__pyx_k_ChunkedArray_slice_line_839_ZL25__pyx_k_ChunkedArray_sort_ZL25__pyx_k_ChunkedArray_take_ZL35__pyx_k_ChunkedArray_take_line_1008_ZL29__pyx_k_ChunkedArray_to_numpy_ZL38__pyx_k_ChunkedArray_to_numpy_line_477_ZL30__pyx_k_ChunkedArray_to_pylist_ZL40__pyx_k_ChunkedArray_to_pylist_line_1322_ZL30__pyx_k_ChunkedArray_to_string_ZL39__pyx_k_ChunkedArray_to_string_line_116_ZL40__pyx_k_ChunkedArray_type___get___line_8_ZL39__pyx_k_ChunkedArray_unify_dictionaries_ZL41__pyx_k_ChunkedArray_unify_dictionaries_2_ZL27__pyx_k_ChunkedArray_unique_ZL36__pyx_k_ChunkedArray_unique_line_756_ZL29__pyx_k_ChunkedArray_validate_ZL33__pyx_k_ChunkedArray_value_counts_ZL40__pyx_k_ChunkedArray_value_counts_line_7_ZL29__pyx_k_ChunkedArray_was_NULL_ZL13__pyx_k_Codec_ZL29__pyx_k_Codec___reduce_cython_ZL31__pyx_k_Codec___setstate_cython_ZL22__pyx_k_Codec_compress_ZL24__pyx_k_Codec_decompress_ZL39__pyx_k_Codec_default_compression_level_ZL20__pyx_k_Codec_detect_ZL26__pyx_k_Codec_is_available_ZL39__pyx_k_Codec_maximum_compression_level_ZL39__pyx_k_Codec_minimum_compression_level_ZL40__pyx_k_Codec_supports_compression_level_ZL39__pyx_k_Column_does_not_exist_in_schema_ZL26__pyx_k_Column_r_not_found_ZL38__pyx_k_Compare_contents_of_this_array_ZL29__pyx_k_CompressedInputStream_ZL40__pyx_k_CompressedInputStream___reduce_c_ZL40__pyx_k_CompressedInputStream___setstate_ZL30__pyx_k_CompressedOutputStream_ZL39__pyx_k_CompressedOutputStream___reduce_ZL40__pyx_k_CompressedOutputStream___setstat_ZL40__pyx_k_Compression_type_must_be_lz4_zst_ZL39__pyx_k_Compute_counts_of_unique_elemen_ZL39__pyx_k_Compute_dictionary_encoded_repr_ZL39__pyx_k_Compute_distinct_elements_in_ar_ZL39__pyx_k_Compute_zero_copy_slice_of_this_ZL41__pyx_k_Compute_zero_copy_slice_of_this_2_ZL41__pyx_k_Compute_zero_copy_slice_of_this_3_ZL39__pyx_k_Concatenate_pyarrow_Table_objec_ZL39__pyx_k_Concatenate_the_given_arrays_Th_ZL39__pyx_k_Construct_FixedSizeListArray_fr_ZL39__pyx_k_Construct_LargeListViewArray_fr_ZL39__pyx_k_Construct_ListArray_from_arrays_ZL39__pyx_k_Construct_ListViewArray_from_ar_ZL38__pyx_k_Construct_MapArray_from_arrays_ZL38__pyx_k_Construct_a_RecordBatch_from_a_ZL39__pyx_k_Construct_a_RecordBatch_from_mu_ZL39__pyx_k_Construct_a_Table_from_Arrow_ar_ZL39__pyx_k_Construct_a_Table_from_a_Struct_ZL39__pyx_k_Construct_a_Table_from_a_sequen_ZL39__pyx_k_Construct_a_Table_or_RecordBatc_ZL41__pyx_k_Construct_a_Table_or_RecordBatc_2_ZL39__pyx_k_Construct_chunked_array_from_li_ZL39__pyx_k_Construct_pyarrow_Schema_from_c_ZL38__pyx_k_Convert_NumPy_dtype_to_pyarrow_ZL39__pyx_k_Convert_Table_to_a_list_of_Reco_ZL39__pyx_k_Convert_arrow_Tensor_to_numpy_n_ZL38__pyx_k_Convert_numpy_tensors_ndarrays_ZL38__pyx_k_Convert_pandas_DataFrame_to_an_ZL40__pyx_k_Convert_pandas_DataFrame_to_an_2_ZL39__pyx_k_Convert_the_Table_or_RecordBatc_ZL41__pyx_k_Convert_the_Table_or_RecordBatc_2_ZL39__pyx_k_Convert_the_Table_to_a_RecordBa_ZL39__pyx_k_Convert_to_a_class_pyarrow_Tens_ZL39__pyx_k_Convert_to_a_list_of_native_Pyt_ZL39__pyx_k_Convert_to_a_list_of_single_chu_ZL38__pyx_k_Convert_to_a_pandas_compatible_ZL39__pyx_k_Convert_to_an_iterator_of_Chunk_ZL39__pyx_k_Converting_to_Python_dictionary_ZL22__pyx_k_Could_not_cast_ZL39__pyx_k_Create_LargeListType_instance_f_ZL39__pyx_k_Create_LargeListViewType_instan_ZL39__pyx_k_Create_ListType_instance_from_c_ZL39__pyx_k_Create_ListViewType_instance_fr_ZL39__pyx_k_Create_MapType_instance_from_ke_ZL39__pyx_k_Create_StructType_instance_from_ZL39__pyx_k_Create_UTF8_variable_length_str_ZL41__pyx_k_Create_UTF8_variable_length_str_2_ZL39__pyx_k_Create_a_Tensor_from_a_numpy_ar_ZL39__pyx_k_Create_a_file_of_the_given_size_ZL39__pyx_k_Create_a_pyarrow_Field_instance_ZL39__pyx_k_Create_a_pyarrow_RecordBatch_fr_ZL39__pyx_k_Create_a_pyarrow_Scalar_instanc_ZL39__pyx_k_Create_a_pyarrow_Table_from_a_P_ZL39__pyx_k_Create_a_strongly_typed_Array_i_ZL39__pyx_k_Create_a_variable_length_binary_ZL38__pyx_k_Create_an_Array_instance_whose_ZL39__pyx_k_Create_an_Arrow_input_stream_Pa_ZL39__pyx_k_Create_an_Arrow_output_stream_P_ZL39__pyx_k_Create_decimal_type_with_precis_ZL39__pyx_k_Create_double_precision_floatin_ZL38__pyx_k_Create_half_precision_floating_ZL38__pyx_k_Create_instance_of_32_bit_date_ZL38__pyx_k_Create_instance_of_32_bit_time_ZL38__pyx_k_Create_instance_of_64_bit_date_ZL38__pyx_k_Create_instance_of_64_bit_time_ZL39__pyx_k_Create_instance_of_a_duration_t_ZL38__pyx_k_Create_instance_of_an_interval_ZL39__pyx_k_Create_instance_of_boolean_type_ZL38__pyx_k_Create_instance_of_fixed_shape_ZL39__pyx_k_Create_instance_of_null_type_Ex_ZL39__pyx_k_Create_instance_of_signed_int16_ZL39__pyx_k_Create_instance_of_signed_int32_ZL39__pyx_k_Create_instance_of_signed_int64_ZL38__pyx_k_Create_instance_of_signed_int8_ZL39__pyx_k_Create_instance_of_timestamp_ty_ZL39__pyx_k_Create_instance_of_unsigned_int_ZL39__pyx_k_Create_instance_of_unsigned_uin_ZL41__pyx_k_Create_instance_of_unsigned_uin_2_ZL41__pyx_k_Create_instance_of_unsigned_uin_3_ZL39__pyx_k_Create_large_UTF8_variable_leng_ZL39__pyx_k_Create_large_variable_length_bi_ZL39__pyx_k_Create_new_RecordBatch_with_the_ZL39__pyx_k_Create_new_Table_with_the_indic_ZL39__pyx_k_Create_new_field_without_metada_ZL39__pyx_k_Create_new_record_batch_with_co_ZL39__pyx_k_Create_new_schema_without_metad_ZL39__pyx_k_Create_new_table_with_columns_r_ZL39__pyx_k_Create_pyarrow_Array_instance_f_ZL39__pyx_k_Create_shallow_copy_of_record_b_ZL39__pyx_k_Create_shallow_copy_of_table_by_ZL39__pyx_k_Create_single_precision_floatin_ZL39__pyx_k_Create_variable_length_or_fixed_ZL27__pyx_k_DEFAULT_BUFFER_SIZE_ZL17__pyx_k_DataFrame_ZL16__pyx_k_DataType_ZL33__pyx_k_DataType___arrow_c_schema_ZL25__pyx_k_DataType___reduce_ZL29__pyx_k_DataType__export_to_c_ZL31__pyx_k_DataType__import_from_c_ZL39__pyx_k_DataType__import_from_c_capsule_ZL39__pyx_k_DataType_bit_width___get___line_ZL40__pyx_k_DataType_byte_width___get___line_ZL23__pyx_k_DataType_equals_ZL32__pyx_k_DataType_equals_line_338_ZL31__pyx_k_DataType_expected_got_r_ZL22__pyx_k_DataType_field_ZL40__pyx_k_DataType_num_buffers___get___lin_ZL40__pyx_k_DataType_num_fields___get___line_ZL32__pyx_k_DataType_to_pandas_dtype_ZL40__pyx_k_DataType_to_pandas_dtype_line_36_ZL19__pyx_k_Date32Array_ZL20__pyx_k_Date32Scalar_ZL26__pyx_k_Date32Scalar_as_py_ZL19__pyx_k_Date64Array_ZL20__pyx_k_Date64Scalar_ZL26__pyx_k_Date64Scalar_as_py_ZL23__pyx_k_DatetimeTZDtype_ZL15__pyx_k_Decimal_ZL23__pyx_k_Decimal128Array_ZL24__pyx_k_Decimal128Scalar_ZL30__pyx_k_Decimal128Scalar_as_py_ZL22__pyx_k_Decimal128Type_ZL31__pyx_k_Decimal128Type___reduce_ZL38__pyx_k_Decimal128Type_precision___get_ZL40__pyx_k_Decimal128Type_scale___get___lin_ZL23__pyx_k_Decimal256Array_ZL24__pyx_k_Decimal256Scalar_ZL30__pyx_k_Decimal256Scalar_as_py_ZL22__pyx_k_Decimal256Type_ZL31__pyx_k_Decimal256Type___reduce_ZL38__pyx_k_Decimal256Type_precision___get_ZL40__pyx_k_Decimal256Type_scale___get___lin_ZL39__pyx_k_Declare_a_grouping_over_the_col_ZL22__pyx_k_DenseUnionType_ZL14__pyx_k_Detail_ZL23__pyx_k_DictionaryArray_ZL40__pyx_k_DictionaryArray_dictionary_decod_ZL40__pyx_k_DictionaryArray_dictionary_encod_ZL35__pyx_k_DictionaryArray_from_arrays_ZL36__pyx_k_DictionaryArray_from_buffers_ZL31__pyx_k_DictionaryEncodeOptions_ZL22__pyx_k_DictionaryMemo_ZL38__pyx_k_DictionaryMemo___reduce_cython_ZL40__pyx_k_DictionaryMemo___setstate_cython_ZL24__pyx_k_DictionaryScalar_ZL33__pyx_k_DictionaryScalar___reduce_ZL37__pyx_k_DictionaryScalar__reconstruct_ZL30__pyx_k_DictionaryScalar_as_py_ZL22__pyx_k_DictionaryType_ZL31__pyx_k_DictionaryType___reduce_ZL39__pyx_k_DictionaryType_index_type___get_ZL40__pyx_k_DictionaryType_ordered___get___l_ZL39__pyx_k_DictionaryType_value_type___get_ZL39__pyx_k_Dictionary_categorical_or_simpl_ZL39__pyx_k_Dimensions_of_the_table_or_reco_ZL19__pyx_k_Do_not_call_ZL40__pyx_k_Do_not_call_Buffer_s_constructor_ZL40__pyx_k_Do_not_call_ChunkedArray_s_const_ZL39__pyx_k_Do_not_call_Field_s_constructor_ZL40__pyx_k_Do_not_call_Schema_s_constructor_ZL40__pyx_k_Do_not_call_SparseCOOTensor_s_co_ZL40__pyx_k_Do_not_call_SparseCSCMatrix_s_co_ZL40__pyx_k_Do_not_call_SparseCSFTensor_s_co_ZL40__pyx_k_Do_not_call_SparseCSRMatrix_s_co_ZL40__pyx_k_Do_not_call_Tensor_s_constructor_ZL40__pyx_k_Do_not_call_s_constructor_direct_ZL42__pyx_k_Do_not_call_s_constructor_direct_2_ZL42__pyx_k_Do_not_call_s_constructor_direct_3_ZL42__pyx_k_Do_not_call_s_constructor_direct_4_ZL42__pyx_k_Do_not_call_s_constructor_direct_5_ZL42__pyx_k_Do_not_call_s_constructor_direct_6_ZL42__pyx_k_Do_not_call_s_constructor_direct_7_ZL42__pyx_k_Do_not_call_s_constructor_direct_8_ZL19__pyx_k_DoubleArray_ZL20__pyx_k_DoubleScalar_ZL26__pyx_k_DoubleScalar_as_py_ZL39__pyx_k_Drop_one_or_more_columns_and_re_ZL40__pyx_k_Duplicate_key_use_pass_all_items_ZL21__pyx_k_DurationArray_ZL22__pyx_k_DurationScalar_ZL28__pyx_k_DurationScalar_as_py_ZL20__pyx_k_DurationType_ZL40__pyx_k_DurationType_unit___get___line_1_ZL16__pyx_k_EOFError_ZL13__pyx_k_Empty_ZL27__pyx_k_End_of_Arrow_stream_ZL16__pyx_k_EnumBase_ZL16__pyx_k_EnumType_ZL40__pyx_k_Expected_1_dimensional_array_for_ZL42__pyx_k_Expected_1_dimensional_array_for_2_ZL42__pyx_k_Expected_1_dimensional_array_for_3_ZL42__pyx_k_Expected_1_dimensional_array_for_4_ZL40__pyx_k_Expected_2_dimensional_array_for_ZL26__pyx_k_Expected_Array_got_ZL27__pyx_k_Expected_Schema_got_ZL40__pyx_k_Expected_a_list_of_1_dimensional_ZL42__pyx_k_Expected_a_list_of_1_dimensional_2_ZL36__pyx_k_Expected_a_non_empty_ndarray_ZL36__pyx_k_Expected_a_pointer_value_got_ZL39__pyx_k_Expected_an_object_implementing_ZL41__pyx_k_Expected_an_object_implementing_2_ZL39__pyx_k_Expected_array_or_chunked_array_ZL40__pyx_k_Expected_file_path_but_0_is_a_di_ZL35__pyx_k_Expected_int_index_got_type_ZL24__pyx_k_Expected_integer_ZL40__pyx_k_Expected_integer_or_string_index_ZL39__pyx_k_Expected_list_of_ndim_np_arrays_ZL41__pyx_k_Expected_list_of_ndim_np_arrays_2_ZL34__pyx_k_Expected_list_or_tuple_got_ZL40__pyx_k_Expected_pandas_DataFrame_or_lis_ZL40__pyx_k_Expected_pandas_DataFrame_python_ZL40__pyx_k_Expected_scipy_sparse_coo_matrix_ZL40__pyx_k_Expected_scipy_sparse_csc_matrix_ZL40__pyx_k_Expected_scipy_sparse_csr_matrix_ZL31__pyx_k_Expected_sparse_COO_got_ZL39__pyx_k_Expected_storage_type_0_but_got_ZL18__pyx_k_Expression_ZL22__pyx_k_ExtensionArray_ZL35__pyx_k_ExtensionArray_from_storage_ZL22__pyx_k_ExtensionDtype_ZL30__pyx_k_ExtensionRegistryNanny_ZL39__pyx_k_ExtensionRegistryNanny___reduce_ZL39__pyx_k_ExtensionRegistryNanny___setsta_ZL38__pyx_k_ExtensionRegistryNanny_release_ZL23__pyx_k_ExtensionScalar_ZL29__pyx_k_ExtensionScalar_as_py_ZL36__pyx_k_ExtensionScalar_from_storage_ZL21__pyx_k_ExtensionType_ZL39__pyx_k_ExtensionType___arrow_ext_class_ZL40__pyx_k_ExtensionType___arrow_ext_deseri_ZL40__pyx_k_ExtensionType___arrow_ext_scalar_ZL40__pyx_k_ExtensionType___arrow_ext_serial_ZL30__pyx_k_ExtensionType___reduce_ZL34__pyx_k_Failed_to_allocate_0_bytes_ZL13__pyx_k_False_ZL13__pyx_k_Field_ZL30__pyx_k_Field___arrow_c_schema_ZL22__pyx_k_Field___reduce_ZL26__pyx_k_Field__export_to_c_ZL28__pyx_k_Field__import_from_c_ZL36__pyx_k_Field__import_from_c_capsule_ZL38__pyx_k_Field_does_not_exist_in_schema_ZL20__pyx_k_Field_equals_ZL30__pyx_k_Field_equals_line_2216_ZL36__pyx_k_Field_exists_times_in_schema_ZL21__pyx_k_Field_flatten_ZL31__pyx_k_Field_flatten_line_2477_ZL40__pyx_k_Field_metadata___get___line_2292_ZL36__pyx_k_Field_name___get___line_2278_ZL40__pyx_k_Field_nullable___get___line_2261_ZL29__pyx_k_Field_remove_metadata_ZL39__pyx_k_Field_remove_metadata_line_2345_ZL27__pyx_k_Field_with_metadata_ZL37__pyx_k_Field_with_metadata_line_2311_ZL23__pyx_k_Field_with_name_ZL33__pyx_k_Field_with_name_line_2406_ZL27__pyx_k_Field_with_nullable_ZL37__pyx_k_Field_with_nullable_line_2438_ZL23__pyx_k_Field_with_type_ZL33__pyx_k_Field_with_type_line_2371_ZL39__pyx_k_File_object_is_malformed_has_no_ZL39__pyx_k_Find_the_first_index_of_a_value_ZL40__pyx_k_First_stride_needs_to_be_largest_ZL29__pyx_k_FixedShapeTensorArray_ZL40__pyx_k_FixedShapeTensorArray_from_numpy_ZL42__pyx_k_FixedShapeTensorArray_from_numpy_2_ZL40__pyx_k_FixedShapeTensorArray_to_numpy_n_ZL39__pyx_k_FixedShapeTensorArray_to_tensor_ZL30__pyx_k_FixedShapeTensorScalar_ZL39__pyx_k_FixedShapeTensorScalar_to_numpy_ZL40__pyx_k_FixedShapeTensorScalar_to_tensor_ZL28__pyx_k_FixedShapeTensorType_ZL40__pyx_k_FixedShapeTensorType___arrow_ext_ZL42__pyx_k_FixedShapeTensorType___arrow_ext_2_ZL37__pyx_k_FixedShapeTensorType___reduce_ZL28__pyx_k_FixedSizeBinaryArray_ZL29__pyx_k_FixedSizeBinaryScalar_ZL27__pyx_k_FixedSizeBinaryType_ZL36__pyx_k_FixedSizeBinaryType___reduce_ZL29__pyx_k_FixedSizeBufferWriter_ZL40__pyx_k_FixedSizeBufferWriter___reduce_c_ZL40__pyx_k_FixedSizeBufferWriter___setstate_ZL40__pyx_k_FixedSizeBufferWriter_set_memcop_ZL42__pyx_k_FixedSizeBufferWriter_set_memcop_2_ZL42__pyx_k_FixedSizeBufferWriter_set_memcop_3_ZL26__pyx_k_FixedSizeListArray_ZL38__pyx_k_FixedSizeListArray_from_arrays_ZL40__pyx_k_FixedSizeListArray_from_arrays_l_ZL39__pyx_k_FixedSizeListArray_values___get_ZL27__pyx_k_FixedSizeListScalar_ZL25__pyx_k_FixedSizeListType_ZL34__pyx_k_FixedSizeListType___reduce_ZL40__pyx_k_FixedSizeListType_list_size___ge_ZL37__pyx_k_FixedSizeListType_value_field_ZL40__pyx_k_FixedSizeListType_value_type___g_ZL39__pyx_k_Flatten_this_ChunkedArray_If_it_ZL38__pyx_k_Flatten_this_ChunkedArray_into_ZL38__pyx_k_Flatten_this_Table_Each_column_ZL38__pyx_k_Flatten_this_field_If_a_struct_ZL18__pyx_k_FloatArray_ZL19__pyx_k_FloatScalar_ZL25__pyx_k_FloatScalar_as_py_ZL26__pyx_k_FloatingPointArray_ZL21__pyx_k_FutureWarning_ZL12__pyx_k_GZIP_ZL9__pyx_k_H_ZL22__pyx_k_HalfFloatArray_ZL23__pyx_k_HalfFloatScalar_ZL29__pyx_k_HalfFloatScalar_as_py_ZL9__pyx_k_I_ZL14__pyx_k_IOBase_ZL15__pyx_k_IOError_ZL40__pyx_k_IO_thread_count_must_be_strictly_ZL40__pyx_k_IPC_read_statistics_Parameters_n_ZL39__pyx_k_IPC_write_statistics_Parameters_ZL36__pyx_k_I_O_operation_on_closed_file_ZL19__pyx_k_ImportError_ZL40__pyx_k_Incompatible_checksums_0x_x_vs_0_ZL42__pyx_k_Incompatible_checksums_0x_x_vs_0_2_ZL39__pyx_k_Incompatible_storage_type_0_for_ZL37__pyx_k_Incompatible_storage_type_for_ZL13__pyx_k_Index_ZL18__pyx_k_IndexError_ZL40__pyx_k_Index_must_either_be_string_or_i_ZL36__pyx_k_Indices_must_be_integer_type_ZL18__pyx_k_Int16Array_ZL19__pyx_k_Int16Scalar_ZL25__pyx_k_Int16Scalar_as_py_ZL18__pyx_k_Int32Array_ZL19__pyx_k_Int32Scalar_ZL25__pyx_k_Int32Scalar_as_py_ZL18__pyx_k_Int64Array_ZL19__pyx_k_Int64Scalar_ZL25__pyx_k_Int64Scalar_as_py_ZL17__pyx_k_Int8Array_ZL18__pyx_k_Int8Scalar_ZL24__pyx_k_Int8Scalar_as_py_ZL15__pyx_k_IntEnum_ZL15__pyx_k_IntFlag_ZL20__pyx_k_IntegerArray_ZL21__pyx_k_IntervalDtype_ZL27__pyx_k_Invalid_file_mode_0_ZL26__pyx_k_Invalid_merge_mode_ZL31__pyx_k_Invalid_promote_options_ZL25__pyx_k_Invalid_time_unit_ZL36__pyx_k_Invalid_time_unit_for_time32_ZL36__pyx_k_Invalid_time_unit_for_time64_ZL30__pyx_k_Invalid_union_mode_0_r_ZL39__pyx_k_Invalid_value_for_compression_r_ZL40__pyx_k_Invalid_value_for_maps_as_pydict_ZL33__pyx_k_Invalid_value_of_whence_0_ZL22__pyx_k_IpcReadOptions_ZL38__pyx_k_IpcReadOptions___reduce_cython_ZL40__pyx_k_IpcReadOptions___setstate_cython_ZL23__pyx_k_IpcWriteOptions_ZL39__pyx_k_IpcWriteOptions___reduce_cython_ZL40__pyx_k_IpcWriteOptions___setstate_cytho_ZL39__pyx_k_Is_this_tensor_contiguous_in_me_ZL39__pyx_k_Is_this_tensor_mutable_or_immut_ZL40__pyx_k_Iterable_should_contain_Array_ob_ZL39__pyx_k_Iterator_over_all_columns_in_th_ZL16__pyx_k_KeyError_ZL24__pyx_k_KeyValueMetadata_ZL33__pyx_k_KeyValueMetadata___reduce_ZL31__pyx_k_KeyValueMetadata_equals_ZL32__pyx_k_KeyValueMetadata_get_all_ZL30__pyx_k_KeyValueMetadata_items_ZL28__pyx_k_KeyValueMetadata_key_ZL29__pyx_k_KeyValueMetadata_keys_ZL32__pyx_k_KeyValueMetadata_to_dict_ZL30__pyx_k_KeyValueMetadata_value_ZL31__pyx_k_KeyValueMetadata_values_ZL11__pyx_k_LZ4_ZL17__pyx_k_LZ4_FRAME_ZL15__pyx_k_LZ4_RAW_ZL24__pyx_k_LargeBinaryArray_ZL25__pyx_k_LargeBinaryScalar_ZL22__pyx_k_LargeListArray_ZL34__pyx_k_LargeListArray_from_arrays_ZL40__pyx_k_LargeListArray_values___get___li_ZL23__pyx_k_LargeListScalar_ZL21__pyx_k_LargeListType_ZL30__pyx_k_LargeListType___reduce_ZL38__pyx_k_LargeListType_value_type___get_ZL26__pyx_k_LargeListViewArray_ZL34__pyx_k_LargeListViewArray_flatten_ZL39__pyx_k_LargeListViewArray_flatten_line_ZL38__pyx_k_LargeListViewArray_from_arrays_ZL40__pyx_k_LargeListViewArray_from_arrays_l_ZL40__pyx_k_LargeListViewArray_offsets___get_ZL38__pyx_k_LargeListViewArray_sizes___get_ZL39__pyx_k_LargeListViewArray_values___get_ZL27__pyx_k_LargeListViewScalar_ZL25__pyx_k_LargeListViewType_ZL34__pyx_k_LargeListViewType___reduce_ZL37__pyx_k_LargeListViewType_value_field_ZL40__pyx_k_LargeListViewType_value_type___g_ZL39__pyx_k_LargeListView_requires_DataType_ZL24__pyx_k_LargeStringArray_ZL37__pyx_k_LargeStringArray_from_buffers_ZL25__pyx_k_LargeStringScalar_ZL35__pyx_k_Length_must_be_non_negative_ZL40__pyx_k_Length_of_names_does_not_match_l_ZL26__pyx_k_Less_than_one_byte_ZL17__pyx_k_ListArray_ZL29__pyx_k_ListArray_from_arrays_ZL39__pyx_k_ListArray_from_arrays_line_2210_ZL40__pyx_k_ListArray_offsets___get___line_2_ZL40__pyx_k_ListArray_values___get___line_22_ZL18__pyx_k_ListScalar_ZL24__pyx_k_ListScalar_as_py_ZL16__pyx_k_ListType_ZL25__pyx_k_ListType___reduce_ZL40__pyx_k_ListType_value_field___get___lin_ZL40__pyx_k_ListType_value_type___get___line_ZL21__pyx_k_ListViewArray_ZL29__pyx_k_ListViewArray_flatten_ZL39__pyx_k_ListViewArray_flatten_line_2750_ZL33__pyx_k_ListViewArray_from_arrays_ZL40__pyx_k_ListViewArray_from_arrays_line_2_ZL40__pyx_k_ListViewArray_offsets___get___li_ZL40__pyx_k_ListViewArray_sizes___get___line_ZL40__pyx_k_ListViewArray_values___get___lin_ZL22__pyx_k_ListViewScalar_ZL20__pyx_k_ListViewType_ZL29__pyx_k_ListViewType___reduce_ZL38__pyx_k_ListViewType_value_field___get_ZL37__pyx_k_ListViewType_value_type___get_ZL40__pyx_k_ListView_requires_DataType_or_Fi_ZL39__pyx_k_List_of_all_columns_in_numerica_ZL39__pyx_k_List_requires_DataType_or_Field_ZL12__pyx_k_Lock_ZL25__pyx_k_LoggingMemoryPool_ZL40__pyx_k_LoggingMemoryPool___reduce_cytho_ZL40__pyx_k_LoggingMemoryPool___setstate_cyt_ZL9__pyx_k_M_ZL39__pyx_k_Make_a_new_table_by_combining_t_ZL16__pyx_k_MapArray_ZL28__pyx_k_MapArray_from_arrays_ZL38__pyx_k_MapArray_from_arrays_line_3107_ZL17__pyx_k_MapScalar_ZL24__pyx_k_MapScalar___iter_ZL23__pyx_k_MapScalar_as_py_ZL15__pyx_k_MapType_ZL24__pyx_k_MapType___reduce_ZL39__pyx_k_MapType_item_field___get___line_ZL40__pyx_k_MapType_item_type___get___line_7_ZL40__pyx_k_MapType_key_field___get___line_7_ZL40__pyx_k_MapType_key_type___get___line_73_ZL40__pyx_k_MapType_keys_sorted___get___line_ZL40__pyx_k_Map_key_field_should_be_non_null_ZL15__pyx_k_Mapping_ZL39__pyx_k_Mask_is_a_different_length_from_ZL29__pyx_k_Mask_must_be_1D_array_ZL39__pyx_k_Mask_must_be_a_numpy_array_when_ZL39__pyx_k_Mask_must_be_a_pyarrow_Array_of_ZL34__pyx_k_Mask_must_be_boolean_dtype_ZL35__pyx_k_Mask_must_not_contain_nulls_ZL19__pyx_k_MaskedArray_ZL19__pyx_k_MemoryError_ZL24__pyx_k_MemoryMappedFile_ZL40__pyx_k_MemoryMappedFile___reduce_cython_ZL40__pyx_k_MemoryMappedFile___setstate_cyth_ZL30__pyx_k_MemoryMappedFile__open_ZL31__pyx_k_MemoryMappedFile_create_ZL31__pyx_k_MemoryMappedFile_fileno_ZL31__pyx_k_MemoryMappedFile_resize_ZL18__pyx_k_MemoryPool_ZL34__pyx_k_MemoryPool___reduce_cython_ZL36__pyx_k_MemoryPool___setstate_cython_ZL34__pyx_k_MemoryPool_bytes_allocated_ZL29__pyx_k_MemoryPool_max_memory_ZL33__pyx_k_MemoryPool_release_unused_ZL15__pyx_k_Message_ZL21__pyx_k_MessageReader_ZL37__pyx_k_MessageReader___reduce_cython_ZL39__pyx_k_MessageReader___setstate_cython_ZL33__pyx_k_MessageReader_open_stream_ZL39__pyx_k_MessageReader_read_next_message_ZL31__pyx_k_Message___reduce_cython_ZL33__pyx_k_Message___setstate_cython_ZL22__pyx_k_Message_equals_ZL25__pyx_k_Message_serialize_ZL28__pyx_k_Message_serialize_to_ZL16__pyx_k_Metadata_ZL23__pyx_k_MetadataVersion_ZL24__pyx_k_MockOutputStream_ZL40__pyx_k_MockOutputStream___reduce_cython_ZL40__pyx_k_MockOutputStream___setstate_cyth_ZL29__pyx_k_MockOutputStream_size_ZL20__pyx_k_MonthDayNano_ZL33__pyx_k_MonthDayNanoIntervalArray_ZL40__pyx_k_MonthDayNanoIntervalArray_to_pyl_ZL34__pyx_k_MonthDayNanoIntervalScalar_ZL40__pyx_k_MonthDayNanoIntervalScalar_as_py_ZL40__pyx_k_Must_pass_a_DictionaryType_insta_ZL35__pyx_k_Must_pass_decompressed_size_ZL40__pyx_k_Must_pass_either_names_or_fields_ZL42__pyx_k_Must_pass_either_names_or_fields_2_ZL40__pyx_k_Must_pass_names_or_schema_when_c_ZL40__pyx_k_Must_pass_schema_or_at_least_one_ZL10__pyx_k_NA_ZL12__pyx_k_NULL_ZL39__pyx_k_Names_of_the_Table_or_RecordBat_ZL39__pyx_k_Names_of_this_tensor_dimensions_ZL40__pyx_k_Nanosecond_duration_is_not_safel_ZL40__pyx_k_Nanosecond_resolution_temporal_t_ZL18__pyx_k_NativeFile_ZL26__pyx_k_NativeFile___enter_ZL25__pyx_k_NativeFile___exit_ZL34__pyx_k_NativeFile___reduce_cython_ZL36__pyx_k_NativeFile___setstate_cython_ZL31__pyx_k_NativeFile__assert_open_ZL35__pyx_k_NativeFile__assert_readable_ZL35__pyx_k_NativeFile__assert_seekable_ZL35__pyx_k_NativeFile__assert_writable_ZL24__pyx_k_NativeFile_close_ZL27__pyx_k_NativeFile_download_ZL25__pyx_k_NativeFile_fileno_ZL24__pyx_k_NativeFile_flush_ZL29__pyx_k_NativeFile_get_stream_ZL25__pyx_k_NativeFile_isatty_ZL27__pyx_k_NativeFile_metadata_ZL23__pyx_k_NativeFile_read_ZL24__pyx_k_NativeFile_read1_ZL26__pyx_k_NativeFile_read_at_ZL30__pyx_k_NativeFile_read_buffer_ZL27__pyx_k_NativeFile_readable_ZL26__pyx_k_NativeFile_readall_ZL27__pyx_k_NativeFile_readinto_ZL27__pyx_k_NativeFile_readline_ZL28__pyx_k_NativeFile_readlines_ZL23__pyx_k_NativeFile_seek_ZL27__pyx_k_NativeFile_seekable_ZL23__pyx_k_NativeFile_size_ZL23__pyx_k_NativeFile_tell_ZL27__pyx_k_NativeFile_truncate_ZL25__pyx_k_NativeFile_upload_ZL27__pyx_k_NativeFile_writable_ZL24__pyx_k_NativeFile_write_ZL29__pyx_k_NativeFile_writelines_ZL27__pyx_k_No_type_alias_for_0_ZL28__pyx_k_Non_fixed_width_type_ZL22__pyx_k_NotImplemented_ZL27__pyx_k_NotImplementedError_ZL30__pyx_k_Not_a_metadata_version_ZL33__pyx_k_Not_an_ArrowSchema_object_ZL17__pyx_k_NullArray_ZL19__pyx_k_NullOptions_ZL18__pyx_k_NullScalar_ZL24__pyx_k_NullScalar_as_py_ZL38__pyx_k_Null_pointer_value_before_cast_ZL39__pyx_k_Number_of_columns_Returns_int_E_ZL39__pyx_k_Number_of_columns_in_this_table_ZL39__pyx_k_Number_of_data_buffers_required_ZL38__pyx_k_Number_of_null_entries_Returns_ZL39__pyx_k_Number_of_rows_Due_to_the_defin_ZL39__pyx_k_Number_of_rows_in_this_table_Du_ZL39__pyx_k_Number_of_underlying_chunks_Ret_ZL20__pyx_k_NumericArray_ZL14__pyx_k_OSFile_ZL30__pyx_k_OSFile___reduce_cython_ZL32__pyx_k_OSFile___setstate_cython_ZL21__pyx_k_OSFile_fileno_ZL35__pyx_k_Offset_must_be_non_negative_ZL40__pyx_k_Only_extension_types_can_be_regi_ZL37__pyx_k_Only_stream_None_is_supported_ZL39__pyx_k_Open_memory_map_at_file_path_Si_ZL34__pyx_k_Operation_on_closed_reader_ZL34__pyx_k_Operation_on_closed_writer_ZL19__pyx_k_OrderedDict_ZL31__pyx_k_PYARROW_IGNORE_TIMEZONE_ZL21__pyx_k_PandasAPIShim_ZL37__pyx_k_PandasAPIShim___reduce_cython_ZL39__pyx_k_PandasAPIShim___setstate_cython_ZL32__pyx_k_PandasAPIShim_data_frame_ZL39__pyx_k_PandasAPIShim_get_rangeindex_at_ZL32__pyx_k_PandasAPIShim_get_values_ZL33__pyx_k_PandasAPIShim_infer_dtype_ZL35__pyx_k_PandasAPIShim_is_array_like_ZL36__pyx_k_PandasAPIShim_is_categorical_ZL35__pyx_k_PandasAPIShim_is_data_frame_ZL35__pyx_k_PandasAPIShim_is_datetimetz_ZL39__pyx_k_PandasAPIShim_is_extension_arra_ZL31__pyx_k_PandasAPIShim_is_ge_v21_ZL30__pyx_k_PandasAPIShim_is_ge_v3_ZL30__pyx_k_PandasAPIShim_is_index_ZL31__pyx_k_PandasAPIShim_is_series_ZL31__pyx_k_PandasAPIShim_is_sparse_ZL27__pyx_k_PandasAPIShim_is_v1_ZL34__pyx_k_PandasAPIShim_pandas_dtype_ZL28__pyx_k_PandasAPIShim_series_ZL25__pyx_k_PandasConvertible_ZL39__pyx_k_PandasConvertible___reduce_cyth_ZL39__pyx_k_PandasConvertible___setstate_cy_ZL35__pyx_k_PandasConvertible_to_pandas_ZL39__pyx_k_PandasConvertible_to_pandas_lin_ZL40__pyx_k_Passing_a_pointer_value_as_a_flo_ZL39__pyx_k_Perform_a_join_between_this_tab_ZL39__pyx_k_Perform_an_aggregation_over_the_ZL39__pyx_k_Perform_an_asof_join_between_th_ZL19__pyx_k_PeriodDtype_ZL20__pyx_k_PickleBuffer_ZL19__pyx_k_PickleError_ZL35__pyx_k_Please_implement_0___reduce_ZL40__pyx_k_Property_compression_must_be_Non_ZL39__pyx_k_Provide_an_empty_table_accordin_ZL23__pyx_k_ProxyMemoryPool_ZL39__pyx_k_ProxyMemoryPool___reduce_cython_ZL40__pyx_k_ProxyMemoryPool___setstate_cytho_ZL24__pyx_k_PyArrowDataFrame_ZL23__pyx_k_PyExtensionType_ZL40__pyx_k_PyExtensionType___arrow_ext_dese_ZL40__pyx_k_PyExtensionType___arrow_ext_seri_ZL32__pyx_k_PyExtensionType___reduce_ZL37__pyx_k_PyExtensionType_set_auto_load_ZL18__pyx_k_PythonFile_ZL34__pyx_k_PythonFile___reduce_cython_ZL36__pyx_k_PythonFile___setstate_cython_ZL27__pyx_k_PythonFile_readline_ZL28__pyx_k_PythonFile_readlines_ZL27__pyx_k_PythonFile_truncate_ZL20__pyx_k_Pyx_EnumBase_ZL26__pyx_k_Pyx_EnumBase___new_ZL27__pyx_k_Pyx_EnumBase___repr_ZL26__pyx_k_Pyx_EnumBase___str_ZL36__pyx_k_Pyx_EnumMeta___reduce_cython_ZL38__pyx_k_Pyx_EnumMeta___setstate_cython_ZL20__pyx_k_Pyx_FlagBase_ZL26__pyx_k_Pyx_FlagBase___new_ZL27__pyx_k_Pyx_FlagBase___repr_ZL26__pyx_k_Pyx_FlagBase___str_ZL9__pyx_k_Q_ZL13__pyx_k_Queue_ZL18__pyx_k_QueueEmpty_ZL23__pyx_k_ReadPandasMixin_ZL35__pyx_k_ReadPandasMixin_read_pandas_ZL19__pyx_k_ReadStats_2_ZL16__pyx_k_Received_ZL19__pyx_k_RecordBatch_ZL29__pyx_k_RecordBatchFileReader_ZL37__pyx_k_RecordBatchFileReader___enter_ZL36__pyx_k_RecordBatchFileReader___exit_ZL38__pyx_k_RecordBatchFileReader___reduce_ZL39__pyx_k_RecordBatchFileReader___setstat_ZL35__pyx_k_RecordBatchFileReader__open_ZL39__pyx_k_RecordBatchFileReader_get_batch_ZL41__pyx_k_RecordBatchFileReader_get_batch_2_ZL38__pyx_k_RecordBatchFileReader_read_all_ZL29__pyx_k_RecordBatchFileWriter_ZL38__pyx_k_RecordBatchFileWriter___reduce_ZL39__pyx_k_RecordBatchFileWriter___setstat_ZL35__pyx_k_RecordBatchFileWriter__open_ZL25__pyx_k_RecordBatchReader_ZL40__pyx_k_RecordBatchReader___arrow_c_stre_ZL33__pyx_k_RecordBatchReader___enter_ZL32__pyx_k_RecordBatchReader___exit_ZL40__pyx_k_RecordBatchReader___reduce_cytho_ZL40__pyx_k_RecordBatchReader___setstate_cyt_ZL38__pyx_k_RecordBatchReader__export_to_c_ZL40__pyx_k_RecordBatchReader__import_from_c_ZL42__pyx_k_RecordBatchReader__import_from_c_2_ZL30__pyx_k_RecordBatchReader_cast_ZL31__pyx_k_RecordBatchReader_close_ZL38__pyx_k_RecordBatchReader_from_batches_ZL37__pyx_k_RecordBatchReader_from_stream_ZL40__pyx_k_RecordBatchReader_iter_batches_w_ZL34__pyx_k_RecordBatchReader_read_all_ZL40__pyx_k_RecordBatchReader_read_next_batc_ZL42__pyx_k_RecordBatchReader_read_next_batc_2_ZL31__pyx_k_RecordBatchStreamReader_ZL39__pyx_k_RecordBatchStreamReader___reduc_ZL39__pyx_k_RecordBatchStreamReader___setst_ZL37__pyx_k_RecordBatchStreamReader__open_ZL31__pyx_k_RecordBatchStreamWriter_ZL39__pyx_k_RecordBatchStreamWriter___reduc_ZL39__pyx_k_RecordBatchStreamWriter___setst_ZL37__pyx_k_RecordBatchStreamWriter__open_ZL33__pyx_k_RecordBatchWithMetadata_2_ZL35__pyx_k_RecordBatch___arrow_c_array_ZL36__pyx_k_RecordBatch___arrow_c_stream_ZL28__pyx_k_RecordBatch___reduce_ZL28__pyx_k_RecordBatch___sizeof_ZL27__pyx_k_RecordBatch__column_ZL32__pyx_k_RecordBatch__export_to_c_ZL39__pyx_k_RecordBatch__export_to_c_device_ZL34__pyx_k_RecordBatch__import_from_c_ZL40__pyx_k_RecordBatch__import_from_c_capsu_ZL40__pyx_k_RecordBatch__import_from_c_devic_ZL35__pyx_k_RecordBatch__is_initialized_ZL30__pyx_k_RecordBatch__to_pandas_ZL30__pyx_k_RecordBatch_add_column_ZL40__pyx_k_RecordBatch_add_column_line_2663_ZL24__pyx_k_RecordBatch_cast_ZL34__pyx_k_RecordBatch_cast_line_3130_ZL26__pyx_k_RecordBatch_equals_ZL36__pyx_k_RecordBatch_equals_line_3028_ZL26__pyx_k_RecordBatch_filter_ZL36__pyx_k_RecordBatch_filter_line_2976_ZL31__pyx_k_RecordBatch_from_arrays_ZL40__pyx_k_RecordBatch_from_arrays_line_329_ZL31__pyx_k_RecordBatch_from_pandas_ZL40__pyx_k_RecordBatch_from_pandas_line_319_ZL37__pyx_k_RecordBatch_from_struct_array_ZL40__pyx_k_RecordBatch_from_struct_array_li_ZL40__pyx_k_RecordBatch_get_total_buffer_siz_ZL42__pyx_k_RecordBatch_get_total_buffer_siz_2_ZL39__pyx_k_RecordBatch_nbytes___get___line_ZL37__pyx_k_RecordBatch_num_columns___get_ZL40__pyx_k_RecordBatch_num_rows___get___lin_ZL33__pyx_k_RecordBatch_remove_column_ZL40__pyx_k_RecordBatch_remove_column_line_2_ZL34__pyx_k_RecordBatch_rename_columns_ZL39__pyx_k_RecordBatch_rename_columns_line_ZL40__pyx_k_RecordBatch_replace_schema_metad_ZL42__pyx_k_RecordBatch_replace_schema_metad_2_ZL39__pyx_k_RecordBatch_schema___get___line_ZL26__pyx_k_RecordBatch_select_ZL36__pyx_k_RecordBatch_select_line_3076_ZL29__pyx_k_RecordBatch_serialize_ZL39__pyx_k_RecordBatch_serialize_line_2871_ZL30__pyx_k_RecordBatch_set_column_ZL40__pyx_k_RecordBatch_set_column_line_2771_ZL25__pyx_k_RecordBatch_slice_ZL35__pyx_k_RecordBatch_slice_line_2919_ZL35__pyx_k_RecordBatch_to_struct_array_ZL29__pyx_k_RecordBatch_to_tensor_ZL39__pyx_k_RecordBatch_to_tensor_line_3438_ZL28__pyx_k_RecordBatch_validate_ZL40__pyx_k_RecordBatch_with_its_custom_meta_ZL39__pyx_k_Register_a_Python_extension_typ_ZL39__pyx_k_Remove_missing_values_from_a_ch_ZL39__pyx_k_Remove_rows_that_contain_missin_ZL39__pyx_k_Remove_the_field_at_index_i_fro_ZL38__pyx_k_Render_a_pretty_printed_string_ZL39__pyx_k_Replace_a_field_at_position_i_i_ZL39__pyx_k_Replace_column_in_RecordBatch_a_ZL39__pyx_k_Replace_column_in_Table_at_posi_ZL39__pyx_k_Replace_each_null_element_in_va_ZL23__pyx_k_ResizableBuffer_ZL30__pyx_k_ResizableBuffer_resize_ZL39__pyx_k_Return_a_NumPy_copy_of_this_arr_ZL39__pyx_k_Return_a_child_field_by_its_num_ZL38__pyx_k_Return_array_of_same_length_as_ZL39__pyx_k_Return_boolean_array_indicating_ZL41__pyx_k_Return_boolean_array_indicating_2_ZL41__pyx_k_Return_boolean_array_indicating_3_ZL39__pyx_k_Return_data_type_of_a_ChunkedAr_ZL39__pyx_k_Return_deserialized_from_JSON_p_ZL39__pyx_k_Return_index_of_the_unique_fiel_ZL41__pyx_k_Return_index_of_the_unique_fiel_2_ZL39__pyx_k_Return_integers_array_with_valu_ZL39__pyx_k_Return_length_of_a_ChunkedArray_ZL39__pyx_k_Return_sorted_list_of_indices_f_ZL41__pyx_k_Return_sorted_list_of_indices_f_2_ZL39__pyx_k_Return_the_equivalent_NumPy_Pan_ZL39__pyx_k_Return_the_list_offsets_as_an_i_ZL41__pyx_k_Return_the_list_offsets_as_an_i_2_ZL39__pyx_k_Return_the_list_sizes_as_an_int_ZL39__pyx_k_Return_the_list_view_offsets_as_ZL39__pyx_k_Return_the_list_view_sizes_as_a_ZL39__pyx_k_Return_the_process_global_memor_ZL38__pyx_k_Return_the_underlying_array_of_ZL40__pyx_k_Return_the_underlying_array_of_2_ZL40__pyx_k_Return_the_underlying_array_of_3_ZL40__pyx_k_Return_the_underlying_array_of_4_ZL40__pyx_k_Return_the_underlying_array_of_5_ZL39__pyx_k_Return_true_if_the_tensors_cont_ZL39__pyx_k_Return_true_if_type_is_equivale_ZL38__pyx_k_Return_whether_the_contents_of_ZL39__pyx_k_Returns_implied_schema_from_dat_ZL39__pyx_k_Returns_the_name_of_the_i_th_te_ZL26__pyx_k_RunEndEncodedArray_ZL39__pyx_k_RunEndEncodedArray__from_arrays_ZL40__pyx_k_RunEndEncodedArray_find_physical_ZL42__pyx_k_RunEndEncodedArray_find_physical_2_ZL38__pyx_k_RunEndEncodedArray_from_arrays_ZL39__pyx_k_RunEndEncodedArray_from_buffers_ZL27__pyx_k_RunEndEncodedScalar_ZL33__pyx_k_RunEndEncodedScalar_as_py_ZL25__pyx_k_RunEndEncodedType_ZL34__pyx_k_RunEndEncodedType___reduce_ZL40__pyx_k_RunEndEncodedType_expects_None_a_ZL40__pyx_k_RunEndEncodedType_s_expected_nul_ZL40__pyx_k_RunEndEncodedType_s_expected_num_ZL42__pyx_k_RunEndEncodedType_s_expected_num_2_ZL20__pyx_k_RuntimeError_ZL22__pyx_k_RuntimeWarning_ZL14__pyx_k_SIGINT_ZL15__pyx_k_SIGTERM_ZL15__pyx_k_SIG_DFL_ZL15__pyx_k_SIG_IGN_ZL14__pyx_k_SNAPPY_ZL14__pyx_k_Scalar_ZL30__pyx_k_ScalarAggregateOptions_ZL23__pyx_k_Scalar___reduce_ZL20__pyx_k_Scalar_as_py_ZL19__pyx_k_Scalar_cast_ZL33__pyx_k_Scalar_data_type_was_NULL_ZL21__pyx_k_Scalar_equals_ZL33__pyx_k_Scalar_type_not_supported_ZL23__pyx_k_Scalar_validate_ZL23__pyx_k_Scalar_was_NULL_ZL14__pyx_k_Schema_ZL31__pyx_k_Schema___arrow_c_schema_ZL21__pyx_k_Schema___iter_ZL23__pyx_k_Schema___reduce_ZL23__pyx_k_Schema___sizeof_ZL27__pyx_k_Schema__export_to_c_ZL21__pyx_k_Schema__field_ZL29__pyx_k_Schema__import_from_c_ZL37__pyx_k_Schema__import_from_c_capsule_ZL27__pyx_k_Schema_add_metadata_ZL40__pyx_k_Schema_and_number_of_arrays_uneq_ZL21__pyx_k_Schema_append_ZL31__pyx_k_Schema_append_line_3024_ZL26__pyx_k_Schema_empty_table_ZL36__pyx_k_Schema_empty_table_line_2759_ZL21__pyx_k_Schema_equals_ZL31__pyx_k_Schema_equals_line_2787_ZL20__pyx_k_Schema_field_ZL28__pyx_k_Schema_field_by_name_ZL30__pyx_k_Schema_field_line_2872_ZL40__pyx_k_Schema_field_name_corresponds_to_ZL26__pyx_k_Schema_from_pandas_ZL36__pyx_k_Schema_from_pandas_line_2826_ZL36__pyx_k_Schema_get_all_field_indices_ZL40__pyx_k_Schema_get_all_field_indices_lin_ZL30__pyx_k_Schema_get_field_index_ZL40__pyx_k_Schema_get_field_index_line_2956_ZL21__pyx_k_Schema_insert_ZL31__pyx_k_Schema_insert_line_3063_ZL40__pyx_k_Schema_metadata___get___line_272_ZL40__pyx_k_Schema_must_be_an_instance_of_py_ZL38__pyx_k_Schema_names___get___line_2674_ZL39__pyx_k_Schema_of_the_RecordBatch_and_i_ZL39__pyx_k_Schema_of_the_table_and_its_col_ZL40__pyx_k_Schema_pandas_metadata___get___l_ZL40__pyx_k_Schema_passed_to_names_option_pl_ZL21__pyx_k_Schema_remove_ZL31__pyx_k_Schema_remove_line_3101_ZL30__pyx_k_Schema_remove_metadata_ZL40__pyx_k_Schema_remove_metadata_line_3252_ZL24__pyx_k_Schema_serialize_ZL34__pyx_k_Schema_serialize_line_3218_ZL18__pyx_k_Schema_set_ZL28__pyx_k_Schema_set_line_3132_ZL24__pyx_k_Schema_to_string_ZL38__pyx_k_Schema_types___get___line_2702_ZL28__pyx_k_Schema_with_metadata_ZL38__pyx_k_Schema_with_metadata_line_3182_ZL39__pyx_k_Select_a_chunk_by_its_index_Par_ZL39__pyx_k_Select_a_field_by_its_column_na_ZL41__pyx_k_Select_a_field_by_its_column_na_2_ZL39__pyx_k_Select_a_schema_field_by_its_co_ZL39__pyx_k_Select_columns_of_the_RecordBat_ZL39__pyx_k_Select_columns_of_the_Table_Ret_ZL39__pyx_k_Select_rows_from_a_Table_or_Rec_ZL39__pyx_k_Select_rows_from_the_record_bat_ZL38__pyx_k_Select_rows_from_the_table_The_ZL39__pyx_k_Select_single_column_from_Table_ZL38__pyx_k_Select_values_from_the_chunked_ZL40__pyx_k_Select_values_from_the_chunked_2_ZL14__pyx_k_Series_ZL39__pyx_k_Should_specify_one_of_list_size_ZL39__pyx_k_Should_the_entries_be_sorted_ac_ZL25__pyx_k_SignalStopHandler_ZL33__pyx_k_SignalStopHandler___enter_ZL32__pyx_k_SignalStopHandler___exit_ZL40__pyx_k_SignalStopHandler___reduce_cytho_ZL40__pyx_k_SignalStopHandler___setstate_cyt_ZL39__pyx_k_SignalStopHandler__init_signals_ZL19__pyx_k_SortOptions_ZL39__pyx_k_Sort_the_Table_or_RecordBatch_b_ZL23__pyx_k_SparseCOOTensor_ZL40__pyx_k_SparseCOOTensor___get___locals_g_ZL39__pyx_k_SparseCOOTensor___reduce_cython_ZL40__pyx_k_SparseCOOTensor___setstate_cytho_ZL32__pyx_k_SparseCOOTensor_dim_name_ZL30__pyx_k_SparseCOOTensor_equals_ZL40__pyx_k_SparseCOOTensor_from_dense_numpy_ZL34__pyx_k_SparseCOOTensor_from_numpy_ZL40__pyx_k_SparseCOOTensor_from_pydata_spar_ZL34__pyx_k_SparseCOOTensor_from_scipy_ZL35__pyx_k_SparseCOOTensor_from_tensor_ZL32__pyx_k_SparseCOOTensor_to_numpy_ZL40__pyx_k_SparseCOOTensor_to_pydata_sparse_ZL32__pyx_k_SparseCOOTensor_to_scipy_ZL33__pyx_k_SparseCOOTensor_to_tensor_ZL32__pyx_k_SparseCOOTensor_was_NULL_ZL23__pyx_k_SparseCSCMatrix_ZL40__pyx_k_SparseCSCMatrix___get___locals_g_ZL39__pyx_k_SparseCSCMatrix___reduce_cython_ZL40__pyx_k_SparseCSCMatrix___setstate_cytho_ZL32__pyx_k_SparseCSCMatrix_dim_name_ZL30__pyx_k_SparseCSCMatrix_equals_ZL40__pyx_k_SparseCSCMatrix_from_dense_numpy_ZL34__pyx_k_SparseCSCMatrix_from_numpy_ZL34__pyx_k_SparseCSCMatrix_from_scipy_ZL35__pyx_k_SparseCSCMatrix_from_tensor_ZL32__pyx_k_SparseCSCMatrix_to_numpy_ZL32__pyx_k_SparseCSCMatrix_to_scipy_ZL33__pyx_k_SparseCSCMatrix_to_tensor_ZL32__pyx_k_SparseCSCMatrix_was_NULL_ZL23__pyx_k_SparseCSFTensor_ZL40__pyx_k_SparseCSFTensor___get___locals_g_ZL39__pyx_k_SparseCSFTensor___reduce_cython_ZL40__pyx_k_SparseCSFTensor___setstate_cytho_ZL32__pyx_k_SparseCSFTensor_dim_name_ZL30__pyx_k_SparseCSFTensor_equals_ZL40__pyx_k_SparseCSFTensor_from_dense_numpy_ZL34__pyx_k_SparseCSFTensor_from_numpy_ZL35__pyx_k_SparseCSFTensor_from_tensor_ZL32__pyx_k_SparseCSFTensor_to_numpy_ZL33__pyx_k_SparseCSFTensor_to_tensor_ZL32__pyx_k_SparseCSFTensor_was_NULL_ZL23__pyx_k_SparseCSRMatrix_ZL40__pyx_k_SparseCSRMatrix___get___locals_g_ZL39__pyx_k_SparseCSRMatrix___reduce_cython_ZL40__pyx_k_SparseCSRMatrix___setstate_cytho_ZL32__pyx_k_SparseCSRMatrix_dim_name_ZL30__pyx_k_SparseCSRMatrix_equals_ZL40__pyx_k_SparseCSRMatrix_from_dense_numpy_ZL34__pyx_k_SparseCSRMatrix_from_numpy_ZL34__pyx_k_SparseCSRMatrix_from_scipy_ZL35__pyx_k_SparseCSRMatrix_from_tensor_ZL32__pyx_k_SparseCSRMatrix_to_numpy_ZL32__pyx_k_SparseCSRMatrix_to_scipy_ZL33__pyx_k_SparseCSRMatrix_to_tensor_ZL32__pyx_k_SparseCSRMatrix_was_NULL_ZL19__pyx_k_SparseDtype_ZL23__pyx_k_SparseUnionType_ZL21__pyx_k_StopIteration_ZL17__pyx_k_StopToken_ZL33__pyx_k_StopToken___reduce_cython_ZL35__pyx_k_StopToken___setstate_cython_ZL39__pyx_k_Strides_of_this_tensor_Examples_ZL19__pyx_k_StringArray_ZL32__pyx_k_StringArray_from_buffers_ZL21__pyx_k_StringBuilder_ZL37__pyx_k_StringBuilder___reduce_cython_ZL39__pyx_k_StringBuilder___setstate_cython_ZL28__pyx_k_StringBuilder_append_ZL35__pyx_k_StringBuilder_append_values_ZL28__pyx_k_StringBuilder_finish_ZL40__pyx_k_StringBuilder_only_accepts_strin_ZL20__pyx_k_StringScalar_ZL26__pyx_k_StringScalar_as_py_ZL23__pyx_k_StringViewArray_ZL25__pyx_k_StringViewBuilder_ZL40__pyx_k_StringViewBuilder___reduce_cytho_ZL40__pyx_k_StringViewBuilder___setstate_cyt_ZL32__pyx_k_StringViewBuilder_append_ZL39__pyx_k_StringViewBuilder_append_values_ZL32__pyx_k_StringViewBuilder_finish_ZL40__pyx_k_StringViewBuilder_only_accepts_s_ZL24__pyx_k_StringViewScalar_ZL19__pyx_k_StructArray_ZL36__pyx_k_StructArray__flattened_field_ZL25__pyx_k_StructArray_field_ZL27__pyx_k_StructArray_flatten_ZL31__pyx_k_StructArray_from_arrays_ZL24__pyx_k_StructArray_sort_ZL20__pyx_k_StructScalar_ZL27__pyx_k_StructScalar___iter_ZL33__pyx_k_StructScalar__as_py_tuple_ZL26__pyx_k_StructScalar_as_py_ZL26__pyx_k_StructScalar_items_ZL18__pyx_k_StructType_ZL25__pyx_k_StructType___iter_ZL27__pyx_k_StructType___reduce_ZL24__pyx_k_StructType_field_ZL33__pyx_k_StructType_field_line_947_ZL40__pyx_k_StructType_get_all_field_indices_ZL42__pyx_k_StructType_get_all_field_indices_2_ZL34__pyx_k_StructType_get_field_index_ZL39__pyx_k_StructType_get_field_index_line_ZL40__pyx_k_Struct_field_name_corresponds_to_ZL19__pyx_k_SystemError_ZL9__pyx_k_T_ZL13__pyx_k_Table_ZL20__pyx_k_TableGroupBy_ZL27__pyx_k_TableGroupBy___init_ZL30__pyx_k_TableGroupBy_aggregate_ZL40__pyx_k_TableGroupBy_aggregate_line_6102_ZL30__pyx_k_Table___arrow_c_stream_ZL22__pyx_k_Table___reduce_ZL22__pyx_k_Table___sizeof_ZL21__pyx_k_Table__column_ZL29__pyx_k_Table__is_initialized_ZL24__pyx_k_Table__to_pandas_ZL24__pyx_k_Table_add_column_ZL34__pyx_k_Table_add_column_line_5062_ZL18__pyx_k_Table_cast_ZL28__pyx_k_Table_cast_line_4399_ZL28__pyx_k_Table_combine_chunks_ZL38__pyx_k_Table_combine_chunks_line_4246_ZL18__pyx_k_Table_drop_ZL20__pyx_k_Table_equals_ZL30__pyx_k_Table_equals_line_4351_ZL20__pyx_k_Table_filter_ZL30__pyx_k_Table_filter_line_3995_ZL21__pyx_k_Table_flatten_ZL31__pyx_k_Table_flatten_line_4181_ZL25__pyx_k_Table_from_arrays_ZL35__pyx_k_Table_from_arrays_line_4542_ZL26__pyx_k_Table_from_batches_ZL36__pyx_k_Table_from_batches_line_4699_ZL25__pyx_k_Table_from_pandas_ZL35__pyx_k_Table_from_pandas_line_4462_ZL31__pyx_k_Table_from_struct_array_ZL40__pyx_k_Table_from_struct_array_line_464_ZL35__pyx_k_Table_get_total_buffer_size_ZL40__pyx_k_Table_get_total_buffer_size_line_ZL22__pyx_k_Table_group_by_ZL32__pyx_k_Table_group_by_line_5288_ZL18__pyx_k_Table_join_ZL23__pyx_k_Table_join_asof_ZL33__pyx_k_Table_join_asof_line_5446_ZL28__pyx_k_Table_join_line_5330_ZL38__pyx_k_Table_nbytes___get___line_4998_ZL40__pyx_k_Table_num_columns___get___line_4_ZL40__pyx_k_Table_num_rows___get___line_4974_ZL27__pyx_k_Table_remove_column_ZL37__pyx_k_Table_remove_column_line_5136_ZL28__pyx_k_Table_rename_columns_ZL38__pyx_k_Table_rename_columns_line_5229_ZL37__pyx_k_Table_replace_schema_metadata_ZL40__pyx_k_Table_replace_schema_metadata_li_ZL38__pyx_k_Table_schema___get___line_4910_ZL20__pyx_k_Table_select_ZL30__pyx_k_Table_select_line_4067_ZL24__pyx_k_Table_set_column_ZL34__pyx_k_Table_set_column_line_5170_ZL19__pyx_k_Table_slice_ZL29__pyx_k_Table_slice_line_3930_ZL24__pyx_k_Table_to_batches_ZL34__pyx_k_Table_to_batches_line_4772_ZL23__pyx_k_Table_to_reader_ZL33__pyx_k_Table_to_reader_line_4842_ZL29__pyx_k_Table_to_struct_array_ZL32__pyx_k_Table_unify_dictionaries_ZL40__pyx_k_Table_unify_dictionaries_line_42_ZL22__pyx_k_Table_validate_ZL15__pyx_k_Tabular_ZL23__pyx_k_Tabular___array_ZL27__pyx_k_Tabular___dataframe_ZL31__pyx_k_Tabular___reduce_cython_ZL33__pyx_k_Tabular___setstate_cython_ZL23__pyx_k_Tabular__column_ZL37__pyx_k_Tabular__ensure_integer_index_ZL31__pyx_k_Tabular__is_initialized_ZL26__pyx_k_Tabular_add_column_ZL29__pyx_k_Tabular_append_column_ZL39__pyx_k_Tabular_append_column_line_2290_ZL22__pyx_k_Tabular_column_ZL32__pyx_k_Tabular_column_line_1683_ZL39__pyx_k_Tabular_column_names___get___li_ZL39__pyx_k_Tabular_columns___get___line_17_ZL28__pyx_k_Tabular_drop_columns_ZL38__pyx_k_Tabular_drop_columns_line_2223_ZL25__pyx_k_Tabular_drop_null_ZL35__pyx_k_Tabular_drop_null_line_1795_ZL21__pyx_k_Tabular_field_ZL31__pyx_k_Tabular_field_line_1829_ZL27__pyx_k_Tabular_from_pydict_ZL37__pyx_k_Tabular_from_pydict_line_1858_ZL27__pyx_k_Tabular_from_pylist_ZL37__pyx_k_Tabular_from_pylist_line_1925_ZL27__pyx_k_Tabular_itercolumns_ZL37__pyx_k_Tabular_itercolumns_line_1989_ZL29__pyx_k_Tabular_remove_column_ZL39__pyx_k_Tabular_shape___get___line_2023_ZL23__pyx_k_Tabular_sort_by_ZL33__pyx_k_Tabular_sort_by_line_2047_ZL20__pyx_k_Tabular_take_ZL30__pyx_k_Tabular_take_line_2097_ZL25__pyx_k_Tabular_to_pydict_ZL35__pyx_k_Tabular_to_pydict_line_2135_ZL25__pyx_k_Tabular_to_pylist_ZL35__pyx_k_Tabular_to_pylist_line_2161_ZL25__pyx_k_Tabular_to_string_ZL39__pyx_k_Target_schema_s_field_names_are_ZL41__pyx_k_Target_schema_s_field_names_are_2_ZL41__pyx_k_Target_schema_s_field_names_are_3_ZL14__pyx_k_Tensor_ZL30__pyx_k_Tensor___reduce_cython_ZL32__pyx_k_Tensor___setstate_cython_ZL40__pyx_k_Tensor__make_shape_or_strides_bu_ZL23__pyx_k_Tensor_dim_name_ZL32__pyx_k_Tensor_dim_name_line_145_ZL40__pyx_k_Tensor_dim_names___get___line_16_ZL21__pyx_k_Tensor_equals_ZL30__pyx_k_Tensor_equals_line_115_ZL25__pyx_k_Tensor_from_numpy_ZL33__pyx_k_Tensor_from_numpy_line_61_ZL40__pyx_k_Tensor_is_contiguous___get___lin_ZL40__pyx_k_Tensor_is_mutable___get___line_1_ZL36__pyx_k_Tensor_ndim___get___line_215_ZL37__pyx_k_Tensor_shape___get___line_247_ZL36__pyx_k_Tensor_size___get___line_231_ZL39__pyx_k_Tensor_strides___get___line_264_ZL23__pyx_k_Tensor_to_numpy_ZL31__pyx_k_Tensor_to_numpy_line_96_ZL23__pyx_k_Tensor_was_NULL_ZL38__pyx_k_Test_if_this_field_is_equal_to_ZL39__pyx_k_Test_if_this_schema_is_equal_to_ZL18__pyx_k_TextIOBase_ZL40__pyx_k_The_Array_passed_as_dictionary_m_ZL40__pyx_k_The_Scalar_value_passed_as_index_ZL40__pyx_k_The_add_metadata_method_is_depre_ZL39__pyx_k_The_data_type_of_dictionary_ind_ZL39__pyx_k_The_data_type_of_items_in_the_m_ZL39__pyx_k_The_data_type_of_keys_in_the_ma_ZL39__pyx_k_The_data_type_of_large_list_val_ZL41__pyx_k_The_data_type_of_large_list_val_2_ZL39__pyx_k_The_data_type_of_large_list_vie_ZL39__pyx_k_The_data_type_of_list_values_Ex_ZL39__pyx_k_The_data_type_of_list_view_valu_ZL39__pyx_k_The_decimal_precision_in_number_ZL41__pyx_k_The_decimal_precision_in_number_2_ZL39__pyx_k_The_decimal_scale_an_integer_Ex_ZL41__pyx_k_The_decimal_scale_an_integer_Ex_2_ZL40__pyx_k_The_dictionary_index_type_should_ZL39__pyx_k_The_dictionary_value_type_The_d_ZL38__pyx_k_The_dimension_n_of_this_tensor_ZL40__pyx_k_The_dtype_of_the_categories_of_t_ZL39__pyx_k_The_duration_unit_s_ms_us_or_ns_ZL40__pyx_k_The_field_by_name_method_is_depr_ZL38__pyx_k_The_field_for_items_in_the_map_ZL39__pyx_k_The_field_for_keys_in_the_map_e_ZL39__pyx_k_The_field_for_large_list_view_v_ZL39__pyx_k_The_field_for_list_values_Examp_ZL41__pyx_k_The_field_for_list_values_Examp_2_ZL38__pyx_k_The_field_for_list_view_values_ZL39__pyx_k_The_field_metadata_Examples_imp_ZL38__pyx_k_The_field_name_Examples_import_ZL38__pyx_k_The_field_nullability_Examples_ZL38__pyx_k_The_mode_of_the_union_dense_or_ZL40__pyx_k_The_names_and_metadata_arguments_ZL42__pyx_k_The_names_and_metadata_arguments_2_ZL39__pyx_k_The_names_argument_is_not_valid_ZL39__pyx_k_The_number_of_child_fields_Exam_ZL40__pyx_k_The_object_s___arrow_array___met_ZL40__pyx_k_The_ordered_flag_of_the_passed_c_ZL40__pyx_k_The_passed_mapping_doesn_t_conta_ZL40__pyx_k_The_run_end_type_should_be_int16_ZL39__pyx_k_The_schema_s_field_names_Return_ZL39__pyx_k_The_schema_s_field_types_Return_ZL39__pyx_k_The_schema_s_metadata_Returns_m_ZL39__pyx_k_The_shape_of_this_tensor_Exampl_ZL39__pyx_k_The_size_of_the_fixed_size_list_ZL39__pyx_k_The_size_of_this_tensor_Example_ZL39__pyx_k_The_sum_of_bytes_in_each_buffer_ZL41__pyx_k_The_sum_of_bytes_in_each_buffer_2_ZL41__pyx_k_The_sum_of_bytes_in_each_buffer_3_ZL38__pyx_k_The_time_unit_s_or_ms_Examples_ZL39__pyx_k_The_time_unit_us_or_ns_Examples_ZL38__pyx_k_The_timestamp_time_zone_if_any_ZL39__pyx_k_The_timestamp_unit_s_ms_us_or_n_ZL38__pyx_k_The_type_code_to_indicate_each_ZL40__pyx_k_This_object_s_internal_pointer_i_ZL14__pyx_k_Thread_ZL19__pyx_k_Time32Array_ZL20__pyx_k_Time32Scalar_ZL26__pyx_k_Time32Scalar_as_py_ZL18__pyx_k_Time32Type_ZL40__pyx_k_Time32Type_unit___get___line_126_ZL19__pyx_k_Time64Array_ZL20__pyx_k_Time64Scalar_ZL26__pyx_k_Time64Scalar_as_py_ZL18__pyx_k_Time64Type_ZL40__pyx_k_Time64Type_unit___get___line_129_ZL40__pyx_k_Time_zones_are_not_available_fro_ZL17__pyx_k_Timedelta_ZL17__pyx_k_Timestamp_ZL22__pyx_k_TimestampArray_ZL23__pyx_k_TimestampScalar_ZL29__pyx_k_TimestampScalar_as_py_ZL21__pyx_k_TimestampType_ZL30__pyx_k_TimestampType___reduce_ZL40__pyx_k_TimestampType_tz___get___line_12_ZL39__pyx_k_TimestampType_unit___get___line_ZL38__pyx_k_Total_number_of_bytes_consumed_ZL40__pyx_k_Total_number_of_bytes_consumed_2_ZL40__pyx_k_Total_number_of_bytes_consumed_3_ZL18__pyx_k_Transcoder_ZL25__pyx_k_Transcoder___call_ZL25__pyx_k_Transcoder___init_ZL28__pyx_k_TransformInputStream_ZL40__pyx_k_TransformInputStream___reduce_cy_ZL39__pyx_k_TransformInputStream___setstate_ZL12__pyx_k_True_ZL17__pyx_k_TypeError_ZL19__pyx_k_Type_BINARY_ZL24__pyx_k_Type_BINARY_VIEW_ZL17__pyx_k_Type_BOOL_ZL19__pyx_k_Type_DATE32_ZL19__pyx_k_Type_DATE64_ZL23__pyx_k_Type_DECIMAL128_ZL23__pyx_k_Type_DECIMAL256_ZL24__pyx_k_Type_DENSE_UNION_ZL23__pyx_k_Type_DICTIONARY_ZL19__pyx_k_Type_DOUBLE_ZL21__pyx_k_Type_DURATION_ZL30__pyx_k_Type_FIXED_SIZE_BINARY_ZL28__pyx_k_Type_FIXED_SIZE_LIST_ZL18__pyx_k_Type_FLOAT_ZL23__pyx_k_Type_HALF_FLOAT_ZL18__pyx_k_Type_INT16_ZL18__pyx_k_Type_INT32_ZL18__pyx_k_Type_INT64_ZL17__pyx_k_Type_INT8_ZL36__pyx_k_Type_INTERVAL_MONTH_DAY_NANO_ZL25__pyx_k_Type_LARGE_BINARY_ZL23__pyx_k_Type_LARGE_LIST_ZL28__pyx_k_Type_LARGE_LIST_VIEW_ZL25__pyx_k_Type_LARGE_STRING_ZL17__pyx_k_Type_LIST_ZL22__pyx_k_Type_LIST_VIEW_ZL16__pyx_k_Type_MAP_ZL15__pyx_k_Type_NA_ZL28__pyx_k_Type_RUN_END_ENCODED_ZL25__pyx_k_Type_SPARSE_UNION_ZL19__pyx_k_Type_STRING_ZL24__pyx_k_Type_STRING_VIEW_ZL19__pyx_k_Type_STRUCT_ZL19__pyx_k_Type_TIME32_ZL19__pyx_k_Type_TIME64_ZL22__pyx_k_Type_TIMESTAMP_ZL19__pyx_k_Type_UINT16_ZL19__pyx_k_Type_UINT32_ZL19__pyx_k_Type_UINT64_ZL18__pyx_k_Type_UINT8_ZL40__pyx_k_Type_s_expected_number_of_buffer_ZL40__pyx_k_Type_s_expected_number_of_childr_ZL19__pyx_k_UInt16Array_ZL20__pyx_k_UInt16Scalar_ZL26__pyx_k_UInt16Scalar_as_py_ZL19__pyx_k_UInt32Array_ZL20__pyx_k_UInt32Scalar_ZL26__pyx_k_UInt32Scalar_as_py_ZL19__pyx_k_UInt64Array_ZL20__pyx_k_UInt64Scalar_ZL26__pyx_k_UInt64Scalar_as_py_ZL18__pyx_k_UInt8Array_ZL19__pyx_k_UInt8Scalar_ZL25__pyx_k_UInt8Scalar_as_py_ZL40__pyx_k_Unable_to_avoid_a_copy_while_cre_ZL42__pyx_k_Unable_to_avoid_a_copy_while_cre_2_ZL42__pyx_k_Unable_to_avoid_a_copy_while_cre_3_ZL40__pyx_k_Unable_to_read_message_from_obje_ZL40__pyx_k_Unable_to_wrap_Datum_in_a_Python_ZL40__pyx_k_Unable_to_write_to_object_of_typ_ZL39__pyx_k_Unify_dictionaries_across_all_c_ZL41__pyx_k_Unify_dictionaries_across_all_c_2_ZL18__pyx_k_UnionArray_ZL24__pyx_k_UnionArray_child_ZL38__pyx_k_UnionArray_does_not_have_child_ZL24__pyx_k_UnionArray_field_ZL29__pyx_k_UnionArray_from_dense_ZL30__pyx_k_UnionArray_from_sparse_ZL23__pyx_k_UnionMode_DENSE_ZL24__pyx_k_UnionMode_SPARSE_ZL19__pyx_k_UnionScalar_ZL25__pyx_k_UnionScalar_as_py_ZL17__pyx_k_UnionType_ZL24__pyx_k_UnionType___iter_ZL26__pyx_k_UnionType___reduce_ZL23__pyx_k_UnionType_field_ZL33__pyx_k_UnionType_field_line_1110_ZL40__pyx_k_UnionType_mode___get___line_1062_ZL40__pyx_k_UnionType_type_codes___get___lin_ZL28__pyx_k_UnknownExtensionType_ZL40__pyx_k_UnknownExtensionType___arrow_ext_ZL28__pyx_k_Unknown_enum_value_s_ZL38__pyx_k_Unnest_this_LargeListViewArray_ZL39__pyx_k_Unnest_this_ListViewArray_by_on_ZL39__pyx_k_Unregister_a_Python_extension_t_ZL28__pyx_k_UnsupportedOperation_ZL19__pyx_k_UserWarning_ZL10__pyx_k_V1_ZL10__pyx_k_V2_ZL10__pyx_k_V3_ZL10__pyx_k_V4_ZL10__pyx_k_V5_ZL18__pyx_k_ValueError_ZL15__pyx_k_Version_ZL19__pyx_k_Weakrefable_ZL39__pyx_k_Whether_the_dictionary_is_order_ZL31__pyx_k_Wrapping_scalar_of_type_ZL40__pyx_k_Writable_buffer_requested_but_Ar_ZL20__pyx_k_WriteStats_2_ZL38__pyx_k_Write_RecordBatch_to_Buffer_as_ZL39__pyx_k_Write_Schema_to_Buffer_as_encap_ZL22__pyx_k_Y_m_dT_H_M_S_z_ZL12__pyx_k_ZSTD_ZL12__pyx_k__108_ZL12__pyx_k__109_ZL12__pyx_k__110_ZL12__pyx_k__111_ZL11__pyx_k__12_ZL12__pyx_k__121_ZL11__pyx_k__13_ZL13__pyx_k__1313_ZL11__pyx_k__14_ZL11__pyx_k__15_ZL11__pyx_k__17_ZL11__pyx_k__19_ZL10__pyx_k__3_ZL10__pyx_k__7_ZL11__pyx_k__72_ZL11__pyx_k__77_ZL9__pyx_k_a_ZL10__pyx_k_ab_ZL11__pyx_k_abc_ZL40__pyx_k_accessing_nonexistent_buffer_seg_ZL13__pyx_k_acero_ZL10__pyx_k_ad_ZL18__pyx_k_add_column_ZL20__pyx_k_add_metadata_ZL15__pyx_k_address_ZL17__pyx_k_address_2_ZL12__pyx_k_aggr_ZL17__pyx_k_aggr_name_ZL17__pyx_k_aggregate_ZL20__pyx_k_aggregations_ZL13__pyx_k_alias_ZL17__pyx_k_alignment_ZL23__pyx_k_allocate_buffer_ZL19__pyx_k_allow_64bit_ZL18__pyx_k_allow_copy_ZL18__pyx_k_allow_none_ZL27__pyx_k_allow_none_for_type_ZL39__pyx_k_always_results_in_a_copy_If_usi_ZL11__pyx_k_any_ZL11__pyx_k_api_ZL14__pyx_k_append_ZL21__pyx_k_append_column_ZL21__pyx_k_append_values_ZL14__pyx_k_arange_ZL12__pyx_k_arg0_ZL21__pyx_k_arg_dict_memo_ZL12__pyx_k_args_ZL15__pyx_k_argsort_ZL11__pyx_k_arr_ZL13__pyx_k_array_ZL15__pyx_k_array_2_ZL21__pyx_k_array_capsule_ZL18__pyx_k_array_data_ZL22__pyx_k_array_line_121_ZL14__pyx_k_arrays_ZL23__pyx_k_arrow_ext_class_ZL29__pyx_k_arrow_ext_deserialize_ZL30__pyx_k_arrow_ext_scalar_class_ZL27__pyx_k_arrow_ext_serialize_ZL17__pyx_k_arrow_obj_ZL31__pyx_k_arrow_py_extension_type_ZL18__pyx_k_arrow_type_ZL17__pyx_k_as_buffer_ZL13__pyx_k_as_py_ZL19__pyx_k_as_py_tuple_ZL15__pyx_k_asarray_ZL15__pyx_k_asbytes_ZL17__pyx_k_ascending_ZL19__pyx_k_assert_open_ZL23__pyx_k_assert_readable_ZL23__pyx_k_assert_seekable_ZL23__pyx_k_assert_writable_ZL18__pyx_k_astimezone_ZL14__pyx_k_astype_ZL26__pyx_k_asyncio_coroutines_ZL14__pyx_k_atexit_ZL12__pyx_k_axis_ZL18__pyx_k_axis_order_ZL16__pyx_k_bBhHiIqQ_ZL15__pyx_k_backend_ZL20__pyx_k_backend_name_ZL22__pyx_k_backend_name_2_ZL16__pyx_k_backends_ZL12__pyx_k_base_ZL27__pyx_k_batch_with_metadata_ZL15__pyx_k_batches_ZL36__pyx_k_benchmark_PandasObjectIsNull_ZL16__pyx_k_bg_write_ZL40__pyx_k_binary_file_expected_got_text_fi_ZL24__pyx_k_binary_line_4369_ZL29__pyx_k_binary_view_line_4506_ZL17__pyx_k_bit_width_ZL17__pyx_k_blocksize_ZL12__pyx_k_body_ZL19__pyx_k_body_length_ZL12__pyx_k_bool_ZL23__pyx_k_bool__line_3579_ZL15__pyx_k_boolean_ZL39__pyx_k_break_traceback_cycle_from_fram_ZL10__pyx_k_bs_ZL11__pyx_k_buf_ZL15__pyx_k_buf_len_ZL14__pyx_k_buffer_ZL19__pyx_k_buffer_size_ZL16__pyx_k_buffered_ZL15__pyx_k_buffers_ZL14__pyx_k_bufobj_ZL10__pyx_k_by_ZL18__pyx_k_byte_width_ZL23__pyx_k_bytes_allocated_ZL25__pyx_k_bytes_allocated_2_ZL18__pyx_k_bytes_read_ZL11__pyx_k_bz2_ZL13__pyx_k_bz2_2_ZL9__pyx_k_c_ZL14__pyx_k_c_addr_ZL13__pyx_k_c_arr_ZL15__pyx_k_c_array_ZL16__pyx_k_c_arrays_ZL20__pyx_k_c_axis_order_ZL15__pyx_k_c_batch_ZL17__pyx_k_c_batches_ZL13__pyx_k_c_buf_ZL16__pyx_k_c_buffer_ZL21__pyx_k_c_buffer_size_ZL17__pyx_k_c_buffers_ZL24__pyx_k_c_check_metadata_ZL20__pyx_k_c_child_data_ZL23__pyx_k_c_chunked_array_ZL22__pyx_k_c_concatenated_ZL14__pyx_k_c_data_ZL18__pyx_k_c_datatype_ZL19__pyx_k_c_dim_names_ZL24__pyx_k_c_extension_name_ZL15__pyx_k_c_field_ZL21__pyx_k_c_field_names_ZL16__pyx_k_c_fields_ZL21__pyx_k_c_file_offset_ZL21__pyx_k_c_from_pandas_ZL17__pyx_k_c_indices_ZL14__pyx_k_c_info_ZL14__pyx_k_c_mask_ZL23__pyx_k_c_max_chunksize_ZL14__pyx_k_c_meta_ZL18__pyx_k_c_metadata_ZL14__pyx_k_c_mode_ZL15__pyx_k_c_names_ZL16__pyx_k_c_nbytes_ZL18__pyx_k_c_nullable_ZL16__pyx_k_c_offset_ZL17__pyx_k_c_options_ZL17__pyx_k_c_ordered_ZL14__pyx_k_c_path_ZL21__pyx_k_c_permutation_ZL14__pyx_k_c_pool_ZL13__pyx_k_c_ptr_ZL13__pyx_k_c_raw_ZL16__pyx_k_c_reader_ZL22__pyx_k_c_record_batch_ZL16__pyx_k_c_result_ZL22__pyx_k_c_result_table_ZL19__pyx_k_c_rz_buffer_ZL16__pyx_k_c_scalar_ZL16__pyx_k_c_schema_ZL20__pyx_k_c_schema_ptr_ZL17__pyx_k_c_schemas_ZL15__pyx_k_c_shape_ZL23__pyx_k_c_shrink_to_fit_ZL14__pyx_k_c_sink_ZL14__pyx_k_c_size_ZL22__pyx_k_c_storage_type_ZL16__pyx_k_c_stream_ZL15__pyx_k_c_table_ZL16__pyx_k_c_tables_ZL16__pyx_k_c_tensor_ZL25__pyx_k_c_tensor_ext_type_ZL18__pyx_k_c_timezone_ZL14__pyx_k_c_type_ZL20__pyx_k_c_type_codes_ZL19__pyx_k_c_type_name_ZL18__pyx_k_c_type_ptr_ZL12__pyx_k_call_ZL21__pyx_k_call_function_ZL40__pyx_k_cannot_specify_type_when_creatin_ZL15__pyx_k_capsule_ZL12__pyx_k_cast_ZL14__pyx_k_casted_ZL20__pyx_k_casted_array_ZL20__pyx_k_casted_batch_ZL11__pyx_k_cat_ZL24__pyx_k_categorical_type_ZL18__pyx_k_categories_ZL22__pyx_k_check_metadata_ZL13__pyx_k_child_ZL20__pyx_k_child_fields_ZL37__pyx_k_child_is_deprecated_use_field_ZL16__pyx_k_children_ZL13__pyx_k_chunk_ZL15__pyx_k_chunked_ZL21__pyx_k_chunked_array_ZL31__pyx_k_chunked_array_line_1411_ZL14__pyx_k_chunks_ZL13__pyx_k_class_ZL21__pyx_k_class_getitem_ZL15__pyx_k_cleanup_ZL26__pyx_k_cline_in_traceback_ZL14__pyx_k_closed_ZL19__pyx_k_cloudpickle_ZL11__pyx_k_cls_ZL21__pyx_k_coalesce_keys_ZL13__pyx_k_codec_ZL14__pyx_k_codecs_ZL13__pyx_k_coder_ZL13__pyx_k_codes_ZL20__pyx_k_coerce_to_ns_ZL11__pyx_k_col_ZL19__pyx_k_collections_ZL23__pyx_k_collections_abc_ZL14__pyx_k_column_ZL16__pyx_k_column_2_ZL21__pyx_k_column_arrays_ZL15__pyx_k_columns_ZL22__pyx_k_combine_chunks_ZL16__pyx_k_combined_ZL14__pyx_k_compat_ZL16__pyx_k_compress_ZL19__pyx_k_compression_ZL25__pyx_k_compression_level_ZL27__pyx_k_compression_level_2_ZL15__pyx_k_compute_ZL21__pyx_k_concat_arrays_ZL31__pyx_k_concat_arrays_line_4420_ZL21__pyx_k_concat_tables_ZL31__pyx_k_concat_tables_line_5885_ZL24__pyx_k_container_window_ZL18__pyx_k_contextlib_ZL22__pyx_k_contextmanager_ZL24__pyx_k_converted_arrays_ZL18__pyx_k_coo_matrix_ZL14__pyx_k_coords_ZL12__pyx_k_copy_ZL40__pyx_k_could_not_infer_open_mode_for_fi_ZL13__pyx_k_count_ZL13__pyx_k_cpool_ZL22__pyx_k_cpp_build_info_ZL16__pyx_k_cpp_file_ZL19__pyx_k_cpp_version_ZL24__pyx_k_cpp_version_info_ZL17__pyx_k_cpu_count_ZL20__pyx_k_cpy_ext_type_ZL14__pyx_k_create_ZL25__pyx_k_create_memory_map_ZL35__pyx_k_create_memory_map_line_1076_ZL18__pyx_k_csc_matrix_ZL22__pyx_k_csparse_tensor_ZL18__pyx_k_csr_matrix_ZL15__pyx_k_ctensor_ZL22__pyx_k_current_thread_ZL9__pyx_k_d_ZL12__pyx_k_data_ZL18__pyx_k_data_frame_ZL17__pyx_k_dataframe_ZL27__pyx_k_dataframe_to_arrays_ZL26__pyx_k_dataframe_to_types_ZL12__pyx_k_date_ZL18__pyx_k_date32_day_ZL24__pyx_k_date32_line_4107_ZL24__pyx_k_date64_line_4128_ZL17__pyx_k_date64_ms_ZL16__pyx_k_datetime_ZL21__pyx_k_datetime64_ms_ZL21__pyx_k_datetime64_ns_ZL20__pyx_k_datetime64_s_ZL21__pyx_k_datetime64_us_ZL25__pyx_k_datetime_from_int_ZL12__pyx_k_days_ZL11__pyx_k_dct_ZL19__pyx_k_debug_print_ZL16__pyx_k_decay_ms_ZL15__pyx_k_decimal_ZL18__pyx_k_decimal128_ZL28__pyx_k_decimal128_line_4233_ZL18__pyx_k_decimal256_ZL14__pyx_k_decode_ZL15__pyx_k_decoder_ZL17__pyx_k_decoder_2_ZL18__pyx_k_decompress_ZL25__pyx_k_decompressed_size_ZL15__pyx_k_default_ZL26__pyx_k_default_chunk_size_ZL33__pyx_k_default_compression_level_ZL27__pyx_k_default_memory_pool_ZL36__pyx_k_default_memory_pool_line_124_ZL13__pyx_k_delta_ZL13__pyx_k_dense_ZL19__pyx_k_dense_union_ZL12__pyx_k_dest_ZL18__pyx_k_dest_codec_ZL21__pyx_k_dest_encoding_ZL14__pyx_k_detach_ZL26__pyx_k_detect_compression_ZL14__pyx_k_device_ZL10__pyx_k_df_ZL12__pyx_k_dict_ZL14__pyx_k_dict_2_ZL20__pyx_k_dictionary_2_ZL20__pyx_k_dictionary_3_ZL25__pyx_k_dictionary_decode_ZL28__pyx_k_dictionary_line_4810_ZL23__pyx_k_dictionary_memo_ZL12__pyx_k_diff_ZL16__pyx_k_dim_name_ZL17__pyx_k_dim_names_ZL15__pyx_k_disable_ZL18__pyx_k_dlm_tensor_ZL14__pyx_k_dlpack_ZL21__pyx_k_dlpack_device_ZL11__pyx_k_doc_ZL12__pyx_k_done_ZL14__pyx_k_double_ZL16__pyx_k_download_ZL32__pyx_k_download_locals_bg_write_ZL31__pyx_k_download_locals_cleanup_ZL20__pyx_k_drop_columns_ZL17__pyx_k_drop_null_ZL10__pyx_k_dt_ZL13__pyx_k_dtype_ZL13__pyx_k_dumps_ZL16__pyx_k_duration_ZL26__pyx_k_duration_line_4036_ZL19__pyx_k_duration_ms_ZL19__pyx_k_duration_ns_ZL18__pyx_k_duration_s_ZL19__pyx_k_duration_us_ZL30__pyx_k_emit_dictionary_deltas_ZL13__pyx_k_empty_ZL19__pyx_k_empty_array_ZL19__pyx_k_empty_table_ZL30__pyx_k_enable_signal_handlers_ZL14__pyx_k_encode_ZL24__pyx_k_encode_file_path_ZL20__pyx_k_encoded_path_ZL15__pyx_k_encoder_ZL17__pyx_k_encoder_2_ZL11__pyx_k_end_ZL16__pyx_k_endswith_ZL28__pyx_k_ensure_integer_index_ZL23__pyx_k_ensure_metadata_ZL28__pyx_k_ensure_native_endian_ZL19__pyx_k_ensure_type_ZL13__pyx_k_enter_ZL15__pyx_k_entries_ZL12__pyx_k_enum_ZL17__pyx_k_enumerate_ZL15__pyx_k_environ_ZL14__pyx_k_equals_ZL14__pyx_k_errors_ZL16__pyx_k_exc_info_ZL14__pyx_k_exc_tb_ZL16__pyx_k_exc_type_ZL15__pyx_k_exc_val_ZL17__pyx_k_exc_value_ZL12__pyx_k_exit_ZL16__pyx_k_expected_ZL19__pyx_k_export_to_c_ZL26__pyx_k_export_to_c_device_ZL17__pyx_k_ext_array_ZL18__pyx_k_ext_scalar_ZL16__pyx_k_ext_type_ZL25__pyx_k_extension_columns_ZL22__pyx_k_extension_name_ZL22__pyx_k_extension_type_ZL18__pyx_k_extensions_ZL10__pyx_k_f2_ZL10__pyx_k_f4_ZL10__pyx_k_f8_ZL13__pyx_k_field_ZL15__pyx_k_field_2_ZL15__pyx_k_field_3_ZL21__pyx_k_field_by_name_ZL19__pyx_k_field_index_ZL21__pyx_k_field_indices_ZL23__pyx_k_field_line_3465_ZL19__pyx_k_field_names_ZL40__pyx_k_field_or_tuple_expected_got_None_ZL14__pyx_k_fields_ZL23__pyx_k_file_descriptor_ZL19__pyx_k_file_offset_ZL14__pyx_k_fileno_ZL17__pyx_k_fill_null_ZL18__pyx_k_fill_value_ZL14__pyx_k_filter_ZL20__pyx_k_filter_table_ZL13__pyx_k_final_ZL28__pyx_k_find_physical_length_ZL28__pyx_k_find_physical_offset_ZL14__pyx_k_finish_ZL26__pyx_k_fixed_shape_tensor_ZL36__pyx_k_fixed_shape_tensor_line_5091_ZL30__pyx_k_fixed_size_binary_type_ZL13__pyx_k_flags_ZL15__pyx_k_flatten_ZL17__pyx_k_flattened_ZL23__pyx_k_flattened_field_ZL13__pyx_k_float_ZL25__pyx_k_float16_line_4149_ZL25__pyx_k_float32_line_4179_ZL25__pyx_k_float64_line_4206_ZL13__pyx_k_flush_ZL21__pyx_k_footer_offset_ZL22__pyx_k_foreign_buffer_ZL14__pyx_k_format_ZL31__pyx_k_from__functions_instead_ZL19__pyx_k_from_arrays_ZL21__pyx_k_from_arrays_2_ZL20__pyx_k_from_batches_ZL20__pyx_k_from_buffers_ZL18__pyx_k_from_codes_ZL18__pyx_k_from_dense_ZL24__pyx_k_from_dense_numpy_ZL28__pyx_k_from_network_metrics_ZL18__pyx_k_from_numpy_ZL24__pyx_k_from_numpy_dtype_ZL34__pyx_k_from_numpy_dtype_line_5356_ZL26__pyx_k_from_numpy_ndarray_ZL19__pyx_k_from_pandas_ZL26__pyx_k_from_pydata_sparse_ZL19__pyx_k_from_pydict_ZL21__pyx_k_from_pydict_2_ZL19__pyx_k_from_pylist_ZL21__pyx_k_from_pylist_2_ZL18__pyx_k_from_scipy_ZL19__pyx_k_from_sparse_ZL20__pyx_k_from_storage_ZL19__pyx_k_from_stream_ZL25__pyx_k_from_struct_array_ZL19__pyx_k_from_tensor_ZL17__pyx_k_frombytes_ZL12__pyx_k_full_ZL12__pyx_k_func_ZL19__pyx_k_func_nohash_ZL10__pyx_k_gc_ZL24__pyx_k_gdb_test_session_ZL15__pyx_k_genexpr_ZL11__pyx_k_get_ZL15__pyx_k_get_all_ZL29__pyx_k_get_all_field_indices_ZL17__pyx_k_get_batch_ZL38__pyx_k_get_batch_with_custom_metadata_ZL27__pyx_k_get_datetimetz_type_ZL23__pyx_k_get_field_index_ZL23__pyx_k_get_pandas_type_ZL26__pyx_k_get_pandas_tz_type_ZL32__pyx_k_get_rangeindex_attribute_ZL24__pyx_k_get_record_batch_ZL29__pyx_k_get_record_batch_size_ZL18__pyx_k_get_stream_ZL23__pyx_k_get_tensor_size_ZL29__pyx_k_get_total_buffer_size_ZL18__pyx_k_get_values_ZL16__pyx_k_getframe_ZL17__pyx_k_getsignal_ZL17__pyx_k_getsizeof_ZL16__pyx_k_getstate_ZL16__pyx_k_getvalue_ZL11__pyx_k_got_ZL23__pyx_k_got_null_buffer_ZL16__pyx_k_group_by_ZL18__pyx_k_group_by_2_ZL22__pyx_k_group_by_aggrs_ZL10__pyx_k_gz_ZL12__pyx_k_gzip_ZL9__pyx_k_h_ZL17__pyx_k_halffloat_ZL14__pyx_k_handle_ZL35__pyx_k_handle_arrow_array_protocol_ZL28__pyx_k_has_canonical_format_ZL12__pyx_k_hash_ZL20__pyx_k_have_libhdfs_ZL19__pyx_k_have_pandas_ZL28__pyx_k_have_signal_refcycle_ZL11__pyx_k_hex_ZL12__pyx_k_hint_ZL23__pyx_k_hole_size_limit_ZL10__pyx_k_i1_ZL10__pyx_k_i2_ZL10__pyx_k_i4_ZL10__pyx_k_i8_ZL10__pyx_k_id_ZL40__pyx_k_ideal_bandwidth_utilization_frac_ZL13__pyx_k_ident_ZL11__pyx_k_idx_ZL23__pyx_k_ignore_metadata_ZL14__pyx_k_import_ZL21__pyx_k_import_from_c_ZL29__pyx_k_import_from_c_capsule_ZL28__pyx_k_import_from_c_device_ZL14__pyx_k_in_ptr_ZL17__pyx_k_in_stream_ZL23__pyx_k_included_fields_ZL26__pyx_k_incrementaldecoder_ZL26__pyx_k_incrementalencoder_ZL14__pyx_k_indent_ZL13__pyx_k_index_ZL15__pyx_k_index_2_ZL27__pyx_k_index_out_of_bounds_ZL18__pyx_k_index_type_ZL17__pyx_k_indices_2_ZL14__pyx_k_indptr_ZL19__pyx_k_infer_dtype_ZL18__pyx_k_infer_type_ZL12__pyx_k_init_ZL29__pyx_k_init___locals_genexpr_ZL20__pyx_k_init_signals_ZL21__pyx_k_init_subclass_ZL20__pyx_k_initializing_ZL19__pyx_k_inner_array_ZL19__pyx_k_inner_batch_ZL20__pyx_k_input_stream_ZL30__pyx_k_input_stream_line_2628_ZL14__pyx_k_insert_ZL23__pyx_k_int16_line_3683_ZL23__pyx_k_int32_line_3737_ZL23__pyx_k_int64_line_3791_ZL22__pyx_k_int8_line_3629_ZL17__pyx_k_int_index_ZL14__pyx_k_invert_ZL10__pyx_k_io_ZL23__pyx_k_io_thread_count_ZL16__pyx_k_is_alive_ZL21__pyx_k_is_array_like_ZL20__pyx_k_is_available_ZL24__pyx_k_is_boolean_value_ZL22__pyx_k_is_categorical_ZL20__pyx_k_is_coroutine_ZL14__pyx_k_is_cpu_ZL16__pyx_k_is_cpu_2_ZL21__pyx_k_is_data_frame_ZL21__pyx_k_is_datetimetz_ZL32__pyx_k_is_extension_array_dtype_ZL22__pyx_k_is_float_value_ZL17__pyx_k_is_ge_v21_ZL16__pyx_k_is_ge_v3_ZL16__pyx_k_is_index_ZL22__pyx_k_is_initialized_ZL24__pyx_k_is_integer_value_ZL18__pyx_k_is_mutable_ZL20__pyx_k_is_mutable_2_ZL24__pyx_k_is_pandas_object_ZL20__pyx_k_is_path_like_ZL20__pyx_k_is_primitive_ZL19__pyx_k_is_readable_ZL19__pyx_k_is_seekable_ZL17__pyx_k_is_series_ZL17__pyx_k_is_sparse_ZL13__pyx_k_is_v1_ZL16__pyx_k_is_valid_ZL19__pyx_k_is_writable_ZL14__pyx_k_isatty_ZL13__pyx_k_isdir_ZL17__pyx_k_isenabled_ZL18__pyx_k_item_field_ZL17__pyx_k_item_type_ZL13__pyx_k_items_ZL15__pyx_k_items_2_ZL28__pyx_k_items_locals_genexpr_ZL12__pyx_k_iter_ZL40__pyx_k_iter_batches_with_custom_metadat_ZL18__pyx_k_iterchunks_ZL19__pyx_k_itercolumns_ZL28__pyx_k_jemalloc_memory_pool_ZL29__pyx_k_jemalloc_set_decay_ms_ZL12__pyx_k_join_ZL17__pyx_k_join_asof_ZL17__pyx_k_join_type_ZL12__pyx_k_json_ZL9__pyx_k_k_ZL17__pyx_k_key_field_ZL16__pyx_k_key_type_ZL12__pyx_k_keys_ZL14__pyx_k_keys_2_ZL19__pyx_k_keys_sorted_ZL12__pyx_k_kind_ZL14__pyx_k_kwargs_ZL30__pyx_k_large_binary_line_4421_ZL18__pyx_k_large_list_ZL28__pyx_k_large_list_line_4602_ZL23__pyx_k_large_list_view_ZL33__pyx_k_large_list_view_line_4697_ZL17__pyx_k_large_str_ZL30__pyx_k_large_string_line_4449_ZL28__pyx_k_large_utf8_line_4479_ZL12__pyx_k_lazy_ZL18__pyx_k_left_outer_ZL19__pyx_k_left_suffix_ZL14__pyx_k_length_ZL13__pyx_k_level_ZL15__pyx_k_lexsort_ZL11__pyx_k_lib_ZL12__pyx_k_line_ZL13__pyx_k_lines_ZL12__pyx_k_list_ZL23__pyx_k_list__line_4536_ZL20__pyx_k_list_flatten_ZL27__pyx_k_list_parent_indices_ZL17__pyx_k_list_size_ZL19__pyx_k_list_size_2_ZL40__pyx_k_list_size_should_be_a_positive_i_ZL17__pyx_k_list_type_ZL25__pyx_k_list_value_length_ZL17__pyx_k_list_view_ZL27__pyx_k_list_view_line_4659_ZL13__pyx_k_loads_ZL30__pyx_k_log_memory_allocations_ZL27__pyx_k_logging_memory_pool_ZL22__pyx_k_logical_length_ZL24__pyx_k_logical_length_2_ZL22__pyx_k_logical_offset_ZL24__pyx_k_logical_offset_2_ZL14__pyx_k_lookup_ZL13__pyx_k_lossy_ZL13__pyx_k_lower_ZL11__pyx_k_lz4_ZL13__pyx_k_lz4_2_ZL10__pyx_k_ma_ZL12__pyx_k_main_ZL19__pyx_k_main_thread_ZL13__pyx_k_major_ZL23__pyx_k_make_datetimetz_ZL36__pyx_k_make_shape_or_strides_buffer_ZL21__pyx_k_make_tz_aware_ZL11__pyx_k_map_ZL22__pyx_k_map__line_4735_ZL15__pyx_k_mapping_ZL39__pyx_k_mask_not_implemented_with_Arrow_ZL21__pyx_k_max_chunksize_ZL39__pyx_k_max_chunksize_should_be_strictl_ZL34__pyx_k_max_ideal_request_size_mib_ZL18__pyx_k_max_memory_ZL20__pyx_k_max_memory_2_ZL23__pyx_k_max_output_size_ZL33__pyx_k_maximum_compression_level_ZL21__pyx_k_maybe_py_list_ZL20__pyx_k_member_names_ZL15__pyx_k_members_ZL18__pyx_k_memory_map_ZL28__pyx_k_memory_map_line_1034_ZL19__pyx_k_memory_pool_ZL15__pyx_k_message_ZL12__pyx_k_meta_ZL17__pyx_k_metaclass_ZL16__pyx_k_metadata_ZL23__pyx_k_metadata_length_ZL24__pyx_k_metadata_version_ZL20__pyx_k_microseconds_ZL20__pyx_k_milliseconds_ZL28__pyx_k_mimalloc_memory_pool_ZL33__pyx_k_minimum_compression_level_ZL13__pyx_k_minor_ZL15__pyx_k_missing_ZL12__pyx_k_mmap_ZL12__pyx_k_mode_ZL14__pyx_k_module_ZL16__pyx_k_module_2_ZL40__pyx_k_month_day_nano_interval_line_408_ZL19__pyx_k_mro_entries_ZL10__pyx_k_ms_ZL14__pyx_k_n_rows_ZL12__pyx_k_name_ZL14__pyx_k_name_2_ZL14__pyx_k_name_3_ZL14__pyx_k_name_4_ZL18__pyx_k_namedtuple_ZL13__pyx_k_names_ZL11__pyx_k_nan_ZL19__pyx_k_nan_as_null_ZL19__pyx_k_nan_is_null_ZL16__pyx_k_nbatches_ZL14__pyx_k_nbytes_ZL15__pyx_k_ndarray_ZL29__pyx_k_ndarray_to_arrow_type_ZL12__pyx_k_ndim_ZL11__pyx_k_new_ZL17__pyx_k_new_field_ZL18__pyx_k_new_schema_ZL16__pyx_k_new_size_ZL16__pyx_k_new_type_ZL15__pyx_k_newcols_ZL10__pyx_k_nf_ZL40__pyx_k_no_default___reduce___due_to_non_ZL14__pyx_k_nomask_ZL12__pyx_k_none_ZL23__pyx_k_normalize_slice_ZL39__pyx_k_not_supported_for_buffer_protoc_ZL10__pyx_k_np_ZL10__pyx_k_ns_ZL10__pyx_k_nt_ZL16__pyx_k_nthreads_ZL19__pyx_k_null_bitmap_ZL18__pyx_k_null_count_ZL21__pyx_k_null_encoding_ZL22__pyx_k_null_line_3557_ZL31__pyx_k_null_selection_behavior_ZL19__pyx_k_null_to_nan_ZL16__pyx_k_nullable_ZL13__pyx_k_nulls_ZL22__pyx_k_nulls_line_388_ZL18__pyx_k_num_arrays_ZL19__pyx_k_num_buffers_ZL18__pyx_k_num_chunks_ZL19__pyx_k_num_columns_ZL30__pyx_k_num_dictionary_batches_ZL29__pyx_k_num_dictionary_deltas_ZL18__pyx_k_num_fields_ZL20__pyx_k_num_messages_ZL33__pyx_k_num_replaced_dictionaries_ZL16__pyx_k_num_rows_ZL19__pyx_k_num_threads_ZL13__pyx_k_numpy_ZL9__pyx_k_o_ZL11__pyx_k_obj_ZL16__pyx_k_object_2_ZL14__pyx_k_offset_ZL15__pyx_k_offsets_ZL17__pyx_k_offsets_2_ZL10__pyx_k_on_ZL40__pyx_k_only_slices_with_step_1_supporte_ZL36__pyx_k_only_valid_on_readable_files_ZL36__pyx_k_only_valid_on_seekable_files_ZL36__pyx_k_only_valid_on_writable_files_ZL12__pyx_k_open_ZL14__pyx_k_open_2_ZL19__pyx_k_open_stream_ZL11__pyx_k_opt_ZL15__pyx_k_options_ZL13__pyx_k_order_ZL20__pyx_k_ordered_dict_ZL10__pyx_k_os_ZL13__pyx_k_other_ZL17__pyx_k_other_arr_ZL19__pyx_k_other_batch_ZL19__pyx_k_other_table_ZL18__pyx_k_other_type_ZL11__pyx_k_out_ZL17__pyx_k_out_array_ZL15__pyx_k_out_buf_ZL18__pyx_k_out_coords_ZL16__pyx_k_out_data_ZL19__pyx_k_out_indices_ZL18__pyx_k_out_indptr_ZL15__pyx_k_out_ptr_ZL18__pyx_k_out_schema_ZL22__pyx_k_out_schema_ptr_ZL14__pyx_k_output_ZL21__pyx_k_output_buffer_ZL21__pyx_k_output_length_ZL19__pyx_k_output_size_ZL21__pyx_k_output_stream_ZL31__pyx_k_output_stream_line_2714_ZL19__pyx_k_output_type_ZL16__pyx_k_own_file_ZL17__pyx_k_owned_buf_ZL40__pyx_k_pa_input_stream_called_with_inst_ZL40__pyx_k_pa_output_stream_called_with_ins_ZL11__pyx_k_pac_ZL13__pyx_k_pac_2_ZL13__pyx_k_pac_3_ZL12__pyx_k_pack_ZL18__pyx_k_pandas_api_ZL21__pyx_k_pandas_compat_ZL20__pyx_k_pandas_dtype_ZL19__pyx_k_pandas_type_ZL14__pyx_k_parent_ZL15__pyx_k_parents_ZL13__pyx_k_patch_ZL12__pyx_k_path_ZL10__pyx_k_pc_ZL12__pyx_k_pc_2_ZL12__pyx_k_pc_3_ZL10__pyx_k_pd_ZL20__pyx_k_perform_join_ZL25__pyx_k_perform_join_asof_ZL18__pyx_k_permissive_ZL19__pyx_k_permutation_ZL14__pyx_k_pickle_ZL39__pyx_k_pickle_based_deserialization_of_ZL14__pyx_k_pieces_ZL33__pyx_k_please_pass_it_explicitly_ZL11__pyx_k_pos_ZL16__pyx_k_position_ZL17__pyx_k_precision_ZL40__pyx_k_precision_should_be_between_1_an_ZL42__pyx_k_precision_should_be_between_1_an_2_ZL22__pyx_k_prefetch_limit_ZL15__pyx_k_prepare_ZL15__pyx_k_present_ZL22__pyx_k_preserve_index_ZL20__pyx_k_preview_cols_ZL12__pyx_k_prod_ZL15__pyx_k_promote_ZL40__pyx_k_promote_has_been_superseded_by_p_ZL23__pyx_k_promote_options_ZL16__pyx_k_protocol_ZL25__pyx_k_proxy_memory_pool_ZL18__pyx_k_put_nowait_ZL14__pyx_k_py_buf_ZL17__pyx_k_py_buffer_ZL35__pyx_k_py_extension_type_auto_load_ZL16__pyx_k_py_field_ZL15__pyx_k_py_list_ZL15__pyx_k_pyarrow_ZL17__pyx_k_pyarrow_2_ZL17__pyx_k_pyarrow_3_ZL23__pyx_k_pyarrow_Field_0_ZL39__pyx_k_pyarrow_Message_type_0_metadata_ZL37__pyx_k_pyarrow_Message_uninitialized_ZL40__pyx_k_pyarrow_PyExtensionType_is_depre_ZL38__pyx_k_pyarrow_SparseCOOTensor_type_0_ZL38__pyx_k_pyarrow_SparseCSCMatrix_type_0_ZL38__pyx_k_pyarrow_SparseCSFTensor_type_0_ZL38__pyx_k_pyarrow_SparseCSRMatrix_type_0_ZL39__pyx_k_pyarrow_Tensor_type_0_type_shap_ZL21__pyx_k_pyarrow_acero_ZL25__pyx_k_pyarrow_array_pxi_ZL29__pyx_k_pyarrow_benchmark_pxi_ZL27__pyx_k_pyarrow_builder_pxi_ZL26__pyx_k_pyarrow_compat_pxi_ZL23__pyx_k_pyarrow_compute_ZL26__pyx_k_pyarrow_config_pxi_ZL25__pyx_k_pyarrow_error_pxi_ZL37__pyx_k_pyarrow_interchange_dataframe_ZL22__pyx_k_pyarrow_io_pxi_ZL23__pyx_k_pyarrow_ipc_pxi_ZL19__pyx_k_pyarrow_lib_ZL23__pyx_k_pyarrow_lib_pyx_ZL26__pyx_k_pyarrow_memory_pxi_ZL29__pyx_k_pyarrow_pandas_compat_ZL31__pyx_k_pyarrow_pandas_shim_pxi_ZL17__pyx_k_pyarrow_r_ZL40__pyx_k_pyarrow_requires_pandas_1_0_0_or_ZL42__pyx_k_pyarrow_requires_pandas_1_0_0_or_2_ZL26__pyx_k_pyarrow_scalar_pxi_ZL25__pyx_k_pyarrow_table_pxi_ZL26__pyx_k_pyarrow_tensor_pxi_ZL25__pyx_k_pyarrow_types_pxi_ZL20__pyx_k_pyarrow_util_ZL32__pyx_k_pyarrow_vendored_version_ZL13__pyx_k_pybuf_ZL17__pyx_k_pydecimal_ZL14__pyx_k_pydict_ZL14__pyx_k_pylist_ZL13__pyx_k_pyobj_ZL39__pyx_k_python_extension_types_registry_ZL23__pyx_k_pyx_PickleError_ZL20__pyx_k_pyx_checksum_ZL18__pyx_k_pyx_result_ZL17__pyx_k_pyx_state_ZL16__pyx_k_pyx_type_ZL35__pyx_k_pyx_unpickle__PandasAPIShim_ZL38__pyx_k_pyx_unpickle__PandasConvertibl_ZL29__pyx_k_pyx_unpickle__Tabular_ZL35__pyx_k_pyx_unpickle___Pyx_EnumMeta_ZL18__pyx_k_pyx_vtable_ZL9__pyx_k_q_ZL13__pyx_k_qsize_ZL16__pyx_k_qualname_ZL13__pyx_k_queue_ZL9__pyx_k_r_ZL11__pyx_k_r_2_ZL11__pyx_k_r_b_ZL16__pyx_k_r_extptr_ZL13__pyx_k_range_ZL24__pyx_k_range_size_limit_ZL13__pyx_k_ravel_ZL11__pyx_k_raw_ZL10__pyx_k_rb_ZL12__pyx_k_rb_2_ZL17__pyx_k_rd_handle_ZL10__pyx_k_re_ZL13__pyx_k_read1_ZL16__pyx_k_read_all_ZL15__pyx_k_read_at_ZL19__pyx_k_read_buffer_ZL20__pyx_k_read_message_ZL23__pyx_k_read_next_batch_ZL40__pyx_k_read_next_batch_with_custom_meta_ZL25__pyx_k_read_next_message_ZL19__pyx_k_read_pandas_ZL25__pyx_k_read_record_batch_ZL19__pyx_k_read_schema_ZL19__pyx_k_read_tensor_ZL16__pyx_k_readable_ZL30__pyx_k_readable_file_expected_ZL15__pyx_k_readall_ZL14__pyx_k_reader_ZL16__pyx_k_readinto_ZL16__pyx_k_readline_ZL17__pyx_k_readlines_ZL19__pyx_k_reconstruct_ZL32__pyx_k_reconstruct_record_batch_ZL25__pyx_k_reconstruct_table_ZL20__pyx_k_record_batch_ZL30__pyx_k_record_batch_line_5559_ZL14__pyx_k_reduce_ZL21__pyx_k_reduce_cython_ZL17__pyx_k_reduce_ex_ZL17__pyx_k_ree_array_ZL16__pyx_k_ree_type_ZL16__pyx_k_register_ZL31__pyx_k_register_extension_type_ZL40__pyx_k_register_extension_type_line_190_ZL34__pyx_k_register_py_extension_type_ZL22__pyx_k_registry_nanny_ZL24__pyx_k_release_registry_ZL22__pyx_k_release_unused_ZL14__pyx_k_remove_ZL21__pyx_k_remove_column_ZL23__pyx_k_remove_metadata_ZL22__pyx_k_rename_columns_ZL14__pyx_k_repeat_ZL23__pyx_k_repeat_line_438_ZL31__pyx_k_replace_schema_metadata_ZL12__pyx_k_repr_ZL17__pyx_k_requested_ZL24__pyx_k_requested_schema_ZL22__pyx_k_requested_type_ZL15__pyx_k_require_ZL20__pyx_k_requirements_ZL11__pyx_k_res_ZL17__pyx_k_resizable_ZL14__pyx_k_resize_ZL21__pyx_k_restore_array_ZL14__pyx_k_result_ZL18__pyx_k_result_obj_ZL15__pyx_k_results_ZL11__pyx_k_ret_ZL16__pyx_k_right_by_ZL18__pyx_k_right_keys_ZL16__pyx_k_right_on_ZL20__pyx_k_right_suffix_ZL19__pyx_k_right_table_ZL11__pyx_k_row_ZL17__pyx_k_row_major_ZL22__pyx_k_run_end_encode_ZL23__pyx_k_run_end_encoded_ZL20__pyx_k_run_end_type_ZL22__pyx_k_run_end_type_2_ZL16__pyx_k_run_ends_ZL18__pyx_k_run_ends_2_ZL20__pyx_k_runtime_info_ZL9__pyx_k_s_ZL38__pyx_k_s_constructor_directly_use_one_ZL11__pyx_k_s_s_ZL13__pyx_k_s_s_d_ZL12__pyx_k_sarr_ZL14__pyx_k_scalar_ZL24__pyx_k_scalar_line_1145_ZL13__pyx_k_scale_ZL14__pyx_k_schema_ZL24__pyx_k_schema_as_string_ZL22__pyx_k_schema_capsule_ZL24__pyx_k_schema_line_5286_ZL15__pyx_k_schemas_ZL13__pyx_k_scipy_ZL20__pyx_k_scipy_sparse_ZL15__pyx_k_seconds_ZL12__pyx_k_seek_ZL16__pyx_k_seekable_ZL14__pyx_k_select_ZL12__pyx_k_self_ZL40__pyx_k_self_c_options_cannot_be_convert_ZL40__pyx_k_self_logging_pool_self_pool_cann_ZL40__pyx_k_self_pool_cannot_be_converted_to_ZL40__pyx_k_self_pool_self_proxy_pool_cannot_ZL39__pyx_k_self_reader_cannot_be_converted_ZL40__pyx_k_self_sp_sparse_tensor_self_stp_c_ZL40__pyx_k_self_sp_tensor_self_tp_cannot_be_ZL40__pyx_k_self_stop_token_cannot_be_conver_ZL40__pyx_k_self_wrapped_cannot_be_converted_ZL39__pyx_k_self_writer_cannot_be_converted_ZL12__pyx_k_send_ZL17__pyx_k_serialize_ZL20__pyx_k_serialize_to_ZL18__pyx_k_serialized_ZL14__pyx_k_series_ZL11__pyx_k_set_ZL21__pyx_k_set_auto_load_ZL18__pyx_k_set_column_ZL21__pyx_k_set_cpu_count_ZL27__pyx_k_set_io_thread_count_ZL29__pyx_k_set_memcopy_blocksize_ZL27__pyx_k_set_memcopy_threads_ZL29__pyx_k_set_memcopy_threshold_ZL23__pyx_k_set_memory_pool_ZL16__pyx_k_set_name_ZL28__pyx_k_set_timezone_db_path_ZL16__pyx_k_setstate_ZL23__pyx_k_setstate_cython_ZL13__pyx_k_shape_ZL27__pyx_k_show_field_metadata_ZL21__pyx_k_show_metadata_ZL28__pyx_k_show_schema_metadata_ZL21__pyx_k_shrink_to_fit_ZL11__pyx_k_sig_ZL14__pyx_k_signal_ZL14__pyx_k_signum_ZL12__pyx_k_sink_ZL12__pyx_k_size_ZL14__pyx_k_size_2_ZL14__pyx_k_sizeof_ZL13__pyx_k_sizes_ZL15__pyx_k_sizes_2_ZL22__pyx_k_skip_new_lines_ZL14__pyx_k_skipna_ZL13__pyx_k_sleep_ZL13__pyx_k_slice_ZL13__pyx_k_slots_ZL14__pyx_k_snappy_ZL12__pyx_k_sort_ZL15__pyx_k_sort_by_ZL20__pyx_k_sort_indices_ZL17__pyx_k_sort_keys_ZL15__pyx_k_sorting_ZL14__pyx_k_source_ZL19__pyx_k_source_path_ZL10__pyx_k_sp_ZL17__pyx_k_sp_scalar_ZL18__pyx_k_sp_storage_ZL17__pyx_k_sp_tensor_ZL20__pyx_k_sparse_union_ZL12__pyx_k_spec_ZL17__pyx_k_src_codec_ZL20__pyx_k_src_encoding_ZL14__pyx_k_stable_ZL13__pyx_k_stack_ZL18__pyx_k_stacklevel_ZL13__pyx_k_start_ZL18__pyx_k_startswith_ZL13__pyx_k_state_ZL20__pyx_k_staticmethod_ZL12__pyx_k_step_ZL12__pyx_k_stop_ZL15__pyx_k_storage_ZL20__pyx_k_storage_type_ZL11__pyx_k_str_ZL13__pyx_k_str_2_ZL14__pyx_k_stream_ZL22__pyx_k_stream_capsule_ZL22__pyx_k_stream_or_path_ZL16__pyx_k_strftime_ZL14__pyx_k_strict_ZL15__pyx_k_strides_ZL24__pyx_k_string_line_4319_ZL24__pyx_k_string_to_tzinfo_ZL29__pyx_k_string_view_line_4521_ZL22__pyx_k_stringify_path_ZL20__pyx_k_stringsource_ZL14__pyx_k_struct_ZL16__pyx_k_struct_2_ZL20__pyx_k_struct_array_ZL24__pyx_k_struct_line_4869_ZL19__pyx_k_struct_type_ZL22__pyx_k_sum_duplicates_ZL13__pyx_k_super_ZL33__pyx_k_supported_memory_backends_ZL34__pyx_k_supports_compression_level_ZL11__pyx_k_sys_ZL26__pyx_k_system_memory_pool_ZL16__pyx_k_t_reader_ZL13__pyx_k_table_ZL15__pyx_k_table_2_ZL23__pyx_k_table_line_5723_ZL22__pyx_k_table_or_batch_ZL23__pyx_k_table_to_blocks_ZL26__pyx_k_table_to_dataframe_ZL14__pyx_k_tables_ZL12__pyx_k_take_ZL14__pyx_k_target_ZL21__pyx_k_target_schema_ZL19__pyx_k_target_type_ZL10__pyx_k_tb_ZL12__pyx_k_tell_ZL17__pyx_k_temp_memo_ZL14__pyx_k_tensor_ZL12__pyx_k_test_ZL16__pyx_k_this_arr_ZL18__pyx_k_this_batch_ZL18__pyx_k_this_table_ZL17__pyx_k_threading_ZL17__pyx_k_threshold_ZL13__pyx_k_throw_ZL12__pyx_k_time_ZL14__pyx_k_time32_ZL24__pyx_k_time32_line_3950_ZL17__pyx_k_time32_ms_ZL16__pyx_k_time32_s_ZL14__pyx_k_time64_ZL24__pyx_k_time64_line_3993_ZL17__pyx_k_time64_ns_ZL17__pyx_k_time64_us_ZL33__pyx_k_time_to_first_byte_millis_ZL17__pyx_k_timedelta_ZL22__pyx_k_timedelta64_ms_ZL22__pyx_k_timedelta64_ns_ZL21__pyx_k_timedelta64_s_ZL22__pyx_k_timedelta64_us_ZL15__pyx_k_timeout_ZL17__pyx_k_timestamp_ZL27__pyx_k_timestamp_line_3891_ZL20__pyx_k_timestamp_ms_ZL20__pyx_k_timestamp_ns_ZL19__pyx_k_timestamp_s_ZL20__pyx_k_timestamp_us_ZL16__pyx_k_timezone_ZL13__pyx_k_title_ZL18__pyx_k_to_batches_ZL15__pyx_k_to_dict_ZL16__pyx_k_to_numpy_ZL24__pyx_k_to_numpy_ndarray_ZL17__pyx_k_to_pandas_ZL19__pyx_k_to_pandas_2_ZL23__pyx_k_to_pandas_dtype_ZL25__pyx_k_to_pandas_dtype_2_ZL26__pyx_k_to_pointer_address_ZL18__pyx_k_to_pybytes_ZL24__pyx_k_to_pydata_sparse_ZL17__pyx_k_to_pydict_ZL17__pyx_k_to_pylist_ZL17__pyx_k_to_reader_ZL27__pyx_k_to_requested_schema_ZL25__pyx_k_to_requested_type_ZL16__pyx_k_to_scipy_ZL17__pyx_k_to_string_ZL23__pyx_k_to_struct_array_ZL17__pyx_k_to_tensor_ZL15__pyx_k_tobytes_ZL17__pyx_k_tolerance_ZL14__pyx_k_tolist_ZL24__pyx_k_top_level_indent_ZL14__pyx_k_tosort_ZL29__pyx_k_total_allocated_bytes_ZL25__pyx_k_total_buffer_size_ZL19__pyx_k_total_bytes_ZL17__pyx_k_traceback_ZL32__pyx_k_transcoding_input_stream_ZL38__pyx_k_transfer_bandwidth_mib_per_sec_ZL22__pyx_k_transform_func_ZL16__pyx_k_truncate_ZL25__pyx_k_truncate_metadata_ZL40__pyx_k_trying_to_write_an_immutable_buf_ZL10__pyx_k_ty_ZL11__pyx_k_typ_ZL12__pyx_k_type_ZL14__pyx_k_type_2_ZL14__pyx_k_type_3_ZL14__pyx_k_type_4_ZL18__pyx_k_type_codes_ZL39__pyx_k_type_codes_should_have_the_same_ZL22__pyx_k_type_for_alias_ZL15__pyx_k_type_id_ZL17__pyx_k_type_name_ZL13__pyx_k_types_ZL20__pyx_k_types_mapper_ZL14__pyx_k_tzinfo_ZL24__pyx_k_tzinfo_to_string_ZL10__pyx_k_u1_ZL10__pyx_k_u2_ZL10__pyx_k_u4_ZL10__pyx_k_u8_ZL24__pyx_k_uint16_line_3656_ZL24__pyx_k_uint32_line_3710_ZL24__pyx_k_uint64_line_3764_ZL23__pyx_k_uint8_line_3602_ZL26__pyx_k_unify_dictionaries_ZL21__pyx_k_unify_schemas_ZL13__pyx_k_union_ZL17__pyx_k_unit_code_ZL33__pyx_k_unregister_extension_type_ZL40__pyx_k_unregister_extension_type_line_1_ZL37__pyx_k_unregister_py_extension_types_ZL14__pyx_k_update_ZL14__pyx_k_upload_ZL30__pyx_k_upload_locals_bg_write_ZL13__pyx_k_upper_ZL10__pyx_k_us_ZL25__pyx_k_use_legacy_format_ZL28__pyx_k_use_pandas_sentinels_ZL20__pyx_k_use_setstate_ZL21__pyx_k_use_threads_2_ZL11__pyx_k_utc_ZL22__pyx_k_utf8_line_4344_ZL13__pyx_k_utf_8_ZL9__pyx_k_v_ZL11__pyx_k_val_ZL16__pyx_k_validate_ZL19__pyx_k_value_field_ZL21__pyx_k_value_lengths_ZL21__pyx_k_value_offsets_ZL28__pyx_k_value_parent_indices_ZL18__pyx_k_value_type_ZL20__pyx_k_value_type_2_ZL14__pyx_k_values_ZL16__pyx_k_values_2_ZL17__pyx_k_version_2_ZL12__pyx_k_view_ZL14__pyx_k_vstack_ZL9__pyx_k_w_ZL12__pyx_k_warn_ZL16__pyx_k_warnings_ZL10__pyx_k_wb_ZL14__pyx_k_whence_ZL14__pyx_k_window_ZL21__pyx_k_with_metadata_ZL17__pyx_k_with_name_ZL21__pyx_k_with_nullable_ZL17__pyx_k_with_type_ZL17__pyx_k_wr_handle_ZL18__pyx_k_wrap_array_ZL15__pyx_k_wrapped_ZL16__pyx_k_writable_ZL30__pyx_k_writable_file_expected_ZL19__pyx_k_write_batch_ZL19__pyx_k_write_queue_ZL19__pyx_k_write_table_ZL20__pyx_k_write_tensor_ZL17__pyx_k_writeable_ZL18__pyx_k_writelines_ZL14__pyx_k_writer_ZL21__pyx_k_writer_thread_ZL9__pyx_k_x_ZL40__pyx_k_zero_copy_only_must_be_False_for_ZL11__pyx_k_zip_ZL11__pyx_k_zst_ZL12__pyx_k_zstd_ZL19__Pyx_InitConstantsv_ZL34__pyx_umethod_PySlice_Type_indices_ZL35__pyx_umethod_PyUnicode_Type_format_ZL34__pyx_umethod_PyUnicode_Type_upper_ZL15__Pyx_GetVtableP11_typeobject.isra.0_ZL18__Pyx_MergeVtablesP11_typeobject_ZL21__Pyx_FetchCommonTypeP11_typeobject_ZL20__Pyx__ExceptionSaveP3_tsPP7_objectS3_S3_.isra.0_ZL30__Pyx_RaiseUnexpectedTypeErrorPKcP7_object.isra.0_ZL30__Pyx_modinit_global_init_codev.isra.0_ZL32__pyx_v_7pyarrow_3lib_pandas_api_ZL42__pyx_v_7pyarrow_3lib__default_memory_pool_ZL42__pyx_v_7pyarrow_3lib__logging_memory_pool_ZL38__pyx_v_7pyarrow_3lib__pandas_type_map_ZL39__pyx_v_7pyarrow_3lib__pep3118_type_map_ZL33__pyx_v_7pyarrow_3lib__type_cache_ZL37__pyx_v_7pyarrow_3lib_PRIMITIVE_TYPES_ZL43__pyx_v_7pyarrow_3lib__timestamp_type_cache_ZL38__pyx_v_7pyarrow_3lib__time_type_cache_ZL42__pyx_v_7pyarrow_3lib__duration_type_cache_ZL35__pyx_v_7pyarrow_3lib__type_aliases_ZL37__pyx_v_7pyarrow_3lib__scalar_classes_ZL36__pyx_v_7pyarrow_3lib__array_classes_ZL17__Pyx_OrderedDict_ZL14__Pyx_EnumBase_ZL14__Pyx_FlagBase_ZL13__Pyx_globals_ZL28__Pyx_CyFunction_get_closureP22__pyx_CyFunctionObjectPv_ZL52__pyx_pw_7pyarrow_3lib_17RecordBatchReader_1__iter__P7_object_ZL23__Pyx_CyFunction_reduceP22__pyx_CyFunctionObjectP7_object_ZL21__Pyx_PyBool_FromLongl_ZL48__pyx_tp_dealloc_7pyarrow_3lib_LoggingMemoryPoolP7_object_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEaSEOS4_.isra.0_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE12_M_constructIPcEEvT_S7_St20forward_iterator_tag.isra.0_ZL42__pyx_tp_new_7pyarrow_3lib_IpcWriteOptionsP11_typeobjectP7_objectS2__ZL38__pyx_tp_dealloc_7pyarrow_3lib_MessageP7_object_ZL44__pyx_tp_dealloc_7pyarrow_3lib_MessageReaderP7_object_ZNSt6vectorIiSaIiEEaSERKS1_.isra.0_ZL45__Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDSP7_objectPKS0_mS0__ZL52__Pyx_CyFunction_Vectorcall_FASTCALL_KEYWORDS_METHODP7_objectPKS0_mS0__ZL34__Pyx_CyFunction_Vectorcall_NOARGSP7_objectPKS0_mS0__ZL26__Pyx__IsSameCyOrCFunctionP7_objectPv_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE14_M_move_assignEOSI_St17integral_constantIbLb1EE.isra.0_ZL38__Pyx_PyErr_GivenExceptionMatchesTupleP7_objectS0__ZL27__Pyx_GetItemInt_Tuple_FastP7_objectlii.constprop.0_ZL45__pyx_tp_dealloc_7pyarrow_3lib_IpcReadOptionsP7_object_ZL53__pyx_f_7pyarrow_8includes_6common_PyObject_to_objectP7_object_ZL38__pyx_sq_item_7pyarrow_3lib_StructTypeP7_objectl_ZL44__pyx_sq_item_7pyarrow_3lib_KeyValueMetadataP7_objectl_ZL34__pyx_sq_item_7pyarrow_3lib_SchemaP7_objectl_ZL33__pyx_sq_item_7pyarrow_3lib_ArrayP7_objectl_ZL40__pyx_sq_item_7pyarrow_3lib_ChunkedArrayP7_objectl_ZL36__pyx_sq_item_7pyarrow_3lib__TabularP7_objectl_ZL34__pyx_sq_item_7pyarrow_3lib_BufferP7_objectl_ZL37__pyx_sq_item_7pyarrow_3lib_UnionTypeP7_objectl_ZL38__pyx_sq_item_7pyarrow_3lib_ListScalarP7_objectl_ZL40__pyx_sq_item_7pyarrow_3lib_StructScalarP7_objectl_ZL37__pyx_sq_item_7pyarrow_3lib_MapScalarP7_objectl_ZL28__pyx_sq_item___Pyx_EnumMetaP7_objectl_ZL18__Pyx_SelflessCallP7_objectS0_S0__ZL55__pyx_tp_clear_7pyarrow_3lib___pyx_scope_struct_8_itemsP7_object_ZL46__pyx_tp_clear_7pyarrow_3lib_SignalStopHandlerP7_object_ZL30__Pyx_CyFunction_init_defaultsP22__pyx_CyFunctionObject_ZL31__Pyx_CyFunction_get_kwdefaultsP22__pyx_CyFunctionObjectPv_ZL29__Pyx_CyFunction_get_defaultsP22__pyx_CyFunctionObjectPv_ZL22__Pyx_PyInt_BoolNeObjCP7_objectS0_ll.constprop.0_ZL24__Pyx_CyFunction_set_docP22__pyx_CyFunctionObjectP7_objectPv_ZL12__Pyx_ImportP7_objectS0_i.constprop.0_ZL57__pyx_tp_clear_7pyarrow_3lib___pyx_scope_struct_20_uploadP7_object_ZL21__Pyx__ExceptionResetP3_tsP7_objectS2_S2_.isra.0_ZL35__pyx_tp_clear_7pyarrow_3lib_TensorP7_object_ZL44__pyx_tp_clear_7pyarrow_3lib_SparseCSCMatrixP7_object_ZL44__pyx_tp_clear_7pyarrow_3lib_SparseCSRMatrixP7_object_ZL34__pyx_tp_clear_7pyarrow_3lib_FieldP7_object_ZL40__pyx_tp_clear_7pyarrow_3lib_RecordBatchP7_object_ZL41__pyx_tp_clear_7pyarrow_3lib_BufferReaderP7_object_ZL39__pyx_tp_clear_7pyarrow_3lib_PythonFileP7_object_ZL45__pyx_tp_clear_7pyarrow_3lib_MemoryMappedFileP7_object_ZL35__pyx_tp_clear_7pyarrow_3lib_OSFileP7_object_ZL44__pyx_tp_clear_7pyarrow_3lib_SparseCSFTensorP7_object_ZL51__pyx_tp_clear_7pyarrow_3lib__RecordBatchFileReaderP7_object_ZL41__pyx_tp_clear_7pyarrow_3lib_ChunkedArrayP7_object_ZL44__pyx_tp_clear_7pyarrow_3lib_SparseCOOTensorP7_object_ZL32__Pyx_CyFunction_set_annotationsP22__pyx_CyFunctionObjectP7_objectPv_ZL23__Pyx_ErrRestoreInStateP3_tsP7_objectS2_S2__ZL25__Pyx_CyFunction_set_nameP22__pyx_CyFunctionObjectP7_objectPv_ZL28__Pyx_Coroutine_set_qualnameP21__pyx_CoroutineObjectP7_objectPv_ZL29__Pyx_CyFunction_set_qualnameP22__pyx_CyFunctionObjectP7_objectPv_ZL24__Pyx_Coroutine_set_nameP21__pyx_CoroutineObjectP7_objectPv_ZL29__Pyx_CyFunction_set_defaultsP22__pyx_CyFunctionObjectP7_objectPv_ZL31__Pyx_CyFunction_set_kwdefaultsP22__pyx_CyFunctionObjectP7_objectPv_ZL48__pyx_tp_new_7pyarrow_3lib_FixedSizeBinaryScalarP11_typeobjectP7_objectS2__ZL49__pyx_vtabptr_7pyarrow_3lib_FixedSizeBinaryScalar_ZL41__pyx_tp_new_7pyarrow_3lib_ListViewScalarP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_ListViewScalar_ZL42__pyx_tp_new_7pyarrow_3lib_LargeListScalarP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_LargeListScalar_ZL49__pyx_tp_new_7pyarrow_3lib_FixedShapeTensorScalarP11_typeobjectP7_objectS2__ZL50__pyx_vtabptr_7pyarrow_3lib_FixedShapeTensorScalar_ZL46__pyx_tp_new_7pyarrow_3lib_FixedSizeListScalarP11_typeobjectP7_objectS2__ZL47__pyx_vtabptr_7pyarrow_3lib_FixedSizeListScalar_ZL46__pyx_tp_new_7pyarrow_3lib_LargeListViewScalarP11_typeobjectP7_objectS2__ZL47__pyx_vtabptr_7pyarrow_3lib_LargeListViewScalar_ZL36__pyx_tp_new_7pyarrow_3lib_MapScalarP11_typeobjectP7_objectS2__ZL37__pyx_vtabptr_7pyarrow_3lib_MapScalar_ZL43__pyx_tp_new_7pyarrow_3lib_BinaryViewScalarP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_BinaryViewScalar_ZL44__pyx_tp_new_7pyarrow_3lib_LargeBinaryScalarP11_typeobjectP7_objectS2__ZL45__pyx_vtabptr_7pyarrow_3lib_LargeBinaryScalar_ZL39__pyx_tp_new_7pyarrow_3lib_StringScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_StringScalar_ZL25__Pyx_CyFunction_set_dictP22__pyx_CyFunctionObjectP7_objectPv_ZL44__pyx_tp_new_7pyarrow_3lib_LargeStringScalarP11_typeobjectP7_objectS2__ZL45__pyx_vtabptr_7pyarrow_3lib_LargeStringScalar_ZL43__pyx_tp_new_7pyarrow_3lib_StringViewScalarP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_StringViewScalar_ZL59__pyx_tp_clear_7pyarrow_3lib___pyx_scope_struct_19_downloadP7_object_ZL34__pyx_tp_clear_7pyarrow_3lib_ArrayP7_object_ZL44__pyx_tp_clear_7pyarrow_3lib_DictionaryArrayP7_object_ZL13__Pyx_HasAttrP7_objectS0__ZL14__Pyx_TypeTestP7_objectP11_typeobject_ZL59__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct____iter__P7_object_ZL58__pyx_freecount_7pyarrow_3lib___pyx_scope_struct____iter___ZL57__pyx_freelist_7pyarrow_3lib___pyx_scope_struct____iter___ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_1___iter__P7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_1___iter___ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_1___iter___ZL56__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_3_keysP7_object_ZL55__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_3_keys_ZL54__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_3_keys_ZL58__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_4_valuesP7_object_ZL57__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_4_values_ZL56__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_4_values_ZL57__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_5_itemsP7_object_ZL56__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_5_items_ZL55__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_5_items_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_6___iter__P7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_6___iter___ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_6___iter___ZL57__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_8_itemsP7_object_ZL56__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_8_items_ZL55__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_8_items_ZL61__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_11___iter__P7_object_ZL60__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_11___iter___ZL59__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_11___iter___ZN5arrow8bit_utilL8kBitmaskE_ZL20__Pyx_PyUnicode_JoinP7_objectllj_ZL59__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_20_uploadP7_object_ZL58__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_20_upload_ZL57__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_20_upload_ZL16__Pyx_ImportFromP7_objectS0__ZL61__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_19_downloadP7_object_ZL60__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_19_download_ZL59__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_19_download_ZL64__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_14_itercolumnsP7_object_ZL63__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_14_itercolumns_ZL62__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_14_itercolumns_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_15_genexprP7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_15_genexpr_ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_15_genexpr_ZL63__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_13_iterchunksP7_object_ZL62__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_13_iterchunks_ZL61__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_13_iterchunks_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_18_genexprP7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_18_genexpr_ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_18_genexpr_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_16_genexprP7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_16_genexpr_ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_16_genexpr_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_17_genexprP7_object_ZL59__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_17_genexpr_ZL58__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_17_genexpr_ZL59__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_2_genexprP7_object_ZL58__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_2_genexpr_ZL57__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_2_genexpr_ZL86__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadataP7_object_ZL85__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadata_ZL84__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadata_ZL18__Pyx__ArgTypeTestP7_objectP11_typeobjectPKci_ZL33__Pyx_PyErr_GivenExceptionMatchesP7_objectS0_.part.0_ZL33__Pyx_PyErr_ExceptionMatchesTupleP7_objectS0__ZL61__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_12___iter__P7_object_ZL60__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_12___iter___ZL59__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_12___iter___ZL61__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_10___iter__P7_object_ZL21__Pyx_Coroutine_clearP7_object.isra.0_ZL23__Pyx_Coroutine_deallocP7_object_ZL29__Pyx_TryUnpackUnboundCMethodP21__Pyx_CachedCFunction_ZL24__Pyx_UnboundCMethod_Def_ZL40__pyx_tp_new_7pyarrow_3lib_BaseListArrayP11_typeobjectP7_objectS2__ZL41__pyx_vtabptr_7pyarrow_3lib_BaseListArray_ZL47__pyx_tp_new_7pyarrow_3lib_FixedSizeBinaryArrayP11_typeobjectP7_objectS2__ZL48__pyx_vtabptr_7pyarrow_3lib_FixedSizeBinaryArray_ZL39__pyx_tp_new_7pyarrow_3lib_NumericArrayP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_NumericArray_ZL38__pyx_tp_new_7pyarrow_3lib_BinaryArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_BinaryArray_ZL38__pyx_tp_new_7pyarrow_3lib_StructArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_StructArray_ZL45__pyx_tp_new_7pyarrow_3lib_RunEndEncodedArrayP11_typeobjectP7_objectS2__ZL46__pyx_vtabptr_7pyarrow_3lib_RunEndEncodedArray_ZL40__pyx_tp_new_7pyarrow_3lib_ListViewArrayP11_typeobjectP7_objectS2__ZL41__pyx_vtabptr_7pyarrow_3lib_ListViewArray_ZL36__pyx_tp_new_7pyarrow_3lib_NullArrayP11_typeobjectP7_objectS2__ZL37__pyx_vtabptr_7pyarrow_3lib_NullArray_ZL39__pyx_tp_new_7pyarrow_3lib_BooleanArrayP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_BooleanArray_ZL52__pyx_tp_new_7pyarrow_3lib_MonthDayNanoIntervalArrayP11_typeobjectP7_objectS2__ZL53__pyx_vtabptr_7pyarrow_3lib_MonthDayNanoIntervalArray_ZL41__pyx_tp_new_7pyarrow_3lib_ExtensionArrayP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_ExtensionArray_ZL45__pyx_tp_new_7pyarrow_3lib_LargeListViewArrayP11_typeobjectP7_objectS2__ZL46__pyx_vtabptr_7pyarrow_3lib_LargeListViewArray_ZL42__pyx_tp_new_7pyarrow_3lib_StringViewArrayP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_StringViewArray_ZL42__pyx_tp_new_7pyarrow_3lib_BinaryViewArrayP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_BinaryViewArray_ZL37__pyx_tp_new_7pyarrow_3lib_UnionArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_UnionArray_ZL38__pyx_tp_new_7pyarrow_3lib_StringArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_StringArray_ZL43__pyx_tp_new_7pyarrow_3lib_LargeStringArrayP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_LargeStringArray_ZL43__pyx_tp_new_7pyarrow_3lib_LargeBinaryArrayP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_LargeBinaryArray_ZL59__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_9_genexprP7_object_ZL58__pyx_freecount_7pyarrow_3lib___pyx_scope_struct_9_genexpr_ZL57__pyx_freelist_7pyarrow_3lib___pyx_scope_struct_9_genexpr_ZL20__Pyx__ExceptionSwapP3_tsPP7_objectS3_S3_.isra.0_ZL36__pyx_tp_new_7pyarrow_3lib_ListArrayP11_typeobjectP7_objectS2__ZL37__pyx_vtabptr_7pyarrow_3lib_ListArray_ZL41__pyx_tp_new_7pyarrow_3lib_LargeListArrayP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_LargeListArray_ZL45__pyx_tp_new_7pyarrow_3lib_FloatingPointArrayP11_typeobjectP7_objectS2__ZL46__pyx_vtabptr_7pyarrow_3lib_FloatingPointArray_ZL39__pyx_tp_new_7pyarrow_3lib_IntegerArrayP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_IntegerArray_ZL42__pyx_tp_new_7pyarrow_3lib_Decimal128ArrayP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_Decimal128Array_ZL42__pyx_tp_new_7pyarrow_3lib_Decimal256ArrayP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_Decimal256Array_ZL38__pyx_tp_new_7pyarrow_3lib_Date32ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Date32Array_ZL38__pyx_tp_new_7pyarrow_3lib_Date64ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Date64Array_ZL41__pyx_tp_new_7pyarrow_3lib_TimestampArrayP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_TimestampArray_ZL45__pyx_tp_new_7pyarrow_3lib_FixedSizeListArrayP11_typeobjectP7_objectS2__ZL46__pyx_vtabptr_7pyarrow_3lib_FixedSizeListArray_ZL40__pyx_tp_new_7pyarrow_3lib_DurationArrayP11_typeobjectP7_objectS2__ZL41__pyx_vtabptr_7pyarrow_3lib_DurationArray_ZL38__pyx_tp_new_7pyarrow_3lib_Time64ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Time64Array_ZL38__pyx_tp_new_7pyarrow_3lib_Time32ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Time32Array_ZL48__pyx_tp_new_7pyarrow_3lib_FixedShapeTensorArrayP11_typeobjectP7_objectS2__ZL49__pyx_vtabptr_7pyarrow_3lib_FixedShapeTensorArray_ZL42__pyx_tp_new_7pyarrow_3lib_DictionaryArrayP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_DictionaryArray_ZL35__pyx_tp_new_7pyarrow_3lib_MapArrayP11_typeobjectP7_objectS2__ZL36__pyx_vtabptr_7pyarrow_3lib_MapArray_ZL37__pyx_tp_new_7pyarrow_3lib_Int16ArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_Int16Array_ZL37__pyx_tp_new_7pyarrow_3lib_UInt8ArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_UInt8Array_ZL37__pyx_tp_new_7pyarrow_3lib_Int32ArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_Int32Array_ZL38__pyx_tp_new_7pyarrow_3lib_UInt64ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_UInt64Array_ZL37__pyx_tp_new_7pyarrow_3lib_FloatArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_FloatArray_ZL38__pyx_tp_new_7pyarrow_3lib_UInt16ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_UInt16Array_ZL38__pyx_tp_new_7pyarrow_3lib_UInt32ArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_UInt32Array_ZL36__pyx_tp_new_7pyarrow_3lib_Int8ArrayP11_typeobjectP7_objectS2__ZL37__pyx_vtabptr_7pyarrow_3lib_Int8Array_ZL37__pyx_tp_new_7pyarrow_3lib_Int64ArrayP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_Int64Array_ZL38__pyx_tp_new_7pyarrow_3lib_DoubleArrayP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_DoubleArray_ZL41__pyx_tp_new_7pyarrow_3lib_HalfFloatArrayP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_HalfFloatArray_ZL22__Pyx_Coroutine_SendExP21__pyx_CoroutineObjectP7_objecti.constprop.0_ZL26__Pyx__CallUnboundCMethod0P21__Pyx_CachedCFunctionP7_object.constprop.0_ZL22__Pyx_CyFunction_clearP22__pyx_CyFunctionObject_ZL24__Pyx_CyFunction_deallocP22__pyx_CyFunctionObject_ZL39__Pyx_PyNumber_IntOrLongWrongResultTypeP7_objectPKc_ZL24__Pyx_PyNumber_IntOrLongP7_object_ZL45__pyx_tp_dealloc_7pyarrow_3lib__PandasAPIShimP7_object_ZL43__pyx_tp_clear_7pyarrow_3lib__PandasAPIShimP7_object_ZL20__Pyx_CyFunction_NewP11PyMethodDefiP7_objectS2_S2_S2_S2__ZL53__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_8_itemsP11_typeobjectP7_objectS2__ZL55__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_9_genexprP11_typeobjectP7_objectS2__ZL57__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_11___iter__P11_typeobjectP7_objectS2__ZL55__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct____iter__P11_typeobjectP7_objectS2__ZL82__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadataP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_15_genexprP11_typeobjectP7_objectS2__ZL59__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_13_iterchunksP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_18_genexprP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_16_genexprP11_typeobjectP7_objectS2__ZL60__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_14_itercolumnsP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_17_genexprP11_typeobjectP7_objectS2__ZL57__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_19_downloadP11_typeobjectP7_objectS2__ZL57__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_12___iter__P11_typeobjectP7_objectS2__ZL55__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_20_uploadP11_typeobjectP7_objectS2__ZL53__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_5_itemsP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_6___iter__P11_typeobjectP7_objectS2__ZL52__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_3_keysP11_typeobjectP7_objectS2__ZL54__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_4_valuesP11_typeobjectP7_objectS2__ZL56__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_1___iter__P11_typeobjectP7_objectS2__ZL55__pyx_tp_new_7pyarrow_3lib___pyx_scope_struct_2_genexprP11_typeobjectP7_objectS2__ZL40__pyx_tp_new_7pyarrow_3lib_MessageReaderP11_typeobjectP7_objectS2__ZL34__pyx_tp_new_7pyarrow_3lib_MessageP11_typeobjectP7_objectS2__ZL21__Pyx_WriteUnraisablePKciiS0_ii.constprop.0_ZL48__pyx_tp_dealloc_7pyarrow_3lib_SignalStopHandlerP7_object_ZL45__pyx_f_7pyarrow_3lib_pycapsule_array_deleterP7_object_ZL46__pyx_f_7pyarrow_3lib_pycapsule_schema_deleterP7_object_ZL46__pyx_f_7pyarrow_3lib_pycapsule_stream_deleterP7_object_ZL46__pyx_tp_new_7pyarrow_3lib__CRecordBatchWriterP11_typeobjectP7_objectS2__ZL44__pyx_tp_new_7pyarrow_3lib_RecordBatchReaderP11_typeobjectP7_objectS2__ZL37__pyx_tp_new_7pyarrow_3lib_MemoryPoolP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_MemoryPool_ZL33__pyx_tp_new_7pyarrow_3lib_SchemaP11_typeobjectP7_objectS2__ZL34__pyx_vtabptr_7pyarrow_3lib_Schema_ZL33__pyx_tp_new_7pyarrow_3lib_BufferP11_typeobjectP7_objectS2__ZL34__pyx_vtabptr_7pyarrow_3lib_Buffer_ZL42__pyx_tp_new_7pyarrow_3lib_ResizableBufferP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_ResizableBuffer_ZL23__Pyx_PyIndex_AsSsize_tP7_object_ZL35__Pyx_PyErr_ExceptionMatchesInStateP3_tsP7_object.isra.0_ZL32__pyx_tp_new_7pyarrow_3lib_CodecP11_typeobjectP7_objectS2__ZL33__pyx_vtabptr_7pyarrow_3lib_Codec_ZL33__pyx_tp_new_7pyarrow_3lib_ScalarP11_typeobjectP7_objectS2__ZL34__pyx_vtabptr_7pyarrow_3lib_Scalar_ZL43__pyx_tp_new_7pyarrow_3lib_KeyValueMetadataP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_KeyValueMetadata_ZL21__Pyx_PyInt_As_int8_tP7_object_ZL29__Pyx_CyFunction_CallAsMethodP7_objectS0_S0__ZL23__Pyx_PyObject_GetSliceP7_objectllPS0_S1_S1_iii.constprop.0_ZL39__pyx_tp_new_7pyarrow_3lib_CacheOptionsP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_CacheOptions_ZL42__pyx_tp_new_7pyarrow_3lib_ProxyMemoryPoolP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_ProxyMemoryPool_ZL44__pyx_tp_new_7pyarrow_3lib_LoggingMemoryPoolP11_typeobjectP7_objectS2__ZL45__pyx_vtabptr_7pyarrow_3lib_LoggingMemoryPool_ZL40__pyx_tp_new_7pyarrow_3lib_BooleanScalarP11_typeobjectP7_objectS2__ZL41__pyx_vtabptr_7pyarrow_3lib_BooleanScalar_ZL38__pyx_tp_new_7pyarrow_3lib_Int16ScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Int16Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_UInt16ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_UInt16Scalar_ZL38__pyx_tp_new_7pyarrow_3lib_FloatScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_FloatScalar_ZL38__pyx_tp_new_7pyarrow_3lib_UInt8ScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_UInt8Scalar_ZL38__pyx_tp_new_7pyarrow_3lib_Int32ScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Int32Scalar_ZL37__pyx_tp_new_7pyarrow_3lib_Int8ScalarP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_Int8Scalar_ZL38__pyx_tp_new_7pyarrow_3lib_Int64ScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_Int64Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_UInt32ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_UInt32Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_UInt64ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_UInt64Scalar_ZL42__pyx_tp_new_7pyarrow_3lib_HalfFloatScalarP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_HalfFloatScalar_ZL39__pyx_tp_new_7pyarrow_3lib_DoubleScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_DoubleScalar_ZL41__pyx_tp_new_7pyarrow_3lib_DurationScalarP11_typeobjectP7_objectS2__ZL42__pyx_vtabptr_7pyarrow_3lib_DurationScalar_ZL42__pyx_tp_new_7pyarrow_3lib_SparseCSRMatrixP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_SparseCSRMatrix_ZL42__pyx_tp_new_7pyarrow_3lib_SparseCSFTensorP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_SparseCSFTensor_ZL53__pyx_tp_new_7pyarrow_3lib_MonthDayNanoIntervalScalarP11_typeobjectP7_objectS2__ZL54__pyx_vtabptr_7pyarrow_3lib_MonthDayNanoIntervalScalar_ZL46__pyx_tp_new_7pyarrow_3lib_RunEndEncodedScalarP11_typeobjectP7_objectS2__ZL47__pyx_vtabptr_7pyarrow_3lib_RunEndEncodedScalar_ZL39__pyx_tp_new_7pyarrow_3lib_Time32ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_Time32Scalar_ZL42__pyx_tp_new_7pyarrow_3lib_SparseCSCMatrixP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_SparseCSCMatrix_ZL42__pyx_tp_new_7pyarrow_3lib_SparseCOOTensorP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_SparseCOOTensor_ZL42__pyx_tp_new_7pyarrow_3lib_ExtensionScalarP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_ExtensionScalar_ZL43__pyx_tp_new_7pyarrow_3lib_Decimal128ScalarP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_Decimal128Scalar_ZL43__pyx_tp_new_7pyarrow_3lib_Decimal256ScalarP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_Decimal256Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_Date32ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_Date32Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_Date64ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_Date64Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_StructScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_StructScalar_ZL39__pyx_tp_new_7pyarrow_3lib_Time64ScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_Time64Scalar_ZL39__pyx_tp_new_7pyarrow_3lib_BinaryScalarP11_typeobjectP7_objectS2__ZL40__pyx_vtabptr_7pyarrow_3lib_BinaryScalar_ZL37__pyx_tp_new_7pyarrow_3lib_ListScalarP11_typeobjectP7_objectS2__ZL38__pyx_vtabptr_7pyarrow_3lib_ListScalar_ZL43__pyx_tp_new_7pyarrow_3lib_DictionaryScalarP11_typeobjectP7_objectS2__ZL44__pyx_vtabptr_7pyarrow_3lib_DictionaryScalar_ZL42__pyx_tp_new_7pyarrow_3lib_TimestampScalarP11_typeobjectP7_objectS2__ZL43__pyx_vtabptr_7pyarrow_3lib_TimestampScalar_ZL38__pyx_tp_new_7pyarrow_3lib_UnionScalarP11_typeobjectP7_objectS2__ZL39__pyx_vtabptr_7pyarrow_3lib_UnionScalar_ZL32__pyx_tp_new_7pyarrow_3lib_ArrayP11_typeobjectP7_objectS2__ZL33__pyx_vtabptr_7pyarrow_3lib_Array_ZL33__pyx_tp_new_7pyarrow_3lib_TensorP11_typeobjectP7_objectS2__ZL34__pyx_vtabptr_7pyarrow_3lib_Tensor_ZL32__Pyx_PyObject_GetAttrStrNoErrorP7_objectS0__ZL27__Pyx_setup_reduce_is_namedP7_objectS0__ZL18__Pyx_setup_reduceP7_object_ZL28__Pyx_modinit_type_init_codev_ZL37__pyx_type_7pyarrow_3lib__Weakrefable_ZL40__pyx_type_7pyarrow_3lib_IpcWriteOptions_ZL39__pyx_type_7pyarrow_3lib_IpcReadOptions_ZL32__pyx_type_7pyarrow_3lib_Message_ZL37__pyx_vtable_7pyarrow_3lib_MemoryPool_ZL35__pyx_type_7pyarrow_3lib_MemoryPool_ZL39__pyx_f_7pyarrow_3lib_10MemoryPool_initP34__pyx_obj_7pyarrow_3lib_MemoryPoolPN5arrow10MemoryPoolE_ZL35__pyx_vtable_7pyarrow_3lib_DataType_ZL36__pyx_f_7pyarrow_3lib_8DataType_initP32__pyx_obj_7pyarrow_3lib_DataTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL36__pyx_vtabptr_7pyarrow_3lib_DataType_ZL37__pyx_f_7pyarrow_3lib_8DataType_fieldP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objecti_ZL33__pyx_type_7pyarrow_3lib_DataType_ZL35__pyx_vtable_7pyarrow_3lib_ListType_ZL33__pyx_type_7pyarrow_3lib_ListType_ZL36__pyx_vtabptr_7pyarrow_3lib_ListType_ZL36__pyx_f_7pyarrow_3lib_8ListType_initP32__pyx_obj_7pyarrow_3lib_ListTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL40__pyx_vtable_7pyarrow_3lib_LargeListType_ZL38__pyx_type_7pyarrow_3lib_LargeListType_ZL41__pyx_vtabptr_7pyarrow_3lib_LargeListType_ZL42__pyx_f_7pyarrow_3lib_13LargeListType_initP37__pyx_obj_7pyarrow_3lib_LargeListTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL39__pyx_vtable_7pyarrow_3lib_ListViewType_ZL37__pyx_type_7pyarrow_3lib_ListViewType_ZL40__pyx_vtabptr_7pyarrow_3lib_ListViewType_ZL41__pyx_f_7pyarrow_3lib_12ListViewType_initP36__pyx_obj_7pyarrow_3lib_ListViewTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL44__pyx_vtable_7pyarrow_3lib_LargeListViewType_ZL42__pyx_type_7pyarrow_3lib_LargeListViewType_ZL45__pyx_vtabptr_7pyarrow_3lib_LargeListViewType_ZL46__pyx_f_7pyarrow_3lib_17LargeListViewType_initP41__pyx_obj_7pyarrow_3lib_LargeListViewTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL34__pyx_vtable_7pyarrow_3lib_MapType_ZL32__pyx_type_7pyarrow_3lib_MapType_ZL35__pyx_vtabptr_7pyarrow_3lib_MapType_ZL35__pyx_f_7pyarrow_3lib_7MapType_initP31__pyx_obj_7pyarrow_3lib_MapTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL44__pyx_vtable_7pyarrow_3lib_FixedSizeListType_ZL42__pyx_type_7pyarrow_3lib_FixedSizeListType_ZL45__pyx_vtabptr_7pyarrow_3lib_FixedSizeListType_ZL46__pyx_f_7pyarrow_3lib_17FixedSizeListType_initP41__pyx_obj_7pyarrow_3lib_FixedSizeListTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL37__pyx_vtable_7pyarrow_3lib_StructType_ZL39__pyx_f_7pyarrow_3lib_10StructType_initP34__pyx_obj_7pyarrow_3lib_StructTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL38__pyx_vtabptr_7pyarrow_3lib_StructType_ZL40__pyx_f_7pyarrow_3lib_10StructType_fieldP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objecti_ZL35__pyx_type_7pyarrow_3lib_StructType_ZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_object_ZL45__pyx_doc_7pyarrow_3lib_10StructType_6__len___ZL46__pyx_doc_7pyarrow_3lib_10StructType_8__iter___ZL50__pyx_doc_7pyarrow_3lib_10StructType_11__getitem___ZL39__pyx_type_7pyarrow_3lib_DictionaryMemo_ZL41__pyx_vtable_7pyarrow_3lib_DictionaryType_ZL39__pyx_type_7pyarrow_3lib_DictionaryType_ZL42__pyx_vtabptr_7pyarrow_3lib_DictionaryType_ZL43__pyx_f_7pyarrow_3lib_14DictionaryType_initP38__pyx_obj_7pyarrow_3lib_DictionaryTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL40__pyx_vtable_7pyarrow_3lib_TimestampType_ZL38__pyx_type_7pyarrow_3lib_TimestampType_ZL41__pyx_vtabptr_7pyarrow_3lib_TimestampType_ZL42__pyx_f_7pyarrow_3lib_13TimestampType_initP37__pyx_obj_7pyarrow_3lib_TimestampTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL37__pyx_vtable_7pyarrow_3lib_Time32Type_ZL35__pyx_type_7pyarrow_3lib_Time32Type_ZL38__pyx_vtabptr_7pyarrow_3lib_Time32Type_ZL39__pyx_f_7pyarrow_3lib_10Time32Type_initP34__pyx_obj_7pyarrow_3lib_Time32TypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL37__pyx_vtable_7pyarrow_3lib_Time64Type_ZL35__pyx_type_7pyarrow_3lib_Time64Type_ZL38__pyx_vtabptr_7pyarrow_3lib_Time64Type_ZL39__pyx_f_7pyarrow_3lib_10Time64Type_initP34__pyx_obj_7pyarrow_3lib_Time64TypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL39__pyx_vtable_7pyarrow_3lib_DurationType_ZL37__pyx_type_7pyarrow_3lib_DurationType_ZL40__pyx_vtabptr_7pyarrow_3lib_DurationType_ZL41__pyx_f_7pyarrow_3lib_12DurationType_initP36__pyx_obj_7pyarrow_3lib_DurationTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL46__pyx_vtable_7pyarrow_3lib_FixedSizeBinaryType_ZL44__pyx_type_7pyarrow_3lib_FixedSizeBinaryType_ZL47__pyx_vtabptr_7pyarrow_3lib_FixedSizeBinaryType_ZL48__pyx_f_7pyarrow_3lib_19FixedSizeBinaryType_initP43__pyx_obj_7pyarrow_3lib_FixedSizeBinaryTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL41__pyx_vtable_7pyarrow_3lib_Decimal128Type_ZL39__pyx_type_7pyarrow_3lib_Decimal128Type_ZL42__pyx_vtabptr_7pyarrow_3lib_Decimal128Type_ZL43__pyx_f_7pyarrow_3lib_14Decimal128Type_initP38__pyx_obj_7pyarrow_3lib_Decimal128TypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL41__pyx_vtable_7pyarrow_3lib_Decimal256Type_ZL39__pyx_type_7pyarrow_3lib_Decimal256Type_ZL42__pyx_vtabptr_7pyarrow_3lib_Decimal256Type_ZL43__pyx_f_7pyarrow_3lib_14Decimal256Type_initP38__pyx_obj_7pyarrow_3lib_Decimal256TypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL44__pyx_vtable_7pyarrow_3lib_RunEndEncodedType_ZL42__pyx_type_7pyarrow_3lib_RunEndEncodedType_ZL45__pyx_vtabptr_7pyarrow_3lib_RunEndEncodedType_ZL46__pyx_f_7pyarrow_3lib_17RunEndEncodedType_initP41__pyx_obj_7pyarrow_3lib_RunEndEncodedTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL44__pyx_vtable_7pyarrow_3lib_BaseExtensionType_ZL42__pyx_type_7pyarrow_3lib_BaseExtensionType_ZL45__pyx_vtabptr_7pyarrow_3lib_BaseExtensionType_ZL46__pyx_f_7pyarrow_3lib_17BaseExtensionType_initP41__pyx_obj_7pyarrow_3lib_BaseExtensionTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL40__pyx_vtable_7pyarrow_3lib_ExtensionType_ZL38__pyx_type_7pyarrow_3lib_ExtensionType_ZL41__pyx_vtabptr_7pyarrow_3lib_ExtensionType_ZL42__pyx_f_7pyarrow_3lib_13ExtensionType_initP37__pyx_obj_7pyarrow_3lib_ExtensionTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL49__pyx_doc_7pyarrow_3lib_13ExtensionType_2__init___ZL47__pyx_vtable_7pyarrow_3lib_FixedShapeTensorType_ZL45__pyx_type_7pyarrow_3lib_FixedShapeTensorType_ZL48__pyx_vtabptr_7pyarrow_3lib_FixedShapeTensorType_ZL49__pyx_f_7pyarrow_3lib_20FixedShapeTensorType_initP44__pyx_obj_7pyarrow_3lib_FixedShapeTensorTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL42__pyx_vtable_7pyarrow_3lib_PyExtensionType_ZL40__pyx_type_7pyarrow_3lib_PyExtensionType_ZL43__pyx_vtabptr_7pyarrow_3lib_PyExtensionType_ZL34__pyx_type_7pyarrow_3lib__Metadata_ZL43__pyx_vtable_7pyarrow_3lib_KeyValueMetadata_ZL45__pyx_f_7pyarrow_3lib_16KeyValueMetadata_initP40__pyx_obj_7pyarrow_3lib_KeyValueMetadataRKSt10shared_ptrIKN5arrow16KeyValueMetadataEE_ZL45__pyx_f_7pyarrow_3lib_16KeyValueMetadata_wrapRKSt10shared_ptrIKN5arrow16KeyValueMetadataEE_ZL30__pyx_type_7pyarrow_3lib_Field_ZL47__pyx_f_7pyarrow_3lib_16KeyValueMetadata_unwrapP40__pyx_obj_7pyarrow_3lib_KeyValueMetadata_ZL32__pyx_vtable_7pyarrow_3lib_Field_ZL33__pyx_vtabptr_7pyarrow_3lib_Field_ZL33__pyx_f_7pyarrow_3lib_5Field_initP29__pyx_obj_7pyarrow_3lib_FieldRKSt10shared_ptrIN5arrow5FieldEE_ZL33__pyx_vtable_7pyarrow_3lib_Schema_ZL34__pyx_f_7pyarrow_3lib_6Schema_initP30__pyx_obj_7pyarrow_3lib_SchemaRKSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS5_EE_ZL41__pyx_f_7pyarrow_3lib_6Schema_init_schemaP30__pyx_obj_7pyarrow_3lib_SchemaRKSt10shared_ptrIN5arrow6SchemaEE_ZL31__pyx_type_7pyarrow_3lib_Schema_ZL33__pyx_vtable_7pyarrow_3lib_Scalar_ZL34__pyx_f_7pyarrow_3lib_6Scalar_initP30__pyx_obj_7pyarrow_3lib_ScalarRKSt10shared_ptrIN5arrow6ScalarEE_ZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEE_ZL31__pyx_type_7pyarrow_3lib_Scalar_ZL36__pyx_f_7pyarrow_3lib_6Scalar_unwrapP30__pyx_obj_7pyarrow_3lib_Scalar_ZL43__pyx_type_7pyarrow_3lib__PandasConvertible_ZL32__pyx_vtable_7pyarrow_3lib_Array_ZL33__pyx_f_7pyarrow_3lib_5Array_initP29__pyx_obj_7pyarrow_3lib_ArrayRKSt10shared_ptrIN5arrow5ArrayEE_ZL36__pyx_f_7pyarrow_3lib_5Array_getitemP29__pyx_obj_7pyarrow_3lib_Arrayl_ZL30__pyx_type_7pyarrow_3lib_Array_ZL35__pyx_f_7pyarrow_3lib_5Array_lengthP29__pyx_obj_7pyarrow_3lib_Array_ZL44__pyx_doc_7pyarrow_3lib_5Array_53__getitem___ZL33__pyx_vtable_7pyarrow_3lib_Tensor_ZL31__pyx_type_7pyarrow_3lib_Tensor_ZL34__pyx_f_7pyarrow_3lib_6Tensor_initP30__pyx_obj_7pyarrow_3lib_TensorRKSt10shared_ptrIN5arrow6TensorEE_ZL42__pyx_vtable_7pyarrow_3lib_SparseCSRMatrix_ZL40__pyx_type_7pyarrow_3lib_SparseCSRMatrix_ZL44__pyx_f_7pyarrow_3lib_15SparseCSRMatrix_initP39__pyx_obj_7pyarrow_3lib_SparseCSRMatrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSRIndexEEEE_ZL42__pyx_vtable_7pyarrow_3lib_SparseCSCMatrix_ZL40__pyx_type_7pyarrow_3lib_SparseCSCMatrix_ZL44__pyx_f_7pyarrow_3lib_15SparseCSCMatrix_initP39__pyx_obj_7pyarrow_3lib_SparseCSCMatrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSCIndexEEEE_ZL42__pyx_vtable_7pyarrow_3lib_SparseCOOTensor_ZL40__pyx_type_7pyarrow_3lib_SparseCOOTensor_ZL44__pyx_f_7pyarrow_3lib_15SparseCOOTensor_initP39__pyx_obj_7pyarrow_3lib_SparseCOOTensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCOOIndexEEEE_ZL42__pyx_vtable_7pyarrow_3lib_SparseCSFTensor_ZL40__pyx_type_7pyarrow_3lib_SparseCSFTensor_ZL44__pyx_f_7pyarrow_3lib_15SparseCSFTensor_initP39__pyx_obj_7pyarrow_3lib_SparseCSFTensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSFIndexEEEE_ZL36__pyx_vtable_7pyarrow_3lib_NullArray_ZL34__pyx_type_7pyarrow_3lib_NullArray_ZL39__pyx_vtable_7pyarrow_3lib_BooleanArray_ZL37__pyx_type_7pyarrow_3lib_BooleanArray_ZL39__pyx_vtable_7pyarrow_3lib_NumericArray_ZL37__pyx_type_7pyarrow_3lib_NumericArray_ZL39__pyx_vtable_7pyarrow_3lib_IntegerArray_ZL37__pyx_type_7pyarrow_3lib_IntegerArray_ZL45__pyx_vtable_7pyarrow_3lib_FloatingPointArray_ZL43__pyx_type_7pyarrow_3lib_FloatingPointArray_ZL36__pyx_vtable_7pyarrow_3lib_Int8Array_ZL34__pyx_type_7pyarrow_3lib_Int8Array_ZL37__pyx_vtable_7pyarrow_3lib_UInt8Array_ZL35__pyx_type_7pyarrow_3lib_UInt8Array_ZL37__pyx_vtable_7pyarrow_3lib_Int16Array_ZL35__pyx_type_7pyarrow_3lib_Int16Array_ZL38__pyx_vtable_7pyarrow_3lib_UInt16Array_ZL36__pyx_type_7pyarrow_3lib_UInt16Array_ZL37__pyx_vtable_7pyarrow_3lib_Int32Array_ZL35__pyx_type_7pyarrow_3lib_Int32Array_ZL38__pyx_vtable_7pyarrow_3lib_UInt32Array_ZL36__pyx_type_7pyarrow_3lib_UInt32Array_ZL37__pyx_vtable_7pyarrow_3lib_Int64Array_ZL35__pyx_type_7pyarrow_3lib_Int64Array_ZL38__pyx_vtable_7pyarrow_3lib_UInt64Array_ZL36__pyx_type_7pyarrow_3lib_UInt64Array_ZL41__pyx_vtable_7pyarrow_3lib_HalfFloatArray_ZL39__pyx_type_7pyarrow_3lib_HalfFloatArray_ZL37__pyx_vtable_7pyarrow_3lib_FloatArray_ZL35__pyx_type_7pyarrow_3lib_FloatArray_ZL38__pyx_vtable_7pyarrow_3lib_DoubleArray_ZL36__pyx_type_7pyarrow_3lib_DoubleArray_ZL47__pyx_vtable_7pyarrow_3lib_FixedSizeBinaryArray_ZL45__pyx_type_7pyarrow_3lib_FixedSizeBinaryArray_ZL42__pyx_vtable_7pyarrow_3lib_Decimal128Array_ZL40__pyx_type_7pyarrow_3lib_Decimal128Array_ZL42__pyx_vtable_7pyarrow_3lib_Decimal256Array_ZL40__pyx_type_7pyarrow_3lib_Decimal256Array_ZL38__pyx_vtable_7pyarrow_3lib_StructArray_ZL36__pyx_type_7pyarrow_3lib_StructArray_ZL40__pyx_vtable_7pyarrow_3lib_BaseListArray_ZL38__pyx_type_7pyarrow_3lib_BaseListArray_ZL36__pyx_vtable_7pyarrow_3lib_ListArray_ZL34__pyx_type_7pyarrow_3lib_ListArray_ZL41__pyx_vtable_7pyarrow_3lib_LargeListArray_ZL39__pyx_type_7pyarrow_3lib_LargeListArray_ZL40__pyx_vtable_7pyarrow_3lib_ListViewArray_ZL38__pyx_type_7pyarrow_3lib_ListViewArray_ZL45__pyx_vtable_7pyarrow_3lib_LargeListViewArray_ZL43__pyx_type_7pyarrow_3lib_LargeListViewArray_ZL35__pyx_vtable_7pyarrow_3lib_MapArray_ZL33__pyx_type_7pyarrow_3lib_MapArray_ZL45__pyx_vtable_7pyarrow_3lib_FixedSizeListArray_ZL43__pyx_type_7pyarrow_3lib_FixedSizeListArray_ZL37__pyx_vtable_7pyarrow_3lib_UnionArray_ZL35__pyx_type_7pyarrow_3lib_UnionArray_ZL38__pyx_vtable_7pyarrow_3lib_StringArray_ZL36__pyx_type_7pyarrow_3lib_StringArray_ZL38__pyx_vtable_7pyarrow_3lib_BinaryArray_ZL36__pyx_type_7pyarrow_3lib_BinaryArray_ZL42__pyx_vtable_7pyarrow_3lib_StringViewArray_ZL40__pyx_type_7pyarrow_3lib_StringViewArray_ZL42__pyx_vtable_7pyarrow_3lib_BinaryViewArray_ZL40__pyx_type_7pyarrow_3lib_BinaryViewArray_ZL42__pyx_vtable_7pyarrow_3lib_DictionaryArray_ZL40__pyx_type_7pyarrow_3lib_DictionaryArray_ZL41__pyx_vtable_7pyarrow_3lib_ExtensionArray_ZL39__pyx_type_7pyarrow_3lib_ExtensionArray_ZL52__pyx_vtable_7pyarrow_3lib_MonthDayNanoIntervalArray_ZL50__pyx_type_7pyarrow_3lib_MonthDayNanoIntervalArray_ZL39__pyx_vtable_7pyarrow_3lib_ChunkedArray_ZL41__pyx_f_7pyarrow_3lib_12ChunkedArray_initP36__pyx_obj_7pyarrow_3lib_ChunkedArrayRKSt10shared_ptrIN5arrow12ChunkedArrayEE_ZL40__pyx_vtabptr_7pyarrow_3lib_ChunkedArray_ZL44__pyx_f_7pyarrow_3lib_12ChunkedArray_getitemP36__pyx_obj_7pyarrow_3lib_ChunkedArrayl_ZL37__pyx_type_7pyarrow_3lib_ChunkedArray_ZL52__pyx_doc_7pyarrow_3lib_12ChunkedArray_27__getitem___ZL33__pyx_type_7pyarrow_3lib__Tabular_ZL46__pyx_doc_7pyarrow_3lib_8_Tabular_8__getitem___ZL32__pyx_vtable_7pyarrow_3lib_Table_ZL30__pyx_type_7pyarrow_3lib_Table_ZL33__pyx_vtabptr_7pyarrow_3lib_Table_ZL33__pyx_f_7pyarrow_3lib_5Table_initP29__pyx_obj_7pyarrow_3lib_TableRKSt10shared_ptrIN5arrow5TableEE_ZL38__pyx_vtable_7pyarrow_3lib_RecordBatch_ZL36__pyx_type_7pyarrow_3lib_RecordBatch_ZL39__pyx_vtabptr_7pyarrow_3lib_RecordBatch_ZL40__pyx_f_7pyarrow_3lib_11RecordBatch_initP35__pyx_obj_7pyarrow_3lib_RecordBatchRKSt10shared_ptrIN5arrow11RecordBatchEE_ZL33__pyx_vtable_7pyarrow_3lib_Buffer_ZL34__pyx_f_7pyarrow_3lib_6Buffer_initP30__pyx_obj_7pyarrow_3lib_BufferRKSt10shared_ptrIN5arrow6BufferEE_ZL37__pyx_f_7pyarrow_3lib_6Buffer_getitemP30__pyx_obj_7pyarrow_3lib_Bufferl_ZL31__pyx_type_7pyarrow_3lib_Buffer_ZL42__pyx_vtable_7pyarrow_3lib_ResizableBuffer_ZL47__pyx_f_7pyarrow_3lib_15ResizableBuffer_init_rzP39__pyx_obj_7pyarrow_3lib_ResizableBufferRKSt10shared_ptrIN5arrow15ResizableBufferEE_ZL40__pyx_type_7pyarrow_3lib_ResizableBuffer_ZL37__pyx_vtable_7pyarrow_3lib_NativeFile_ZL57__pyx_f_7pyarrow_3lib_10NativeFile_set_random_access_fileP34__pyx_obj_7pyarrow_3lib_NativeFileSt10shared_ptrIN5arrow2io16RandomAccessFileEE_ZL38__pyx_vtabptr_7pyarrow_3lib_NativeFile_ZL51__pyx_f_7pyarrow_3lib_10NativeFile_set_input_streamP34__pyx_obj_7pyarrow_3lib_NativeFileSt10shared_ptrIN5arrow2io11InputStreamEE_ZL52__pyx_f_7pyarrow_3lib_10NativeFile_set_output_streamP34__pyx_obj_7pyarrow_3lib_NativeFileSt10shared_ptrIN5arrow2io12OutputStreamEE_ZL57__pyx_f_7pyarrow_3lib_10NativeFile_get_random_access_fileP34__pyx_obj_7pyarrow_3lib_NativeFile_ZL51__pyx_f_7pyarrow_3lib_10NativeFile_get_input_streamP34__pyx_obj_7pyarrow_3lib_NativeFile_ZL52__pyx_f_7pyarrow_3lib_10NativeFile_get_output_streamP34__pyx_obj_7pyarrow_3lib_NativeFile_ZL35__pyx_type_7pyarrow_3lib_NativeFile_ZL46__pyx_vtable_7pyarrow_3lib_BufferedInputStream_ZL47__pyx_vtabptr_7pyarrow_3lib_BufferedInputStream_ZL44__pyx_type_7pyarrow_3lib_BufferedInputStream_ZL47__pyx_vtable_7pyarrow_3lib_BufferedOutputStream_ZL48__pyx_vtabptr_7pyarrow_3lib_BufferedOutputStream_ZL45__pyx_type_7pyarrow_3lib_BufferedOutputStream_ZL48__pyx_vtable_7pyarrow_3lib_CompressedInputStream_ZL49__pyx_vtabptr_7pyarrow_3lib_CompressedInputStream_ZL46__pyx_type_7pyarrow_3lib_CompressedInputStream_ZL49__pyx_vtable_7pyarrow_3lib_CompressedOutputStream_ZL50__pyx_vtabptr_7pyarrow_3lib_CompressedOutputStream_ZL47__pyx_type_7pyarrow_3lib_CompressedOutputStream_ZL44__pyx_type_7pyarrow_3lib__CRecordBatchWriter_ZL42__pyx_type_7pyarrow_3lib_RecordBatchReader_ZL39__pyx_vtable_7pyarrow_3lib_CacheOptions_ZL41__pyx_f_7pyarrow_3lib_12CacheOptions_initP36__pyx_obj_7pyarrow_3lib_CacheOptionsN5arrow2io12CacheOptionsE_ZL43__pyx_f_7pyarrow_3lib_12CacheOptions_unwrapP36__pyx_obj_7pyarrow_3lib_CacheOptions_ZL37__pyx_type_7pyarrow_3lib_CacheOptions_ZL41__pyx_f_7pyarrow_3lib_12CacheOptions_wrapN5arrow2io12CacheOptionsE_ZL32__pyx_vtable_7pyarrow_3lib_Codec_ZL30__pyx_type_7pyarrow_3lib_Codec_ZL35__pyx_f_7pyarrow_3lib_5Codec_unwrapP29__pyx_obj_7pyarrow_3lib_Codec_ZL36__pyx_vtable_7pyarrow_3lib_StopToken_ZL34__pyx_type_7pyarrow_3lib_StopToken_ZL37__pyx_f_7pyarrow_3lib_9StopToken_initP33__pyx_obj_7pyarrow_3lib_StopTokenN5arrow9StopTokenE_ZL42__pyx_type_7pyarrow_3lib_SignalStopHandler_ZL41__pyx_vtable_7pyarrow_3lib__PandasAPIShim_ZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim__import_pandasP38__pyx_obj_7pyarrow_3lib__PandasAPIShimi_ZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim__check_importP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP59__pyx_opt_args_7pyarrow_3lib_14_PandasAPIShim__check_import_ZL60__pyx_f_7pyarrow_3lib_14_PandasAPIShim__have_pandas_internalP38__pyx_obj_7pyarrow_3lib__PandasAPIShim_ZL50__pyx_f_7pyarrow_3lib_14_PandasAPIShim_infer_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL51__pyx_f_7pyarrow_3lib_14_PandasAPIShim_pandas_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_array_likeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_categoricalP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_datetimetzP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL39__pyx_type_7pyarrow_3lib__PandasAPIShim_ZL63__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_extension_array_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_sparseP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_data_frameP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_seriesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL47__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_indexP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL49__pyx_f_7pyarrow_3lib_14_PandasAPIShim_get_valuesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objecti_ZL44__pyx_vtable_7pyarrow_3lib_LoggingMemoryPool_ZL42__pyx_type_7pyarrow_3lib_LoggingMemoryPool_ZL42__pyx_vtable_7pyarrow_3lib_ProxyMemoryPool_ZL40__pyx_type_7pyarrow_3lib_ProxyMemoryPool_ZL36__pyx_vtable_7pyarrow_3lib_UnionType_ZL37__pyx_f_7pyarrow_3lib_9UnionType_initP33__pyx_obj_7pyarrow_3lib_UnionTypeRKSt10shared_ptrIN5arrow8DataTypeEE_ZL37__pyx_vtabptr_7pyarrow_3lib_UnionType_ZL38__pyx_f_7pyarrow_3lib_9UnionType_fieldP33__pyx_obj_7pyarrow_3lib_UnionTypeP7_objecti_ZL34__pyx_type_7pyarrow_3lib_UnionType_ZL42__pyx_doc_7pyarrow_3lib_9UnionType___len___ZL44__pyx_doc_7pyarrow_3lib_9UnionType_2__iter___ZL47__pyx_doc_7pyarrow_3lib_9UnionType_7__getitem___ZL42__pyx_vtable_7pyarrow_3lib_SparseUnionType_ZL40__pyx_type_7pyarrow_3lib_SparseUnionType_ZL43__pyx_vtabptr_7pyarrow_3lib_SparseUnionType_ZL41__pyx_vtable_7pyarrow_3lib_DenseUnionType_ZL39__pyx_type_7pyarrow_3lib_DenseUnionType_ZL42__pyx_vtabptr_7pyarrow_3lib_DenseUnionType_ZL47__pyx_vtable_7pyarrow_3lib_UnknownExtensionType_ZL45__pyx_type_7pyarrow_3lib_UnknownExtensionType_ZL48__pyx_vtabptr_7pyarrow_3lib_UnknownExtensionType_ZL48__pyx_type_7pyarrow_3lib__ExtensionRegistryNanny_ZL37__pyx_vtable_7pyarrow_3lib_NullScalar_ZL35__pyx_type_7pyarrow_3lib_NullScalar_ZL38__pyx_vtabptr_7pyarrow_3lib_NullScalar_ZL40__pyx_vtable_7pyarrow_3lib_BooleanScalar_ZL38__pyx_type_7pyarrow_3lib_BooleanScalar_ZL38__pyx_vtable_7pyarrow_3lib_UInt8Scalar_ZL36__pyx_type_7pyarrow_3lib_UInt8Scalar_ZL37__pyx_vtable_7pyarrow_3lib_Int8Scalar_ZL35__pyx_type_7pyarrow_3lib_Int8Scalar_ZL39__pyx_vtable_7pyarrow_3lib_UInt16Scalar_ZL37__pyx_type_7pyarrow_3lib_UInt16Scalar_ZL38__pyx_vtable_7pyarrow_3lib_Int16Scalar_ZL36__pyx_type_7pyarrow_3lib_Int16Scalar_ZL39__pyx_vtable_7pyarrow_3lib_UInt32Scalar_ZL37__pyx_type_7pyarrow_3lib_UInt32Scalar_ZL38__pyx_vtable_7pyarrow_3lib_Int32Scalar_ZL36__pyx_type_7pyarrow_3lib_Int32Scalar_ZL39__pyx_vtable_7pyarrow_3lib_UInt64Scalar_ZL37__pyx_type_7pyarrow_3lib_UInt64Scalar_ZL38__pyx_vtable_7pyarrow_3lib_Int64Scalar_ZL36__pyx_type_7pyarrow_3lib_Int64Scalar_ZL42__pyx_vtable_7pyarrow_3lib_HalfFloatScalar_ZL40__pyx_type_7pyarrow_3lib_HalfFloatScalar_ZL38__pyx_vtable_7pyarrow_3lib_FloatScalar_ZL36__pyx_type_7pyarrow_3lib_FloatScalar_ZL39__pyx_vtable_7pyarrow_3lib_DoubleScalar_ZL37__pyx_type_7pyarrow_3lib_DoubleScalar_ZL43__pyx_vtable_7pyarrow_3lib_Decimal128Scalar_ZL41__pyx_type_7pyarrow_3lib_Decimal128Scalar_ZL43__pyx_vtable_7pyarrow_3lib_Decimal256Scalar_ZL41__pyx_type_7pyarrow_3lib_Decimal256Scalar_ZL39__pyx_vtable_7pyarrow_3lib_Date32Scalar_ZL37__pyx_type_7pyarrow_3lib_Date32Scalar_ZL39__pyx_vtable_7pyarrow_3lib_Date64Scalar_ZL37__pyx_type_7pyarrow_3lib_Date64Scalar_ZL39__pyx_vtable_7pyarrow_3lib_Time32Scalar_ZL37__pyx_type_7pyarrow_3lib_Time32Scalar_ZL39__pyx_vtable_7pyarrow_3lib_Time64Scalar_ZL37__pyx_type_7pyarrow_3lib_Time64Scalar_ZL42__pyx_vtable_7pyarrow_3lib_TimestampScalar_ZL40__pyx_type_7pyarrow_3lib_TimestampScalar_ZL51__pyx_doc_7pyarrow_3lib_15TimestampScalar_2__repr___ZL41__pyx_vtable_7pyarrow_3lib_DurationScalar_ZL39__pyx_type_7pyarrow_3lib_DurationScalar_ZL53__pyx_vtable_7pyarrow_3lib_MonthDayNanoIntervalScalar_ZL51__pyx_type_7pyarrow_3lib_MonthDayNanoIntervalScalar_ZL39__pyx_vtable_7pyarrow_3lib_BinaryScalar_ZL37__pyx_type_7pyarrow_3lib_BinaryScalar_ZL44__pyx_vtable_7pyarrow_3lib_LargeBinaryScalar_ZL42__pyx_type_7pyarrow_3lib_LargeBinaryScalar_ZL48__pyx_vtable_7pyarrow_3lib_FixedSizeBinaryScalar_ZL46__pyx_type_7pyarrow_3lib_FixedSizeBinaryScalar_ZL39__pyx_vtable_7pyarrow_3lib_StringScalar_ZL37__pyx_type_7pyarrow_3lib_StringScalar_ZL44__pyx_vtable_7pyarrow_3lib_LargeStringScalar_ZL42__pyx_type_7pyarrow_3lib_LargeStringScalar_ZL43__pyx_vtable_7pyarrow_3lib_BinaryViewScalar_ZL41__pyx_type_7pyarrow_3lib_BinaryViewScalar_ZL43__pyx_vtable_7pyarrow_3lib_StringViewScalar_ZL41__pyx_type_7pyarrow_3lib_StringViewScalar_ZL37__pyx_vtable_7pyarrow_3lib_ListScalar_ZL35__pyx_type_7pyarrow_3lib_ListScalar_ZL44__pyx_doc_7pyarrow_3lib_10ListScalar___len___ZL49__pyx_doc_7pyarrow_3lib_10ListScalar_2__getitem___ZL46__pyx_doc_7pyarrow_3lib_10ListScalar_4__iter___ZL46__pyx_vtable_7pyarrow_3lib_FixedSizeListScalar_ZL44__pyx_type_7pyarrow_3lib_FixedSizeListScalar_ZL42__pyx_vtable_7pyarrow_3lib_LargeListScalar_ZL40__pyx_type_7pyarrow_3lib_LargeListScalar_ZL41__pyx_vtable_7pyarrow_3lib_ListViewScalar_ZL39__pyx_type_7pyarrow_3lib_ListViewScalar_ZL46__pyx_vtable_7pyarrow_3lib_LargeListViewScalar_ZL44__pyx_type_7pyarrow_3lib_LargeListViewScalar_ZL39__pyx_vtable_7pyarrow_3lib_StructScalar_ZL34__pyx_type_7pyarrow_3lib_MapScalar_ZL36__pyx_vtable_7pyarrow_3lib_MapScalar_ZL46__pyx_doc_7pyarrow_3lib_9MapScalar___getitem___ZL44__pyx_doc_7pyarrow_3lib_9MapScalar_2__iter___ZL43__pyx_vtable_7pyarrow_3lib_DictionaryScalar_ZL41__pyx_type_7pyarrow_3lib_DictionaryScalar_ZL46__pyx_vtable_7pyarrow_3lib_RunEndEncodedScalar_ZL44__pyx_type_7pyarrow_3lib_RunEndEncodedScalar_ZL38__pyx_vtable_7pyarrow_3lib_UnionScalar_ZL36__pyx_type_7pyarrow_3lib_UnionScalar_ZL42__pyx_vtable_7pyarrow_3lib_ExtensionScalar_ZL40__pyx_type_7pyarrow_3lib_ExtensionScalar_ZL49__pyx_vtable_7pyarrow_3lib_FixedShapeTensorScalar_ZL47__pyx_type_7pyarrow_3lib_FixedShapeTensorScalar_ZL38__pyx_vtable_7pyarrow_3lib_Date32Array_ZL36__pyx_type_7pyarrow_3lib_Date32Array_ZL38__pyx_vtable_7pyarrow_3lib_Date64Array_ZL36__pyx_type_7pyarrow_3lib_Date64Array_ZL41__pyx_vtable_7pyarrow_3lib_TimestampArray_ZL39__pyx_type_7pyarrow_3lib_TimestampArray_ZL38__pyx_vtable_7pyarrow_3lib_Time32Array_ZL36__pyx_type_7pyarrow_3lib_Time32Array_ZL38__pyx_vtable_7pyarrow_3lib_Time64Array_ZL36__pyx_type_7pyarrow_3lib_Time64Array_ZL40__pyx_vtable_7pyarrow_3lib_DurationArray_ZL38__pyx_type_7pyarrow_3lib_DurationArray_ZL43__pyx_vtable_7pyarrow_3lib_LargeStringArray_ZL41__pyx_type_7pyarrow_3lib_LargeStringArray_ZL43__pyx_vtable_7pyarrow_3lib_LargeBinaryArray_ZL41__pyx_type_7pyarrow_3lib_LargeBinaryArray_ZL45__pyx_vtable_7pyarrow_3lib_RunEndEncodedArray_ZL43__pyx_type_7pyarrow_3lib_RunEndEncodedArray_ZL48__pyx_vtable_7pyarrow_3lib_FixedShapeTensorArray_ZL46__pyx_type_7pyarrow_3lib_FixedShapeTensorArray_ZL38__pyx_type_7pyarrow_3lib_StringBuilder_ZL42__pyx_type_7pyarrow_3lib_StringViewBuilder_ZL37__pyx_vtable_7pyarrow_3lib_PythonFile_ZL38__pyx_vtabptr_7pyarrow_3lib_PythonFile_ZL35__pyx_type_7pyarrow_3lib_PythonFile_ZL43__pyx_vtable_7pyarrow_3lib_MemoryMappedFile_ZL44__pyx_vtabptr_7pyarrow_3lib_MemoryMappedFile_ZL41__pyx_type_7pyarrow_3lib_MemoryMappedFile_ZL33__pyx_vtable_7pyarrow_3lib_OSFile_ZL44__pyx_f_7pyarrow_3lib_6OSFile__open_readableP30__pyx_obj_7pyarrow_3lib_OSFileNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPN5arrow10MemoryPoolE_ZL34__pyx_vtabptr_7pyarrow_3lib_OSFile_ZL44__pyx_f_7pyarrow_3lib_6OSFile__open_writableP30__pyx_obj_7pyarrow_3lib_OSFileNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEP51__pyx_opt_args_7pyarrow_3lib_6OSFile__open_writable_ZL31__pyx_type_7pyarrow_3lib_OSFile_ZL48__pyx_vtable_7pyarrow_3lib_FixedSizeBufferWriter_ZL49__pyx_vtabptr_7pyarrow_3lib_FixedSizeBufferWriter_ZL46__pyx_type_7pyarrow_3lib_FixedSizeBufferWriter_ZL45__pyx_vtable_7pyarrow_3lib_BufferOutputStream_ZL46__pyx_vtabptr_7pyarrow_3lib_BufferOutputStream_ZL43__pyx_type_7pyarrow_3lib_BufferOutputStream_ZL43__pyx_vtable_7pyarrow_3lib_MockOutputStream_ZL44__pyx_vtabptr_7pyarrow_3lib_MockOutputStream_ZL41__pyx_type_7pyarrow_3lib_MockOutputStream_ZL39__pyx_vtable_7pyarrow_3lib_BufferReader_ZL40__pyx_vtabptr_7pyarrow_3lib_BufferReader_ZL37__pyx_type_7pyarrow_3lib_BufferReader_ZL47__pyx_vtable_7pyarrow_3lib_TransformInputStream_ZL48__pyx_vtabptr_7pyarrow_3lib_TransformInputStream_ZL56__pyx_f_7pyarrow_3lib_20TransformInputStream_make_nativeSt10shared_ptrIN5arrow2io11InputStreamEEP7_object_ZL45__pyx_type_7pyarrow_3lib_TransformInputStream_ZL38__pyx_type_7pyarrow_3lib_MessageReader_ZL49__pyx_type_7pyarrow_3lib__RecordBatchStreamWriter_ZL49__pyx_type_7pyarrow_3lib__RecordBatchStreamReader_ZL47__pyx_type_7pyarrow_3lib__RecordBatchFileWriter_ZL47__pyx_type_7pyarrow_3lib__RecordBatchFileReader_ZL53__pyx_type_7pyarrow_3lib___pyx_scope_struct____iter___ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_1___iter___ZL53__pyx_type_7pyarrow_3lib___pyx_scope_struct_2_genexpr_ZL50__pyx_type_7pyarrow_3lib___pyx_scope_struct_3_keys_ZL52__pyx_type_7pyarrow_3lib___pyx_scope_struct_4_values_ZL51__pyx_type_7pyarrow_3lib___pyx_scope_struct_5_items_ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_6___iter___ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_7___iter___ZL51__pyx_type_7pyarrow_3lib___pyx_scope_struct_8_items_ZL53__pyx_type_7pyarrow_3lib___pyx_scope_struct_9_genexpr_ZL55__pyx_type_7pyarrow_3lib___pyx_scope_struct_10___iter___ZL55__pyx_type_7pyarrow_3lib___pyx_scope_struct_11___iter___ZL55__pyx_type_7pyarrow_3lib___pyx_scope_struct_12___iter___ZL57__pyx_type_7pyarrow_3lib___pyx_scope_struct_13_iterchunks_ZL58__pyx_type_7pyarrow_3lib___pyx_scope_struct_14_itercolumns_ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_15_genexpr_ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_16_genexpr_ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_17_genexpr_ZL54__pyx_type_7pyarrow_3lib___pyx_scope_struct_18_genexpr_ZL55__pyx_type_7pyarrow_3lib___pyx_scope_struct_19_download_ZL53__pyx_type_7pyarrow_3lib___pyx_scope_struct_20_upload_ZL80__pyx_type_7pyarrow_3lib___pyx_scope_struct_21_iter_batches_with_custom_metadata_ZL14__Pyx_EnumMeta_ZL18__Pyx_PyInt_As_intP7_object_ZL22__Pyx_PyInt_As_int32_tP7_object_ZL20__Pyx_GetBuiltinNameP7_object_ZL26__Pyx__GetModuleGlobalNameP7_objectPmPS0__ZL21__Pyx__GetNameInClassP7_objectS0__ZL24__Pyx_InitCachedBuiltinsv_ZL25__pyx_builtin_ImportError_ZL24__pyx_builtin_ValueError_ZL25__pyx_builtin_MemoryError_ZL22__pyx_builtin_KeyError_ZL23__pyx_builtin_TypeError_ZL33__pyx_builtin_NotImplementedError_ZL24__pyx_builtin_IndexError_ZL21__pyx_builtin_IOError_ZL26__pyx_builtin_staticmethod_ZL19__pyx_builtin_super_ZL27__pyx_builtin_BaseException_ZL28__pyx_builtin_AttributeError_ZL28__pyx_builtin_AssertionError_ZL28__pyx_builtin_NotImplemented_ZL19__pyx_builtin_range_ZL17__pyx_builtin_zip_ZL26__pyx_builtin_RuntimeError_ZL20__pyx_builtin_object_ZL17__pyx_builtin_any_ZL27__pyx_builtin_StopIteration_ZL18__pyx_builtin_open_ZL17__pyx_builtin_hex_ZL25__pyx_builtin_BufferError_ZL22__pyx_builtin_EOFError_ZL52__Pyx_PyInt_As_enum____arrow_3a__3a_Type_3a__3a_typeP7_object_ZL58__Pyx_PyInt_As_enum____pyx_t_7pyarrow_3lib_MetadataVersionP7_object_ZL56__Pyx_PyInt_As_enum____arrow_3a__3a_TimeUnit_3a__3a_typeP7_object_ZL22__Pyx_PyUnicode_EqualsP7_objectS0_i_ZL25__Pyx_GetKwValue_FASTCALLP7_objectPKS0_S0__ZL33__Pyx_CyFunction_get_is_coroutineP22__pyx_CyFunctionObjectPv_ZL16__Pyx_IterFinishv_ZL19__Pyx__GetExceptionP3_tsPP7_objectS3_S3__ZL37__Pyx_Generator_Replace_StopIterationi.constprop.0_ZL21__Pyx_GetItemInt_FastP7_objectliii.constprop.0_ZL27__Pyx_ParseOptionalKeywordsP7_objectPKS0_PPS0_S0_S3_lPKc_ZL28__Pyx_IternextUnpackEndCheckP7_objectl_ZL20__Pyx_dict_iter_nextP7_objectlPlPS0_S2_S2_i.part.0.constprop.0_ZL20__Pyx_dict_iter_nextP7_objectlPlPS0_S2_S2_i.constprop.0_ZL23__Pyx_PyObject_GetIndexP7_objectS0__ZL43__pyx_pw_7pyarrow_3lib_8DataType_1__cinit__P7_objectS0_S0_.constprop.0_ZL35__pyx_tp_new_7pyarrow_3lib_DataTypeP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_DictionaryTypeP11_typeobjectP7_objectS2__ZL35__pyx_tp_new_7pyarrow_3lib_ListTypeP11_typeobjectP7_objectS2__ZL40__pyx_tp_new_7pyarrow_3lib_LargeListTypeP11_typeobjectP7_objectS2__ZL39__pyx_tp_new_7pyarrow_3lib_ListViewTypeP11_typeobjectP7_objectS2__ZL44__pyx_tp_new_7pyarrow_3lib_LargeListViewTypeP11_typeobjectP7_objectS2__ZL34__pyx_tp_new_7pyarrow_3lib_MapTypeP11_typeobjectP7_objectS2__ZL44__pyx_tp_new_7pyarrow_3lib_FixedSizeListTypeP11_typeobjectP7_objectS2__ZL37__pyx_tp_new_7pyarrow_3lib_StructTypeP11_typeobjectP7_objectS2__ZL36__pyx_tp_new_7pyarrow_3lib_UnionTypeP11_typeobjectP7_objectS2__ZL37__pyx_tp_new_7pyarrow_3lib_Time32TypeP11_typeobjectP7_objectS2__ZL37__pyx_tp_new_7pyarrow_3lib_Time64TypeP11_typeobjectP7_objectS2__ZL40__pyx_tp_new_7pyarrow_3lib_TimestampTypeP11_typeobjectP7_objectS2__ZL39__pyx_tp_new_7pyarrow_3lib_DurationTypeP11_typeobjectP7_objectS2__ZL46__pyx_tp_new_7pyarrow_3lib_FixedSizeBinaryTypeP11_typeobjectP7_objectS2__ZL44__pyx_tp_new_7pyarrow_3lib_RunEndEncodedTypeP11_typeobjectP7_objectS2__ZL44__pyx_tp_new_7pyarrow_3lib_BaseExtensionTypeP11_typeobjectP7_objectS2__ZL42__pyx_tp_new_7pyarrow_3lib_SparseUnionTypeP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_DenseUnionTypeP11_typeobjectP7_objectS2__ZL47__pyx_tp_new_7pyarrow_3lib_FixedShapeTensorTypeP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_Decimal256TypeP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_Decimal128TypeP11_typeobjectP7_objectS2__ZL51__pyx_tp_new_7pyarrow_3lib__RecordBatchStreamWriterP11_typeobjectP7_objectS2__ZL32__pyx_tp_new_7pyarrow_3lib_FieldP11_typeobjectP7_objectS2__ZL55__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_9__enter__P7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_11__enter__P7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_17RecordBatchReader_16__enter__P7_objectPKS0_lS0__ZL36__Pyx_PyGen__FetchStopIterationValueP3_tsPP7_object.constprop.0.isra.0_ZL42__pyx_pw_7pyarrow_3lib_10NullScalar_5as_pyP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_15DictionaryArray_1dictionary_encodeP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_5__enter__P7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10NullScalar_3__init__P7_objectS0_S0__ZL11__Pyx_RaiseP7_objectS0_S0_S0__ZL46__pyx_f_7pyarrow_3lib_dlpack_pycapsule_deleterP7_object_ZL63__pyx_pw_7pyarrow_3lib_13ExtensionType_9__arrow_ext_serialize__P7_objectPKS0_lS0__ZL70__pyx_pw_7pyarrow_3lib_17BaseExtensionType_3__arrow_ext_scalar_class__P7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_13ExtensionType_17__arrow_ext_scalar_class__P7_objectPKS0_lS0__ZL73__pyx_pw_7pyarrow_3lib_20FixedShapeTensorType_5__arrow_ext_scalar_class__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_20FixedShapeTensorType_1__arrow_ext_class__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_13ExtensionType_15__arrow_ext_class__P7_objectPKS0_lS0__ZL63__pyx_pw_7pyarrow_3lib_17BaseExtensionType_1__arrow_ext_class__P7_objectPKS0_lS0__ZL18__Pyx_AddTracebackPKciiS0_.constprop.0_ZL16__pyx_code_cache_ZL33__pyx_pw_7pyarrow_3lib_1cpu_countP7_objectS0__ZL44__pyx_pw_7pyarrow_3lib_23default_memory_poolP7_objectS0__ZL43__pyx_pw_7pyarrow_3lib_29system_memory_poolP7_objectS0__ZL46__pyx_pw_7pyarrow_3lib_39total_allocated_bytesP7_objectS0__ZL39__pyx_pw_7pyarrow_3lib_5Field_3__init__P7_objectS0_S0__ZL40__pyx_pw_7pyarrow_3lib_6Schema_3__init__P7_objectS0_S0__ZL47__pyx_pw_7pyarrow_3lib_12ChunkedArray_3__init__P7_objectS0_S0__ZL49__pyx_getprop_7pyarrow_3lib_8_Tabular_num_columnsP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_8_Tabular_num_rowsP7_objectPv_ZL44__pyx_getprop_7pyarrow_3lib_8_Tabular_schemaP7_objectPv_ZL40__pyx_pw_7pyarrow_3lib_6Tensor_1__init__P7_objectS0_S0__ZL50__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_1__init__P7_objectS0_S0__ZL50__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_1__init__P7_objectS0_S0__ZL50__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_1__init__P7_objectS0_S0__ZL50__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_1__init__P7_objectS0_S0__ZL41__pyx_pw_7pyarrow_3lib_215io_thread_countP7_objectS0__ZL40__pyx_pw_7pyarrow_3lib_6Buffer_3__init__P7_objectS0_S0__ZL58__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_13__exit__P7_objectPKS0_lS0__ZL39__pyx_tp_new_7pyarrow_3lib_ChunkedArrayP11_typeobjectP7_objectS2__Z26pyarrow_wrap_chunked_arrayRKSt10shared_ptrIN5arrow12ChunkedArrayEE.localalias_ZL39__pyx_pw_7pyarrow_3lib_6Tensor_11equalsP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_23equalsP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_7Message_5equalsP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_17StringViewBuilder_9__len__P7_object_ZL58__pyx_getprop_7pyarrow_3lib_17StringViewBuilder_null_countP7_objectPv_ZL47__pyx_pw_7pyarrow_3lib_13StringBuilder_9__len__P7_object_ZL54__pyx_getprop_7pyarrow_3lib_13StringBuilder_null_countP7_objectPv_ZL49__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_19equalsP7_objectPKS0_lS0__Z19pyarrow_wrap_schemaRKSt10shared_ptrIN5arrow6SchemaEE.localalias_ZL49__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_19equalsP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_14IpcReadOptions_1__init__P7_objectS0_S0__ZL59__pyx_convert_string_from_py_6libcpp_6string_std__in_stringP7_object_ZL59__pyx_convert_string_from_py_6libcpp_6string_std__in_stringP7_object.cold_ZL49__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_15equalsP7_objectPKS0_lS0__Z18pyarrow_wrap_fieldRKSt10shared_ptrIN5arrow5FieldEE.part.0_Z18pyarrow_wrap_fieldRKSt10shared_ptrIN5arrow5FieldEE.localalias_ZL50__pyx_pw_7pyarrow_3lib_10NativeFile_23_assert_openP7_objectPKS0_lS0__Z22pyarrow_wrap_data_typeRKSt10shared_ptrIN5arrow8DataTypeEE.localalias_ZL48__pyx_getprop_7pyarrow_3lib_8ListType_value_typeP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_13LargeListType_value_typeP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_12ListViewType_value_typeP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_17LargeListViewType_value_typeP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_17FixedSizeListType_value_typeP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_14DictionaryType_index_typeP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_14DictionaryType_value_typeP7_objectPv_ZL60__pyx_getprop_7pyarrow_3lib_17RunEndEncodedType_run_end_typeP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_17RunEndEncodedType_value_typeP7_objectPv_ZL60__pyx_getprop_7pyarrow_3lib_17BaseExtensionType_storage_typeP7_objectPv_ZL40__pyx_getprop_7pyarrow_3lib_6Scalar_typeP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_12ChunkedArray_typeP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_8DataType_num_buffersP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_8DataType_num_buffersP7_objectPv.cold_ZL49__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_3equalsP7_objectPKS0_lS0__ZL61__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_non_zero_lengthP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_non_zero_lengthP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_non_zero_lengthP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_non_zero_lengthP7_objectPv_ZL47__pyx_pw_7pyarrow_3lib_12StructScalar_3__iter__P7_object_ZL49__pyx_gb_7pyarrow_3lib_12StructScalar_4generator6P21__pyx_CoroutineObjectP3_tsP7_object_ZL41__pyx_pw_7pyarrow_3lib_6Scalar_15__hash__P7_object_ZL46__pyx_getprop_7pyarrow_3lib_5Table_num_columnsP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_5Array_offsetP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_11BinaryArray_total_values_lengthP7_objectPv_ZL66__pyx_getprop_7pyarrow_3lib_16LargeBinaryArray_total_values_lengthP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_6Tensor_is_mutableP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_is_mutableP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_is_mutableP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_is_mutableP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_is_mutableP7_objectPv_Z29pyarrow_wrap_resizable_bufferRKSt10shared_ptrIN5arrow15ResizableBufferEE.localalias_ZL57__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_allow_64bitP7_objectPv_ZL57__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_allow_64bitP7_objectS0_Pv_ZL63__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_use_legacy_formatP7_objectPv_ZL63__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_use_legacy_formatP7_objectS0_Pv_ZL57__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_use_threadsP7_objectPv_ZL57__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_use_threadsP7_objectS0_Pv_ZL68__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_emit_dictionary_deltasP7_objectPv_ZL68__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_emit_dictionary_deltasP7_objectS0_Pv_ZL64__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_unify_dictionariesP7_objectPv_ZL64__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_unify_dictionariesP7_objectS0_Pv_ZL65__pyx_getprop_7pyarrow_3lib_14IpcReadOptions_ensure_native_endianP7_objectPv_ZL65__pyx_setprop_7pyarrow_3lib_14IpcReadOptions_ensure_native_endianP7_objectS0_Pv_ZL56__pyx_getprop_7pyarrow_3lib_14IpcReadOptions_use_threadsP7_objectPv_ZL56__pyx_setprop_7pyarrow_3lib_14IpcReadOptions_use_threadsP7_objectS0_Pv_ZL42__pyx_pw_7pyarrow_3lib_8DataType_9__hash__P7_object_ZL40__pyx_getprop_7pyarrow_3lib_8DataType_idP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_8DataType_bit_widthP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_8DataType_num_fieldsP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_8ListType_value_fieldP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_13LargeListType_value_fieldP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_12ListViewType_value_fieldP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_17LargeListViewType_value_fieldP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_7MapType_keys_sortedP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_17FixedSizeListType_value_fieldP7_objectPv_ZL57__pyx_getprop_7pyarrow_3lib_17FixedSizeListType_list_sizeP7_objectPv_ZL44__pyx_pw_7pyarrow_3lib_10StructType_7__len__P7_object_ZL49__pyx_pw_7pyarrow_3lib_10StructType_12__getitem__P7_objectS0__ZL47__pyx_gb_7pyarrow_3lib_10StructType_10generatorP21__pyx_CoroutineObjectP3_tsP7_object_ZL45__pyx_pw_7pyarrow_3lib_10StructType_9__iter__P7_object_ZL52__pyx_getprop_7pyarrow_3lib_14DictionaryType_orderedP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_13TimestampType_unitP7_objectPv_ZL45__pyx_getprop_7pyarrow_3lib_10Time32Type_unitP7_objectPv_ZL45__pyx_getprop_7pyarrow_3lib_10Time64Type_unitP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_12DurationType_unitP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_14Decimal128Type_precisionP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_14Decimal128Type_scaleP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_14Decimal256Type_precisionP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_14Decimal256Type_scaleP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_5Field_nullableP7_objectPv_ZL39__pyx_getprop_7pyarrow_3lib_5Field_typeP7_objectPv_ZL39__pyx_pw_7pyarrow_3lib_6Schema_5__len__P7_object_ZL41__pyx_pw_7pyarrow_3lib_6Schema_14__hash__P7_object_ZL43__pyx_gb_7pyarrow_3lib_6Schema_10generator5P21__pyx_CoroutineObjectP3_tsP7_object_ZL40__pyx_pw_7pyarrow_3lib_6Schema_9__iter__P7_object_ZL44__pyx_getprop_7pyarrow_3lib_6Scalar_is_validP7_objectPv_ZL39__pyx_pw_7pyarrow_3lib_5Array_44__len__P7_object_ZL42__pyx_gb_7pyarrow_3lib_5Array_30generator8P21__pyx_CoroutineObjectP3_tsP7_object_ZL40__pyx_pw_7pyarrow_3lib_5Array_29__iter__P7_object_ZL45__pyx_getprop_7pyarrow_3lib_5Array_null_countP7_objectPv_ZL39__pyx_getprop_7pyarrow_3lib_5Array_typeP7_objectPv_ZL40__pyx_getprop_7pyarrow_3lib_5Array__nameP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_6Tensor_is_contiguousP7_objectPv_ZL40__pyx_getprop_7pyarrow_3lib_6Tensor_ndimP7_objectPv_ZL40__pyx_getprop_7pyarrow_3lib_6Tensor_sizeP7_objectPv_ZL40__pyx_getprop_7pyarrow_3lib_6Tensor_typeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_6Tensor__ssize_t_shapeP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_6Tensor__ssize_t_stridesP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_ndimP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_sizeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_typeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_ndimP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_sizeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_typeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_ndimP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_sizeP7_objectPv_ZL66__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_has_canonical_formatP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_typeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_ndimP7_objectPv_ZL34__pyx_convert_vector_to_py_int64_tRKSt6vectorIlSaIlEE_ZL51__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_shapeP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_shapeP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_shapeP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_shapeP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_6Tensor_stridesP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_6Tensor_shapeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_sizeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_typeP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_12BooleanArray_false_countP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_12BooleanArray_true_countP7_objectPv_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_25__iter__P7_object_ZL50__pyx_gb_7pyarrow_3lib_12ChunkedArray_26generator9P21__pyx_CoroutineObjectP3_tsP7_object_ZL53__pyx_getprop_7pyarrow_3lib_12ChunkedArray_null_countP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_12ChunkedArray_num_chunksP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_12ChunkedArray__nameP7_objectPv_ZL42__pyx_pw_7pyarrow_3lib_8_Tabular_11__len__P7_object_Z19pyarrow_wrap_bufferRKSt10shared_ptrIN5arrow6BufferEE.localalias_ZL43__pyx_getprop_7pyarrow_3lib_8_Tabular_shapeP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_5Table_schemaP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_5Table_num_rowsP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_11RecordBatch_num_columnsP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_11RecordBatch_num_rowsP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_11RecordBatch_schemaP7_objectPv_ZL39__pyx_pw_7pyarrow_3lib_6Buffer_5__len__P7_object_ZL46__pyx_pw_7pyarrow_3lib_6Buffer_23__getbuffer__P7_objectP9Py_bufferi_ZL40__pyx_getprop_7pyarrow_3lib_6Buffer_sizeP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_6Buffer_addressP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_6Buffer_is_mutableP7_objectPv_ZL42__pyx_getprop_7pyarrow_3lib_6Buffer_is_cpuP7_objectPv_ZL45__pyx_pf_7pyarrow_3lib_10NativeFile_8__repr__P34__pyx_obj_7pyarrow_3lib_NativeFile_ZL45__pyx_pw_7pyarrow_3lib_10NativeFile_9__repr__P7_object_ZL65__pyx_specialmethod___pyx_pw_7pyarrow_3lib_10NativeFile_9__repr__P7_objectS0__ZL47__pyx_getprop_7pyarrow_3lib_10NativeFile_closedP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_12CacheOptions_hole_size_limitP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_12CacheOptions_range_size_limitP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_12CacheOptions_lazyP7_objectPv_ZL47__pyx_setprop_7pyarrow_3lib_12CacheOptions_lazyP7_objectS0_Pv_ZL57__pyx_getprop_7pyarrow_3lib_12CacheOptions_prefetch_limitP7_objectPv_ZL47__pyx_pw_7pyarrow_3lib_12CacheOptions_1__init__P7_objectS0_S0__ZL40__pyx_pf_7pyarrow_3lib_5Codec_18__repr__P29__pyx_obj_7pyarrow_3lib_Codec_ZL40__pyx_pw_7pyarrow_3lib_5Codec_19__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_5Codec_19__repr__P7_objectS0__ZL52__pyx_getprop_7pyarrow_3lib_5Codec_compression_levelP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_17SignalStopHandler_stop_tokenP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__loose_versionP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__versionP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__pdP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__types_apiP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__compat_moduleP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__data_frameP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__indexP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__seriesP7_objectPv_ZL62__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__categorical_typeP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__datetimetz_typeP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__extension_arrayP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__extension_dtypeP7_objectPv_ZL62__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__array_like_typesP7_objectPv_ZL70__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__is_extension_array_dtypeP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__lockP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_has_sparseP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__pd024P7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__is_v1P7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__is_ge_v21P7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim__is_ge_v3P7_objectPv_ZL42__pyx_pw_7pyarrow_3lib_9UnionType_1__len__P7_object_ZL46__pyx_pw_7pyarrow_3lib_9UnionType_8__getitem__P7_objectS0__ZL45__pyx_gb_7pyarrow_3lib_9UnionType_4generator1P21__pyx_CoroutineObjectP3_tsP7_object_ZL43__pyx_pw_7pyarrow_3lib_9UnionType_3__iter__P7_object_ZL43__pyx_getprop_7pyarrow_3lib_9UnionType_modeP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_9UnionType_type_codesP7_objectPv_ZL40__pyx_tp_new_7pyarrow_3lib_ExtensionTypeP11_typeobjectP7_objectS2__ZL42__pyx_tp_new_7pyarrow_3lib_PyExtensionTypeP11_typeobjectP7_objectS2__ZL47__pyx_tp_new_7pyarrow_3lib_UnknownExtensionTypeP11_typeobjectP7_objectS2__ZL48__pyx_getprop_7pyarrow_3lib_12Date32Scalar_valueP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_12Date64Scalar_valueP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_12Time32Scalar_valueP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_12Time64Scalar_valueP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_15TimestampScalar_valueP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_14DurationScalar_valueP7_objectPv_ZL44__pyx_pw_7pyarrow_3lib_10ListScalar_1__len__P7_object_ZL45__pyx_pw_7pyarrow_3lib_10ListScalar_5__iter__P7_object_ZL43__pyx_pw_7pyarrow_3lib_9MapScalar_3__iter__P7_object_ZL45__pyx_gb_7pyarrow_3lib_9MapScalar_4generator7P21__pyx_CoroutineObjectP3_tsP7_object_ZL51__pyx_getprop_7pyarrow_3lib_11UnionScalar_type_codeP7_objectPv_ZL47__pyx_pw_7pyarrow_3lib_12BufferReader_1__init__P7_objectS0_S0__ZL37__pyx_tp_new_7pyarrow_3lib_NativeFileP11_typeobjectP7_objectS2__ZL47__pyx_tp_new_7pyarrow_3lib_TransformInputStreamP11_typeobjectP7_objectS2__ZL43__pyx_tp_new_7pyarrow_3lib_MemoryMappedFileP11_typeobjectP7_objectS2__ZL49__pyx_tp_new_7pyarrow_3lib_CompressedOutputStreamP11_typeobjectP7_objectS2__ZL48__pyx_tp_new_7pyarrow_3lib_CompressedInputStreamP11_typeobjectP7_objectS2__ZL47__pyx_tp_new_7pyarrow_3lib_BufferedOutputStreamP11_typeobjectP7_objectS2__ZL46__pyx_tp_new_7pyarrow_3lib_BufferedInputStreamP11_typeobjectP7_objectS2__ZL73__pyx_getprop_7pyarrow_3lib_24_RecordBatchStreamWriter__use_legacy_formatP7_objectPv_ZL71__pyx_getprop_7pyarrow_3lib_22_RecordBatchFileReader_num_record_batchesP7_objectPv_ZL59__pyx_getprop_7pyarrow_3lib_22_RecordBatchFileReader_schemaP7_objectPv_ZL54__pyx_pw_8EnumBase_14__Pyx_EnumMeta_7__reduce_cython__P7_objectPKS0_lS0__ZZL54__pyx_pf_8EnumBase_14__Pyx_EnumMeta_6__reduce_cython__P24__pyx_obj___Pyx_EnumMetaE18__pyx_dict_version_ZZL54__pyx_pf_8EnumBase_14__Pyx_EnumMeta_6__reduce_cython__P24__pyx_obj___Pyx_EnumMetaE23__pyx_dict_cached_value_ZZL54__pyx_pf_8EnumBase_14__Pyx_EnumMeta_6__reduce_cython__P24__pyx_obj___Pyx_EnumMetaE18__pyx_dict_version_0_ZZL54__pyx_pf_8EnumBase_14__Pyx_EnumMeta_6__reduce_cython__P24__pyx_obj___Pyx_EnumMetaE23__pyx_dict_cached_value_0_ZL45__pyx_pw_8EnumBase_14__Pyx_EnumBase_3__repr__P7_objectPKS0_lS0__ZL44__pyx_pw_8EnumBase_14__Pyx_EnumBase_5__str__P7_objectPKS0_lS0__ZL45__pyx_pw_8EnumBase_14__Pyx_FlagBase_3__repr__P7_objectPKS0_lS0__ZL44__pyx_pw_8EnumBase_14__Pyx_FlagBase_5__str__P7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_21FixedSizeBufferWriter_3set_memcopy_threadsP7_objectPKS0_lS0__ZL38__pyx_tp_new_7pyarrow_3lib_RecordBatchP11_typeobjectP7_objectS2__Z18pyarrow_wrap_batchRKSt10shared_ptrIN5arrow11RecordBatchEE.localalias_ZL39__pyx_pw_7pyarrow_3lib_6Buffer_15equalsP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_10MemoryPool_5bytes_allocatedP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_10MemoryPool_7max_memoryP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_6Buffer_21to_pybytesP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_16MockOutputStream_3sizeP7_objectPKS0_lS0__ZL77__pyx_pw_7pyarrow_3lib_17RecordBatchReader_9iter_batches_with_custom_metadataP7_objectPKS0_lS0__ZL56__pyx_gb_7pyarrow_3lib_17RecordBatchReader_10generator12P21__pyx_CoroutineObjectP3_tsP7_object_ZL38__pyx_pw_7pyarrow_3lib_45_is_primitiveP7_objectPKS0_lS0__ZL36__pyx_f_7pyarrow_3lib_alloc_c_streamPP16ArrowArrayStream_ZL45__pyx_pw_7pyarrow_3lib_8DataType_11__reduce__P7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_8DataType_10__reduce__P32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_8DataType_10__reduce__P32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_ZL32__pyx_tp_new_7pyarrow_3lib_TableP11_typeobjectP7_objectS2__Z18pyarrow_wrap_tableRKSt10shared_ptrIN5arrow5TableEE.localalias_ZL51__pyx_pw_7pyarrow_3lib_14DictionaryType_1__reduce__P7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_14DictionaryType___reduce__P38__pyx_obj_7pyarrow_3lib_DictionaryTypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_14DictionaryType___reduce__P38__pyx_obj_7pyarrow_3lib_DictionaryTypeE23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_8ListType_1__reduce__P7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_8ListType___reduce__P32__pyx_obj_7pyarrow_3lib_ListTypeE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_8ListType___reduce__P32__pyx_obj_7pyarrow_3lib_ListTypeE23__pyx_dict_cached_value_ZL50__pyx_pw_7pyarrow_3lib_13LargeListType_1__reduce__P7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_13LargeListType___reduce__P37__pyx_obj_7pyarrow_3lib_LargeListTypeE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_13LargeListType___reduce__P37__pyx_obj_7pyarrow_3lib_LargeListTypeE23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_12ListViewType_1__reduce__P7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_12ListViewType___reduce__P36__pyx_obj_7pyarrow_3lib_ListViewTypeE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_12ListViewType___reduce__P36__pyx_obj_7pyarrow_3lib_ListViewTypeE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_17LargeListViewType_1__reduce__P7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_17LargeListViewType___reduce__P41__pyx_obj_7pyarrow_3lib_LargeListViewTypeE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_17LargeListViewType___reduce__P41__pyx_obj_7pyarrow_3lib_LargeListViewTypeE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_7MapType_1__reduce__P7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_7MapType___reduce__P31__pyx_obj_7pyarrow_3lib_MapTypeE18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_7MapType___reduce__P31__pyx_obj_7pyarrow_3lib_MapTypeE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_17FixedSizeListType_1__reduce__P7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_17FixedSizeListType___reduce__P41__pyx_obj_7pyarrow_3lib_FixedSizeListTypeE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_17FixedSizeListType___reduce__P41__pyx_obj_7pyarrow_3lib_FixedSizeListTypeE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_10StructType_14__reduce__P7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_10StructType_13__reduce__P34__pyx_obj_7pyarrow_3lib_StructTypeE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_10StructType_13__reduce__P34__pyx_obj_7pyarrow_3lib_StructTypeE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_9UnionType_10__reduce__P7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_9UnionType_9__reduce__P33__pyx_obj_7pyarrow_3lib_UnionTypeE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_9UnionType_9__reduce__P33__pyx_obj_7pyarrow_3lib_UnionTypeE23__pyx_dict_cached_value_ZL50__pyx_pw_7pyarrow_3lib_13TimestampType_1__reduce__P7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_13TimestampType___reduce__P37__pyx_obj_7pyarrow_3lib_TimestampTypeE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_13TimestampType___reduce__P37__pyx_obj_7pyarrow_3lib_TimestampTypeE23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_19FixedSizeBinaryType_1__reduce__P7_objectPKS0_lS0__ZZL55__pyx_pf_7pyarrow_3lib_19FixedSizeBinaryType___reduce__P43__pyx_obj_7pyarrow_3lib_FixedSizeBinaryTypeE18__pyx_dict_version_ZZL55__pyx_pf_7pyarrow_3lib_19FixedSizeBinaryType___reduce__P43__pyx_obj_7pyarrow_3lib_FixedSizeBinaryTypeE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_14Decimal128Type_1__reduce__P7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_14Decimal128Type___reduce__P38__pyx_obj_7pyarrow_3lib_Decimal128TypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_14Decimal128Type___reduce__P38__pyx_obj_7pyarrow_3lib_Decimal128TypeE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_14Decimal256Type_1__reduce__P7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_14Decimal256Type___reduce__P38__pyx_obj_7pyarrow_3lib_Decimal256TypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_14Decimal256Type___reduce__P38__pyx_obj_7pyarrow_3lib_Decimal256TypeE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_17RunEndEncodedType_1__reduce__P7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_17RunEndEncodedType___reduce__P41__pyx_obj_7pyarrow_3lib_RunEndEncodedTypeE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_17RunEndEncodedType___reduce__P41__pyx_obj_7pyarrow_3lib_RunEndEncodedTypeE23__pyx_dict_cached_value_ZL66__pyx_pw_7pyarrow_3lib_13ExtensionType_11__arrow_ext_deserialize__P7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_20FixedShapeTensorType_3__reduce__P7_objectPKS0_lS0__ZZL57__pyx_pf_7pyarrow_3lib_20FixedShapeTensorType_2__reduce__P44__pyx_obj_7pyarrow_3lib_FixedShapeTensorTypeE18__pyx_dict_version_ZZL57__pyx_pf_7pyarrow_3lib_20FixedShapeTensorType_2__reduce__P44__pyx_obj_7pyarrow_3lib_FixedShapeTensorTypeE23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_15PyExtensionType_11set_auto_loadP7_objectPKS0_lS0__Z30pyarrow_wrap_sparse_csf_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSFIndexEEEE.localalias_ZL71__pyx_specialmethod___pyx_pw_7pyarrow_3lib_16KeyValueMetadata_5__repr__P7_objectS0__ZL51__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_11__len__P7_object_ZL48__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_25keysP7_objectPKS0_lS0__ZL54__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_26generator2P21__pyx_CoroutineObjectP3_tsP7_object_ZL50__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_28valuesP7_objectPKS0_lS0__ZL54__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_29generator3P21__pyx_CoroutineObjectP3_tsP7_object_ZL49__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_31itemsP7_objectPKS0_lS0__ZL54__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_32generator4P21__pyx_CoroutineObjectP3_tsP7_object_ZL37__pyx_pw_7pyarrow_3lib_5Field_5equalsP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Field_9__reduce__P7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Field_8__reduce__P29__pyx_obj_7pyarrow_3lib_FieldE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Field_8__reduce__P29__pyx_obj_7pyarrow_3lib_FieldE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_6Schema_12__reduce__P7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_6Schema_11__reduce__P30__pyx_obj_7pyarrow_3lib_SchemaE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_6Schema_11__reduce__P30__pyx_obj_7pyarrow_3lib_SchemaE23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_6Schema_22equalsP7_objectPKS0_lS0__Z30pyarrow_wrap_sparse_csc_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSCIndexEEEE.localalias_ZL30__pyx_convert_vector_to_py_intRKSt6vectorIiSaIiEE_ZL60__pyx_getprop_7pyarrow_3lib_14IpcReadOptions_included_fieldsP7_objectPv_Z30pyarrow_wrap_sparse_csr_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSRIndexEEEE.localalias_Z30pyarrow_wrap_sparse_coo_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCOOIndexEEEE.localalias_Z19pyarrow_wrap_tensorRKSt10shared_ptrIN5arrow6TensorEE.localalias_ZL45__pyx_pw_7pyarrow_3lib_13BooleanScalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11UInt8Scalar_1as_pyP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10Int8Scalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12UInt16Scalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11Int16Scalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12UInt32Scalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11Int32Scalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12UInt64Scalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11Int64Scalar_1as_pyP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_15HalfFloatScalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11FloatScalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12DoubleScalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12Date32Scalar_1as_pyP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_12Date32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date32ScalarE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_12Date32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date32ScalarE23__pyx_dict_cached_value_ZZL43__pyx_pf_7pyarrow_3lib_12Date32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date32ScalarE18__pyx_dict_version_0_ZZL43__pyx_pf_7pyarrow_3lib_12Date32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date32ScalarE23__pyx_dict_cached_value_0_ZL44__pyx_pw_7pyarrow_3lib_12Date64Scalar_1as_pyP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_12Date64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date64ScalarE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_12Date64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date64ScalarE23__pyx_dict_cached_value_ZZL43__pyx_pf_7pyarrow_3lib_12Date64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date64ScalarE18__pyx_dict_version_0_ZZL43__pyx_pf_7pyarrow_3lib_12Date64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Date64ScalarE23__pyx_dict_cached_value_0_ZL48__pyx_pw_7pyarrow_3lib_12BinaryScalar_1as_bufferP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_12StructScalar_8__contains__P7_objectS0__ZL46__pyx_pw_7pyarrow_3lib_12StructScalar_1__len__P7_object_ZL44__pyx_pw_7pyarrow_3lib_12StructScalar_6itemsP7_objectPKS0_lS0__ZL57__pyx_gb_7pyarrow_3lib_12StructScalar_5items_2generator14P21__pyx_CoroutineObjectP3_tsP7_object_ZL40__pyx_pw_7pyarrow_3lib_9MapScalar_6as_pyP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_16DictionaryScalar_3__reduce__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_18_PandasConvertible_3__reduce_cython__P7_objectPKS0_lS0__ZZL62__pyx_pf_7pyarrow_3lib_18_PandasConvertible_2__reduce_cython__P42__pyx_obj_7pyarrow_3lib__PandasConvertibleE18__pyx_dict_version_ZZL62__pyx_pf_7pyarrow_3lib_18_PandasConvertible_2__reduce_cython__P42__pyx_obj_7pyarrow_3lib__PandasConvertibleE23__pyx_dict_cached_value_ZZL62__pyx_pf_7pyarrow_3lib_18_PandasConvertible_2__reduce_cython__P42__pyx_obj_7pyarrow_3lib__PandasConvertibleE18__pyx_dict_version_0_ZZL62__pyx_pf_7pyarrow_3lib_18_PandasConvertible_2__reduce_cython__P42__pyx_obj_7pyarrow_3lib__PandasConvertibleE23__pyx_dict_cached_value_0_ZL43__pyx_pw_7pyarrow_3lib_5Array_19from_pandasP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_5Array_18from_pandasP7_objectS0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_5Array_18from_pandasP7_objectS0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL53__pyx_pw_7pyarrow_3lib_5Array_25get_total_buffer_sizeP7_objectPKS0_lS0__ZL39__pyx_f_7pyarrow_3lib__codes_to_indicesP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPool_ZZL39__pyx_f_7pyarrow_3lib__codes_to_indicesP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL39__pyx_f_7pyarrow_3lib__codes_to_indicesP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL65__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_7find_physical_offsetP7_objectPKS0_lS0__ZL65__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_9find_physical_lengthP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_12CacheOptions_9__reduce__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_5__reduce__P7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_4__reduce__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_4__reduce__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_12ChunkedArray_7lengthP7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_12ChunkedArray_21get_total_buffer_sizeP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_40equalsP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_12ChunkedArray_76iterchunksP7_objectPKS0_lS0__ZL51__pyx_gb_7pyarrow_3lib_12ChunkedArray_77generator10P21__pyx_CoroutineObjectP3_tsP7_object_ZL42__pyx_pw_7pyarrow_3lib_8_Tabular_15_columnP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_8_Tabular_27from_pydictP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_8_Tabular_26from_pydictP11_typeobjectP7_objectS2_S2_E18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_8_Tabular_26from_pydictP11_typeobjectP7_objectS2_S2_E23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_8_Tabular_29from_pylistP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_8_Tabular_28from_pylistP11_typeobjectP7_objectS2_S2_E18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_8_Tabular_28from_pylistP11_typeobjectP7_objectS2_S2_E23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_8_Tabular_31itercolumnsP7_objectPKS0_lS0__ZL46__pyx_gb_7pyarrow_3lib_8_Tabular_32generator11P21__pyx_CoroutineObjectP3_tsP7_object_ZL48__pyx_pw_7pyarrow_3lib_8_Tabular_44remove_columnP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_8_Tabular_52__reduce_cython__P7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_8_Tabular_51__reduce_cython__P32__pyx_obj_7pyarrow_3lib__TabularE18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_8_Tabular_51__reduce_cython__P32__pyx_obj_7pyarrow_3lib__TabularE23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_8_Tabular_51__reduce_cython__P32__pyx_obj_7pyarrow_3lib__TabularE18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_8_Tabular_51__reduce_cython__P32__pyx_obj_7pyarrow_3lib__TabularE23__pyx_dict_cached_value_0_ZL53__pyx_pw_7pyarrow_3lib_11RecordBatch_3_is_initializedP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_11RecordBatch_5__reduce__P7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_11RecordBatch_4__reduce__P35__pyx_obj_7pyarrow_3lib_RecordBatchE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_11RecordBatch_4__reduce__P35__pyx_obj_7pyarrow_3lib_RecordBatchE23__pyx_dict_cached_value_ZL60__pyx_pw_7pyarrow_3lib_11RecordBatch_13get_total_buffer_sizeP7_objectPKS0_lS0__ZL35__pyx_f_7pyarrow_3lib_alloc_c_arrayPP10ArrowArray_ZL36__pyx_f_7pyarrow_3lib_alloc_c_schemaPP11ArrowSchema_ZL46__pyx_pw_7pyarrow_3lib_5Table_3_is_initializedP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Table_7__reduce__P7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Table_6__reduce__P29__pyx_obj_7pyarrow_3lib_TableE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Table_6__reduce__P29__pyx_obj_7pyarrow_3lib_TableE23__pyx_dict_cached_value_ZL38__pyx_f_7pyarrow_3lib__normalize_indexll_ZL48__pyx_pw_7pyarrow_3lib_10ListScalar_3__getitem__P7_objectS0__ZL53__pyx_pw_7pyarrow_3lib_5Table_45get_total_buffer_sizeP7_objectPKS0_lS0__ZL65__pyx_convert_PyBytes_string_to_py_6libcpp_6string_std__in_stringRKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE.isra.0_ZL51__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_5__repr__P7_object_ZL44__pyx_convert_vector_to_py_std_3a__3a_stringRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE_ZL55__pyx_getprop_7pyarrow_3lib_15SparseCSFTensor_dim_namesP7_objectPv_ZL73__pyx_gb_7pyarrow_3lib_15SparseCSFTensor_9dim_names_7__get___2generator18P21__pyx_CoroutineObjectP3_tsP7_object_ZL55__pyx_getprop_7pyarrow_3lib_15SparseCOOTensor_dim_namesP7_objectPv_ZL73__pyx_gb_7pyarrow_3lib_15SparseCOOTensor_9dim_names_7__get___2generator15P21__pyx_CoroutineObjectP3_tsP7_object_ZL55__pyx_getprop_7pyarrow_3lib_15SparseCSCMatrix_dim_namesP7_objectPv_ZL73__pyx_gb_7pyarrow_3lib_15SparseCSCMatrix_9dim_names_7__get___2generator17P21__pyx_CoroutineObjectP3_tsP7_object_ZL55__pyx_getprop_7pyarrow_3lib_15SparseCSRMatrix_dim_namesP7_objectPv_ZL73__pyx_gb_7pyarrow_3lib_15SparseCSRMatrix_9dim_names_7__get___2generator16P21__pyx_CoroutineObjectP3_tsP7_object_ZL35__pyx_pw_7pyarrow_3lib_6Buffer_9hexP7_objectPKS0_lS0__ZL35__pyx_pw_7pyarrow_3lib_6Buffer_9hexP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_5Field_15__hash__P7_object_ZL50__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_7__str__P7_object_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_6__str__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_6__str__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataE23__pyx_dict_cached_value_ZL50__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_7__str__P7_object.cold_ZL41__pyx_pw_7pyarrow_3lib_8DataType_7__str__P7_object_ZZL41__pyx_pf_7pyarrow_3lib_8DataType_6__str__P32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_8DataType_6__str__P32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_8DataType_7__str__P7_object.cold_ZL20__Pyx_Generator_NextP7_object_ZL48__pyx_getprop_7pyarrow_3lib_8DataType_byte_widthP7_objectPv_ZL42__pyx_pw_7pyarrow_3lib_5Table_41_to_pandasP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_6Schema_28_fieldP7_objectPKS0_lS0__ZL24__Pyx_PyObject_GetMethodP7_objectS0_PS0_.constprop.0_ZL40__pyx_pw_7pyarrow_3lib_5Table_59group_byP7_objectPKS0_lS0__ZZL40__pyx_pf_7pyarrow_3lib_5Table_58group_byP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E18__pyx_dict_version_ZZL40__pyx_pf_7pyarrow_3lib_5Table_58group_byP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E23__pyx_dict_cached_value_ZL57__pyx_pw_7pyarrow_3lib_10NativeFile_75__setstate_cython__P7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_5Codec_23__setstate_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_31__setstate_cython__P7_objectPKS0_lS0__ZL68__pyx_pw_7pyarrow_3lib_21FixedSizeBufferWriter_11__setstate_cython__P7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_7Message_15__setstate_cython__P7_objectPKS0_lS0__ZL65__pyx_pw_7pyarrow_3lib_19BufferedInputStream_7__setstate_cython__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_7__setstate_cython__P7_objectPKS0_lS0__ZL69__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_17__setstate_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_13MessageReader_15__setstate_cython__P7_objectPKS0_lS0__ZL68__pyx_pw_7pyarrow_3lib_22_RecordBatchFileWriter_5__setstate_cython__P7_objectPKS0_lS0__ZL69__pyx_pw_7pyarrow_3lib_23_ExtensionRegistryNanny_7__setstate_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_17RecordBatchReader_36__setstate_cython__P7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_12BufferReader_7__setstate_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_23__setstate_cython__P7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_6Tensor_21__setstate_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_27__setstate_cython__P7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_9StopToken_3__setstate_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_16MockOutputStream_7__setstate_cython__P7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_6OSFile_7__setstate_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_17StringViewBuilder_13__setstate_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_14IpcReadOptions_5__setstate_cython__P7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_10PythonFile_11__setstate_cython__P7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_21CompressedInputStream_5__setstate_cython__P7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_10MemoryPool_13__setstate_cython__P7_objectPKS0_lS0__ZL70__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamReader_7__setstate_cython__P7_objectPKS0_lS0__ZL63__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_11__setstate_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_14DictionaryMemo_5__setstate_cython__P7_objectPKS0_lS0__ZL70__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamWriter_9__setstate_cython__P7_objectPKS0_lS0__ZL63__pyx_pw_7pyarrow_3lib_17LoggingMemoryPool_5__setstate_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_13StringBuilder_13__setstate_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_17SignalStopHandler_13__setstate_cython__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_20TransformInputStream_5__setstate_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_18BufferOutputStream_7__setstate_cython__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_15__setstate_cython__P7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_15ProxyMemoryPool_5__setstate_cython__P7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_15IpcWriteOptions_5__setstate_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_27__setstate_cython__P7_objectPKS0_lS0__ZL68__pyx_pw_7pyarrow_3lib_22CompressedOutputStream_5__setstate_cython__P7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_18_PandasConvertible_1to_pandasP7_objectPKS0_lS0__ZL41__pyx_getprop_7pyarrow_3lib_6Schema_typesP7_objectPv_ZL45__pyx_pw_7pyarrow_3lib_10Transcoder_1__init__P7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_27logging_memory_poolP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_27logging_memory_poolP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_25proxy_memory_poolP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_25proxy_memory_poolP7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_17enable_signal_handlersP7_objectPKS0_lS0__ZL45__pyx_v_7pyarrow_3lib_signal_handlers_enabled_ZL51__pyx_pw_7pyarrow_3lib_197_reconstruct_record_batchP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_201_reconstruct_tableP7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_6Tensor_3_make_shape_or_strides_bufferP7_objectPKS0_lS0__ZZL61__pyx_pf_7pyarrow_3lib_6Tensor_2_make_shape_or_strides_bufferP30__pyx_obj_7pyarrow_3lib_TensorP7_objectE18__pyx_dict_version_ZZL61__pyx_pf_7pyarrow_3lib_6Tensor_2_make_shape_or_strides_bufferP30__pyx_obj_7pyarrow_3lib_TensorP7_objectE23__pyx_dict_cached_value_ZL14DIGIT_PAIRS_10_ZL50__pyx_pw_7pyarrow_3lib_8_Tabular_19_is_initializedP7_objectPKS0_lS0__ZL32__pyx_f_7pyarrow_3lib_get_valuesP7_objectPi_ZZL32__pyx_f_7pyarrow_3lib_get_valuesP7_objectPiE18__pyx_dict_version_ZZL32__pyx_f_7pyarrow_3lib_get_valuesP7_objectPiE23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_5Table_23equalsP7_objectPKS0_lS0__ZL27__Pyx_PyObject_FastCallDictP7_objectPS0_mS0__ZL25__Pyx_PyObject_CallOneArgP7_objectS0__ZL25__Pyx_PEP560_update_basesP7_object_ZL24__Pyx_PyObject_CallNoArgP7_object_ZL25__Pyx_Py3MetaclassPrepareP7_objectS0_S0_S0_S0_S0_S0__ZL56__pyx_f_7pyarrow_3lib___pyx_unpickle__Tabular__set_stateP32__pyx_obj_7pyarrow_3lib__TabularP7_object_ZL66__pyx_f_7pyarrow_3lib___pyx_unpickle__PandasConvertible__set_stateP42__pyx_obj_7pyarrow_3lib__PandasConvertibleP7_object_ZL56__pyx_pw_7pyarrow_3lib_17RecordBatchReader_30from_streamP7_objectPKS0_lS0__ZL41__pyx_f_7pyarrow_3lib__ensure_compressionP7_object_ZL38__pyx_pw_7pyarrow_3lib_6OSFile_3filenoP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_7filenoP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10PythonFile_7readlinesP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10PythonFile_5readlineP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10PythonFile_3truncateP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_10NativeFile_8download_1cleanupP7_objectS0__ZL48__pyx_pw_7pyarrow_3lib_10NativeFile_67writelinesP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10NativeFile_51readallP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_49read1P7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_10NativeFile_29_assert_seekableP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_10NativeFile_27_assert_writableP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_10NativeFile_25_assert_readableP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_10NativeFile_17isattyP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_15seekableP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_13writableP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_11readableP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_5from_dense_numpyP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_5from_dense_numpyP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_5from_dense_numpyP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_5from_dense_numpyP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_5Table_65__arrow_c_stream__P7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_5Table_57dropP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_5Table_47__sizeof__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_5Table_31from_struct_arrayP7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_5Table_25castP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_11RecordBatch_55__arrow_c_stream__P7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11RecordBatch_35castP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_11RecordBatch_29filterP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_11RecordBatch_28filterP35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectS2_E18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_11RecordBatch_28filterP35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectS2_E23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_15__sizeof__P7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_8_Tabular_50append_columnP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_8_Tabular_36takeP7_objectPKS0_lS0__ZZL39__pyx_pf_7pyarrow_3lib_8_Tabular_35takeP32__pyx_obj_7pyarrow_3lib__TabularP7_objectE18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_8_Tabular_35takeP32__pyx_obj_7pyarrow_3lib__TabularP7_objectE23__pyx_dict_cached_value_ZL40__pyx_pw_7pyarrow_3lib_8_Tabular_25fieldP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_8_Tabular_23drop_nullP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_8_Tabular_22drop_nullP32__pyx_obj_7pyarrow_3lib__TabularE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_8_Tabular_22drop_nullP32__pyx_obj_7pyarrow_3lib__TabularE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_8_Tabular_21columnP7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_8_Tabular_17_ensure_integer_indexP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_8_Tabular_5__dataframe__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_68drop_nullP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_67drop_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_67drop_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_12ChunkedArray_66takeP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_65takeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_65takeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_12ChunkedArray_64indexP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_12ChunkedArray_63indexP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_S2_S2_E18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_12ChunkedArray_63indexP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_S2_S2_E23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_62filterP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_61filterP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_61filterP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E23__pyx_dict_cached_value_ZL52__pyx_pw_7pyarrow_3lib_12ChunkedArray_58value_countsP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_12ChunkedArray_57value_countsP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_12ChunkedArray_57value_countsP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_56uniqueP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_55uniqueP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_55uniqueP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL57__pyx_pw_7pyarrow_3lib_12ChunkedArray_50dictionary_encodeP7_objectPKS0_lS0__ZZL57__pyx_pf_7pyarrow_3lib_12ChunkedArray_49dictionary_encodeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL57__pyx_pf_7pyarrow_3lib_12ChunkedArray_49dictionary_encodeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZZL57__pyx_pf_7pyarrow_3lib_12ChunkedArray_49dictionary_encodeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_0_ZZL57__pyx_pf_7pyarrow_3lib_12ChunkedArray_49dictionary_encodeP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_0_ZL44__pyx_pw_7pyarrow_3lib_12ChunkedArray_48castP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_47castP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_S2_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_47castP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_S2_E23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_46__array__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_38fill_nullP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_37fill_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_37fill_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_34is_validP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_33is_validP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_33is_validP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_32is_nanP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_31is_nanP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_31is_nanP36__pyx_obj_7pyarrow_3lib_ChunkedArrayE23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_12ChunkedArray_30is_nullP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_12ChunkedArray_29is_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_12ChunkedArray_29is_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZZL47__pyx_pf_7pyarrow_3lib_12ChunkedArray_29is_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_0_ZZL47__pyx_pf_7pyarrow_3lib_12ChunkedArray_29is_nullP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_0_ZL50__pyx_pw_7pyarrow_3lib_12ChunkedArray_23__sizeof__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_21FixedShapeTensorArray_1to_numpy_ndarrayP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_15DictionaryArray_3dictionary_decodeP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_13BaseListArray_5value_lengthsP7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_13BaseListArray_4value_lengthsP37__pyx_obj_7pyarrow_3lib_BaseListArrayE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_13BaseListArray_4value_lengthsP37__pyx_obj_7pyarrow_3lib_BaseListArrayE23__pyx_dict_cached_value_ZL60__pyx_pw_7pyarrow_3lib_13BaseListArray_3value_parent_indicesP7_objectPKS0_lS0__ZZL60__pyx_pf_7pyarrow_3lib_13BaseListArray_2value_parent_indicesP37__pyx_obj_7pyarrow_3lib_BaseListArrayE18__pyx_dict_version_ZZL60__pyx_pf_7pyarrow_3lib_13BaseListArray_2value_parent_indicesP37__pyx_obj_7pyarrow_3lib_BaseListArrayE23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_13BaseListArray_1flattenP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_13BaseListArray_flattenP37__pyx_obj_7pyarrow_3lib_BaseListArrayE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_13BaseListArray_flattenP37__pyx_obj_7pyarrow_3lib_BaseListArrayE23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_5Array_76tolistP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Array_74to_pylistP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_5Array_64indexP7_objectPKS0_lS0__ZZL37__pyx_pf_7pyarrow_3lib_5Array_63indexP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_S2_E18__pyx_dict_version_ZZL37__pyx_pf_7pyarrow_3lib_5Array_63indexP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_S2_E23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_5Array_60drop_nullP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Array_59drop_nullP29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Array_59drop_nullP29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL36__pyx_pw_7pyarrow_3lib_5Array_58takeP7_objectPKS0_lS0__ZZL36__pyx_pf_7pyarrow_3lib_5Array_57takeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_5Array_57takeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_5Array_52fill_nullP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Array_51fill_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Array_51fill_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZL40__pyx_pw_7pyarrow_3lib_5Array_50is_validP7_objectPKS0_lS0__ZZL40__pyx_pf_7pyarrow_3lib_5Array_49is_validP29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL40__pyx_pf_7pyarrow_3lib_5Array_49is_validP29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_5Array_48is_nanP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_5Array_47is_nanP29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_5Array_47is_nanP29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_5Array_46is_nullP7_objectPKS0_lS0__ZZL39__pyx_pf_7pyarrow_3lib_5Array_45is_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_5Array_45is_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZZL39__pyx_pf_7pyarrow_3lib_5Array_45is_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_0_ZZL39__pyx_pf_7pyarrow_3lib_5Array_45is_nullP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_0_ZL42__pyx_pw_7pyarrow_3lib_5Array_27__sizeof__P7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_5Array_17value_countsP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_5Array_16value_countsP29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_5Array_16value_countsP29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_5Array_15dictionary_encodeP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_5Array_14dictionary_encodeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_5Array_14dictionary_encodeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZZL49__pyx_pf_7pyarrow_3lib_5Array_14dictionary_encodeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_0_ZZL49__pyx_pf_7pyarrow_3lib_5Array_14dictionary_encodeP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_0_ZL38__pyx_pw_7pyarrow_3lib_5Array_13uniqueP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_5Array_12uniqueP29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_5Array_12uniqueP29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL35__pyx_pw_7pyarrow_3lib_5Array_7castP7_objectPKS0_lS0__ZZL35__pyx_pf_7pyarrow_3lib_5Array_6castP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_S2_E18__pyx_dict_version_ZZL35__pyx_pf_7pyarrow_3lib_5Array_6castP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_S2_E23__pyx_dict_cached_value_ZL57__pyx_pw_7pyarrow_3lib_22FixedShapeTensorScalar_1to_numpyP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_15ExtensionScalar_1as_pyP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_11UnionScalar_1as_pyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_19RunEndEncodedScalar_1as_pyP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_16DictionaryScalar_5as_pyP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_12StructScalar_18__str__P7_object_ZL48__pyx_pf_7pyarrow_3lib_12StructScalar_15__repr__P36__pyx_obj_7pyarrow_3lib_StructScalar_ZL48__pyx_pw_7pyarrow_3lib_12StructScalar_16__repr__P7_object_ZL68__pyx_specialmethod___pyx_pw_7pyarrow_3lib_12StructScalar_16__repr__P7_objectS0__ZL42__pyx_pw_7pyarrow_3lib_10ListScalar_7as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12StringScalar_1as_pyP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_12BinaryScalar_3as_pyP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_14DurationScalar_1as_pyP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_2_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_2_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_4_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_4_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_1_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_1_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_0_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_0_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE18__pyx_dict_version_3_ZZL45__pyx_pf_7pyarrow_3lib_14DurationScalar_as_pyP38__pyx_obj_7pyarrow_3lib_DurationScalarE23__pyx_dict_cached_value_3_ZL44__pyx_pw_7pyarrow_3lib_12Time64Scalar_1as_pyP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_12Time64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Time64ScalarE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_12Time64Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Time64ScalarE23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_12Time32Scalar_1as_pyP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_12Time32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Time32ScalarE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_12Time32Scalar_as_pyP36__pyx_obj_7pyarrow_3lib_Time32ScalarE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_6Scalar_17__reduce__P7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_6Scalar_16__reduce__P30__pyx_obj_7pyarrow_3lib_ScalarE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_6Scalar_16__reduce__P30__pyx_obj_7pyarrow_3lib_ScalarE23__pyx_dict_cached_value_ZL36__pyx_pw_7pyarrow_3lib_6Scalar_3castP7_objectPKS0_lS0__ZZL36__pyx_pf_7pyarrow_3lib_6Scalar_2castP30__pyx_obj_7pyarrow_3lib_ScalarP7_objectS2_S2_S2_E18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_6Scalar_2castP30__pyx_obj_7pyarrow_3lib_ScalarP7_objectS2_S2_S2_E23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_6Schema_44add_metadataP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_6Schema_43add_metadataP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_6Schema_43add_metadataP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_6Schema_36appendP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_6Schema_26fieldP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_6Schema_20empty_tableP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_6Schema_19empty_tableP30__pyx_obj_7pyarrow_3lib_SchemaE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_6Schema_19empty_tableP30__pyx_obj_7pyarrow_3lib_SchemaE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_36to_dictP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_35to_dictP40__pyx_obj_7pyarrow_3lib_KeyValueMetadataE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_35to_dictP40__pyx_obj_7pyarrow_3lib_KeyValueMetadataE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_34get_allP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_33get_allP40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_33get_allP40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_19__reduce__P7_objectPKS0_lS0__ZL64__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_8__init___2generator13P21__pyx_CoroutineObjectP3_tsP7_object_ZZL64__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_8__init___2generator13P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL64__pyx_gb_7pyarrow_3lib_16KeyValueMetadata_8__init___2generator13P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZL52__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_17__iter__P7_object_ZL65__pyx_pw_7pyarrow_3lib_15PyExtensionType_7__arrow_ext_serialize__P7_objectPKS0_lS0__ZZL65__pyx_pf_7pyarrow_3lib_15PyExtensionType_6__arrow_ext_serialize__P39__pyx_obj_7pyarrow_3lib_PyExtensionTypeE18__pyx_dict_version_ZZL65__pyx_pf_7pyarrow_3lib_15PyExtensionType_6__arrow_ext_serialize__P39__pyx_obj_7pyarrow_3lib_PyExtensionTypeE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_13ExtensionType_13__reduce__P7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_8DataType_19to_pandas_dtypeP7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_8DataType_18to_pandas_dtypeP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_8DataType_18to_pandas_dtypeP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_ZL40__pyx_unpickle___Pyx_EnumMeta__set_stateP24__pyx_obj___Pyx_EnumMetaP7_object_ZL45__pyx_pw_8EnumBase_14__Pyx_EnumMeta_1__init__P7_objectS0_S0__ZL45__pyx_pw_8EnumBase_14__Pyx_EnumMeta_3__iter__P7_object_ZL38__pyx_f_7pyarrow_3lib__wrap_read_statsN5arrow3ipc9ReadStatsE_ZZL38__pyx_f_7pyarrow_3lib__wrap_read_statsN5arrow3ipc9ReadStatsEE18__pyx_dict_version_ZZL38__pyx_f_7pyarrow_3lib__wrap_read_statsN5arrow3ipc9ReadStatsEE23__pyx_dict_cached_value_ZL58__pyx_getprop_7pyarrow_3lib_22_RecordBatchFileReader_statsP7_objectPv_ZL60__pyx_getprop_7pyarrow_3lib_24_RecordBatchStreamReader_statsP7_objectPv_ZL44__pyx_f_7pyarrow_3lib__wrap_metadata_versionN5arrow3ipc15MetadataVersionE_ZZL44__pyx_f_7pyarrow_3lib__wrap_metadata_versionN5arrow3ipc15MetadataVersionEE18__pyx_dict_version_ZZL44__pyx_f_7pyarrow_3lib__wrap_metadata_versionN5arrow3ipc15MetadataVersionEE23__pyx_dict_cached_value_ZL72__pyx_getprop_7pyarrow_3lib_24_RecordBatchStreamWriter__metadata_versionP7_objectPv_ZL62__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_metadata_versionP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_7Message_metadata_versionP7_objectPv_ZL48__pyx_pf_7pyarrow_3lib_13MessageReader_8__next__P37__pyx_obj_7pyarrow_3lib_MessageReader_ZL48__pyx_pw_7pyarrow_3lib_13MessageReader_9__next__P7_object_ZL68__pyx_specialmethod___pyx_pw_7pyarrow_3lib_13MessageReader_9__next__P7_objectS0__ZL62__pyx_getprop_7pyarrow_3lib_26MonthDayNanoIntervalScalar_valueP7_objectPv_ZL55__pyx_pw_7pyarrow_3lib_20UnknownExtensionType_1__init__P7_objectS0_S0__ZL49__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_1__init__P7_objectS0_S0__ZZL48__pyx_pf_7pyarrow_3lib_14_PandasAPIShim___init__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_14_PandasAPIShim___init__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE23__pyx_dict_cached_value_ZL39__pyx_getprop_7pyarrow_3lib_5Codec_nameP7_objectPv_ZZL43__pyx_pf_7pyarrow_3lib_5Codec_4name___get__P29__pyx_obj_7pyarrow_3lib_CodecE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_5Codec_4name___get__P29__pyx_obj_7pyarrow_3lib_CodecE23__pyx_dict_cached_value_ZL52__pyx_pf_7pyarrow_3lib_17RecordBatchReader_2__next__P41__pyx_obj_7pyarrow_3lib_RecordBatchReader_ZL52__pyx_pw_7pyarrow_3lib_17RecordBatchReader_3__next__P7_object_ZL72__pyx_specialmethod___pyx_pw_7pyarrow_3lib_17RecordBatchReader_3__next__P7_objectS0__ZL46__pyx_pf_7pyarrow_3lib_10NativeFile_60__next__P34__pyx_obj_7pyarrow_3lib_NativeFile_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_61__next__P7_object_ZL66__pyx_specialmethod___pyx_pw_7pyarrow_3lib_10NativeFile_61__next__P7_objectS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_59__iter__P7_object_ZL39__pyx_pf_7pyarrow_3lib_6Buffer_16__eq__P30__pyx_obj_7pyarrow_3lib_BufferP7_object_ZZL39__pyx_pf_7pyarrow_3lib_6Buffer_16__eq__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_6Buffer_16__eq__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE23__pyx_dict_cached_value_ZL41__pyx_tp_richcompare_7pyarrow_3lib_BufferP7_objectS0_i_ZL44__pyx_pw_7pyarrow_3lib_6Buffer_11__getitem__P7_objectS0__ZZL44__pyx_pf_7pyarrow_3lib_6Buffer_10__getitem__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_6Buffer_10__getitem__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_8_Tabular_9__getitem__P7_objectS0__ZZL45__pyx_pf_7pyarrow_3lib_8_Tabular_8__getitem__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_8_Tabular_8__getitem__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectE23__pyx_dict_cached_value_ZL49__pyx_getprop_7pyarrow_3lib_12ChunkedArray_chunksP7_objectPv_ZL47__pyx_pw_7pyarrow_3lib_12ChunkedArray_17__str__P7_object_ZL51__pyx_pw_7pyarrow_3lib_12ChunkedArray_28__getitem__P7_objectS0__ZZL51__pyx_pf_7pyarrow_3lib_12ChunkedArray_27__getitem__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_12ChunkedArray_27__getitem__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_9__len__P7_object_ZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_10__repr__P36__pyx_obj_7pyarrow_3lib_ChunkedArray_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_11__repr__P7_object_ZL68__pyx_specialmethod___pyx_pw_7pyarrow_3lib_12ChunkedArray_11__repr__P7_objectS0__ZL49__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_16__eq__P39__pyx_obj_7pyarrow_3lib_SparseCSFTensorP7_object_ZL50__pyx_tp_richcompare_7pyarrow_3lib_SparseCSFTensorP7_objectS0_i_ZL50__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_2__repr__P39__pyx_obj_7pyarrow_3lib_SparseCSFTensor_ZL50__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_3__repr__P7_object_ZL70__pyx_specialmethod___pyx_pw_7pyarrow_3lib_15SparseCSFTensor_3__repr__P7_objectS0__ZL49__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_24__eq__P39__pyx_obj_7pyarrow_3lib_SparseCOOTensorP7_object_ZL50__pyx_tp_richcompare_7pyarrow_3lib_SparseCOOTensorP7_objectS0_i_ZL50__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_2__repr__P39__pyx_obj_7pyarrow_3lib_SparseCOOTensor_ZL50__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_3__repr__P7_object_ZL70__pyx_specialmethod___pyx_pw_7pyarrow_3lib_15SparseCOOTensor_3__repr__P7_objectS0__ZL49__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_20__eq__P39__pyx_obj_7pyarrow_3lib_SparseCSCMatrixP7_object_ZL50__pyx_tp_richcompare_7pyarrow_3lib_SparseCSCMatrixP7_objectS0_i_ZL50__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_2__repr__P39__pyx_obj_7pyarrow_3lib_SparseCSCMatrix_ZL50__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_3__repr__P7_object_ZL70__pyx_specialmethod___pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_3__repr__P7_objectS0__ZL49__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_20__eq__P39__pyx_obj_7pyarrow_3lib_SparseCSRMatrixP7_object_ZL50__pyx_tp_richcompare_7pyarrow_3lib_SparseCSRMatrixP7_objectS0_i_ZL50__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_2__repr__P39__pyx_obj_7pyarrow_3lib_SparseCSRMatrix_ZL50__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_3__repr__P7_object_ZL70__pyx_specialmethod___pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_3__repr__P7_objectS0__ZL39__pyx_pf_7pyarrow_3lib_6Tensor_12__eq__P30__pyx_obj_7pyarrow_3lib_TensorP7_object_ZL41__pyx_tp_richcompare_7pyarrow_3lib_TensorP7_objectS0_i_ZL40__pyx_pf_7pyarrow_3lib_6Tensor_4__repr__P30__pyx_obj_7pyarrow_3lib_Tensor_ZL40__pyx_pw_7pyarrow_3lib_6Tensor_5__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_6Tensor_5__repr__P7_objectS0__ZL39__pyx_pw_7pyarrow_3lib_5Array_38__str__P7_object_ZL43__pyx_pw_7pyarrow_3lib_5Array_54__getitem__P7_objectS0__ZZL43__pyx_pf_7pyarrow_3lib_5Array_53__getitem__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_5Array_53__getitem__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZL40__pyx_pf_7pyarrow_3lib_5Array_31__repr__P29__pyx_obj_7pyarrow_3lib_Array_ZL40__pyx_pw_7pyarrow_3lib_5Array_32__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_5Array_32__repr__P7_objectS0__ZL39__pyx_pw_7pyarrow_3lib_6Scalar_9__str__P7_object_ZL40__pyx_pf_7pyarrow_3lib_6Scalar_6__repr__P30__pyx_obj_7pyarrow_3lib_Scalar_ZL40__pyx_pw_7pyarrow_3lib_6Scalar_7__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_6Scalar_7__repr__P7_objectS0__ZL44__pyx_getprop_7pyarrow_3lib_6Schema_metadataP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_6Schema_namesP7_objectPv_ZZL45__pyx_pf_7pyarrow_3lib_6Schema_5names___get__P30__pyx_obj_7pyarrow_3lib_SchemaE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_6Schema_5names___get__P30__pyx_obj_7pyarrow_3lib_SchemaE23__pyx_dict_cached_value_ZL40__pyx_pw_7pyarrow_3lib_6Schema_58__str__P7_object_ZL43__pyx_pw_7pyarrow_3lib_6Schema_7__getitem__P7_objectS0__ZL41__pyx_pf_7pyarrow_3lib_6Schema_59__repr__P30__pyx_obj_7pyarrow_3lib_Schema_ZL41__pyx_pw_7pyarrow_3lib_6Schema_60__repr__P7_object_ZL61__pyx_specialmethod___pyx_pw_7pyarrow_3lib_6Schema_60__repr__P7_objectS0__ZL40__pyx_pf_7pyarrow_3lib_5Field_12__repr__P29__pyx_obj_7pyarrow_3lib_Field_ZL40__pyx_pw_7pyarrow_3lib_5Field_13__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_5Field_13__repr__P7_objectS0__ZL50__pyx_pw_7pyarrow_3lib_15PyExtensionType_3__init__P7_objectS0_S0__ZZL50__pyx_pf_7pyarrow_3lib_15PyExtensionType_2__init__P39__pyx_obj_7pyarrow_3lib_PyExtensionTypeP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_15PyExtensionType_2__init__P39__pyx_obj_7pyarrow_3lib_PyExtensionTypeP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_ZL43__pyx_pf_7pyarrow_3lib_8DataType_12__repr__P32__pyx_obj_7pyarrow_3lib_DataType_ZL43__pyx_pw_7pyarrow_3lib_8DataType_13__repr__P7_object_ZL63__pyx_specialmethod___pyx_pw_7pyarrow_3lib_8DataType_13__repr__P7_objectS0__ZL45__pyx_pf_7pyarrow_3lib_10MemoryPool_8__repr__P34__pyx_obj_7pyarrow_3lib_MemoryPool_ZL45__pyx_pw_7pyarrow_3lib_10MemoryPool_9__repr__P7_object_ZL65__pyx_specialmethod___pyx_pw_7pyarrow_3lib_10MemoryPool_9__repr__P7_objectS0__ZL33__pyx_f_7pyarrow_3lib__build_infov_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_0_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_0_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_1_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_1_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_2_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_2_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_3_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_3_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_4_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_4_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_5_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_5_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_6_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_6_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_7_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_7_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_8_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_8_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version_9_ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value_9_ZZL33__pyx_f_7pyarrow_3lib__build_infovE18__pyx_dict_version__10__ZZL33__pyx_f_7pyarrow_3lib__build_infovE23__pyx_dict_cached_value__10__ZL48__pyx_getprop_7pyarrow_3lib_10UnionArray_offsetsP7_objectPv_ZZL52__pyx_pf_7pyarrow_3lib_10UnionArray_7offsets___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_10UnionArray_7offsets___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_10UnionArray_7offsets___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_10UnionArray_7offsets___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE23__pyx_dict_cached_value_ZL51__pyx_getprop_7pyarrow_3lib_10UnionArray_type_codesP7_objectPv_ZZL56__pyx_pf_7pyarrow_3lib_10UnionArray_10type_codes___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE18__pyx_dict_version_ZZL56__pyx_pf_7pyarrow_3lib_10UnionArray_10type_codes___get__P34__pyx_obj_7pyarrow_3lib_UnionArrayE23__pyx_dict_cached_value_ZL57__pyx_getprop_7pyarrow_3lib_15IpcWriteOptions_compressionP7_objectPv_ZZL62__pyx_pf_7pyarrow_3lib_15IpcWriteOptions_11compression___get__P39__pyx_obj_7pyarrow_3lib_IpcWriteOptionsE18__pyx_dict_version_ZZL62__pyx_pf_7pyarrow_3lib_15IpcWriteOptions_11compression___get__P39__pyx_obj_7pyarrow_3lib_IpcWriteOptionsE23__pyx_dict_cached_value_ZL48__pyx_f_7pyarrow_3lib_get_scalar_class_from_typeRKSt10shared_ptrIN5arrow8DataTypeEE_Z18pyarrow_wrap_arrayRKSt10shared_ptrIN5arrow5ArrayEE.localalias_ZL45__pyx_getprop_7pyarrow_3lib_9ListArray_valuesP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_14LargeListArray_valuesP7_objectPv_ZL50__pyx_getprop_7pyarrow_3lib_13ListViewArray_valuesP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_18LargeListViewArray_valuesP7_objectPv_ZL42__pyx_getprop_7pyarrow_3lib_8MapArray_keysP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_8MapArray_itemsP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_18FixedSizeListArray_valuesP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_15DictionaryArray_dictionaryP7_objectPv_ZL53__pyx_getprop_7pyarrow_3lib_15DictionaryArray_indicesP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_14ExtensionArray_storageP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_10ListScalar_valuesP7_objectPv_ZL57__pyx_getprop_7pyarrow_3lib_16DictionaryScalar_dictionaryP7_objectPv_ZL57__pyx_getprop_7pyarrow_3lib_18RunEndEncodedArray_run_endsP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_18RunEndEncodedArray_valuesP7_objectPv_ZL45__pyx_pw_7pyarrow_3lib_12ChunkedArray_74chunkP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_16Decimal256Scalar_1as_pyP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_16Decimal256Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal256ScalarE18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal256Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal256ScalarE23__pyx_dict_cached_value_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal256Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal256ScalarE18__pyx_dict_version_0_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal256Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal256ScalarE23__pyx_dict_cached_value_0_ZL48__pyx_pw_7pyarrow_3lib_16Decimal256Scalar_1as_pyP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_16Decimal128Scalar_1as_pyP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_16Decimal128Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal128ScalarE18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal128Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal128ScalarE23__pyx_dict_cached_value_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal128Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal128ScalarE18__pyx_dict_version_0_ZZL47__pyx_pf_7pyarrow_3lib_16Decimal128Scalar_as_pyP40__pyx_obj_7pyarrow_3lib_Decimal128ScalarE23__pyx_dict_cached_value_0_ZL48__pyx_pw_7pyarrow_3lib_16Decimal128Scalar_1as_pyP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_5Field_11__str__P7_object_ZZL39__pyx_pf_7pyarrow_3lib_5Field_10__str__P29__pyx_obj_7pyarrow_3lib_FieldE18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_5Field_10__str__P29__pyx_obj_7pyarrow_3lib_FieldE23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_5Field_11__str__P7_object.cold_ZL62__pyx_getprop_7pyarrow_3lib_17BaseExtensionType_extension_nameP7_objectPv_ZZL67__pyx_pf_7pyarrow_3lib_17BaseExtensionType_14extension_name___get__P41__pyx_obj_7pyarrow_3lib_BaseExtensionTypeE18__pyx_dict_version_ZZL67__pyx_pf_7pyarrow_3lib_17BaseExtensionType_14extension_name___get__P41__pyx_obj_7pyarrow_3lib_BaseExtensionTypeE23__pyx_dict_cached_value_ZL62__pyx_getprop_7pyarrow_3lib_17BaseExtensionType_extension_nameP7_objectPv.cold_ZL53__pyx_getprop_7pyarrow_3lib_10MemoryPool_backend_nameP7_objectPv_ZZL58__pyx_pf_7pyarrow_3lib_10MemoryPool_12backend_name___get__P34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL58__pyx_pf_7pyarrow_3lib_10MemoryPool_12backend_name___get__P34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL53__pyx_getprop_7pyarrow_3lib_10MemoryPool_backend_nameP7_objectPv.cold_ZL48__pyx_pw_7pyarrow_3lib_6Schema_32get_field_indexP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_6Schema_31get_field_indexP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_6Schema_31get_field_indexP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_6Schema_32get_field_indexP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_13__contains__P7_objectS0__ZZL56__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_12__contains__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE18__pyx_dict_version_ZZL56__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_12__contains__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_13__contains__P7_objectS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_10StructType_1get_field_indexP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_10StructType_get_field_indexP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_10StructType_get_field_indexP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE23__pyx_dict_cached_value_ZL52__pyx_pw_7pyarrow_3lib_10StructType_1get_field_indexP7_objectPKS0_lS0_.cold_ZL42__pyx_pf_7pyarrow_3lib_7Message_10__repr__P31__pyx_obj_7pyarrow_3lib_Message_ZL42__pyx_pw_7pyarrow_3lib_7Message_11__repr__P7_object_ZL62__pyx_specialmethod___pyx_pw_7pyarrow_3lib_7Message_11__repr__P7_objectS0__ZL41__pyx_getprop_7pyarrow_3lib_7Message_typeP7_objectPv_ZZL45__pyx_pf_7pyarrow_3lib_7Message_4type___get__P31__pyx_obj_7pyarrow_3lib_MessageE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_7Message_4type___get__P31__pyx_obj_7pyarrow_3lib_MessageE23__pyx_dict_cached_value_ZL41__pyx_getprop_7pyarrow_3lib_7Message_typeP7_objectPv.cold_ZL20__Pyx_Py3ClassCreateP7_objectS0_S0_S0_S0_ii.constprop.0_ZL35__pyx_pw_7pyarrow_3lib_231as_bufferP7_objectPKS0_lS0__ZZL35__pyx_pf_7pyarrow_3lib_230as_bufferP7_objectS0_E18__pyx_dict_version_ZZL35__pyx_pf_7pyarrow_3lib_230as_bufferP7_objectS0_E23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_10Transcoder_3__call__P7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_65truncateP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_64truncateP34__pyx_obj_7pyarrow_3lib_NativeFileE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_64truncateP34__pyx_obj_7pyarrow_3lib_NativeFileE23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_10NativeFile_57readlinesP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_10NativeFile_56readlinesP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_10NativeFile_56readlinesP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_55readlineP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_54readlineP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_54readlineP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_10NativeFile_19filenoP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_18filenoP34__pyx_obj_7pyarrow_3lib_NativeFileE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_18filenoP34__pyx_obj_7pyarrow_3lib_NativeFileE23__pyx_dict_cached_value_ZL42__pyx_pw_7pyarrow_3lib_187_normalize_sliceP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_186_normalize_sliceP7_objectS0_S0_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_186_normalize_sliceP7_objectS0_S0_E23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_167_unregister_py_extension_typesP7_objectS0__ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE18__pyx_dict_version_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE23__pyx_dict_cached_value_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE18__pyx_dict_version_0_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE23__pyx_dict_cached_value_0_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE18__pyx_dict_version_1_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE23__pyx_dict_cached_value_1_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE18__pyx_dict_version_2_ZZL56__pyx_pf_7pyarrow_3lib_166_unregister_py_extension_typesP7_objectE23__pyx_dict_cached_value_2_ZL40__pyx_pw_7pyarrow_3lib_151type_for_aliasP7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_121large_utf8P7_objectS0__ZZL36__pyx_pf_7pyarrow_3lib_120large_utf8P7_objectE18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_120large_utf8P7_objectE23__pyx_dict_cached_value_ZL30__pyx_pw_7pyarrow_3lib_113utf8P7_objectS0__ZZL30__pyx_pf_7pyarrow_3lib_112utf8P7_objectE18__pyx_dict_version_ZZL30__pyx_pf_7pyarrow_3lib_112utf8P7_objectE23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_6Schema_24from_pandasP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_6Schema_23from_pandasP11_typeobjectP7_objectS2_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_6Schema_23from_pandasP11_typeobjectP7_objectS2_E23__pyx_dict_cached_value_ZZL44__pyx_pf_7pyarrow_3lib_6Schema_23from_pandasP11_typeobjectP7_objectS2_E18__pyx_dict_version_0_ZZL44__pyx_pf_7pyarrow_3lib_6Schema_23from_pandasP11_typeobjectP7_objectS2_E23__pyx_dict_cached_value_0_ZL44__pyx_pw_7pyarrow_3lib_49_get_pandas_tz_typeP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_47_get_pandas_typeP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_46_get_pandas_typeP7_objectS0_S0_E18__pyx_dict_version_0_ZZL41__pyx_pf_7pyarrow_3lib_46_get_pandas_typeP7_objectS0_S0_E23__pyx_dict_cached_value_0_ZZL41__pyx_pf_7pyarrow_3lib_46_get_pandas_typeP7_objectS0_S0_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_46_get_pandas_typeP7_objectS0_S0_E23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_37log_memory_allocationsP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_36log_memory_allocationsP7_objectS0_E18__pyx_dict_version_0_ZZL47__pyx_pf_7pyarrow_3lib_36log_memory_allocationsP7_objectS0_E23__pyx_dict_cached_value_0_ZZL47__pyx_pf_7pyarrow_3lib_36log_memory_allocationsP7_objectS0_E18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_36log_memory_allocationsP7_objectS0_E23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_13ArrowKeyError_1__str__P7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_13ArrowKeyError___str__P7_objectS0_E18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_13ArrowKeyError___str__P7_objectS0_E23__pyx_dict_cached_value_ZL34__pyx_pw_7pyarrow_3lib_15frombytesP7_objectPKS0_lS0__ZL32__pyx_pw_7pyarrow_3lib_13tobytesP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_11encode_file_pathP7_objectPKS0_lS0__ZL55__pyx_getprop_7pyarrow_3lib_19_CRecordBatchWriter_statsP7_objectPv_ZZL39__pyx_f_7pyarrow_3lib__wrap_write_statsN5arrow3ipc10WriteStatsEE18__pyx_dict_version_ZZL39__pyx_f_7pyarrow_3lib__wrap_write_statsN5arrow3ipc10WriteStatsEE23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_6Scalar_19as_pyP7_objectPKS0_lS0__ZL25__Pyx_Coroutine_CloseIterP21__pyx_CoroutineObjectP7_object_ZL21__Pyx_Coroutine_CloseP7_object_ZL19__Pyx_Coroutine_delP7_object_ZL28__Pyx_Coroutine_Close_MethodP7_objectS0__ZL22__Pyx__Coroutine_ThrowP7_objectS0_S0_S0_S0_i.constprop.0_ZL21__Pyx_Coroutine_ThrowP7_objectS0__ZL27__Pyx_PyObject_GetItem_SlowP7_objectS0__ZL22__Pyx_PyObject_GetItemP7_objectS0__ZL48__pyx_pw_8EnumBase_14__Pyx_EnumMeta_5__getitem__P7_objectS0__ZL52__pyx_pw_7pyarrow_3lib_12StructScalar_14_as_py_tupleP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_12StructScalar_12as_pyP7_objectPKS0_lS0__ZL34__pyx_pw_7pyarrow_3lib_235compressP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_15PyExtensionType_5__reduce__P7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_7Message_9serializeP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_15ProxyMemoryPool_1__init__P7_objectS0_S0__ZL52__pyx_pw_7pyarrow_3lib_17LoggingMemoryPool_1__init__P7_objectS0_S0__ZL48__pyx_pw_7pyarrow_3lib_13MessageReader_3__init__P7_objectS0_S0__ZL41__pyx_pw_7pyarrow_3lib_7Message_3__init__P7_objectS0_S0__ZL39__pyx_pw_7pyarrow_3lib_5Array_1__init__P7_objectS0_S0__ZL40__pyx_pw_7pyarrow_3lib_6Scalar_1__init__P7_objectS0_S0__ZL45__pyx_pw_7pyarrow_3lib_10MemoryPool_1__init__P7_objectS0_S0__ZL42__pyx_pw_7pyarrow_3lib_8DataType_3__init__P7_objectS0_S0__ZL37__pyx_f_7pyarrow_3lib_ensure_metadataP7_objectiP44__pyx_opt_args_7pyarrow_3lib_ensure_metadata.constprop.0_ZL50__pyx_getprop_7pyarrow_3lib_8_Tabular_column_namesP7_objectPv_ZL45__pyx_getprop_7pyarrow_3lib_8_Tabular_columnsP7_objectPv_ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_79to_pylistP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_8_Tabular_38to_pydictP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_8_Tabular_37to_pydictP32__pyx_obj_7pyarrow_3lib__TabularE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_8_Tabular_37to_pydictP32__pyx_obj_7pyarrow_3lib__TabularE23__pyx_dict_cached_value_ZL26__Pyx_PyObject_CallMethod1P7_objectS0_S0__ZL20__Pyx_Coroutine_SendP7_objectS0__ZL57__pyx_pw_7pyarrow_3lib_17StringViewBuilder_5append_valuesP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_13StringBuilder_5append_valuesP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_8_Tabular_1__init__P7_objectS0_S0__ZL44__pyx_pw_7pyarrow_3lib_8_Tabular_40to_pylistP7_objectPKS0_lS0__ZL40__pyx_pf_7pyarrow_3lib_6Buffer_6__repr__P30__pyx_obj_7pyarrow_3lib_Buffer_ZL40__pyx_pw_7pyarrow_3lib_6Buffer_7__repr__P7_object_ZL60__pyx_specialmethod___pyx_pw_7pyarrow_3lib_6Buffer_7__repr__P7_objectS0__ZL43__pyx_pw_7pyarrow_3lib_8_Tabular_3__array__P7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E23__pyx_dict_cached_value_ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E18__pyx_dict_version_0_ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E23__pyx_dict_cached_value_0_ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E18__pyx_dict_version_1_ZZL43__pyx_pf_7pyarrow_3lib_8_Tabular_2__array__P32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E23__pyx_dict_cached_value_1_ZL51__pyx_pw_7pyarrow_3lib_12CacheOptions_7_reconstructP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_1writeP7_objectPKS0_lS0__ZL19__Pyx_MergeKeywordsP7_objectS0__ZZL37__pyx_f_7pyarrow_3lib_8DataType_fieldP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objectiE22__pyx_obj_dict_version_ZZL37__pyx_f_7pyarrow_3lib_8DataType_fieldP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objectiE21__pyx_tp_dict_version_ZL39__pyx_pw_7pyarrow_3lib_8DataType_5fieldP7_objectPKS0_lS0__ZZL38__pyx_f_7pyarrow_3lib_9UnionType_fieldP33__pyx_obj_7pyarrow_3lib_UnionTypeP7_objectiE22__pyx_obj_dict_version_ZZL38__pyx_f_7pyarrow_3lib_9UnionType_fieldP33__pyx_obj_7pyarrow_3lib_UnionTypeP7_objectiE21__pyx_tp_dict_version_ZL40__pyx_pw_7pyarrow_3lib_9UnionType_6fieldP7_objectPKS0_lS0__ZZL40__pyx_f_7pyarrow_3lib_10StructType_fieldP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectiE22__pyx_obj_dict_version_ZZL40__pyx_f_7pyarrow_3lib_10StructType_fieldP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectiE21__pyx_tp_dict_version_ZL42__pyx_pw_7pyarrow_3lib_10StructType_3fieldP7_objectPKS0_lS0__ZL36__pyx_f_7pyarrow_3lib__check_is_fileP7_object_ZZL36__pyx_f_7pyarrow_3lib__check_is_fileP7_objectE18__pyx_dict_version_ZZL36__pyx_f_7pyarrow_3lib__check_is_fileP7_objectE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_6OSFile_1__cinit__P7_objectS0_S0__ZZL40__pyx_pf_7pyarrow_3lib_6OSFile___cinit__P30__pyx_obj_7pyarrow_3lib_OSFileP7_objectS2_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL40__pyx_pf_7pyarrow_3lib_6OSFile___cinit__P30__pyx_obj_7pyarrow_3lib_OSFileP7_objectS2_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_6OSFile_1__cinit__P7_objectS0_S0_.cold_ZL33__pyx_tp_new_7pyarrow_3lib_OSFileP11_typeobjectP7_objectS2__ZL36__pyx_pw_7pyarrow_3lib_219memory_mapP7_objectPKS0_lS0__ZL33__pyx_pw_7pyarrow_3lib_179asarrayP7_objectPKS0_lS0__ZZL33__pyx_pf_7pyarrow_3lib_178asarrayP7_objectS0_S0_E18__pyx_dict_version_ZZL33__pyx_pf_7pyarrow_3lib_178asarrayP7_objectS0_S0_E23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_257benchmark_PandasObjectIsNullP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_9StopToken_1__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_21__reduce_cython__P7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_14IpcReadOptions_3__reduce_cython__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_22_RecordBatchFileWriter_3__reduce_cython__P7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_6Tensor_19__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_9__reduce_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_13__reduce_cython__P7_objectPKS0_lS0__ZL66__pyx_pw_7pyarrow_3lib_22CompressedOutputStream_3__reduce_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_17SignalStopHandler_11__reduce_cython__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_6OSFile_5__reduce_cython__P7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_15__reduce_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_18BufferOutputStream_5__reduce_cython__P7_objectPKS0_lS0__ZL65__pyx_pw_7pyarrow_3lib_21FixedSizeBufferWriter_9__reduce_cython__P7_objectPKS0_lS0__ZL68__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamWriter_7__reduce_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_17RecordBatchReader_34__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_29__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_25__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_25__reduce_cython__P7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_15IpcWriteOptions_3__reduce_cython__P7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_10PythonFile_9__reduce_cython__P7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_10NativeFile_73__reduce_cython__P7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_13MessageReader_13__reduce_cython__P7_objectPKS0_lS0__ZL68__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamReader_5__reduce_cython__P7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_13StringBuilder_11__reduce_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_5__reduce_cython__P7_objectPKS0_lS0__ZL63__pyx_pw_7pyarrow_3lib_19BufferedInputStream_5__reduce_cython__P7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_23_ExtensionRegistryNanny_5__reduce_cython__P7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_17StringViewBuilder_11__reduce_cython__P7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_10MemoryPool_11__reduce_cython__P7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_12BufferReader_5__reduce_cython__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_5Codec_21__reduce_cython__P7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_17LoggingMemoryPool_3__reduce_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_20TransformInputStream_3__reduce_cython__P7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_14DictionaryMemo_3__reduce_cython__P7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_15ProxyMemoryPool_3__reduce_cython__P7_objectPKS0_lS0__ZL65__pyx_pw_7pyarrow_3lib_21CompressedInputStream_3__reduce_cython__P7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_7Message_13__reduce_cython__P7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_16MockOutputStream_5__reduce_cython__P7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_9MapScalar_1__getitem__P7_objectS0__ZL50__pyx_pf_7pyarrow_3lib_15TimestampScalar_2__repr__P39__pyx_obj_7pyarrow_3lib_TimestampScalar_ZZL50__pyx_pf_7pyarrow_3lib_15TimestampScalar_2__repr__P39__pyx_obj_7pyarrow_3lib_TimestampScalarE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_15TimestampScalar_2__repr__P39__pyx_obj_7pyarrow_3lib_TimestampScalarE23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_15TimestampScalar_2__repr__P39__pyx_obj_7pyarrow_3lib_TimestampScalarE18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_15TimestampScalar_2__repr__P39__pyx_obj_7pyarrow_3lib_TimestampScalarE23__pyx_dict_cached_value_0_ZL50__pyx_pw_7pyarrow_3lib_15TimestampScalar_3__repr__P7_object_ZL70__pyx_specialmethod___pyx_pw_7pyarrow_3lib_15TimestampScalar_3__repr__P7_objectS0__ZL47__pyx_pw_7pyarrow_3lib_12TableGroupBy_1__init__P7_objectPKS0_lS0__ZL39__pyx_pf_7pyarrow_3lib_6Schema_17__eq__P30__pyx_obj_7pyarrow_3lib_SchemaP7_object_ZL41__pyx_tp_richcompare_7pyarrow_3lib_SchemaP7_objectS0_i_ZL46__pyx_pf_7pyarrow_3lib_12ChunkedArray_35__eq__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_object_ZL47__pyx_tp_richcompare_7pyarrow_3lib_ChunkedArrayP7_objectS0_i_ZL39__pyx_pf_7pyarrow_3lib_6Scalar_12__eq__P30__pyx_obj_7pyarrow_3lib_ScalarP7_object_ZL41__pyx_tp_richcompare_7pyarrow_3lib_ScalarP7_objectS0_i_ZL38__pyx_pf_7pyarrow_3lib_5Array_39__eq__P29__pyx_obj_7pyarrow_3lib_ArrayP7_object_ZL40__pyx_tp_richcompare_7pyarrow_3lib_ArrayP7_objectS0_i_ZL37__pyx_pf_7pyarrow_3lib_5Field_6__eq__P29__pyx_obj_7pyarrow_3lib_FieldP7_object_ZL40__pyx_tp_richcompare_7pyarrow_3lib_FieldP7_objectS0_i_ZL40__pyx_pf_7pyarrow_3lib_8_Tabular_6__eq__P32__pyx_obj_7pyarrow_3lib__TabularP7_object_ZL43__pyx_tp_richcompare_7pyarrow_3lib__TabularP7_objectS0_i_ZL51__pyx_pw_7pyarrow_3lib_10MemoryPool_3release_unusedP7_objectPKS0_lS0__ZL48__pyx_pf_7pyarrow_3lib_13ExtensionType_6__repr__P37__pyx_obj_7pyarrow_3lib_ExtensionType_ZL48__pyx_pw_7pyarrow_3lib_13ExtensionType_7__repr__P7_object_ZL68__pyx_specialmethod___pyx_pw_7pyarrow_3lib_13ExtensionType_7__repr__P7_objectS0__ZL40__pyx_tp_new_7pyarrow_3lib_StringBuilderP11_typeobjectP7_objectS2__ZN5arrow4util8internalL14kNonNullFillerE_ZL47__pyx_pw_7pyarrow_3lib_15TimestampScalar_1as_pyP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE18__pyx_dict_version_1_ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE23__pyx_dict_cached_value_1_ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE23__pyx_dict_cached_value_ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE18__pyx_dict_version_0_ZZL46__pyx_pf_7pyarrow_3lib_15TimestampScalar_as_pyP39__pyx_obj_7pyarrow_3lib_TimestampScalarE23__pyx_dict_cached_value_0_ZL40__pyx_pw_7pyarrow_3lib_35set_memory_poolP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_37_to_pandasP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_16_ReadPandasMixin_1read_pandasP7_objectPKS0_lS0__ZL44__pyx_tp_new_7pyarrow_3lib_StringViewBuilderP11_typeobjectP7_objectS2__ZL47__pyx_pw_7pyarrow_3lib_5Table_33to_struct_arrayP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_5Table_32to_struct_arrayP29__pyx_obj_7pyarrow_3lib_TableP7_objectE18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_5Table_32to_struct_arrayP29__pyx_obj_7pyarrow_3lib_TableP7_objectE23__pyx_dict_cached_value_ZL40__pyx_pw_7pyarrow_3lib_57ensure_metadataP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10NativeFile_7__exit__P7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_17RecordBatchReader_18__exit__P7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_11__exit__P7_objectPKS0_lS0__ZL78__Pyx_Enum_7pyarrow_3lib_enum__dunderpyx_t_7pyarrow_3lib_MetadataVersion_to_py37__pyx_t_7pyarrow_3lib_MetadataVersion_ZZL78__Pyx_Enum_7pyarrow_3lib_enum__dunderpyx_t_7pyarrow_3lib_MetadataVersion_to_py37__pyx_t_7pyarrow_3lib_MetadataVersionE18__pyx_dict_version_ZZL78__Pyx_Enum_7pyarrow_3lib_enum__dunderpyx_t_7pyarrow_3lib_MetadataVersion_to_py37__pyx_t_7pyarrow_3lib_MetadataVersionE23__pyx_dict_cached_value_ZL62__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_metadata_versionP7_objectS0_Pv_ZL45__pyx_pw_7pyarrow_3lib_233_detect_compressionP7_objectPKS0_lS0__ZL19__Pyx_PyInt_As_longP7_object.part.0_ZL22__Pyx_PyInt_As_int64_tP7_object.part.0_ZL22__Pyx_PyInt_As_int64_tP7_object_ZL19__Pyx_PyInt_As_longP7_object_ZL49__pyx_pw_7pyarrow_3lib_263__pyx_unpickle__TabularP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_261__pyx_unpickle__PandasConvertibleP7_objectPKS0_lS0__ZL49__pyx_pw_8EnumBase_1__pyx_unpickle___Pyx_EnumMetaP7_objectPKS0_lS0__ZL57__pyx_setprop_7pyarrow_3lib_12CacheOptions_prefetch_limitP7_objectS0_Pv_ZL59__pyx_setprop_7pyarrow_3lib_12CacheOptions_range_size_limitP7_objectS0_Pv_ZL58__pyx_setprop_7pyarrow_3lib_12CacheOptions_hole_size_limitP7_objectS0_Pv_ZL59__pyx_pw_7pyarrow_3lib_12CacheOptions_5from_network_metricsP7_objectPKS0_lS0__ZL69__pyx_pw_7pyarrow_3lib_21FixedSizeBufferWriter_7set_memcopy_thresholdP7_objectPKS0_lS0__ZL69__pyx_pw_7pyarrow_3lib_21FixedSizeBufferWriter_5set_memcopy_blocksizeP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_8_Tabular_42to_stringP7_objectPKS0_lS0__ZL70__pyx_pw_7pyarrow_3lib_20UnknownExtensionType_3__arrow_ext_serialize__P7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_5Codec_3detectP7_objectPKS0_lS0__ZZL37__pyx_pf_7pyarrow_3lib_5Codec_2detectP7_objectE18__pyx_dict_version_ZZL37__pyx_pf_7pyarrow_3lib_5Codec_2detectP7_objectE23__pyx_dict_cached_value_ZZL37__pyx_pf_7pyarrow_3lib_5Codec_2detectP7_objectE18__pyx_dict_version_0_ZZL37__pyx_pf_7pyarrow_3lib_5Codec_2detectP7_objectE23__pyx_dict_cached_value_0_ZL44__pyx_pw_7pyarrow_3lib_169_datetime_from_intP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_1_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_1_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_5_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_5_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_6_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_6_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_2_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_2_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_3_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_3_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_0_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_0_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E18__pyx_dict_version_4_ZZL44__pyx_pf_7pyarrow_3lib_168_datetime_from_intP7_objectlN5arrow8TimeUnit4typeES0_E23__pyx_dict_cached_value_4_ZL24__Pyx_ImportDottedModuleP7_objectS0__ZL42__pyx_pw_7pyarrow_3lib_10UnionArray_1childP7_objectPKS0_lS0__ZL47__pyx_getprop_7pyarrow_3lib_12ChunkedArray_dataP7_objectPv_ZL20__pyx_pymod_exec_libP7_object_ZL25__pyx_CyFunctionType_type_ZL24__pyx_GeneratorType_type_ZL56__pyx_mdef_8EnumBase_14__Pyx_EnumMeta_7__reduce_cython___ZL58__pyx_mdef_8EnumBase_14__Pyx_EnumMeta_9__setstate_cython___ZL46__pyx_mdef_8EnumBase_14__Pyx_EnumBase_1__new___ZL47__pyx_mdef_8EnumBase_14__Pyx_EnumBase_3__repr___ZL46__pyx_mdef_8EnumBase_14__Pyx_EnumBase_5__str___ZL46__pyx_mdef_8EnumBase_14__Pyx_FlagBase_1__new___ZL47__pyx_mdef_8EnumBase_14__Pyx_FlagBase_3__repr___ZL46__pyx_mdef_8EnumBase_14__Pyx_FlagBase_5__str___ZL51__pyx_mdef_8EnumBase_1__pyx_unpickle___Pyx_EnumMeta_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_0_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_0_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_1_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_1_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_2_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_2_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_3_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_3_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_4_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_4_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version_5_ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value_5_ZL35__pyx_mdef_7pyarrow_3lib_1cpu_count_ZL39__pyx_mdef_7pyarrow_3lib_3set_cpu_count_ZL29__pyx_mdef_7pyarrow_3lib_5_pc_ZL30__pyx_mdef_7pyarrow_3lib_7_pac_ZL43__pyx_mdef_7pyarrow_3lib_9_gdb_test_session_ZL43__pyx_mdef_7pyarrow_3lib_11encode_file_path_ZL34__pyx_mdef_7pyarrow_3lib_13tobytes_ZL36__pyx_mdef_7pyarrow_3lib_15frombytes_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__11__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__11__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__12__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__12__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__13__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__13__ZL49__pyx_mdef_7pyarrow_3lib_13ArrowKeyError_1__str___ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__14__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__14__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__15__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__15__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__16__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__16__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__17__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__17__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__18__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__18__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__19__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__19__ZL51__pyx_mdef_7pyarrow_3lib_14ArrowCancelled_1__init___ZL54__pyx_mdef_7pyarrow_3lib_9StopToken_1__reduce_cython___ZL56__pyx_mdef_7pyarrow_3lib_9StopToken_3__setstate_cython___ZL49__pyx_mdef_7pyarrow_3lib_17enable_signal_handlers_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__20__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__20__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__21__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__21__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__22__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__22__ZL59__pyx_mdef_7pyarrow_3lib_17SignalStopHandler_3_init_signals_ZL55__pyx_mdef_7pyarrow_3lib_17SignalStopHandler_5__enter___ZL54__pyx_mdef_7pyarrow_3lib_17SignalStopHandler_7__exit___ZL64__pyx_mdef_7pyarrow_3lib_17SignalStopHandler_11__reduce_cython___ZL66__pyx_mdef_7pyarrow_3lib_17SignalStopHandler_13__setstate_cython___ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__23__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__23__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__24__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__24__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__25__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__25__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__26__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__26__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__27__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__27__ZL39__pyx_mdef_7pyarrow_3lib_19runtime_info_ZL47__pyx_mdef_7pyarrow_3lib_21set_timezone_db_path_ZL49__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_3series_ZL53__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_5data_frame_ZL54__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_7infer_dtype_ZL55__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_9pandas_dtype_ZL49__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_11is_v1_ZL53__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_13is_ge_v21_ZL52__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_15is_ge_v3_ZL57__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_17is_array_like_ZL58__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_19is_categorical_ZL57__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_21is_datetimetz_ZL68__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_23is_extension_array_dtype_ZL53__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_25is_sparse_ZL57__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_27is_data_frame_ZL53__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_29is_series_ZL52__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_31is_index_ZL54__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_33get_values_ZL68__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_35get_rangeindex_attribute_ZL61__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_37__reduce_cython___ZL63__pyx_mdef_7pyarrow_3lib_14_PandasAPIShim_39__setstate_cython___ZL53__pyx_mdef_7pyarrow_3lib_10MemoryPool_3release_unused_ZL54__pyx_mdef_7pyarrow_3lib_10MemoryPool_5bytes_allocated_ZL49__pyx_mdef_7pyarrow_3lib_10MemoryPool_7max_memory_ZL57__pyx_mdef_7pyarrow_3lib_10MemoryPool_11__reduce_cython___ZL59__pyx_mdef_7pyarrow_3lib_10MemoryPool_13__setstate_cython___ZL63__pyx_mdef_7pyarrow_3lib_17LoggingMemoryPool_3__reduce_cython___ZL65__pyx_mdef_7pyarrow_3lib_17LoggingMemoryPool_5__setstate_cython___ZL61__pyx_mdef_7pyarrow_3lib_15ProxyMemoryPool_3__reduce_cython___ZL63__pyx_mdef_7pyarrow_3lib_15ProxyMemoryPool_5__setstate_cython___ZL46__pyx_mdef_7pyarrow_3lib_23default_memory_pool_ZL44__pyx_mdef_7pyarrow_3lib_25proxy_memory_pool_ZL46__pyx_mdef_7pyarrow_3lib_27logging_memory_pool_ZL45__pyx_mdef_7pyarrow_3lib_29system_memory_pool_ZL47__pyx_mdef_7pyarrow_3lib_31jemalloc_memory_pool_ZL47__pyx_mdef_7pyarrow_3lib_33mimalloc_memory_pool_ZL42__pyx_mdef_7pyarrow_3lib_35set_memory_pool_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__28__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__28__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__29__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__29__ZL49__pyx_mdef_7pyarrow_3lib_37log_memory_allocations_ZL48__pyx_mdef_7pyarrow_3lib_39total_allocated_bytes_ZL48__pyx_mdef_7pyarrow_3lib_41jemalloc_set_decay_ms_ZL52__pyx_mdef_7pyarrow_3lib_43supported_memory_backends_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__30__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__30__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__31__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__31__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__32__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__32__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__33__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__33__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__34__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__34__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__35__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__35__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__36__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__36__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__37__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__37__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__38__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__38__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__39__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__39__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__40__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__40__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__41__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__41__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__42__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__42__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__43__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__43__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__44__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__44__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__45__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__45__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__46__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__46__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__47__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__47__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__48__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__48__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__49__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__49__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__50__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__50__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__51__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__51__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__52__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__52__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__53__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__53__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__54__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__54__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__55__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__55__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__56__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__56__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__57__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__57__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__58__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__58__ZL40__pyx_mdef_7pyarrow_3lib_45_is_primitive_ZL43__pyx_mdef_7pyarrow_3lib_47_get_pandas_type_ZL46__pyx_mdef_7pyarrow_3lib_49_get_pandas_tz_type_ZL43__pyx_mdef_7pyarrow_3lib_51_to_pandas_dtype_ZL41__pyx_mdef_7pyarrow_3lib_8DataType_5field_ZL47__pyx_mdef_7pyarrow_3lib_8DataType_11__reduce___ZL43__pyx_mdef_7pyarrow_3lib_8DataType_17equals_ZL52__pyx_mdef_7pyarrow_3lib_8DataType_19to_pandas_dtype_ZL49__pyx_mdef_7pyarrow_3lib_8DataType_21_export_to_c_ZL51__pyx_mdef_7pyarrow_3lib_8DataType_23_import_from_c_ZL55__pyx_mdef_7pyarrow_3lib_8DataType_25__arrow_c_schema___ZL59__pyx_mdef_7pyarrow_3lib_8DataType_27_import_from_c_capsule_ZL60__pyx_mdef_7pyarrow_3lib_14DictionaryMemo_3__reduce_cython___ZL62__pyx_mdef_7pyarrow_3lib_14DictionaryMemo_5__setstate_cython___ZL53__pyx_mdef_7pyarrow_3lib_14DictionaryType_1__reduce___ZL46__pyx_mdef_7pyarrow_3lib_8ListType_1__reduce___ZL52__pyx_mdef_7pyarrow_3lib_13LargeListType_1__reduce___ZL51__pyx_mdef_7pyarrow_3lib_12ListViewType_1__reduce___ZL56__pyx_mdef_7pyarrow_3lib_17LargeListViewType_1__reduce___ZL45__pyx_mdef_7pyarrow_3lib_7MapType_1__reduce___ZL56__pyx_mdef_7pyarrow_3lib_17FixedSizeListType_1__reduce___ZL54__pyx_mdef_7pyarrow_3lib_10StructType_1get_field_index_ZL44__pyx_mdef_7pyarrow_3lib_10StructType_3field_ZL60__pyx_mdef_7pyarrow_3lib_10StructType_5get_all_field_indices_ZL50__pyx_mdef_7pyarrow_3lib_10StructType_14__reduce___ZL42__pyx_mdef_7pyarrow_3lib_9UnionType_6field_ZL48__pyx_mdef_7pyarrow_3lib_9UnionType_10__reduce___ZL52__pyx_mdef_7pyarrow_3lib_13TimestampType_1__reduce___ZL58__pyx_mdef_7pyarrow_3lib_19FixedSizeBinaryType_1__reduce___ZL53__pyx_mdef_7pyarrow_3lib_14Decimal128Type_1__reduce___ZL53__pyx_mdef_7pyarrow_3lib_14Decimal256Type_1__reduce___ZL56__pyx_mdef_7pyarrow_3lib_17RunEndEncodedType_1__reduce___ZL65__pyx_mdef_7pyarrow_3lib_17BaseExtensionType_1__arrow_ext_class___ZL72__pyx_mdef_7pyarrow_3lib_17BaseExtensionType_3__arrow_ext_scalar_class___ZL56__pyx_mdef_7pyarrow_3lib_17BaseExtensionType_5wrap_array_ZL65__pyx_mdef_7pyarrow_3lib_13ExtensionType_9__arrow_ext_serialize___ZL68__pyx_mdef_7pyarrow_3lib_13ExtensionType_11__arrow_ext_deserialize___ZL53__pyx_mdef_7pyarrow_3lib_13ExtensionType_13__reduce___ZL62__pyx_mdef_7pyarrow_3lib_13ExtensionType_15__arrow_ext_class___ZL69__pyx_mdef_7pyarrow_3lib_13ExtensionType_17__arrow_ext_scalar_class___ZL68__pyx_mdef_7pyarrow_3lib_20FixedShapeTensorType_1__arrow_ext_class___ZL59__pyx_mdef_7pyarrow_3lib_20FixedShapeTensorType_3__reduce___ZL75__pyx_mdef_7pyarrow_3lib_20FixedShapeTensorType_5__arrow_ext_scalar_class___ZL54__pyx_mdef_7pyarrow_3lib_15PyExtensionType_5__reduce___ZL67__pyx_mdef_7pyarrow_3lib_15PyExtensionType_7__arrow_ext_serialize___ZL69__pyx_mdef_7pyarrow_3lib_15PyExtensionType_9__arrow_ext_deserialize___ZL58__pyx_mdef_7pyarrow_3lib_15PyExtensionType_11set_auto_load_ZL72__pyx_mdef_7pyarrow_3lib_20UnknownExtensionType_3__arrow_ext_serialize___ZL50__pyx_mdef_7pyarrow_3lib_53register_extension_type_ZL52__pyx_mdef_7pyarrow_3lib_55unregister_extension_type_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__59__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__59__ZL41__pyx_type_7pyarrow_3lib_KeyValueMetadata_ZL51__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_3equals_ZL56__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_19__reduce___ZL49__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_21key_ZL51__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_23value_ZL50__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_25keys_ZL52__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_28values_ZL51__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_31items_ZL53__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_34get_all_ZL53__pyx_mdef_7pyarrow_3lib_16KeyValueMetadata_36to_dict_ZL42__pyx_mdef_7pyarrow_3lib_57ensure_metadata_ZL39__pyx_mdef_7pyarrow_3lib_5Field_5equals_ZL43__pyx_mdef_7pyarrow_3lib_5Field_9__reduce___ZL47__pyx_mdef_7pyarrow_3lib_5Field_17with_metadata_ZL49__pyx_mdef_7pyarrow_3lib_5Field_19remove_metadata_ZL43__pyx_mdef_7pyarrow_3lib_5Field_21with_type_ZL43__pyx_mdef_7pyarrow_3lib_5Field_23with_name_ZL47__pyx_mdef_7pyarrow_3lib_5Field_25with_nullable_ZL41__pyx_mdef_7pyarrow_3lib_5Field_27flatten_ZL46__pyx_mdef_7pyarrow_3lib_5Field_29_export_to_c_ZL48__pyx_mdef_7pyarrow_3lib_5Field_31_import_from_c_ZL52__pyx_mdef_7pyarrow_3lib_5Field_33__arrow_c_schema___ZL56__pyx_mdef_7pyarrow_3lib_5Field_35_import_from_c_capsule_ZL45__pyx_mdef_7pyarrow_3lib_6Schema_12__reduce___ZL45__pyx_mdef_7pyarrow_3lib_6Schema_16__sizeof___ZL46__pyx_mdef_7pyarrow_3lib_6Schema_20empty_table_ZL41__pyx_mdef_7pyarrow_3lib_6Schema_22equals_ZL46__pyx_mdef_7pyarrow_3lib_6Schema_24from_pandas_ZL40__pyx_mdef_7pyarrow_3lib_6Schema_26field_ZL41__pyx_mdef_7pyarrow_3lib_6Schema_28_field_ZL48__pyx_mdef_7pyarrow_3lib_6Schema_30field_by_name_ZL50__pyx_mdef_7pyarrow_3lib_6Schema_32get_field_index_ZL56__pyx_mdef_7pyarrow_3lib_6Schema_34get_all_field_indices_ZL41__pyx_mdef_7pyarrow_3lib_6Schema_36append_ZL41__pyx_mdef_7pyarrow_3lib_6Schema_38insert_ZL41__pyx_mdef_7pyarrow_3lib_6Schema_40remove_ZL38__pyx_mdef_7pyarrow_3lib_6Schema_42set_ZL47__pyx_mdef_7pyarrow_3lib_6Schema_44add_metadata_ZL48__pyx_mdef_7pyarrow_3lib_6Schema_46with_metadata_ZL44__pyx_mdef_7pyarrow_3lib_6Schema_48serialize_ZL50__pyx_mdef_7pyarrow_3lib_6Schema_50remove_metadata_ZL44__pyx_mdef_7pyarrow_3lib_6Schema_52to_string_ZL47__pyx_mdef_7pyarrow_3lib_6Schema_54_export_to_c_ZL49__pyx_mdef_7pyarrow_3lib_6Schema_56_import_from_c_ZL53__pyx_mdef_7pyarrow_3lib_6Schema_62__arrow_c_schema___ZL57__pyx_mdef_7pyarrow_3lib_6Schema_64_import_from_c_capsule_ZL40__pyx_mdef_7pyarrow_3lib_59unify_schemas_ZL32__pyx_mdef_7pyarrow_3lib_61field_ZL31__pyx_mdef_7pyarrow_3lib_63null_ZL32__pyx_mdef_7pyarrow_3lib_65bool__ZL32__pyx_mdef_7pyarrow_3lib_67uint8_ZL31__pyx_mdef_7pyarrow_3lib_69int8_ZL33__pyx_mdef_7pyarrow_3lib_71uint16_ZL32__pyx_mdef_7pyarrow_3lib_73int16_ZL33__pyx_mdef_7pyarrow_3lib_75uint32_ZL32__pyx_mdef_7pyarrow_3lib_77int32_ZL33__pyx_mdef_7pyarrow_3lib_79uint64_ZL32__pyx_mdef_7pyarrow_3lib_81int64_ZL43__pyx_mdef_7pyarrow_3lib_83tzinfo_to_string_ZL43__pyx_mdef_7pyarrow_3lib_85string_to_tzinfo_ZL36__pyx_mdef_7pyarrow_3lib_87timestamp_ZL33__pyx_mdef_7pyarrow_3lib_89time32_ZL33__pyx_mdef_7pyarrow_3lib_91time64_ZL35__pyx_mdef_7pyarrow_3lib_93duration_ZL50__pyx_mdef_7pyarrow_3lib_95month_day_nano_interval_ZL33__pyx_mdef_7pyarrow_3lib_97date32_ZL33__pyx_mdef_7pyarrow_3lib_99date64_ZL35__pyx_mdef_7pyarrow_3lib_101float16_ZL35__pyx_mdef_7pyarrow_3lib_103float32_ZL35__pyx_mdef_7pyarrow_3lib_105float64_ZL38__pyx_mdef_7pyarrow_3lib_107decimal128_ZL38__pyx_mdef_7pyarrow_3lib_109decimal256_ZL34__pyx_mdef_7pyarrow_3lib_111string_ZL32__pyx_mdef_7pyarrow_3lib_113utf8_ZL34__pyx_mdef_7pyarrow_3lib_115binary_ZL40__pyx_mdef_7pyarrow_3lib_117large_binary_ZL40__pyx_mdef_7pyarrow_3lib_119large_string_ZL38__pyx_mdef_7pyarrow_3lib_121large_utf8_ZL39__pyx_mdef_7pyarrow_3lib_123binary_view_ZL39__pyx_mdef_7pyarrow_3lib_125string_view_ZL33__pyx_mdef_7pyarrow_3lib_127list__ZL38__pyx_mdef_7pyarrow_3lib_129large_list_ZL37__pyx_mdef_7pyarrow_3lib_131list_view_ZL43__pyx_mdef_7pyarrow_3lib_133large_list_view_ZL32__pyx_mdef_7pyarrow_3lib_135map__ZL38__pyx_mdef_7pyarrow_3lib_137dictionary_ZL34__pyx_mdef_7pyarrow_3lib_139struct_ZL40__pyx_mdef_7pyarrow_3lib_141sparse_union_ZL39__pyx_mdef_7pyarrow_3lib_143dense_union_ZL33__pyx_mdef_7pyarrow_3lib_145union_ZL43__pyx_mdef_7pyarrow_3lib_147run_end_encoded_ZL46__pyx_mdef_7pyarrow_3lib_149fixed_shape_tensor_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__60__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__60__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__61__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__61__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__62__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__62__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__63__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__63__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__64__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__64__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__65__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__65__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__66__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__66__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__67__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__67__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__68__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__68__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__69__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__69__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__70__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__70__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__71__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__71__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__72__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__72__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__73__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__73__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__74__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__74__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__75__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__75__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__76__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__76__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__77__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__77__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__78__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__78__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__79__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__79__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__80__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__80__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__81__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__81__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__82__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__82__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__83__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__83__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__84__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__84__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__85__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__85__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__86__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__86__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__87__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__87__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__88__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__88__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__89__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__89__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__90__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__90__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__91__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__91__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__92__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__92__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__93__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__93__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__94__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__94__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__95__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__95__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__96__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__96__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__97__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__97__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__98__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__98__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__99__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__99__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__100__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__100__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__101__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__101__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__102__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__102__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__103__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__103__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__104__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__104__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__105__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__105__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__106__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__106__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__107__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__107__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__108__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__108__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__109__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__109__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__110__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__110__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__111__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__111__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__112__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__112__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__113__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__113__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__114__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__114__ZL42__pyx_mdef_7pyarrow_3lib_151type_for_alias_ZL39__pyx_mdef_7pyarrow_3lib_153ensure_type_ZL34__pyx_mdef_7pyarrow_3lib_155schema_ZL44__pyx_mdef_7pyarrow_3lib_157from_numpy_dtype_ZL44__pyx_mdef_7pyarrow_3lib_159is_boolean_value_ZL44__pyx_mdef_7pyarrow_3lib_161is_integer_value_ZL42__pyx_mdef_7pyarrow_3lib_163is_float_value_ZL68__pyx_mdef_7pyarrow_3lib_23_ExtensionRegistryNanny_3release_registry_ZL69__pyx_mdef_7pyarrow_3lib_23_ExtensionRegistryNanny_5__reduce_cython___ZL71__pyx_mdef_7pyarrow_3lib_23_ExtensionRegistryNanny_7__setstate_cython___ZL55__pyx_mdef_7pyarrow_3lib_165_register_py_extension_type_ZL58__pyx_mdef_7pyarrow_3lib_167_unregister_py_extension_types_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__115__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__115__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__116__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__116__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__117__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__117__ZL38__pyx_mdef_7pyarrow_3lib_6Scalar_3cast_ZL42__pyx_mdef_7pyarrow_3lib_6Scalar_5validate_ZL41__pyx_mdef_7pyarrow_3lib_6Scalar_11equals_ZL45__pyx_mdef_7pyarrow_3lib_6Scalar_17__reduce___ZL40__pyx_mdef_7pyarrow_3lib_6Scalar_19as_py_ZL44__pyx_mdef_7pyarrow_3lib_10NullScalar_5as_py_ZL47__pyx_mdef_7pyarrow_3lib_13BooleanScalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11UInt8Scalar_1as_py_ZL44__pyx_mdef_7pyarrow_3lib_10Int8Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12UInt16Scalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11Int16Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12UInt32Scalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11Int32Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12UInt64Scalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11Int64Scalar_1as_py_ZL49__pyx_mdef_7pyarrow_3lib_15HalfFloatScalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11FloatScalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12DoubleScalar_1as_py_ZL50__pyx_mdef_7pyarrow_3lib_16Decimal128Scalar_1as_py_ZL50__pyx_mdef_7pyarrow_3lib_16Decimal256Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12Date32Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12Date64Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_169_datetime_from_int_ZL46__pyx_mdef_7pyarrow_3lib_12Time32Scalar_1as_py_ZL46__pyx_mdef_7pyarrow_3lib_12Time64Scalar_1as_py_ZL49__pyx_mdef_7pyarrow_3lib_15TimestampScalar_1as_py_ZL48__pyx_mdef_7pyarrow_3lib_14DurationScalar_1as_py_ZL60__pyx_mdef_7pyarrow_3lib_26MonthDayNanoIntervalScalar_1as_py_ZL50__pyx_mdef_7pyarrow_3lib_12BinaryScalar_1as_buffer_ZL46__pyx_mdef_7pyarrow_3lib_12BinaryScalar_3as_py_ZL46__pyx_mdef_7pyarrow_3lib_12StringScalar_1as_py_ZL44__pyx_mdef_7pyarrow_3lib_10ListScalar_7as_py_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__118__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__118__ZL37__pyx_type_7pyarrow_3lib_StructScalar_ZL51__pyx_doc_7pyarrow_3lib_12StructScalar_9__getitem___ZL46__pyx_mdef_7pyarrow_3lib_12StructScalar_6items_ZL47__pyx_mdef_7pyarrow_3lib_12StructScalar_12as_py_ZL54__pyx_mdef_7pyarrow_3lib_12StructScalar_14_as_py_tuple_ZL42__pyx_mdef_7pyarrow_3lib_9MapScalar_6as_py_ZL57__pyx_mdef_7pyarrow_3lib_16DictionaryScalar_1_reconstruct_ZL55__pyx_mdef_7pyarrow_3lib_16DictionaryScalar_3__reduce___ZL50__pyx_mdef_7pyarrow_3lib_16DictionaryScalar_5as_py_ZL53__pyx_mdef_7pyarrow_3lib_19RunEndEncodedScalar_1as_py_ZL45__pyx_mdef_7pyarrow_3lib_11UnionScalar_1as_py_ZL49__pyx_mdef_7pyarrow_3lib_15ExtensionScalar_1as_py_ZL56__pyx_mdef_7pyarrow_3lib_15ExtensionScalar_3from_storage_ZL59__pyx_mdef_7pyarrow_3lib_22FixedShapeTensorScalar_1to_numpy_ZL60__pyx_mdef_7pyarrow_3lib_22FixedShapeTensorScalar_3to_tensor_ZL34__pyx_mdef_7pyarrow_3lib_171scalar_ZL50__pyx_mdef_7pyarrow_3lib_173_ndarray_to_arrow_type_ZL56__pyx_mdef_7pyarrow_3lib_175_handle_arrow_array_protocol_ZL33__pyx_mdef_7pyarrow_3lib_177array_ZL35__pyx_mdef_7pyarrow_3lib_179asarray_ZL33__pyx_mdef_7pyarrow_3lib_181nulls_ZL34__pyx_mdef_7pyarrow_3lib_183repeat_ZL38__pyx_mdef_7pyarrow_3lib_185infer_type_ZL44__pyx_mdef_7pyarrow_3lib_187_normalize_slice_ZL42__pyx_mdef_7pyarrow_3lib_189_restore_array_ZL56__pyx_mdef_7pyarrow_3lib_18_PandasConvertible_1to_pandas_ZL64__pyx_mdef_7pyarrow_3lib_18_PandasConvertible_3__reduce_cython___ZL66__pyx_mdef_7pyarrow_3lib_18_PandasConvertible_5__setstate_cython___ZL45__pyx_mdef_7pyarrow_3lib_5Array_3_debug_print_ZL37__pyx_mdef_7pyarrow_3lib_5Array_5diff_ZL37__pyx_mdef_7pyarrow_3lib_5Array_7cast_ZL37__pyx_mdef_7pyarrow_3lib_5Array_9view_ZL37__pyx_mdef_7pyarrow_3lib_5Array_11sum_ZL40__pyx_mdef_7pyarrow_3lib_5Array_13unique_ZL51__pyx_mdef_7pyarrow_3lib_5Array_15dictionary_encode_ZL46__pyx_mdef_7pyarrow_3lib_5Array_17value_counts_ZL45__pyx_mdef_7pyarrow_3lib_5Array_19from_pandas_ZL44__pyx_mdef_7pyarrow_3lib_5Array_21__reduce___ZL46__pyx_mdef_7pyarrow_3lib_5Array_23from_buffers_ZL55__pyx_mdef_7pyarrow_3lib_5Array_25get_total_buffer_size_ZL44__pyx_mdef_7pyarrow_3lib_5Array_27__sizeof___ZL43__pyx_mdef_7pyarrow_3lib_5Array_34to_string_ZL40__pyx_mdef_7pyarrow_3lib_5Array_36format_ZL40__pyx_mdef_7pyarrow_3lib_5Array_42equals_ZL41__pyx_mdef_7pyarrow_3lib_5Array_46is_null_ZL40__pyx_mdef_7pyarrow_3lib_5Array_48is_nan_ZL42__pyx_mdef_7pyarrow_3lib_5Array_50is_valid_ZL43__pyx_mdef_7pyarrow_3lib_5Array_52fill_null_ZL39__pyx_mdef_7pyarrow_3lib_5Array_56slice_ZL38__pyx_mdef_7pyarrow_3lib_5Array_58take_ZL43__pyx_mdef_7pyarrow_3lib_5Array_60drop_null_ZL40__pyx_mdef_7pyarrow_3lib_5Array_62filter_ZL39__pyx_mdef_7pyarrow_3lib_5Array_64index_ZL38__pyx_mdef_7pyarrow_3lib_5Array_66sort_ZL44__pyx_mdef_7pyarrow_3lib_5Array_68_to_pandas_ZL43__pyx_mdef_7pyarrow_3lib_5Array_70__array___ZL42__pyx_mdef_7pyarrow_3lib_5Array_72to_numpy_ZL43__pyx_mdef_7pyarrow_3lib_5Array_74to_pylist_ZL40__pyx_mdef_7pyarrow_3lib_5Array_76tolist_ZL42__pyx_mdef_7pyarrow_3lib_5Array_78validate_ZL41__pyx_mdef_7pyarrow_3lib_5Array_80buffers_ZL46__pyx_mdef_7pyarrow_3lib_5Array_82_export_to_c_ZL48__pyx_mdef_7pyarrow_3lib_5Array_84_import_from_c_ZL51__pyx_mdef_7pyarrow_3lib_5Array_86__arrow_c_array___ZL56__pyx_mdef_7pyarrow_3lib_5Array_88_import_from_c_capsule_ZL53__pyx_mdef_7pyarrow_3lib_5Array_90_export_to_c_device_ZL55__pyx_mdef_7pyarrow_3lib_5Array_92_import_from_c_device_ZL44__pyx_mdef_7pyarrow_3lib_5Array_94__dlpack___ZL51__pyx_mdef_7pyarrow_3lib_5Array_96__dlpack_device___ZL63__pyx_mdef_7pyarrow_3lib_25MonthDayNanoIntervalArray_1to_pylist_ZL49__pyx_mdef_7pyarrow_3lib_13BaseListArray_1flatten_ZL62__pyx_mdef_7pyarrow_3lib_13BaseListArray_3value_parent_indices_ZL55__pyx_mdef_7pyarrow_3lib_13BaseListArray_5value_lengths_ZL48__pyx_mdef_7pyarrow_3lib_9ListArray_1from_arrays_ZL54__pyx_mdef_7pyarrow_3lib_14LargeListArray_1from_arrays_ZL53__pyx_mdef_7pyarrow_3lib_13ListViewArray_1from_arrays_ZL49__pyx_mdef_7pyarrow_3lib_13ListViewArray_3flatten_ZL58__pyx_mdef_7pyarrow_3lib_18LargeListViewArray_1from_arrays_ZL54__pyx_mdef_7pyarrow_3lib_18LargeListViewArray_3flatten_ZL47__pyx_mdef_7pyarrow_3lib_8MapArray_1from_arrays_ZL58__pyx_mdef_7pyarrow_3lib_18FixedSizeListArray_1from_arrays_ZL44__pyx_mdef_7pyarrow_3lib_10UnionArray_1child_ZL44__pyx_mdef_7pyarrow_3lib_10UnionArray_3field_ZL49__pyx_mdef_7pyarrow_3lib_10UnionArray_5from_dense_ZL50__pyx_mdef_7pyarrow_3lib_10UnionArray_7from_sparse_ZL52__pyx_mdef_7pyarrow_3lib_11StringArray_1from_buffers_ZL57__pyx_mdef_7pyarrow_3lib_16LargeStringArray_1from_buffers_ZL61__pyx_mdef_7pyarrow_3lib_15DictionaryArray_1dictionary_encode_ZL61__pyx_mdef_7pyarrow_3lib_15DictionaryArray_3dictionary_decode_ZL56__pyx_mdef_7pyarrow_3lib_15DictionaryArray_5from_buffers_ZL55__pyx_mdef_7pyarrow_3lib_15DictionaryArray_7from_arrays_ZL45__pyx_mdef_7pyarrow_3lib_11StructArray_1field_ZL56__pyx_mdef_7pyarrow_3lib_11StructArray_3_flattened_field_ZL47__pyx_mdef_7pyarrow_3lib_11StructArray_5flatten_ZL51__pyx_mdef_7pyarrow_3lib_11StructArray_7from_arrays_ZL44__pyx_mdef_7pyarrow_3lib_11StructArray_9sort_ZL59__pyx_mdef_7pyarrow_3lib_18RunEndEncodedArray_1_from_arrays_ZL58__pyx_mdef_7pyarrow_3lib_18RunEndEncodedArray_3from_arrays_ZL59__pyx_mdef_7pyarrow_3lib_18RunEndEncodedArray_5from_buffers_ZL67__pyx_mdef_7pyarrow_3lib_18RunEndEncodedArray_7find_physical_offset_ZL67__pyx_mdef_7pyarrow_3lib_18RunEndEncodedArray_9find_physical_length_ZL55__pyx_mdef_7pyarrow_3lib_14ExtensionArray_1from_storage_ZL66__pyx_mdef_7pyarrow_3lib_21FixedShapeTensorArray_1to_numpy_ndarray_ZL59__pyx_mdef_7pyarrow_3lib_21FixedShapeTensorArray_3to_tensor_ZL68__pyx_mdef_7pyarrow_3lib_21FixedShapeTensorArray_5from_numpy_ndarray_ZL41__pyx_mdef_7pyarrow_3lib_191concat_arrays_ZL40__pyx_mdef_7pyarrow_3lib_193_empty_array_ZL48__pyx_mdef_7pyarrow_3lib_13StringBuilder_3append_ZL55__pyx_mdef_7pyarrow_3lib_13StringBuilder_5append_values_ZL48__pyx_mdef_7pyarrow_3lib_13StringBuilder_7finish_ZL60__pyx_mdef_7pyarrow_3lib_13StringBuilder_11__reduce_cython___ZL62__pyx_mdef_7pyarrow_3lib_13StringBuilder_13__setstate_cython___ZL52__pyx_mdef_7pyarrow_3lib_17StringViewBuilder_3append_ZL59__pyx_mdef_7pyarrow_3lib_17StringViewBuilder_5append_values_ZL52__pyx_mdef_7pyarrow_3lib_17StringViewBuilder_7finish_ZL64__pyx_mdef_7pyarrow_3lib_17StringViewBuilder_11__reduce_cython___ZL66__pyx_mdef_7pyarrow_3lib_17StringViewBuilder_13__setstate_cython___ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_5__reduce___ZL47__pyx_mdef_7pyarrow_3lib_12ChunkedArray_7length_ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_13to_string_ZL48__pyx_mdef_7pyarrow_3lib_12ChunkedArray_15format_ZL50__pyx_mdef_7pyarrow_3lib_12ChunkedArray_19validate_ZL63__pyx_mdef_7pyarrow_3lib_12ChunkedArray_21get_total_buffer_size_ZL52__pyx_mdef_7pyarrow_3lib_12ChunkedArray_23__sizeof___ZL49__pyx_mdef_7pyarrow_3lib_12ChunkedArray_30is_null_ZL48__pyx_mdef_7pyarrow_3lib_12ChunkedArray_32is_nan_ZL50__pyx_mdef_7pyarrow_3lib_12ChunkedArray_34is_valid_ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_38fill_null_ZL48__pyx_mdef_7pyarrow_3lib_12ChunkedArray_40equals_ZL52__pyx_mdef_7pyarrow_3lib_12ChunkedArray_42_to_pandas_ZL50__pyx_mdef_7pyarrow_3lib_12ChunkedArray_44to_numpy_ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_46__array___ZL46__pyx_mdef_7pyarrow_3lib_12ChunkedArray_48cast_ZL59__pyx_mdef_7pyarrow_3lib_12ChunkedArray_50dictionary_encode_ZL49__pyx_mdef_7pyarrow_3lib_12ChunkedArray_52flatten_ZL56__pyx_mdef_7pyarrow_3lib_12ChunkedArray_54combine_chunks_ZL48__pyx_mdef_7pyarrow_3lib_12ChunkedArray_56unique_ZL54__pyx_mdef_7pyarrow_3lib_12ChunkedArray_58value_counts_ZL47__pyx_mdef_7pyarrow_3lib_12ChunkedArray_60slice_ZL48__pyx_mdef_7pyarrow_3lib_12ChunkedArray_62filter_ZL47__pyx_mdef_7pyarrow_3lib_12ChunkedArray_64index_ZL46__pyx_mdef_7pyarrow_3lib_12ChunkedArray_66take_ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_68drop_null_ZL46__pyx_mdef_7pyarrow_3lib_12ChunkedArray_70sort_ZL60__pyx_mdef_7pyarrow_3lib_12ChunkedArray_72unify_dictionaries_ZL47__pyx_mdef_7pyarrow_3lib_12ChunkedArray_74chunk_ZL52__pyx_mdef_7pyarrow_3lib_12ChunkedArray_76iterchunks_ZL51__pyx_mdef_7pyarrow_3lib_12ChunkedArray_79to_pylist_ZL60__pyx_mdef_7pyarrow_3lib_12ChunkedArray_81__arrow_c_stream___ZL64__pyx_mdef_7pyarrow_3lib_12ChunkedArray_83_import_from_c_capsule_ZL41__pyx_mdef_7pyarrow_3lib_195chunked_array_ZL45__pyx_mdef_7pyarrow_3lib_8_Tabular_3__array___ZL49__pyx_mdef_7pyarrow_3lib_8_Tabular_5__dataframe___ZL44__pyx_mdef_7pyarrow_3lib_8_Tabular_15_column_ZL58__pyx_mdef_7pyarrow_3lib_8_Tabular_17_ensure_integer_index_ZL52__pyx_mdef_7pyarrow_3lib_8_Tabular_19_is_initialized_ZL43__pyx_mdef_7pyarrow_3lib_8_Tabular_21column_ZL46__pyx_mdef_7pyarrow_3lib_8_Tabular_23drop_null_ZL42__pyx_mdef_7pyarrow_3lib_8_Tabular_25field_ZL48__pyx_mdef_7pyarrow_3lib_8_Tabular_27from_pydict_ZL48__pyx_mdef_7pyarrow_3lib_8_Tabular_29from_pylist_ZL48__pyx_mdef_7pyarrow_3lib_8_Tabular_31itercolumns_ZL44__pyx_mdef_7pyarrow_3lib_8_Tabular_34sort_by_ZL41__pyx_mdef_7pyarrow_3lib_8_Tabular_36take_ZL46__pyx_mdef_7pyarrow_3lib_8_Tabular_38to_pydict_ZL46__pyx_mdef_7pyarrow_3lib_8_Tabular_40to_pylist_ZL46__pyx_mdef_7pyarrow_3lib_8_Tabular_42to_string_ZL50__pyx_mdef_7pyarrow_3lib_8_Tabular_44remove_column_ZL49__pyx_mdef_7pyarrow_3lib_8_Tabular_46drop_columns_ZL47__pyx_mdef_7pyarrow_3lib_8_Tabular_48add_column_ZL50__pyx_mdef_7pyarrow_3lib_8_Tabular_50append_column_ZL54__pyx_mdef_7pyarrow_3lib_8_Tabular_52__reduce_cython___ZL56__pyx_mdef_7pyarrow_3lib_8_Tabular_54__setstate_cython___ZL55__pyx_mdef_7pyarrow_3lib_11RecordBatch_3_is_initialized_ZL50__pyx_mdef_7pyarrow_3lib_11RecordBatch_5__reduce___ZL48__pyx_mdef_7pyarrow_3lib_11RecordBatch_7validate_ZL63__pyx_mdef_7pyarrow_3lib_11RecordBatch_9replace_schema_metadata_ZL48__pyx_mdef_7pyarrow_3lib_11RecordBatch_11_column_ZL62__pyx_mdef_7pyarrow_3lib_11RecordBatch_13get_total_buffer_size_ZL51__pyx_mdef_7pyarrow_3lib_11RecordBatch_15__sizeof___ZL51__pyx_mdef_7pyarrow_3lib_11RecordBatch_17add_column_ZL54__pyx_mdef_7pyarrow_3lib_11RecordBatch_19remove_column_ZL51__pyx_mdef_7pyarrow_3lib_11RecordBatch_21set_column_ZL55__pyx_mdef_7pyarrow_3lib_11RecordBatch_23rename_columns_ZL50__pyx_mdef_7pyarrow_3lib_11RecordBatch_25serialize_ZL46__pyx_mdef_7pyarrow_3lib_11RecordBatch_27slice_ZL47__pyx_mdef_7pyarrow_3lib_11RecordBatch_29filter_ZL47__pyx_mdef_7pyarrow_3lib_11RecordBatch_31equals_ZL47__pyx_mdef_7pyarrow_3lib_11RecordBatch_33select_ZL45__pyx_mdef_7pyarrow_3lib_11RecordBatch_35cast_ZL51__pyx_mdef_7pyarrow_3lib_11RecordBatch_37_to_pandas_ZL52__pyx_mdef_7pyarrow_3lib_11RecordBatch_39from_pandas_ZL52__pyx_mdef_7pyarrow_3lib_11RecordBatch_41from_arrays_ZL58__pyx_mdef_7pyarrow_3lib_11RecordBatch_43from_struct_array_ZL56__pyx_mdef_7pyarrow_3lib_11RecordBatch_45to_struct_array_ZL50__pyx_mdef_7pyarrow_3lib_11RecordBatch_47to_tensor_ZL53__pyx_mdef_7pyarrow_3lib_11RecordBatch_49_export_to_c_ZL55__pyx_mdef_7pyarrow_3lib_11RecordBatch_51_import_from_c_ZL58__pyx_mdef_7pyarrow_3lib_11RecordBatch_53__arrow_c_array___ZL59__pyx_mdef_7pyarrow_3lib_11RecordBatch_55__arrow_c_stream___ZL63__pyx_mdef_7pyarrow_3lib_11RecordBatch_57_import_from_c_capsule_ZL60__pyx_mdef_7pyarrow_3lib_11RecordBatch_59_export_to_c_device_ZL62__pyx_mdef_7pyarrow_3lib_11RecordBatch_61_import_from_c_device_ZL53__pyx_mdef_7pyarrow_3lib_197_reconstruct_record_batch_ZL43__pyx_mdef_7pyarrow_3lib_199table_to_blocks_ZL48__pyx_mdef_7pyarrow_3lib_5Table_3_is_initialized_ZL41__pyx_mdef_7pyarrow_3lib_5Table_5validate_ZL43__pyx_mdef_7pyarrow_3lib_5Table_7__reduce___ZL38__pyx_mdef_7pyarrow_3lib_5Table_9slice_ZL40__pyx_mdef_7pyarrow_3lib_5Table_11filter_ZL40__pyx_mdef_7pyarrow_3lib_5Table_13select_ZL57__pyx_mdef_7pyarrow_3lib_5Table_15replace_schema_metadata_ZL41__pyx_mdef_7pyarrow_3lib_5Table_17flatten_ZL48__pyx_mdef_7pyarrow_3lib_5Table_19combine_chunks_ZL52__pyx_mdef_7pyarrow_3lib_5Table_21unify_dictionaries_ZL40__pyx_mdef_7pyarrow_3lib_5Table_23equals_ZL38__pyx_mdef_7pyarrow_3lib_5Table_25cast_ZL45__pyx_mdef_7pyarrow_3lib_5Table_27from_pandas_ZL45__pyx_mdef_7pyarrow_3lib_5Table_29from_arrays_ZL51__pyx_mdef_7pyarrow_3lib_5Table_31from_struct_array_ZL49__pyx_mdef_7pyarrow_3lib_5Table_33to_struct_array_ZL46__pyx_mdef_7pyarrow_3lib_5Table_35from_batches_ZL44__pyx_mdef_7pyarrow_3lib_5Table_37to_batches_ZL43__pyx_mdef_7pyarrow_3lib_5Table_39to_reader_ZL44__pyx_mdef_7pyarrow_3lib_5Table_41_to_pandas_ZL41__pyx_mdef_7pyarrow_3lib_5Table_43_column_ZL55__pyx_mdef_7pyarrow_3lib_5Table_45get_total_buffer_size_ZL44__pyx_mdef_7pyarrow_3lib_5Table_47__sizeof___ZL44__pyx_mdef_7pyarrow_3lib_5Table_49add_column_ZL47__pyx_mdef_7pyarrow_3lib_5Table_51remove_column_ZL44__pyx_mdef_7pyarrow_3lib_5Table_53set_column_ZL48__pyx_mdef_7pyarrow_3lib_5Table_55rename_columns_ZL38__pyx_mdef_7pyarrow_3lib_5Table_57drop_ZL42__pyx_mdef_7pyarrow_3lib_5Table_59group_by_ZL38__pyx_mdef_7pyarrow_3lib_5Table_61join_ZL43__pyx_mdef_7pyarrow_3lib_5Table_63join_asof_ZL52__pyx_mdef_7pyarrow_3lib_5Table_65__arrow_c_stream___ZL46__pyx_mdef_7pyarrow_3lib_201_reconstruct_table_ZL40__pyx_mdef_7pyarrow_3lib_203record_batch_ZL33__pyx_mdef_7pyarrow_3lib_205table_ZL41__pyx_mdef_7pyarrow_3lib_207concat_tables_ZL40__pyx_mdef_7pyarrow_3lib_209_from_pydict_ZL40__pyx_mdef_7pyarrow_3lib_211_from_pylist_ZL49__pyx_mdef_7pyarrow_3lib_12TableGroupBy_1__init___ZL50__pyx_mdef_7pyarrow_3lib_12TableGroupBy_3aggregate_ZL63__pyx_mdef_7pyarrow_3lib_6Tensor_3_make_shape_or_strides_buffer_ZL44__pyx_mdef_7pyarrow_3lib_6Tensor_7from_numpy_ZL42__pyx_mdef_7pyarrow_3lib_6Tensor_9to_numpy_ZL41__pyx_mdef_7pyarrow_3lib_6Tensor_11equals_ZL43__pyx_mdef_7pyarrow_3lib_6Tensor_15dim_name_ZL52__pyx_mdef_7pyarrow_3lib_6Tensor_19__reduce_cython___ZL54__pyx_mdef_7pyarrow_3lib_6Tensor_21__setstate_cython___ZL60__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_5from_dense_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_7from_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_9from_scipy_ZL63__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_11from_pydata_sparse_ZL56__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_13from_tensor_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_15to_numpy_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_17to_scipy_ZL61__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_19to_pydata_sparse_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_21to_tensor_ZL51__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_23equals_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_27dim_name_ZL62__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_29__reduce_cython___ZL64__pyx_mdef_7pyarrow_3lib_15SparseCOOTensor_31__setstate_cython___ZL60__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_5from_dense_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_7from_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_9from_scipy_ZL56__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_11from_tensor_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_13to_numpy_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_15to_scipy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_17to_tensor_ZL51__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_19equals_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_23dim_name_ZL62__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_25__reduce_cython___ZL64__pyx_mdef_7pyarrow_3lib_15SparseCSRMatrix_27__setstate_cython___ZL60__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_5from_dense_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_7from_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_9from_scipy_ZL56__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_11from_tensor_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_13to_numpy_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_15to_scipy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_17to_tensor_ZL51__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_19equals_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_23dim_name_ZL62__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_25__reduce_cython___ZL64__pyx_mdef_7pyarrow_3lib_15SparseCSCMatrix_27__setstate_cython___ZL60__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_5from_dense_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_7from_numpy_ZL55__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_9from_tensor_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_11to_numpy_ZL54__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_13to_tensor_ZL51__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_15equals_ZL53__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_19dim_name_ZL62__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_21__reduce_cython___ZL64__pyx_mdef_7pyarrow_3lib_15SparseCSFTensor_23__setstate_cython___ZL40__pyx_mdef_7pyarrow_3lib_213have_libhdfs_ZL43__pyx_mdef_7pyarrow_3lib_215io_thread_count_ZL47__pyx_mdef_7pyarrow_3lib_217set_io_thread_count_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_5__enter___ZL47__pyx_mdef_7pyarrow_3lib_10NativeFile_7__exit___ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_11readable_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_13writable_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_15seekable_ZL46__pyx_mdef_7pyarrow_3lib_10NativeFile_17isatty_ZL46__pyx_mdef_7pyarrow_3lib_10NativeFile_19fileno_ZL45__pyx_mdef_7pyarrow_3lib_10NativeFile_21close_ZL52__pyx_mdef_7pyarrow_3lib_10NativeFile_23_assert_open_ZL56__pyx_mdef_7pyarrow_3lib_10NativeFile_25_assert_readable_ZL56__pyx_mdef_7pyarrow_3lib_10NativeFile_27_assert_writable_ZL56__pyx_mdef_7pyarrow_3lib_10NativeFile_29_assert_seekable_ZL44__pyx_mdef_7pyarrow_3lib_10NativeFile_31size_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_33metadata_ZL44__pyx_mdef_7pyarrow_3lib_10NativeFile_35tell_ZL44__pyx_mdef_7pyarrow_3lib_10NativeFile_37seek_ZL45__pyx_mdef_7pyarrow_3lib_10NativeFile_39flush_ZL45__pyx_mdef_7pyarrow_3lib_10NativeFile_41write_ZL44__pyx_mdef_7pyarrow_3lib_10NativeFile_43read_ZL50__pyx_mdef_7pyarrow_3lib_10NativeFile_45get_stream_ZL47__pyx_mdef_7pyarrow_3lib_10NativeFile_47read_at_ZL45__pyx_mdef_7pyarrow_3lib_10NativeFile_49read1_ZL47__pyx_mdef_7pyarrow_3lib_10NativeFile_51readall_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_53readinto_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_55readline_ZL49__pyx_mdef_7pyarrow_3lib_10NativeFile_57readlines_ZL51__pyx_mdef_7pyarrow_3lib_10NativeFile_63read_buffer_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_65truncate_ZL50__pyx_mdef_7pyarrow_3lib_10NativeFile_67writelines_ZL48__pyx_mdef_7pyarrow_3lib_10NativeFile_69download_ZL46__pyx_mdef_7pyarrow_3lib_10NativeFile_71upload_ZL57__pyx_mdef_7pyarrow_3lib_10NativeFile_73__reduce_cython___ZL59__pyx_mdef_7pyarrow_3lib_10NativeFile_75__setstate_cython___ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__119__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__119__ZL47__pyx_mdef_7pyarrow_3lib_10PythonFile_3truncate_ZL47__pyx_mdef_7pyarrow_3lib_10PythonFile_5readline_ZL48__pyx_mdef_7pyarrow_3lib_10PythonFile_7readlines_ZL56__pyx_mdef_7pyarrow_3lib_10PythonFile_9__reduce_cython___ZL59__pyx_mdef_7pyarrow_3lib_10PythonFile_11__setstate_cython___ZL51__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_1create_ZL50__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_3_open_ZL51__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_5resize_ZL51__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_7fileno_ZL62__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_9__reduce_cython___ZL65__pyx_mdef_7pyarrow_3lib_16MemoryMappedFile_11__setstate_cython___ZL38__pyx_mdef_7pyarrow_3lib_219memory_map_ZL45__pyx_mdef_7pyarrow_3lib_221create_memory_map_ZL40__pyx_mdef_7pyarrow_3lib_6OSFile_3fileno_ZL51__pyx_mdef_7pyarrow_3lib_6OSFile_5__reduce_cython___ZL53__pyx_mdef_7pyarrow_3lib_6OSFile_7__setstate_cython___ZL69__pyx_mdef_7pyarrow_3lib_21FixedSizeBufferWriter_3set_memcopy_threads_ZL71__pyx_mdef_7pyarrow_3lib_21FixedSizeBufferWriter_5set_memcopy_blocksize_ZL71__pyx_mdef_7pyarrow_3lib_21FixedSizeBufferWriter_7set_memcopy_threshold_ZL67__pyx_mdef_7pyarrow_3lib_21FixedSizeBufferWriter_9__reduce_cython___ZL70__pyx_mdef_7pyarrow_3lib_21FixedSizeBufferWriter_11__setstate_cython___ZL37__pyx_mdef_7pyarrow_3lib_6Buffer_9hex_ZL40__pyx_mdef_7pyarrow_3lib_6Buffer_13slice_ZL41__pyx_mdef_7pyarrow_3lib_6Buffer_15equals_ZL48__pyx_mdef_7pyarrow_3lib_6Buffer_19__reduce_ex___ZL45__pyx_mdef_7pyarrow_3lib_6Buffer_21to_pybytes_ZL50__pyx_mdef_7pyarrow_3lib_15ResizableBuffer_1resize_ZL43__pyx_mdef_7pyarrow_3lib_223allocate_buffer_ZL55__pyx_mdef_7pyarrow_3lib_18BufferOutputStream_3getvalue_ZL64__pyx_mdef_7pyarrow_3lib_18BufferOutputStream_5__reduce_cython___ZL66__pyx_mdef_7pyarrow_3lib_18BufferOutputStream_7__setstate_cython___ZL49__pyx_mdef_7pyarrow_3lib_16MockOutputStream_3size_ZL62__pyx_mdef_7pyarrow_3lib_16MockOutputStream_5__reduce_cython___ZL64__pyx_mdef_7pyarrow_3lib_16MockOutputStream_7__setstate_cython___ZL58__pyx_mdef_7pyarrow_3lib_12BufferReader_5__reduce_cython___ZL60__pyx_mdef_7pyarrow_3lib_12BufferReader_7__setstate_cython___ZL67__pyx_mdef_7pyarrow_3lib_21CompressedInputStream_3__reduce_cython___ZL69__pyx_mdef_7pyarrow_3lib_21CompressedInputStream_5__setstate_cython___ZL68__pyx_mdef_7pyarrow_3lib_22CompressedOutputStream_3__reduce_cython___ZL70__pyx_mdef_7pyarrow_3lib_22CompressedOutputStream_5__setstate_cython___ZL54__pyx_mdef_7pyarrow_3lib_19BufferedInputStream_3detach_ZL65__pyx_mdef_7pyarrow_3lib_19BufferedInputStream_5__reduce_cython___ZL67__pyx_mdef_7pyarrow_3lib_19BufferedInputStream_7__setstate_cython___ZL55__pyx_mdef_7pyarrow_3lib_20BufferedOutputStream_3detach_ZL66__pyx_mdef_7pyarrow_3lib_20BufferedOutputStream_5__reduce_cython___ZL68__pyx_mdef_7pyarrow_3lib_20BufferedOutputStream_7__setstate_cython___ZL66__pyx_mdef_7pyarrow_3lib_20TransformInputStream_3__reduce_cython___ZL68__pyx_mdef_7pyarrow_3lib_20TransformInputStream_5__setstate_cython___ZL47__pyx_mdef_7pyarrow_3lib_10Transcoder_1__init___ZL47__pyx_mdef_7pyarrow_3lib_10Transcoder_3__call___ZL52__pyx_mdef_7pyarrow_3lib_225transcoding_input_stream_ZL37__pyx_mdef_7pyarrow_3lib_227py_buffer_ZL42__pyx_mdef_7pyarrow_3lib_229foreign_buffer_ZL37__pyx_mdef_7pyarrow_3lib_231as_buffer_ZL47__pyx_mdef_7pyarrow_3lib_233_detect_compression_ZL61__pyx_mdef_7pyarrow_3lib_12CacheOptions_5from_network_metrics_ZL53__pyx_mdef_7pyarrow_3lib_12CacheOptions_7_reconstruct_ZL51__pyx_mdef_7pyarrow_3lib_12CacheOptions_9__reduce___ZL39__pyx_mdef_7pyarrow_3lib_5Codec_3detect_ZL45__pyx_mdef_7pyarrow_3lib_5Codec_5is_available_ZL59__pyx_mdef_7pyarrow_3lib_5Codec_7supports_compression_level_ZL58__pyx_mdef_7pyarrow_3lib_5Codec_9default_compression_level_ZL59__pyx_mdef_7pyarrow_3lib_5Codec_11minimum_compression_level_ZL59__pyx_mdef_7pyarrow_3lib_5Codec_13maximum_compression_level_ZL42__pyx_mdef_7pyarrow_3lib_5Codec_15compress_ZL44__pyx_mdef_7pyarrow_3lib_5Codec_17decompress_ZL51__pyx_mdef_7pyarrow_3lib_5Codec_21__reduce_cython___ZL53__pyx_mdef_7pyarrow_3lib_5Codec_23__setstate_cython___ZL36__pyx_mdef_7pyarrow_3lib_235compress_ZL38__pyx_mdef_7pyarrow_3lib_237decompress_ZL40__pyx_mdef_7pyarrow_3lib_239input_stream_ZL41__pyx_mdef_7pyarrow_3lib_241output_stream_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__120__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__120__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__121__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__121__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__122__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__122__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__123__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__123__ZL60__pyx_mdef_7pyarrow_3lib_14IpcReadOptions_3__reduce_cython___ZL62__pyx_mdef_7pyarrow_3lib_14IpcReadOptions_5__setstate_cython___ZL61__pyx_mdef_7pyarrow_3lib_15IpcWriteOptions_3__reduce_cython___ZL63__pyx_mdef_7pyarrow_3lib_15IpcWriteOptions_5__setstate_cython___ZL41__pyx_mdef_7pyarrow_3lib_7Message_5equals_ZL47__pyx_mdef_7pyarrow_3lib_7Message_7serialize_to_ZL44__pyx_mdef_7pyarrow_3lib_7Message_9serialize_ZL53__pyx_mdef_7pyarrow_3lib_7Message_13__reduce_cython___ZL55__pyx_mdef_7pyarrow_3lib_7Message_15__setstate_cython___ZL53__pyx_mdef_7pyarrow_3lib_13MessageReader_5open_stream_ZL60__pyx_mdef_7pyarrow_3lib_13MessageReader_11read_next_message_ZL60__pyx_mdef_7pyarrow_3lib_13MessageReader_13__reduce_cython___ZL62__pyx_mdef_7pyarrow_3lib_13MessageReader_15__setstate_cython___ZL53__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_1write_ZL59__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_3write_batch_ZL59__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_5write_table_ZL53__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_7close_ZL57__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_9__enter___ZL57__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_11__exit___ZL66__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_13__reduce_cython___ZL68__pyx_mdef_7pyarrow_3lib_19_CRecordBatchWriter_15__setstate_cython___ZL58__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamWriter_5_open_ZL70__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamWriter_7__reduce_cython___ZL72__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamWriter_9__setstate_cython___ZL56__pyx_mdef_7pyarrow_3lib_16_ReadPandasMixin_1read_pandas_ZL61__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_5read_next_batch_ZL82__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_7read_next_batch_with_custom_metadata_ZL79__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_9iter_batches_with_custom_metadata_ZL55__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_12read_all_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__124__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__124__ZL52__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_14close_ZL56__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_16__enter___ZL55__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_18__exit___ZL51__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_20cast_ZL59__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_22_export_to_c_ZL61__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_24_import_from_c_ZL65__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_26__arrow_c_stream___ZL69__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_28_import_from_c_capsule_ZL58__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_30from_stream_ZL59__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_32from_batches_ZL64__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_34__reduce_cython___ZL66__pyx_mdef_7pyarrow_3lib_17RecordBatchReader_36__setstate_cython___ZL58__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamReader_3_open_ZL70__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamReader_5__reduce_cython___ZL72__pyx_mdef_7pyarrow_3lib_24_RecordBatchStreamReader_7__setstate_cython___ZL56__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileWriter_1_open_ZL68__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileWriter_3__reduce_cython___ZL70__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileWriter_5__setstate_cython___ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__125__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__125__ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__126__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__126__ZL56__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_3_open_ZL60__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_5get_batch_ZL81__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_7get_batch_with_custom_metadata_ZL59__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_9read_all_ZZL20__pyx_pymod_exec_libP7_objectE18__pyx_dict_version__127__ZZL20__pyx_pymod_exec_libP7_objectE23__pyx_dict_cached_value__127__ZL61__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_11__enter___ZL60__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_13__exit___ZL69__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_15__reduce_cython___ZL71__pyx_mdef_7pyarrow_3lib_22_RecordBatchFileReader_17__setstate_cython___ZL43__pyx_mdef_7pyarrow_3lib_243get_tensor_size_ZL49__pyx_mdef_7pyarrow_3lib_245get_record_batch_size_ZL40__pyx_mdef_7pyarrow_3lib_247write_tensor_ZL39__pyx_mdef_7pyarrow_3lib_249read_tensor_ZL40__pyx_mdef_7pyarrow_3lib_251read_message_ZL39__pyx_mdef_7pyarrow_3lib_253read_schema_ZL45__pyx_mdef_7pyarrow_3lib_255read_record_batch_ZL56__pyx_mdef_7pyarrow_3lib_257benchmark_PandasObjectIsNull_ZL57__pyx_mdef_7pyarrow_3lib_259__pyx_unpickle__PandasAPIShim_ZL61__pyx_mdef_7pyarrow_3lib_261__pyx_unpickle__PandasConvertible_ZL51__pyx_mdef_7pyarrow_3lib_263__pyx_unpickle__Tabular_ZL28__pyx_pw_7pyarrow_3lib_7_pacP7_objectS0__ZZL28__pyx_pf_7pyarrow_3lib_6_pacP7_objectE18__pyx_dict_version_ZZL28__pyx_pf_7pyarrow_3lib_6_pacP7_objectE23__pyx_dict_cached_value_ZZL28__pyx_pf_7pyarrow_3lib_6_pacP7_objectE18__pyx_dict_version_0_ZZL28__pyx_pf_7pyarrow_3lib_6_pacP7_objectE23__pyx_dict_cached_value_0_ZL27__pyx_pw_7pyarrow_3lib_5_pcP7_objectS0__ZZL27__pyx_pf_7pyarrow_3lib_4_pcP7_objectE18__pyx_dict_version_ZZL27__pyx_pf_7pyarrow_3lib_4_pcP7_objectE23__pyx_dict_cached_value_ZZL27__pyx_pf_7pyarrow_3lib_4_pcP7_objectE18__pyx_dict_version_0_ZZL27__pyx_pf_7pyarrow_3lib_4_pcP7_objectE23__pyx_dict_cached_value_0_ZL43__pyx_pw_7pyarrow_3lib_221create_memory_mapP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_6Buffer_19__reduce_ex__P7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_6Buffer_18__reduce_ex__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_6Buffer_18__reduce_ex__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE23__pyx_dict_cached_value_ZZL46__pyx_pf_7pyarrow_3lib_6Buffer_18__reduce_ex__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE18__pyx_dict_version_0_ZZL46__pyx_pf_7pyarrow_3lib_6Buffer_18__reduce_ex__P30__pyx_obj_7pyarrow_3lib_BufferP7_objectE23__pyx_dict_cached_value_0_ZL42__pyx_pw_7pyarrow_3lib_159is_boolean_valueP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_163is_float_valueP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_161is_integer_valueP7_objectPKS0_lS0__ZZL37__pyx_f_7pyarrow_3lib_get_native_fileP7_objectbE18__pyx_dict_version_ZZL37__pyx_f_7pyarrow_3lib_get_native_fileP7_objectbE23__pyx_dict_cached_value_ZZL37__pyx_f_7pyarrow_3lib_get_native_fileP7_objectbE18__pyx_dict_version_0_ZZL37__pyx_f_7pyarrow_3lib_get_native_fileP7_objectbE23__pyx_dict_cached_value_0_ZL42__pyx_pw_7pyarrow_3lib_8_Tabular_34sort_byP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_8_Tabular_33sort_byP32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_8_Tabular_33sort_byP32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E23__pyx_dict_cached_value_ZZL42__pyx_pf_7pyarrow_3lib_8_Tabular_33sort_byP32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E18__pyx_dict_version_0_ZZL42__pyx_pf_7pyarrow_3lib_8_Tabular_33sort_byP32__pyx_obj_7pyarrow_3lib__TabularP7_objectS2_E23__pyx_dict_cached_value_0_ZL51__pyx_getprop_7pyarrow_3lib_6Schema_pandas_metadataP7_objectPv_ZL54__pyx_pw_7pyarrow_3lib_8_Tabular_54__setstate_cython__P7_objectPKS0_lS0__ZL64__pyx_pw_7pyarrow_3lib_18_PandasConvertible_5__setstate_cython__P7_objectPKS0_lS0__ZL56__pyx_pw_8EnumBase_14__Pyx_EnumMeta_9__setstate_cython__P7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_5Table_61joinP7_objectPKS0_lS0__ZZL36__pyx_pf_7pyarrow_3lib_5Table_60joinP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_S2_S2_S2_S2_S2_S2_E18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_5Table_60joinP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_S2_S2_S2_S2_S2_S2_E23__pyx_dict_cached_value_ZL36__pyx_f_7pyarrow_3lib_as_native_fileP7_object_ZL38__pyx_pw_7pyarrow_3lib_5Array_62filterP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_5Array_61filterP29__pyx_obj_7pyarrow_3lib_ArrayS0_P7_objectE18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_5Array_61filterP29__pyx_obj_7pyarrow_3lib_ArrayS0_P7_objectE23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_193_empty_arrayP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_2_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_2_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_0_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_0_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE18__pyx_dict_version_1_ZZL38__pyx_pf_7pyarrow_3lib_192_empty_arrayP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeE23__pyx_dict_cached_value_1_ZL53__pyx_pw_7pyarrow_3lib_10NativeFile_6upload_1bg_writeP7_objectS0__ZZL52__pyx_pf_7pyarrow_3lib_10NativeFile_6upload_bg_writeP7_objectE18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_10NativeFile_6upload_bg_writeP7_objectE23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_10NativeFile_6upload_bg_writeP7_objectE18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_10NativeFile_6upload_bg_writeP7_objectE23__pyx_dict_cached_value_0_ZL41__pyx_pw_7pyarrow_3lib_6Tensor_15dim_nameP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_6Tensor_14dim_nameP30__pyx_obj_7pyarrow_3lib_TensorP7_objectE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_6Tensor_14dim_nameP30__pyx_obj_7pyarrow_3lib_TensorP7_objectE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_23dim_nameP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_22dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSRMatrixP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_22dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSRMatrixP7_objectE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_27dim_nameP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_26dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCOOTensorP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_26dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCOOTensorP7_objectE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_23dim_nameP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_22dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSCMatrixP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_22dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSCMatrixP7_objectE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_19dim_nameP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_18dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSFTensorP7_objectE18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_18dim_nameP39__pyx_obj_7pyarrow_3lib_SparseCSFTensorP7_objectE23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_8_Tabular_46drop_columnsP7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_237decompressP7_objectPKS0_lS0__ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSRMatrix_9dim_names_7__get___2generator16P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSRMatrix_9dim_names_7__get___2generator16P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSCMatrix_9dim_names_7__get___2generator17P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSCMatrix_9dim_names_7__get___2generator17P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSFTensor_9dim_names_7__get___2generator18P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCSFTensor_9dim_names_7__get___2generator18P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCOOTensor_9dim_names_7__get___2generator15P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL73__pyx_gb_7pyarrow_3lib_15SparseCOOTensor_9dim_names_7__get___2generator15P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZL36__pyx_pw_7pyarrow_3lib_5Array_66sortP7_objectPKS0_lS0__ZZL36__pyx_pf_7pyarrow_3lib_5Array_65sortP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_5Array_65sortP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_ZZL36__pyx_pf_7pyarrow_3lib_5Array_65sortP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_0_ZZL36__pyx_pf_7pyarrow_3lib_5Array_65sortP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_0_ZL44__pyx_pw_7pyarrow_3lib_12ChunkedArray_70sortP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_69sortP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_69sortP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E23__pyx_dict_cached_value_ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_69sortP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E18__pyx_dict_version_0_ZZL44__pyx_pf_7pyarrow_3lib_12ChunkedArray_69sortP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectS2_E23__pyx_dict_cached_value_0_ZL38__pyx_pw_7pyarrow_3lib_5Table_11filterP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E23__pyx_dict_cached_value_ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E18__pyx_dict_version_0_ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E23__pyx_dict_cached_value_0_ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E18__pyx_dict_version_1_ZZL38__pyx_pf_7pyarrow_3lib_5Table_10filterP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_E23__pyx_dict_cached_value_1_ZL38__pyx_pw_7pyarrow_3lib_239input_streamP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_1_ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_1_ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL38__pyx_pf_7pyarrow_3lib_238input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZL39__pyx_pw_7pyarrow_3lib_241output_streamP7_objectPKS0_lS0__ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E18__pyx_dict_version_1_ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_1_ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL39__pyx_pf_7pyarrow_3lib_240output_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZL21__Pyx_PyInt_As_size_tP7_object_ZL35__pyx_f_7pyarrow_3lib__as_c_pointerP7_objectP42__pyx_opt_args_7pyarrow_3lib__as_c_pointer_ZZL35__pyx_f_7pyarrow_3lib__as_c_pointerP7_objectP42__pyx_opt_args_7pyarrow_3lib__as_c_pointerE18__pyx_dict_version_ZZL35__pyx_f_7pyarrow_3lib__as_c_pointerP7_objectP42__pyx_opt_args_7pyarrow_3lib__as_c_pointerE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_6Schema_16__sizeof__P7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_6Schema_15__sizeof__P30__pyx_obj_7pyarrow_3lib_SchemaE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_6Schema_15__sizeof__P30__pyx_obj_7pyarrow_3lib_SchemaE23__pyx_dict_cached_value_ZZL43__pyx_pf_7pyarrow_3lib_6Schema_15__sizeof__P30__pyx_obj_7pyarrow_3lib_SchemaE18__pyx_dict_version_0_ZZL43__pyx_pf_7pyarrow_3lib_6Schema_15__sizeof__P30__pyx_obj_7pyarrow_3lib_SchemaE23__pyx_dict_cached_value_0_ZL39__pyx_getprop_7pyarrow_3lib_5Field_nameP7_objectPv_ZZL43__pyx_pf_7pyarrow_3lib_5Field_4name___get__P29__pyx_obj_7pyarrow_3lib_FieldE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_5Field_4name___get__P29__pyx_obj_7pyarrow_3lib_FieldE23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_12CacheOptions_3__eq__P7_objectS0__ZL47__pyx_tp_richcompare_7pyarrow_3lib_CacheOptionsP7_objectS0_i_ZL42__pyx_pw_7pyarrow_3lib_11StructArray_9sortP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_8sortP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectS2_S2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_8sortP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectS2_S2_E23__pyx_dict_cached_value_ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_8sortP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectS2_S2_E18__pyx_dict_version_0_ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_8sortP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectS2_S2_E23__pyx_dict_cached_value_0_ZL56__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_3from_arraysP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_12ChunkedArray_54combine_chunksP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_12ChunkedArray_53combine_chunksP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_0_ZZL54__pyx_pf_7pyarrow_3lib_12ChunkedArray_53combine_chunksP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_0_ZZL54__pyx_pf_7pyarrow_3lib_12ChunkedArray_53combine_chunksP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_12ChunkedArray_53combine_chunksP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL55__pyx_pw_7pyarrow_3lib_16LargeStringArray_1from_buffersP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_16LargeStringArray_from_buffersiP30__pyx_obj_7pyarrow_3lib_BufferS0_S0_iiE18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_16LargeStringArray_from_buffersiP30__pyx_obj_7pyarrow_3lib_BufferS0_S0_iiE23__pyx_dict_cached_value_ZL50__pyx_pw_7pyarrow_3lib_11StringArray_1from_buffersP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_11StringArray_from_buffersiP30__pyx_obj_7pyarrow_3lib_BufferS0_S0_iiE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_11StringArray_from_buffersiP30__pyx_obj_7pyarrow_3lib_BufferS0_S0_iiE23__pyx_dict_cached_value_ZL23__Pyx_PyInt_As_uint64_tP7_object_ZL57__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_5from_buffersP7_objectPKS0_lS0__ZL44__pyx_pw_8EnumBase_14__Pyx_EnumBase_1__new__P7_objectPKS0_lS0__ZL44__pyx_pw_8EnumBase_14__Pyx_FlagBase_1__new__P7_objectPKS0_lS0__ZL31__pyx_pw_7pyarrow_3lib_145unionP7_objectPKS0_lS0__ZZL31__pyx_pf_7pyarrow_3lib_144unionP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL31__pyx_pf_7pyarrow_3lib_144unionP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL31__pyx_pf_7pyarrow_3lib_144unionP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL31__pyx_pf_7pyarrow_3lib_144unionP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZL49__pyx_pw_7pyarrow_3lib_14ArrowCancelled_1__init__P7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_209_from_pydictP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E18__pyx_dict_version_1_ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E18__pyx_dict_version_0_ZZL38__pyx_pf_7pyarrow_3lib_208_from_pydictP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZL54__pyx_pw_7pyarrow_3lib_175_handle_arrow_array_protocolP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_211_from_pylistP7_objectPKS0_lS0__ZZ19pyarrow_wrap_scalarRKSt10shared_ptrIN5arrow6ScalarEEE18__pyx_dict_version_ZZ19pyarrow_wrap_scalarRKSt10shared_ptrIN5arrow6ScalarEEE23__pyx_dict_cached_value_Z19pyarrow_wrap_scalarRKSt10shared_ptrIN5arrow6ScalarEE.localalias_ZL55__pyx_pw_7pyarrow_3lib_10NativeFile_8download_5bg_writeP7_objectS0__ZZL55__pyx_pf_7pyarrow_3lib_10NativeFile_8download_4bg_writeP7_objectE18__pyx_dict_version_ZZL55__pyx_pf_7pyarrow_3lib_10NativeFile_8download_4bg_writeP7_objectE23__pyx_dict_cached_value_ZZL55__pyx_pf_7pyarrow_3lib_10NativeFile_8download_4bg_writeP7_objectE18__pyx_dict_version_0_ZZL55__pyx_pf_7pyarrow_3lib_10NativeFile_8download_4bg_writeP7_objectE23__pyx_dict_cached_value_0_ZL49__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_8__eq__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_object_ZZL49__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_8__eq__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_8__eq__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE23__pyx_dict_cached_value_ZL51__pyx_tp_richcompare_7pyarrow_3lib_KeyValueMetadataP7_objectS0_i_ZZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim__import_pandasP38__pyx_obj_7pyarrow_3lib__PandasAPIShimiE18__pyx_dict_version_ZZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim__import_pandasP38__pyx_obj_7pyarrow_3lib__PandasAPIShimiE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_5Codec_5is_availableP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_5Codec_7supports_compression_levelP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_51_to_pandas_dtypeP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E23__pyx_dict_cached_value_ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E18__pyx_dict_version_0_ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E23__pyx_dict_cached_value_0_ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E18__pyx_dict_version_1_ZZL41__pyx_pf_7pyarrow_3lib_50_to_pandas_dtypeP7_objectS0_S0_E23__pyx_dict_cached_value_1_ZL46__pyx_pf_7pyarrow_3lib_13ExtensionType_4__eq__P37__pyx_obj_7pyarrow_3lib_ExtensionTypeP7_object_ZL48__pyx_tp_richcompare_7pyarrow_3lib_ExtensionTypeP7_objectS0_i_ZL50__pyx_pw_7pyarrow_3lib_225transcoding_input_streamP7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E18__pyx_dict_version_1_ZZL50__pyx_pf_7pyarrow_3lib_224transcoding_input_streamP7_objectS0_S0_S0_E23__pyx_dict_cached_value_1_ZL43__pyx_pf_7pyarrow_3lib_8_Tabular_12__repr__P32__pyx_obj_7pyarrow_3lib__Tabular_ZL43__pyx_pw_7pyarrow_3lib_8_Tabular_13__repr__P7_object_ZL63__pyx_specialmethod___pyx_pw_7pyarrow_3lib_8_Tabular_13__repr__P7_objectS0__ZL45__pyx_pw_7pyarrow_3lib_8_Tabular_48add_columnP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Table_63join_asofP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Table_62join_asofP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_S2_S2_S2_S2_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Table_62join_asofP29__pyx_obj_7pyarrow_3lib_TableP7_objectS2_S2_S2_S2_S2_E23__pyx_dict_cached_value_ZL41__pyx_pf_7pyarrow_3lib_8DataType_14__eq__P32__pyx_obj_7pyarrow_3lib_DataTypeP7_object_ZL43__pyx_tp_richcompare_7pyarrow_3lib_DataTypeP7_objectS0_i_ZL66__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_35get_rangeindex_attributeP7_objectPKS0_lS0__ZZL49__pyx_f_7pyarrow_3lib_14_PandasAPIShim_get_valuesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL49__pyx_f_7pyarrow_3lib_14_PandasAPIShim_get_valuesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL52__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_33get_valuesP7_objectPKS0_lS0__ZZL63__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_extension_array_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL63__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_extension_array_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL66__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_23is_extension_array_dtypeP7_objectPKS0_lS0__ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_array_likeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_array_likeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL55__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_17is_array_likeP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_15is_ge_v3P7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_13is_ge_v21P7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_11is_v1P7_objectPKS0_lS0__ZZL51__pyx_f_7pyarrow_3lib_14_PandasAPIShim_pandas_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL51__pyx_f_7pyarrow_3lib_14_PandasAPIShim_pandas_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL53__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_9pandas_dtypeP7_objectPKS0_lS0__ZZL50__pyx_f_7pyarrow_3lib_14_PandasAPIShim_infer_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL50__pyx_f_7pyarrow_3lib_14_PandasAPIShim_infer_dtypeP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL52__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_7infer_dtypeP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_5data_frameP7_objectS0_S0__ZL47__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_3seriesP7_objectS0_S0__ZL60__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_extension_dtypeP7_objectPv_ZL60__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_datetimetz_typeP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_categorical_typeP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_versionP7_objectPv_ZL58__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_loose_versionP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_pdP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_compatP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_14_PandasAPIShim_have_pandasP7_objectPv_ZZL47__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_indexP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL47__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_indexP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL50__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_31is_indexP7_objectPKS0_lS0__ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_data_frameP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_data_frameP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL55__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_27is_data_frameP7_objectPKS0_lS0__ZZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_seriesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_seriesP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL51__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_29is_seriesP7_objectPKS0_lS0__ZZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_categoricalP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL53__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_categoricalP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL56__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_19is_categoricalP7_objectPKS0_lS0__ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_datetimetzP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL52__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_datetimetzP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL55__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_21is_datetimetzP7_objectPKS0_lS0__ZZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_sparseP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE22__pyx_obj_dict_version_ZZL48__pyx_f_7pyarrow_3lib_14_PandasAPIShim_is_sparseP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_objectiE21__pyx_tp_dict_version_ZL51__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_25is_sparseP7_objectPKS0_lS0__ZL36__pyx_f_7pyarrow_3lib__is_array_likeP7_object_ZZL36__pyx_f_7pyarrow_3lib__is_array_likeP7_objectE18__pyx_dict_version_ZZL36__pyx_f_7pyarrow_3lib__is_array_likeP7_objectE23__pyx_dict_cached_value_ZZL39__pyx_f_7pyarrow_3lib_wrap_array_outputP7_objectE18__pyx_dict_version_ZZL39__pyx_f_7pyarrow_3lib_wrap_array_outputP7_objectE23__pyx_dict_cached_value_ZL45__pyx_getprop_7pyarrow_3lib_10NativeFile_modeP7_objectPv_ZL31__pyx_pw_7pyarrow_3lib_205tableP7_objectPKS0_lS0__ZZL31__pyx_pf_7pyarrow_3lib_204tableP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_ZZL31__pyx_pf_7pyarrow_3lib_204tableP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL31__pyx_pf_7pyarrow_3lib_204tableP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_0_ZZL31__pyx_pf_7pyarrow_3lib_204tableP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZL46__pyx_pw_7pyarrow_3lib_12ChunkedArray_15formatP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_5Array_36formatP7_objectPKS0_lS0__ZL35__pyx_pw_7pyarrow_3lib_5Array_11sumP7_objectPKS0_lS0__ZZL35__pyx_pf_7pyarrow_3lib_5Array_10sumP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL35__pyx_pf_7pyarrow_3lib_5Array_10sumP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZZL35__pyx_pf_7pyarrow_3lib_5Array_10sumP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_0_ZZL35__pyx_pf_7pyarrow_3lib_5Array_10sumP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_0_ZL46__pyx_getprop_7pyarrow_3lib_13TimestampType_tzP7_objectPv_ZZL50__pyx_pf_7pyarrow_3lib_13TimestampType_2tz___get__P37__pyx_obj_7pyarrow_3lib_TimestampTypeE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_13TimestampType_2tz___get__P37__pyx_obj_7pyarrow_3lib_TimestampTypeE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_12TableGroupBy_3aggregateP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_12TableGroupBy_2aggregateP7_objectS0_S0_E18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_12TableGroupBy_2aggregateP7_objectS0_S0_E23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_10NativeFile_71uploadP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_0_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_0_ZL55__pyx_mdef_7pyarrow_3lib_10NativeFile_6upload_1bg_write_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_1_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_1_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_2_ZZL44__pyx_pf_7pyarrow_3lib_10NativeFile_70uploadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_2_ZZL33__pyx_f_7pyarrow_3lib_ensure_typeP7_objectiP40__pyx_opt_args_7pyarrow_3lib_ensure_typeE18__pyx_dict_version_ZZL33__pyx_f_7pyarrow_3lib_ensure_typeP7_objectiP40__pyx_opt_args_7pyarrow_3lib_ensure_typeE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_8DataType_17equalsP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_153ensure_typeP7_objectPKS0_lS0__ZL62__pyx_f_7pyarrow_3lib___pyx_unpickle__PandasAPIShim__set_stateP38__pyx_obj_7pyarrow_3lib__PandasAPIShimP7_object_ZL61__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_39__setstate_cython__P7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_259__pyx_unpickle__PandasAPIShimP7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_15PyExtensionType_9__arrow_ext_deserialize__P7_objectPKS0_lS0__ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E18__pyx_dict_version_ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E23__pyx_dict_cached_value_ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E18__pyx_dict_version_0_ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E23__pyx_dict_cached_value_0_ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E18__pyx_dict_version_1_ZZL67__pyx_pf_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize__P11_typeobjectP7_objectS2_E23__pyx_dict_cached_value_1_ZL35__pyx_pw_7pyarrow_3lib_5Array_5diffP7_objectPKS0_lS0__ZZL35__pyx_pf_7pyarrow_3lib_5Array_4diffP29__pyx_obj_7pyarrow_3lib_ArrayS0_E18__pyx_dict_version_ZZL35__pyx_pf_7pyarrow_3lib_5Array_4diffP29__pyx_obj_7pyarrow_3lib_ArrayS0_E23__pyx_dict_cached_value_ZL35__pyx_pw_7pyarrow_3lib_5Array_5diffP7_objectPKS0_lS0_.cold_ZL66__pyx_pw_7pyarrow_3lib_21FixedShapeTensorArray_5from_numpy_ndarrayP7_objectPKS0_lS0__ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_0_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_0_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_1_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_1_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_2_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_2_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_3_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_3_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE18__pyx_dict_version_4_ZZL66__pyx_pf_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarrayP7_objectE23__pyx_dict_cached_value_4_ZL38__pyx_pw_7pyarrow_3lib_5Array_42equalsP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_21keyP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_23valueP7_objectPKS0_lS0__ZL45__pyx_getprop_7pyarrow_3lib_6Tensor_dim_namesP7_objectPv_ZZL49__pyx_pf_7pyarrow_3lib_6Tensor_9dim_names___get__P30__pyx_obj_7pyarrow_3lib_TensorE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_6Tensor_9dim_names___get__P30__pyx_obj_7pyarrow_3lib_TensorE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_5Array_70__array__P7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_2_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_2_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_0_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_0_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_1_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_1_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Array_69__array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_E23__pyx_dict_cached_value_ZL38__pyx_pw_7pyarrow_3lib_203record_batchP7_objectPKS0_lS0__ZZL38__pyx_pf_7pyarrow_3lib_202record_batchP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL38__pyx_pf_7pyarrow_3lib_202record_batchP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZL59__pyx_pw_7pyarrow_3lib_14_PandasAPIShim_37__reduce_cython__P7_objectPKS0_lS0__ZZL59__pyx_pf_7pyarrow_3lib_14_PandasAPIShim_36__reduce_cython__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE18__pyx_dict_version_ZZL59__pyx_pf_7pyarrow_3lib_14_PandasAPIShim_36__reduce_cython__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE23__pyx_dict_cached_value_ZZL59__pyx_pf_7pyarrow_3lib_14_PandasAPIShim_36__reduce_cython__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE18__pyx_dict_version_0_ZZL59__pyx_pf_7pyarrow_3lib_14_PandasAPIShim_36__reduce_cython__P38__pyx_obj_7pyarrow_3lib__PandasAPIShimE23__pyx_dict_cached_value_0_ZL37__pyx_pw_7pyarrow_3lib_19runtime_infoP7_objectS0__ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE18__pyx_dict_version_ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE23__pyx_dict_cached_value_ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE18__pyx_dict_version_0_ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE23__pyx_dict_cached_value_0_ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE18__pyx_dict_version_1_ZZL37__pyx_pf_7pyarrow_3lib_18runtime_infoP7_objectE23__pyx_dict_cached_value_1_ZL37__pyx_pw_7pyarrow_3lib_19runtime_infoP7_objectS0_.cold_ZN12_GLOBAL__N_115__pyx_moduledefE_ZL36__pyx_f_7pyarrow_3lib_primitive_typeN5arrow4Type4typeE_ZL36__pyx_f_7pyarrow_3lib_primitive_typeN5arrow4Type4typeE.cold_ZL37__pyx_pw_7pyarrow_3lib_125string_viewP7_objectS0__ZL37__pyx_pw_7pyarrow_3lib_123binary_viewP7_objectS0__ZL38__pyx_pw_7pyarrow_3lib_119large_stringP7_objectS0__ZL38__pyx_pw_7pyarrow_3lib_117large_binaryP7_objectS0__ZL32__pyx_pw_7pyarrow_3lib_111stringP7_objectS0__ZL33__pyx_pw_7pyarrow_3lib_105float64P7_objectS0__ZL33__pyx_pw_7pyarrow_3lib_103float32P7_objectS0__ZL33__pyx_pw_7pyarrow_3lib_101float16P7_objectS0__ZL31__pyx_pw_7pyarrow_3lib_99date64P7_objectS0__ZL31__pyx_pw_7pyarrow_3lib_97date32P7_objectS0__ZL48__pyx_pw_7pyarrow_3lib_95month_day_nano_intervalP7_objectS0__ZL30__pyx_pw_7pyarrow_3lib_81int64P7_objectS0__ZL31__pyx_pw_7pyarrow_3lib_79uint64P7_objectS0__ZL30__pyx_pw_7pyarrow_3lib_77int32P7_objectS0__ZL31__pyx_pw_7pyarrow_3lib_75uint32P7_objectS0__ZL30__pyx_pw_7pyarrow_3lib_73int16P7_objectS0__ZL31__pyx_pw_7pyarrow_3lib_71uint16P7_objectS0__ZL29__pyx_pw_7pyarrow_3lib_69int8P7_objectS0__ZL30__pyx_pw_7pyarrow_3lib_67uint8P7_objectS0__ZL30__pyx_pw_7pyarrow_3lib_65bool_P7_objectS0__ZL29__pyx_pw_7pyarrow_3lib_63nullP7_objectS0__ZL39__pyx_pw_7pyarrow_3lib_5Table_43_columnP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_5Table_43_columnP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_11RecordBatch_11_columnP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_11RecordBatch_11_columnP7_objectPKS0_lS0_.cold_ZL32__pyx_f_7pyarrow_3lib_wrap_datumRKN5arrow5DatumE.cold_ZL39__pyx_tp_new_7pyarrow_3lib_BufferReaderP11_typeobjectP7_objectS2__ZZL48__pyx_pf_7pyarrow_3lib_12BufferReader_2__cinit__P36__pyx_obj_7pyarrow_3lib_BufferReaderP7_objectE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_12BufferReader_2__cinit__P36__pyx_obj_7pyarrow_3lib_BufferReaderP7_objectE23__pyx_dict_cached_value_ZL39__pyx_tp_new_7pyarrow_3lib_BufferReaderP11_typeobjectP7_objectS2_.cold_ZL46__pyx_pw_7pyarrow_3lib_10PythonFile_1__cinit__P7_objectS0_S0__ZZL45__pyx_pf_7pyarrow_3lib_10PythonFile___cinit__P34__pyx_obj_7pyarrow_3lib_PythonFileP7_objectS2_E18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_10PythonFile___cinit__P34__pyx_obj_7pyarrow_3lib_PythonFileP7_objectS2_E23__pyx_dict_cached_value_ZZL45__pyx_pf_7pyarrow_3lib_10PythonFile___cinit__P34__pyx_obj_7pyarrow_3lib_PythonFileP7_objectS2_E18__pyx_dict_version_0_ZZL45__pyx_pf_7pyarrow_3lib_10PythonFile___cinit__P34__pyx_obj_7pyarrow_3lib_PythonFileP7_objectS2_E23__pyx_dict_cached_value_0_ZL46__pyx_pw_7pyarrow_3lib_10PythonFile_1__cinit__P7_objectS0_S0_.cold_ZL37__pyx_tp_new_7pyarrow_3lib_PythonFileP11_typeobjectP7_objectS2__ZL46__pyx_pw_7pyarrow_3lib_6Tensor_17__getbuffer__P7_objectP9Py_bufferi_ZL30__pyx_pw_7pyarrow_3lib_135map_P7_objectPKS0_lS0__ZZL26__pyx_f_7pyarrow_3lib_map_P7_objectS0_iP33__pyx_opt_args_7pyarrow_3lib_map_E18__pyx_dict_version_0_ZZL26__pyx_f_7pyarrow_3lib_map_P7_objectS0_iP33__pyx_opt_args_7pyarrow_3lib_map_E23__pyx_dict_cached_value_0_ZZL26__pyx_f_7pyarrow_3lib_map_P7_objectS0_iP33__pyx_opt_args_7pyarrow_3lib_map_E18__pyx_dict_version_ZZL26__pyx_f_7pyarrow_3lib_map_P7_objectS0_iP33__pyx_opt_args_7pyarrow_3lib_map_E23__pyx_dict_cached_value_ZL30__pyx_pw_7pyarrow_3lib_135map_P7_objectPKS0_lS0_.cold_ZL33__pyx_pw_7pyarrow_3lib_93durationP7_objectPKS0_lS0__ZL33__pyx_pw_7pyarrow_3lib_93durationP7_objectPKS0_lS0_.cold_ZL31__pyx_pw_7pyarrow_3lib_91time64P7_objectPKS0_lS0__ZL31__pyx_pw_7pyarrow_3lib_91time64P7_objectPKS0_lS0_.cold_ZL31__pyx_pw_7pyarrow_3lib_89time32P7_objectPKS0_lS0__ZL31__pyx_pw_7pyarrow_3lib_89time32P7_objectPKS0_lS0_.cold_ZL34__pyx_pw_7pyarrow_3lib_87timestampP7_objectPKS0_lS0__ZZL34__pyx_pf_7pyarrow_3lib_86timestampP7_objectS0_S0_E18__pyx_dict_version_ZZL34__pyx_pf_7pyarrow_3lib_86timestampP7_objectS0_S0_E23__pyx_dict_cached_value_ZZL34__pyx_pf_7pyarrow_3lib_86timestampP7_objectS0_S0_E18__pyx_dict_version_0_ZZL34__pyx_pf_7pyarrow_3lib_86timestampP7_objectS0_S0_E23__pyx_dict_cached_value_0_ZL34__pyx_pw_7pyarrow_3lib_87timestampP7_objectPKS0_lS0_.cold_ZL35__pyx_f_7pyarrow_3lib__cb_transformP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4__ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E18__pyx_dict_version_ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E23__pyx_dict_cached_value_ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E18__pyx_dict_version_0_ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E23__pyx_dict_cached_value_0_ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E18__pyx_dict_version_1_ZZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_E23__pyx_dict_cached_value_1_ZL42__pyx_f_7pyarrow_3lib_make_streamwrap_funcP7_objectS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_14ExtensionArray_1from_storageP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_14ExtensionArray_1from_storageP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_15IpcWriteOptions_1__init__P7_objectS0_S0__ZL39__pyx_pw_7pyarrow_3lib_6Scalar_11equalsP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_6Scalar_11equalsP7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_5Table_9sliceP7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_5Table_9sliceP7_objectPKS0_lS0_.cold_ZL34__pyx_f_7pyarrow_3lib_6Tensor_initP30__pyx_obj_7pyarrow_3lib_TensorRKSt10shared_ptrIN5arrow6TensorEE.cold_ZZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEEE18__pyx_dict_version_ZZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEEE23__pyx_dict_cached_value_ZZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEEE18__pyx_dict_version_0_ZZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEEE23__pyx_dict_cached_value_0_ZL34__pyx_f_7pyarrow_3lib_6Scalar_wrapRKSt10shared_ptrIN5arrow6ScalarEE.cold_ZL51__pyx_getprop_7pyarrow_3lib_15ExtensionScalar_valueP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_11UnionScalar_valueP7_objectPv_ZL55__pyx_getprop_7pyarrow_3lib_19RunEndEncodedScalar_valueP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_16DictionaryScalar_indexP7_objectPv_ZL57__pyx_f_7pyarrow_3lib_10NativeFile_get_random_access_fileP34__pyx_obj_7pyarrow_3lib_NativeFile.cold_ZL60__pyx_tp_dealloc_7pyarrow_3lib___pyx_scope_struct_7___iter__P7_object_ZNSt12__shared_ptrIN5arrow8DataTypeELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZNSt12__shared_ptrIN5arrow9ArrayDataELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZNSt12__shared_ptrIN5arrow11RecordBatchELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZNSt12__shared_ptrIN5arrow12ChunkedArrayELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZNSt12__shared_ptrIN5arrow5ArrayELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZL37__pyx_pw_7pyarrow_3lib_5Array_56sliceP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_5Array_56sliceP7_objectPKS0_lS0_.cold_ZNSt12__shared_ptrIKN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EEaSEOS5_.isra.0_ZNSt12__shared_ptrIN5arrow5TableELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZNSt12__shared_ptrIN5arrow6BufferELN9__gnu_cxx12_Lock_policyE2EEaSEOS4_.isra.0_ZL37__pyx_tp_dealloc_7pyarrow_3lib_BufferP7_object_ZL45__pyx_tp_dealloc_7pyarrow_3lib_DictionaryMemoP7_object_ZL37__pyx_tp_dealloc_7pyarrow_3lib_SchemaP7_object_ZL47__pyx_tp_dealloc_7pyarrow_3lib_KeyValueMetadataP7_object_ZL36__pyx_tp_dealloc_7pyarrow_3lib_TableP7_object_ZL37__pyx_tp_dealloc_7pyarrow_3lib_ScalarP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_IpcWriteOptionsP7_object_ZL36__pyx_tp_dealloc_7pyarrow_3lib_CodecP7_object_ZL54__pyx_tp_dealloc_7pyarrow_3lib__ExtensionRegistryNannyP7_object_ZL40__pyx_tp_dealloc_7pyarrow_3lib_StopTokenP7_object_ZL43__pyx_tp_dealloc_7pyarrow_3lib_ChunkedArrayP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_SparseCSRMatrixP7_object_ZL36__pyx_tp_dealloc_7pyarrow_3lib_FieldP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_SparseCSFTensorP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_SparseCOOTensorP7_object_ZL42__pyx_tp_dealloc_7pyarrow_3lib_RecordBatchP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_SparseCSCMatrixP7_object_ZNSt14__shared_countILN9__gnu_cxx12_Lock_policyE2EEaSERKS2_.isra.0_ZL43__pyx_pw_7pyarrow_3lib_11StructArray_1fieldP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_fieldP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectE18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_11StructArray_fieldP35__pyx_obj_7pyarrow_3lib_StructArrayP7_objectE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_11StructArray_1fieldP7_objectPKS0_lS0_.cold_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_0_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_0_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version__11__ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value__11__ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version__12__ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value__12__ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_2_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_2_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_3_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_3_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_1_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_1_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_4_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_4_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_5_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_5_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_6_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_6_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_7_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_7_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_8_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_8_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version_9_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value_9_ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE18__pyx_dict_version__10__ZZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusEE23__pyx_dict_cached_value__10__ZL36__pyx_f_7pyarrow_3lib_convert_statusRKN5arrow6StatusE.cold_ZL34__pyx_f_7pyarrow_3lib_check_statusRKN5arrow6StatusE.part.0_ZL54__pyx_pw_7pyarrow_3lib_17BaseExtensionType_5wrap_arrayP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_17BaseExtensionType_5wrap_arrayP7_objectPKS0_lS0_.cold_ZZL32__pyx_f_7pyarrow_3lib_get_writerP7_objectPSt10shared_ptrIN5arrow2io12OutputStreamEEE18__pyx_dict_version_ZZL32__pyx_f_7pyarrow_3lib_get_writerP7_objectPSt10shared_ptrIN5arrow2io12OutputStreamEEE23__pyx_dict_cached_value_ZL32__pyx_f_7pyarrow_3lib_get_writerP7_objectPSt10shared_ptrIN5arrow2io12OutputStreamEE.cold_ZL39__pyx_tp_dealloc_7pyarrow_3lib_DataTypeP7_object_ZL51__pyx_tp_dealloc_7pyarrow_3lib_UnknownExtensionTypeP7_object_ZL49__pyx_getprop_7pyarrow_3lib_13ListViewArray_sizesP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_13ListViewArray_sizesP7_objectPv.cold_ZL45__pyx_getprop_7pyarrow_3lib_7Message_metadataP7_objectPv_ZL45__pyx_getprop_7pyarrow_3lib_7Message_metadataP7_objectPv.cold_ZL46__pyx_getprop_7pyarrow_3lib_9ListArray_offsetsP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_9ListArray_offsetsP7_objectPv.cold_ZL52__pyx_getprop_7pyarrow_3lib_14LargeListArray_offsetsP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_14LargeListArray_offsetsP7_objectPv.cold_ZL56__pyx_getprop_7pyarrow_3lib_18LargeListViewArray_offsetsP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_18LargeListViewArray_offsetsP7_objectPv.cold_ZL54__pyx_getprop_7pyarrow_3lib_18LargeListViewArray_sizesP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_18LargeListViewArray_sizesP7_objectPv.cold_ZL51__pyx_getprop_7pyarrow_3lib_13ListViewArray_offsetsP7_objectPv_ZL51__pyx_getprop_7pyarrow_3lib_13ListViewArray_offsetsP7_objectPv.cold_ZL36__pyx_tp_dealloc_7pyarrow_3lib_ArrayP7_object_ZL46__pyx_tp_dealloc_7pyarrow_3lib_DictionaryArrayP7_object_ZL61__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_value_typeP7_objectPv_ZL61__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_value_typeP7_objectPv.cold_ZL37__pyx_tp_dealloc_7pyarrow_3lib_TensorP7_object_ZL47__pyx_f_7pyarrow_3lib_15ResizableBuffer_init_rzP39__pyx_obj_7pyarrow_3lib_ResizableBufferRKSt10shared_ptrIN5arrow15ResizableBufferEE.cold_ZL66__pyx_pw_7pyarrow_3lib_23_ExtensionRegistryNanny_3release_registryP7_objectPKS0_lS0__ZL40__pyx_f_7pyarrow_3lib__reduce_array_dataPKN5arrow9ArrayDataE_ZL40__pyx_f_7pyarrow_3lib__reduce_array_dataPKN5arrow9ArrayDataE.cold_ZL42__pyx_pw_7pyarrow_3lib_5Array_21__reduce__P7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_5Array_20__reduce__P29__pyx_obj_7pyarrow_3lib_ArrayE18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_5Array_20__reduce__P29__pyx_obj_7pyarrow_3lib_ArrayE23__pyx_dict_cached_value_ZL63__pyx_pw_7pyarrow_3lib_17RecordBatchReader_26__arrow_c_stream__P7_objectPKS0_lS0__ZL63__pyx_pw_7pyarrow_3lib_17RecordBatchReader_26__arrow_c_stream__P7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_17RecordBatchReader_14closeP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_17RecordBatchReader_14closeP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_7closeP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_7closeP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_5write_tableP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_5write_tableP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_15ResizableBuffer_1resizeP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_15ResizableBuffer_1resizeP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_5resizeP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_5resizeP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_37seekP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_37seekP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_21closeP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_21closeP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_11to_numpyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_11to_numpyP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_15to_scipyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_15to_scipyP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_13to_numpyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_13to_numpyP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_15to_scipyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_15to_scipyP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_13to_numpyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_13to_numpyP7_objectPKS0_lS0_.cold_ZL59__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_19to_pydata_sparseP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_19to_pydata_sparseP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_17to_scipyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_17to_scipyP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_15to_numpyP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_15to_numpyP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_6Tensor_9to_numpyP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_6Tensor_9to_numpyP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_5Table_5validateP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_5Table_5validateP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_11RecordBatch_49_export_to_cP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_11RecordBatch_49_export_to_cP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_11RecordBatch_7validateP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_11RecordBatch_7validateP7_objectPKS0_lS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_12ChunkedArray_81__arrow_c_stream__P7_objectPKS0_lS0__ZZL58__pyx_pf_7pyarrow_3lib_12ChunkedArray_80__arrow_c_stream__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL58__pyx_pf_7pyarrow_3lib_12ChunkedArray_80__arrow_c_stream__P36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZL58__pyx_pw_7pyarrow_3lib_12ChunkedArray_81__arrow_c_stream__P7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_19validateP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_19validateP7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_5Array_82_export_to_cP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_5Array_82_export_to_cP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_5Array_78validateP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_5Array_78validateP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_5Array_3_debug_printP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_5Array_3_debug_printP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_6Scalar_5validateP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_6Scalar_5validateP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_6Schema_62__arrow_c_schema__P7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_6Schema_62__arrow_c_schema__P7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_6Schema_54_export_to_cP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_6Schema_54_export_to_cP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_5Field_33__arrow_c_schema__P7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_5Field_33__arrow_c_schema__P7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_5Field_29_export_to_cP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_5Field_29_export_to_cP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_8DataType_25__arrow_c_schema__P7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_8DataType_25__arrow_c_schema__P7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_8DataType_21_export_to_cP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_8DataType_21_export_to_cP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_17SignalStopHandler_5__enter__P7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_17SignalStopHandler_5__enter__P7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_13to_stringP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_12to_stringP36__pyx_obj_7pyarrow_3lib_ChunkedArrayiiibE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_12ChunkedArray_12to_stringP36__pyx_obj_7pyarrow_3lib_ChunkedArrayiiibE23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_12ChunkedArray_13to_stringP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_6Schema_52to_stringP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_6Schema_51to_stringP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectS2_S2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_6Schema_51to_stringP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectS2_S2_E23__pyx_dict_cached_value_ZL42__pyx_pw_7pyarrow_3lib_6Schema_52to_stringP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_13ExtensionType_3__init__P7_objectS0_S0__ZZL48__pyx_pf_7pyarrow_3lib_13ExtensionType_2__init__P37__pyx_obj_7pyarrow_3lib_ExtensionTypeP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objectE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_13ExtensionType_2__init__P37__pyx_obj_7pyarrow_3lib_ExtensionTypeP32__pyx_obj_7pyarrow_3lib_DataTypeP7_objectE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_13ExtensionType_3__init__P7_objectS0_S0_.cold_ZL44__pyx_tp_new_7pyarrow_3lib_SignalStopHandlerP11_typeobjectP7_objectS2__ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler___cinit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_1_ZL44__pyx_tp_new_7pyarrow_3lib_SignalStopHandlerP11_typeobjectP7_objectS2_.cold_ZL48__pyx_getprop_7pyarrow_3lib_11RecordBatch_nbytesP7_objectPv_ZL48__pyx_getprop_7pyarrow_3lib_11RecordBatch_nbytesP7_objectPv.cold_ZL41__pyx_getprop_7pyarrow_3lib_5Table_nbytesP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_5Table_nbytesP7_objectPv.cold_ZL49__pyx_getprop_7pyarrow_3lib_12ChunkedArray_nbytesP7_objectPv_ZL49__pyx_getprop_7pyarrow_3lib_12ChunkedArray_nbytesP7_objectPv.cold_ZL41__pyx_getprop_7pyarrow_3lib_5Array_nbytesP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_5Array_nbytesP7_objectPv.cold_ZL58__pyx_pw_7pyarrow_3lib_26MonthDayNanoIntervalScalar_1as_pyP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_26MonthDayNanoIntervalScalar_1as_pyP7_objectPKS0_lS0_.cold_ZL61__pyx_pw_7pyarrow_3lib_25MonthDayNanoIntervalArray_1to_pylistP7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_25MonthDayNanoIntervalArray_1to_pylistP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_69downloadP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_0_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_0_ZL56__pyx_mdef_7pyarrow_3lib_10NativeFile_8download_1cleanup_ZL57__pyx_mdef_7pyarrow_3lib_10NativeFile_8download_5bg_write_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_1_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_1_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E18__pyx_dict_version_2_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_68downloadP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectS2_E23__pyx_dict_cached_value_2_ZL56__pyx_mdef_7pyarrow_3lib_10NativeFile_8download_3cleanup_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_69downloadP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_53readintoP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_52readintoP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_52readintoP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_53readintoP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_43readP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_43readP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_5Codec_13maximum_compression_levelP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_5Codec_13maximum_compression_levelP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_5Codec_11minimum_compression_levelP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_5Codec_11minimum_compression_levelP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_5Codec_9default_compression_levelP7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_5Codec_9default_compression_levelP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_5Array_94__dlpack__P7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_5Array_94__dlpack__P7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_5Array_96__dlpack_device__P7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_5Array_96__dlpack_device__P7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_245get_record_batch_sizeP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_245get_record_batch_sizeP7_objectPKS0_lS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_243get_tensor_sizeP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_243get_tensor_sizeP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_229foreign_bufferP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_229foreign_bufferP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_217set_io_thread_countP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_217set_io_thread_countP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_213have_libhdfsP7_objectS0__ZL38__pyx_pw_7pyarrow_3lib_213have_libhdfsP7_objectS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_5Array_34to_stringP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Array_33to_stringP29__pyx_obj_7pyarrow_3lib_ArrayiiiibE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Array_33to_stringP29__pyx_obj_7pyarrow_3lib_ArrayiiiibE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_5Array_34to_stringP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_165_register_py_extension_typeP7_objectS0__ZZL53__pyx_pf_7pyarrow_3lib_164_register_py_extension_typeP7_objectE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_164_register_py_extension_typeP7_objectE23__pyx_dict_cached_value_ZZL53__pyx_pf_7pyarrow_3lib_164_register_py_extension_typeP7_objectE18__pyx_dict_version_0_ZZL53__pyx_pf_7pyarrow_3lib_164_register_py_extension_typeP7_objectE23__pyx_dict_cached_value_0_ZL53__pyx_pw_7pyarrow_3lib_165_register_py_extension_typeP7_objectS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_85string_to_tzinfoP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_85string_to_tzinfoP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_55unregister_extension_typeP7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_54unregister_extension_typeP7_objectS0_E18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_54unregister_extension_typeP7_objectS0_E23__pyx_dict_cached_value_ZL50__pyx_pw_7pyarrow_3lib_55unregister_extension_typeP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_53register_extension_typeP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_52register_extension_typeP7_objectS0_E18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_52register_extension_typeP7_objectS0_E23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_53register_extension_typeP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_41jemalloc_set_decay_msP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_41jemalloc_set_decay_msP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_33mimalloc_memory_poolP7_objectS0__ZL45__pyx_pw_7pyarrow_3lib_33mimalloc_memory_poolP7_objectS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_31jemalloc_memory_poolP7_objectS0__ZL45__pyx_pw_7pyarrow_3lib_31jemalloc_memory_poolP7_objectS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_21set_timezone_db_pathP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_20set_timezone_db_pathP7_objectS0_E18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_20set_timezone_db_pathP7_objectS0_E23__pyx_dict_cached_value_ZL45__pyx_pw_7pyarrow_3lib_21set_timezone_db_pathP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_17SignalStopHandler_7__exit__P7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E23__pyx_dict_cached_value_1_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E18__pyx_dict_version_2_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E23__pyx_dict_cached_value_2_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_17SignalStopHandler_6__exit__P41__pyx_obj_7pyarrow_3lib_SignalStopHandlerP7_objectS2_S2_E23__pyx_dict_cached_value_ZL52__pyx_pw_7pyarrow_3lib_17SignalStopHandler_7__exit__P7_objectPKS0_lS0_.cold_ZL37__pyx_pw_7pyarrow_3lib_3set_cpu_countP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_3set_cpu_countP7_objectPKS0_lS0_.cold_ZL47__pyx_getprop_7pyarrow_3lib_7MapType_item_fieldP7_objectPv_ZL47__pyx_getprop_7pyarrow_3lib_7MapType_item_fieldP7_objectPv.cold_ZL46__pyx_getprop_7pyarrow_3lib_7MapType_key_fieldP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_7MapType_key_fieldP7_objectPv.cold_ZL48__pyx_tp_dealloc_7pyarrow_3lib_RecordBatchReaderP7_object_ZL55__pyx_tp_dealloc_7pyarrow_3lib__RecordBatchStreamReaderP7_object_ZL50__pyx_tp_dealloc_7pyarrow_3lib__CRecordBatchWriterP7_object_ZL55__pyx_tp_dealloc_7pyarrow_3lib__RecordBatchStreamWriterP7_object_ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_5read_next_batchP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_5read_next_batchP7_objectPKS0_lS0_.cold_ZL36__pyx_f_7pyarrow_3lib_6Scalar_unwrapP30__pyx_obj_7pyarrow_3lib_Scalar.cold_ZL47__pyx_f_7pyarrow_3lib_16KeyValueMetadata_unwrapP40__pyx_obj_7pyarrow_3lib_KeyValueMetadata.cold_ZL50__pyx_pw_7pyarrow_3lib_17StringViewBuilder_7finishP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_17StringViewBuilder_7finishP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_13StringBuilder_7finishP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_13StringBuilder_7finishP7_objectPKS0_lS0_.cold_ZL41__pyx_tp_new_7pyarrow_3lib_DictionaryMemoP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_DictionaryMemoP11_typeobjectP7_objectS2_.cold_ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_22_export_to_cP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_22_export_to_cP7_objectPKS0_lS0_.cold_ZL46__pyx_getprop_7pyarrow_3lib_7MapType_item_typeP7_objectPv_ZL46__pyx_getprop_7pyarrow_3lib_7MapType_item_typeP7_objectPv.cold_ZL45__pyx_getprop_7pyarrow_3lib_7MapType_key_typeP7_objectPv_ZL45__pyx_getprop_7pyarrow_3lib_7MapType_key_typeP7_objectPv.cold_ZL51__pyx_pw_7pyarrow_3lib_5Array_90_export_to_c_deviceP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_5Array_90_export_to_c_deviceP7_objectPKS0_lS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_11RecordBatch_59_export_to_c_deviceP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_11RecordBatch_59_export_to_c_deviceP7_objectPKS0_lS0_.cold_ZL41__pyx_tp_dealloc_7pyarrow_3lib_NativeFileP7_object_ZL43__pyx_tp_dealloc_7pyarrow_3lib_BufferReaderP7_object_ZL37__pyx_tp_dealloc_7pyarrow_3lib_OSFileP7_object_ZL41__pyx_tp_dealloc_7pyarrow_3lib_PythonFileP7_object_ZL49__pyx_tp_dealloc_7pyarrow_3lib_BufferOutputStreamP7_object_ZL47__pyx_tp_dealloc_7pyarrow_3lib_MemoryMappedFileP7_object_ZL53__pyx_pw_7pyarrow_3lib_18BufferOutputStream_3getvalueP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_18BufferOutputStream_3getvalueP7_objectPKS0_lS0_.cold_ZL42__pyx_f_7pyarrow_3lib_13ExtensionType_initP37__pyx_obj_7pyarrow_3lib_ExtensionTypeRKSt10shared_ptrIN5arrow8DataTypeEE.cold_ZL43__pyx_tp_new_7pyarrow_3lib_MockOutputStreamP11_typeobjectP7_objectS2__ZL43__pyx_tp_new_7pyarrow_3lib_MockOutputStreamP11_typeobjectP7_objectS2_.cold_ZL41__pyx_getprop_7pyarrow_3lib_7Message_bodyP7_objectPv_ZL41__pyx_getprop_7pyarrow_3lib_7Message_bodyP7_objectPv.cold_ZL53__pyx_tp_dealloc_7pyarrow_3lib__RecordBatchFileReaderP7_object_ZL50__pyx_tp_new_7pyarrow_3lib__ExtensionRegistryNannyP11_typeobjectP7_objectS2__ZL54__pyx_getprop_7pyarrow_3lib_17RecordBatchReader_schemaP7_objectPv_ZL54__pyx_getprop_7pyarrow_3lib_17RecordBatchReader_schemaP7_objectPv.cold_ZL42__pyx_getprop_7pyarrow_3lib_6Buffer_parentP7_objectPv_ZL42__pyx_getprop_7pyarrow_3lib_6Buffer_parentP7_objectPv.cold_ZL48__pyx_tp_new_7pyarrow_3lib_FixedSizeBufferWriterP11_typeobjectP7_objectS2__ZL48__pyx_tp_new_7pyarrow_3lib_FixedSizeBufferWriterP11_typeobjectP7_objectS2_.cold_ZL43__pyx_getprop_7pyarrow_3lib_5Field_metadataP7_objectPv_ZL43__pyx_getprop_7pyarrow_3lib_5Field_metadataP7_objectPv.cold_ZL43__pyx_f_7pyarrow_3lib__append_array_buffersPKN5arrow9ArrayDataEP7_object_ZL43__pyx_f_7pyarrow_3lib__append_array_buffersPKN5arrow9ArrayDataEP7_object.cold_ZL39__pyx_pw_7pyarrow_3lib_5Array_80buffersP7_objectPKS0_lS0__ZZL49__pyx_gb_7pyarrow_3lib_12StructScalar_4generator6P21__pyx_CoroutineObjectP3_tsP7_objectE18__pyx_dict_version_ZZL49__pyx_gb_7pyarrow_3lib_12StructScalar_4generator6P21__pyx_CoroutineObjectP3_tsP7_objectE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_147run_end_encodedP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_147run_end_encodedP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_6Schema_50remove_metadataP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_6Schema_50remove_metadataP7_objectPKS0_lS0_.cold_ZL56__pyx_f_7pyarrow_3lib_20TransformInputStream_make_nativeSt10shared_ptrIN5arrow2io11InputStreamEEP7_object.cold_ZL47__pyx_pw_7pyarrow_3lib_5Field_19remove_metadataP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_5Field_19remove_metadataP7_objectPKS0_lS0_.cold_ZL32__pyx_f_7pyarrow_3lib_get_readerP7_objectbPSt10shared_ptrIN5arrow2io16RandomAccessFileEE.cold_ZL45__pyx_f_7pyarrow_3lib_pyarrow_unwrap_metadataP7_object.cold_ZL44__pyx_f_7pyarrow_3lib_15SparseCOOTensor_initP39__pyx_obj_7pyarrow_3lib_SparseCOOTensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCOOIndexEEEE.cold_ZL44__pyx_f_7pyarrow_3lib_15SparseCSCMatrix_initP39__pyx_obj_7pyarrow_3lib_SparseCSCMatrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSCIndexEEEE.cold_ZL44__pyx_f_7pyarrow_3lib_15SparseCSRMatrix_initP39__pyx_obj_7pyarrow_3lib_SparseCSRMatrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSRIndexEEEE.cold_ZL44__pyx_f_7pyarrow_3lib_15SparseCSFTensor_initP39__pyx_obj_7pyarrow_3lib_SparseCSFTensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS2_14SparseCSFIndexEEEE.cold_ZL54__pyx_f_7pyarrow_3lib__wrap_record_batch_with_metadataN5arrow23RecordBatchWithMetadataE_ZZL54__pyx_f_7pyarrow_3lib__wrap_record_batch_with_metadataN5arrow23RecordBatchWithMetadataEE18__pyx_dict_version_ZZL54__pyx_f_7pyarrow_3lib__wrap_record_batch_with_metadataN5arrow23RecordBatchWithMetadataEE23__pyx_dict_cached_value_ZL54__pyx_f_7pyarrow_3lib__wrap_record_batch_with_metadataN5arrow23RecordBatchWithMetadataE.cold_ZL46__pyx_pw_7pyarrow_3lib_6Schema_46with_metadataP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_6Schema_46with_metadataP7_objectPKS0_lS0_.cold_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEEaSEOS4_.isra.0_ZL45__pyx_pw_7pyarrow_3lib_5Field_17with_metadataP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_5Field_17with_metadataP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_5Table_37to_batchesP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_5Table_37to_batchesP7_objectPKS0_lS0_.cold_Z32pyarrow_unwrap_sparse_csr_matrixP7_object.cold_Z21pyarrow_unwrap_bufferP7_object.cold_Z21pyarrow_unwrap_bufferP7_object.localalias_ZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPool_ZZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_0_ZZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_0_ZL46__pyx_f_7pyarrow_3lib_c_mask_inverted_from_objP7_objectP34__pyx_obj_7pyarrow_3lib_MemoryPool.cold_Z32pyarrow_unwrap_sparse_csc_matrixP7_object.cold_Z20pyarrow_unwrap_tableP7_object.cold_Z21pyarrow_unwrap_tensorP7_object.cold_Z21pyarrow_unwrap_tensorP7_object.localalias_Z20pyarrow_unwrap_fieldP7_object.cold_Z28pyarrow_unwrap_chunked_arrayP7_object.cold_Z24pyarrow_unwrap_data_typeP7_object.cold_Z24pyarrow_unwrap_data_typeP7_object.localalias_Z20pyarrow_unwrap_arrayP7_object.cold_Z20pyarrow_unwrap_arrayP7_object.localalias_ZL49__pyx_pw_7pyarrow_3lib_5Array_86__arrow_c_array__P7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_5Array_85__arrow_c_array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_5Array_85__arrow_c_array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_ZZL49__pyx_pf_7pyarrow_3lib_5Array_85__arrow_c_array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE18__pyx_dict_version_0_ZZL49__pyx_pf_7pyarrow_3lib_5Array_85__arrow_c_array__P29__pyx_obj_7pyarrow_3lib_ArrayP7_objectE23__pyx_dict_cached_value_0_ZL49__pyx_pw_7pyarrow_3lib_5Array_86__arrow_c_array__P7_objectPKS0_lS0_.cold_Z20pyarrow_unwrap_batchP7_object.cold_Z20pyarrow_unwrap_batchP7_object.localalias_ZL56__pyx_pw_7pyarrow_3lib_11RecordBatch_53__arrow_c_array__P7_objectPKS0_lS0__ZZL56__pyx_pf_7pyarrow_3lib_11RecordBatch_52__arrow_c_array__P35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectE18__pyx_dict_version_ZZL56__pyx_pf_7pyarrow_3lib_11RecordBatch_52__arrow_c_array__P35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectE23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_11RecordBatch_53__arrow_c_array__P7_objectPKS0_lS0_.cold_Z32pyarrow_unwrap_sparse_csf_tensorP7_object.cold_Z32pyarrow_unwrap_sparse_coo_tensorP7_object.cold_Z21pyarrow_unwrap_schemaP7_object.cold_Z21pyarrow_unwrap_schemaP7_object.localalias_ZL45__pyx_pw_7pyarrow_3lib_7Message_7serialize_toP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_7Message_7serialize_toP7_objectPKS0_lS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_5Field_23with_nameP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_5Field_22with_nameP29__pyx_obj_7pyarrow_3lib_FieldP7_objectE18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_5Field_22with_nameP29__pyx_obj_7pyarrow_3lib_FieldP7_objectE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_5Field_23with_nameP7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_109decimal256P7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_109decimal256P7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_107decimal128P7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_107decimal128P7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_13from_tensorP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_13from_tensorP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_9from_tensorP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_9from_tensorP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_11from_tensorP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_11from_tensorP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_11from_tensorP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_11from_tensorP7_objectPKS0_lS0_.cold_ZNSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS3_EE13_M_assign_auxIPKS3_EEvT_S9_St20forward_iterator_tag.isra.0_ZL31__pyx_pw_7pyarrow_3lib_127list_P7_objectPKS0_lS0__ZZL31__pyx_pf_7pyarrow_3lib_126list_P7_objectS0_iE18__pyx_dict_version_ZZL31__pyx_pf_7pyarrow_3lib_126list_P7_objectS0_iE23__pyx_dict_cached_value_ZL31__pyx_pw_7pyarrow_3lib_127list_P7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_129large_listP7_objectPKS0_lS0__ZZL32__pyx_f_7pyarrow_3lib_large_listP7_objectiE18__pyx_dict_version_ZZL32__pyx_f_7pyarrow_3lib_large_listP7_objectiE23__pyx_dict_cached_value_ZL36__pyx_pw_7pyarrow_3lib_129large_listP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_39flushP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_39flushP7_objectPKS0_lS0_.cold_ZZL35__pyx_f_7pyarrow_3lib__cb_transformP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E18__pyx_dict_version_ZZL35__pyx_f_7pyarrow_3lib__cb_transformP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E23__pyx_dict_cached_value_ZL35__pyx_f_7pyarrow_3lib__cb_transformP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_.cold_ZL41__pyx_pw_7pyarrow_3lib_5Field_21with_typeP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Field_21with_typeP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_31sizeP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_31sizeP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_247write_tensorP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_247write_tensorP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_5Field_25with_nullableP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_5Field_25with_nullableP7_objectPKS0_lS0_.cold_ZL32__pyx_pw_7pyarrow_3lib_115binaryP7_objectPKS0_lS0__ZL32__pyx_pw_7pyarrow_3lib_115binaryP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_3write_batchP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_19_CRecordBatchWriter_3write_batchP7_objectPKS0_lS0_.cold_ZL55__pyx_pw_7pyarrow_3lib_20TransformInputStream_1__init__P7_objectS0_S0__ZL55__pyx_pw_7pyarrow_3lib_20TransformInputStream_1__init__P7_objectS0_S0_.cold_ZL55__pyx_pw_7pyarrow_3lib_5Table_15replace_schema_metadataP7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_5Table_15replace_schema_metadataP7_objectPKS0_lS0_.cold_ZL61__pyx_pw_7pyarrow_3lib_11RecordBatch_9replace_schema_metadataP7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_11RecordBatch_9replace_schema_metadataP7_objectPKS0_lS0_.cold_ZL35__pyx_pw_7pyarrow_3lib_131list_viewP7_objectPKS0_lS0__ZZL31__pyx_f_7pyarrow_3lib_list_viewP7_objectiE18__pyx_dict_version_ZZL31__pyx_f_7pyarrow_3lib_list_viewP7_objectiE23__pyx_dict_cached_value_ZL35__pyx_pw_7pyarrow_3lib_131list_viewP7_objectPKS0_lS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_133large_list_viewP7_objectPKS0_lS0__ZZL37__pyx_f_7pyarrow_3lib_large_list_viewP7_objectiE18__pyx_dict_version_ZZL37__pyx_f_7pyarrow_3lib_large_list_viewP7_objectiE23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_133large_list_viewP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_35tellP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10NativeFile_35tellP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_10UnionArray_3fieldP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_10UnionArray_3fieldP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_12ChunkedArray_60sliceP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_12ChunkedArray_60sliceP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_11RecordBatch_31equalsP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_11RecordBatch_31equalsP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_10NativeFile_47read_atP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_10NativeFile_47read_atP7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_11RecordBatch_27sliceP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_11RecordBatch_27sliceP7_objectPKS0_lS0_.cold_Z21pyarrow_unwrap_scalarP7_object.cold_Z21pyarrow_unwrap_scalarP7_object.localalias_ZL54__pyx_pw_7pyarrow_3lib_15ExtensionScalar_3from_storageP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_15ExtensionScalar_2from_storageP41__pyx_obj_7pyarrow_3lib_BaseExtensionTypeP7_objectE18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_15ExtensionScalar_2from_storageP41__pyx_obj_7pyarrow_3lib_BaseExtensionTypeP7_objectE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_15ExtensionScalar_3from_storageP7_objectPKS0_lS0_.cold_ZL37__pyx_tp_new_7pyarrow_3lib_NullScalarP11_typeobjectP7_objectS2__ZZL45__pyx_pf_7pyarrow_3lib_10NullScalar___cinit__P34__pyx_obj_7pyarrow_3lib_NullScalarE18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_10NullScalar___cinit__P34__pyx_obj_7pyarrow_3lib_NullScalarE23__pyx_dict_cached_value_ZL37__pyx_tp_new_7pyarrow_3lib_NullScalarP11_typeobjectP7_objectS2_.cold_ZL51__pyx_f_7pyarrow_3lib_10NativeFile_get_input_streamP34__pyx_obj_7pyarrow_3lib_NativeFile.cold_ZL52__pyx_f_7pyarrow_3lib_10NativeFile_get_output_streamP34__pyx_obj_7pyarrow_3lib_NativeFile.cold_ZL46__pyx_pw_7pyarrow_3lib_6Schema_30field_by_nameP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_0_ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_0_ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_1_ZZL46__pyx_pf_7pyarrow_3lib_6Schema_29field_by_nameP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_1_ZL46__pyx_pw_7pyarrow_3lib_6Schema_30field_by_nameP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_5Field_27flattenP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_5Field_27flattenP7_objectPKS0_lS0_.cold_ZZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE18__pyx_dict_version_ZZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE23__pyx_dict_cached_value_ZZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE18__pyx_dict_version_0_ZZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE23__pyx_dict_cached_value_0_ZL48__pyx_f_7pyarrow_3lib_10StructType_field_by_nameP34__pyx_obj_7pyarrow_3lib_StructTypeP7_object.cold_ZL34__pyx_f_7pyarrow_3lib_6Schema_initP30__pyx_obj_7pyarrow_3lib_SchemaRKSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS5_EE.cold_ZL38__pyx_f_7pyarrow_3lib__ndarray_to_typeP7_objectP32__pyx_obj_7pyarrow_3lib_DataType_ZL38__pyx_f_7pyarrow_3lib__ndarray_to_typeP7_objectP32__pyx_obj_7pyarrow_3lib_DataType.cold_ZL39__pyx_f_7pyarrow_3lib__ndarray_to_arrayP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypebbPN5arrow10MemoryPoolE_ZL39__pyx_f_7pyarrow_3lib__ndarray_to_arrayP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypebbPN5arrow10MemoryPoolE.cold_ZL48__pyx_pw_7pyarrow_3lib_173_ndarray_to_arrow_typeP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_173_ndarray_to_arrow_typeP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_8DataType_27_import_from_c_capsuleP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_8DataType_27_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_8DataType_23_import_from_cP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_8DataType_23_import_from_cP7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_185infer_typeP7_objectPKS0_lS0__ZZL36__pyx_pf_7pyarrow_3lib_184infer_typeP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL36__pyx_pf_7pyarrow_3lib_184infer_typeP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL36__pyx_pf_7pyarrow_3lib_184infer_typeP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL36__pyx_pf_7pyarrow_3lib_184infer_typeP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZL36__pyx_pw_7pyarrow_3lib_185infer_typeP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_157from_numpy_dtypeP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_156from_numpy_dtypeP7_objectS0_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_156from_numpy_dtypeP7_objectS0_E23__pyx_dict_cached_value_ZL42__pyx_pw_7pyarrow_3lib_157from_numpy_dtypeP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_6Schema_34get_all_field_indicesP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_6Schema_33get_all_field_indicesP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_6Schema_33get_all_field_indicesP30__pyx_obj_7pyarrow_3lib_SchemaP7_objectE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_6Schema_34get_all_field_indicesP7_objectPKS0_lS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_10StructType_5get_all_field_indicesP7_objectPKS0_lS0__ZZL58__pyx_pf_7pyarrow_3lib_10StructType_4get_all_field_indicesP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE18__pyx_dict_version_ZZL58__pyx_pf_7pyarrow_3lib_10StructType_4get_all_field_indicesP34__pyx_obj_7pyarrow_3lib_StructTypeP7_objectE23__pyx_dict_cached_value_ZL58__pyx_pw_7pyarrow_3lib_10StructType_5get_all_field_indicesP7_objectPKS0_lS0_.cold_ZL41__pyx_tp_new_7pyarrow_3lib_IpcReadOptionsP11_typeobjectP7_objectS2__ZL41__pyx_tp_new_7pyarrow_3lib_IpcReadOptionsP11_typeobjectP7_objectS2_.cold_ZL51__pyx_tp_new_7pyarrow_3lib__RecordBatchStreamReaderP11_typeobjectP7_objectS2__ZL51__pyx_tp_new_7pyarrow_3lib__RecordBatchStreamReaderP11_typeobjectP7_objectS2_.cold_ZL49__pyx_tp_new_7pyarrow_3lib__RecordBatchFileReaderP11_typeobjectP7_objectS2__ZL49__pyx_tp_new_7pyarrow_3lib__RecordBatchFileReaderP11_typeobjectP7_objectS2_.cold_ZL54__pyx_pw_7pyarrow_3lib_11StructArray_3_flattened_fieldP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_11StructArray_3_flattened_fieldP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_18LargeListViewArray_3flattenP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_18LargeListViewArray_3flattenP7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_13ListViewArray_3flattenP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_13ListViewArray_3flattenP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_5Array_88_import_from_c_capsuleP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_5Array_88_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL35__pyx_pw_7pyarrow_3lib_5Array_9viewP7_objectPKS0_lS0__ZL35__pyx_pw_7pyarrow_3lib_5Array_9viewP7_objectPKS0_lS0_.cold_ZL32__pyx_pw_7pyarrow_3lib_183repeatP7_objectPKS0_lS0__ZZL32__pyx_pf_7pyarrow_3lib_182repeatP7_objectS0_S0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL32__pyx_pf_7pyarrow_3lib_182repeatP7_objectS0_S0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL32__pyx_pw_7pyarrow_3lib_183repeatP7_objectPKS0_lS0_.cold_ZL31__pyx_pw_7pyarrow_3lib_181nullsP7_objectPKS0_lS0__ZZL31__pyx_pf_7pyarrow_3lib_180nullsP7_objectS0_S0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL31__pyx_pf_7pyarrow_3lib_180nullsP7_objectS0_S0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL31__pyx_pw_7pyarrow_3lib_181nullsP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_43supported_memory_backendsP7_objectS0__ZL50__pyx_pw_7pyarrow_3lib_43supported_memory_backendsP7_objectS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_5get_batchP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_5get_batchP7_objectPKS0_lS0_.cold_ZL60__pyx_pw_7pyarrow_3lib_11RecordBatch_61_import_from_c_deviceP7_objectPKS0_lS0__ZL60__pyx_pw_7pyarrow_3lib_11RecordBatch_61_import_from_c_deviceP7_objectPKS0_lS0_.cold_ZL61__pyx_pw_7pyarrow_3lib_11RecordBatch_57_import_from_c_capsuleP7_objectPKS0_lS0__ZL61__pyx_pw_7pyarrow_3lib_11RecordBatch_57_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_11RecordBatch_51_import_from_cP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_11RecordBatch_51_import_from_cP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_11RecordBatch_43from_struct_arrayP7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_11RecordBatch_43from_struct_arrayP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_21set_columnP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_20set_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_20set_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E23__pyx_dict_cached_value_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_20set_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E18__pyx_dict_version_0_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_20set_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E23__pyx_dict_cached_value_0_ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_21set_columnP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_11RecordBatch_19remove_columnP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_11RecordBatch_19remove_columnP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_17add_columnP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_16add_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_16add_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E23__pyx_dict_cached_value_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_16add_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E18__pyx_dict_version_0_ZZL49__pyx_pf_7pyarrow_3lib_11RecordBatch_16add_columnP35__pyx_obj_7pyarrow_3lib_RecordBatchiP7_objectS2_E23__pyx_dict_cached_value_0_ZL49__pyx_pw_7pyarrow_3lib_11RecordBatch_17add_columnP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_255read_record_batchP7_objectPKS0_lS0__ZZL43__pyx_pf_7pyarrow_3lib_254read_record_batchP7_objectS0_P30__pyx_obj_7pyarrow_3lib_SchemaP38__pyx_obj_7pyarrow_3lib_DictionaryMemoE18__pyx_dict_version_ZZL43__pyx_pf_7pyarrow_3lib_254read_record_batchP7_objectS0_P30__pyx_obj_7pyarrow_3lib_SchemaP38__pyx_obj_7pyarrow_3lib_DictionaryMemoE23__pyx_dict_cached_value_ZL43__pyx_pw_7pyarrow_3lib_255read_record_batchP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_13StringBuilder_3appendP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_13StringBuilder_2appendP37__pyx_obj_7pyarrow_3lib_StringBuilderP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_13StringBuilder_2appendP37__pyx_obj_7pyarrow_3lib_StringBuilderP7_objectE23__pyx_dict_cached_value_ZZL46__pyx_pf_7pyarrow_3lib_13StringBuilder_2appendP37__pyx_obj_7pyarrow_3lib_StringBuilderP7_objectE18__pyx_dict_version_0_ZZL46__pyx_pf_7pyarrow_3lib_13StringBuilder_2appendP37__pyx_obj_7pyarrow_3lib_StringBuilderP7_objectE23__pyx_dict_cached_value_0_ZL46__pyx_pw_7pyarrow_3lib_13StringBuilder_3appendP7_objectPKS0_lS0_.cold_ZL45__pyx_tp_new_7pyarrow_3lib_BufferOutputStreamP11_typeobjectP7_objectS2__ZL45__pyx_tp_new_7pyarrow_3lib_BufferOutputStreamP11_typeobjectP7_objectS2_.cold_ZL41__pyx_pw_7pyarrow_3lib_223allocate_bufferP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_223allocate_bufferP7_objectPKS0_lS0_.cold_ZL33__pyx_f_7pyarrow_3lib_as_c_bufferP7_object_ZL33__pyx_f_7pyarrow_3lib_as_c_bufferP7_object.cold_ZL42__pyx_pw_7pyarrow_3lib_5Codec_17decompressP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_5Codec_16decompressP29__pyx_obj_7pyarrow_3lib_CodecP7_objectS2_S2_S2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_5Codec_16decompressP29__pyx_obj_7pyarrow_3lib_CodecP7_objectS2_S2_S2_E23__pyx_dict_cached_value_ZL42__pyx_pw_7pyarrow_3lib_5Codec_17decompressP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_5Codec_15compressP7_objectPKS0_lS0__ZZL40__pyx_pf_7pyarrow_3lib_5Codec_14compressP29__pyx_obj_7pyarrow_3lib_CodecP7_objectS2_S2_E18__pyx_dict_version_ZZL40__pyx_pf_7pyarrow_3lib_5Codec_14compressP29__pyx_obj_7pyarrow_3lib_CodecP7_objectS2_S2_E23__pyx_dict_cached_value_ZL40__pyx_pw_7pyarrow_3lib_5Codec_15compressP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_41writeP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_10NativeFile_41writeP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_6Buffer_13sliceP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_6Buffer_13sliceP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_10NativeFile_63read_bufferP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_10NativeFile_62read_bufferP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_10NativeFile_62read_bufferP34__pyx_obj_7pyarrow_3lib_NativeFileP7_objectE23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_10NativeFile_63read_bufferP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_11RecordBatch_25serializeP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_11RecordBatch_25serializeP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_6Schema_48serializeP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_6Schema_48serializeP7_objectPKS0_lS0_.cold_ZL35__pyx_pw_7pyarrow_3lib_227py_bufferP7_objectPKS0_lS0__ZL35__pyx_pw_7pyarrow_3lib_227py_bufferP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_13to_tensorP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_13to_tensorP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_17to_tensorP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_17to_tensorP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_17to_tensorP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_17to_tensorP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_21to_tensorP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_21to_tensorP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_11RecordBatch_47to_tensorP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_11RecordBatch_47to_tensorP7_objectPKS0_lS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_22FixedShapeTensorScalar_3to_tensorP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_22FixedShapeTensorScalar_3to_tensorP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_21FixedShapeTensorArray_3to_tensorP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_21FixedShapeTensorArray_3to_tensorP7_objectPKS0_lS0_.cold_ZL37__pyx_pw_7pyarrow_3lib_249read_tensorP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_249read_tensorP7_objectPKS0_lS0_.cold_ZL56__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_shapeP7_objectPv_ZL56__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_shapeP7_objectPv.cold_ZL62__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_permutationP7_objectPv_ZL62__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_permutationP7_objectPv.cold_ZL55__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_15__getitem__P7_objectS0__ZZL55__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_14__getitem__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE18__pyx_dict_version_ZZL55__pyx_pf_7pyarrow_3lib_16KeyValueMetadata_14__getitem__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectE23__pyx_dict_cached_value_ZL55__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_15__getitem__P7_objectS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_83tzinfo_to_stringP7_objectPKS0_lS0__ZZL41__pyx_pf_7pyarrow_3lib_82tzinfo_to_stringP7_objectS0_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_82tzinfo_to_stringP7_objectS0_E23__pyx_dict_cached_value_ZL41__pyx_pw_7pyarrow_3lib_83tzinfo_to_stringP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_5Field_35_import_from_c_capsuleP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_5Field_35_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_5Field_31_import_from_cP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_5Field_31_import_from_cP7_objectPKS0_lS0_.cold_ZL55__pyx_pw_7pyarrow_3lib_6Schema_64_import_from_c_capsuleP7_objectPKS0_lS0__ZL55__pyx_pw_7pyarrow_3lib_6Schema_64_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_6Schema_56_import_from_cP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_6Schema_56_import_from_cP7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_6Schema_42setP7_objectPKS0_lS0__ZL36__pyx_pw_7pyarrow_3lib_6Schema_42setP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_6Schema_40removeP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_6Schema_40removeP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_6Schema_38insertP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_6Schema_38insertP7_objectPKS0_lS0_.cold_ZL37__pyx_pw_7pyarrow_3lib_253read_schemaP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_253read_schemaP7_objectPKS0_lS0_.cold_ZL36__pyx_pw_7pyarrow_3lib_137dictionaryP7_objectPKS0_lS0__ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_0_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_0_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_1_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_1_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_2_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_2_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_3_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_3_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_4_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_4_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_5_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_5_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE18__pyx_dict_version_6_ZZL32__pyx_f_7pyarrow_3lib_dictionaryP7_objectS0_iP39__pyx_opt_args_7pyarrow_3lib_dictionaryE23__pyx_dict_cached_value_6_ZL36__pyx_pw_7pyarrow_3lib_137dictionaryP7_objectPKS0_lS0_.cold_ZL52__pyx_getprop_7pyarrow_3lib_16DictionaryScalar_valueP7_objectPv_ZL52__pyx_getprop_7pyarrow_3lib_16DictionaryScalar_valueP7_objectPv.cold_ZL44__pyx_f_7pyarrow_3lib_12ChunkedArray_getitemP36__pyx_obj_7pyarrow_3lib_ChunkedArrayl.cold_ZL36__pyx_f_7pyarrow_3lib_5Array_getitemP29__pyx_obj_7pyarrow_3lib_Arrayl.cold_ZL62__pyx_pw_7pyarrow_3lib_12ChunkedArray_83_import_from_c_capsuleP7_objectPKS0_lS0__ZL62__pyx_pw_7pyarrow_3lib_12ChunkedArray_83_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL58__pyx_pw_7pyarrow_3lib_12ChunkedArray_72unify_dictionariesP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_12ChunkedArray_72unify_dictionariesP7_objectPKS0_lS0_.cold_ZL40__pyx_f_7pyarrow_3lib__sequence_to_arrayP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypePN5arrow10MemoryPoolEb_ZZL40__pyx_f_7pyarrow_3lib__sequence_to_arrayP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypePN5arrow10MemoryPoolEbE18__pyx_dict_version_ZZL40__pyx_f_7pyarrow_3lib__sequence_to_arrayP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypePN5arrow10MemoryPoolEbE23__pyx_dict_cached_value_ZL40__pyx_f_7pyarrow_3lib__sequence_to_arrayP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypePN5arrow10MemoryPoolEb.cold_ZL31__pyx_pw_7pyarrow_3lib_177arrayP7_objectPKS0_lS0__ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_0_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_0_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_1_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_1_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_2_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_2_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_3_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_3_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_4_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_4_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_5_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_5_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_6_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_6_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version__11__ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value__11__ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_7_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_7_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version__10__ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value__10__ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_8_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_8_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_9_ZZL31__pyx_pf_7pyarrow_3lib_176arrayP7_objectS0_S0_S0_S0_S0_iP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_9_ZL32__pyx_pw_7pyarrow_3lib_171scalarP7_objectPKS0_lS0__ZL32__pyx_pw_7pyarrow_3lib_171scalarP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_5Array_92_import_from_c_deviceP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_5Array_92_import_from_c_deviceP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_5Array_84_import_from_cP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_5Array_84_import_from_cP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_15DictionaryArray_7from_arraysP7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_15DictionaryArray_6from_arraysP7_objectS0_S0_iiiP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_15DictionaryArray_6from_arraysP7_objectS0_S0_iiiP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZZL53__pyx_pf_7pyarrow_3lib_15DictionaryArray_6from_arraysP7_objectS0_S0_iiiP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_0_ZZL53__pyx_pf_7pyarrow_3lib_15DictionaryArray_6from_arraysP7_objectS0_S0_iiiP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_0_ZL53__pyx_pw_7pyarrow_3lib_15DictionaryArray_7from_arraysP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_11StructArray_5flattenP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_11StructArray_5flattenP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_11RecordBatch_45to_struct_arrayP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_11RecordBatch_45to_struct_arrayP7_objectPKS0_lS0_.cold_ZL47__pyx_pw_7pyarrow_3lib_12ChunkedArray_52flattenP7_objectPKS0_lS0__ZL47__pyx_pw_7pyarrow_3lib_12ChunkedArray_52flattenP7_objectPKS0_lS0_.cold_ZL53__pyx_pw_7pyarrow_3lib_17RecordBatchReader_12read_allP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_17RecordBatchReader_12read_allP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_5Table_53set_columnP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_5Table_52set_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_5Table_52set_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E23__pyx_dict_cached_value_ZZL42__pyx_pf_7pyarrow_3lib_5Table_52set_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E18__pyx_dict_version_0_ZZL42__pyx_pf_7pyarrow_3lib_5Table_52set_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E23__pyx_dict_cached_value_0_ZL42__pyx_pw_7pyarrow_3lib_5Table_53set_columnP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_5Table_51remove_columnP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_5Table_51remove_columnP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_5Table_49add_columnP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_5Table_48add_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_5Table_48add_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E23__pyx_dict_cached_value_ZZL42__pyx_pf_7pyarrow_3lib_5Table_48add_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E18__pyx_dict_version_0_ZZL42__pyx_pf_7pyarrow_3lib_5Table_48add_columnP29__pyx_obj_7pyarrow_3lib_TableiP7_objectS2_E23__pyx_dict_cached_value_0_ZL42__pyx_pw_7pyarrow_3lib_5Table_49add_columnP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_5Table_21unify_dictionariesP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_5Table_21unify_dictionariesP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_5Table_19combine_chunksP7_objectPKS0_lS0__ZL46__pyx_pw_7pyarrow_3lib_5Table_19combine_chunksP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_5Table_17flattenP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_5Table_17flattenP7_objectPKS0_lS0_.cold_ZL41__pyx_pw_7pyarrow_3lib_5Table_39to_readerP7_objectPKS0_lS0__ZL41__pyx_pw_7pyarrow_3lib_5Table_39to_readerP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_33metadataP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_32metadataP34__pyx_obj_7pyarrow_3lib_NativeFileE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_10NativeFile_32metadataP34__pyx_obj_7pyarrow_3lib_NativeFileE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_10NativeFile_33metadataP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_19BufferedInputStream_3detachP7_objectPKS0_lS0__ZL52__pyx_pw_7pyarrow_3lib_19BufferedInputStream_3detachP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_10NativeFile_45get_streamP7_objectPKS0_lS0__ZL48__pyx_pw_7pyarrow_3lib_10NativeFile_45get_streamP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_3_openP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_16MemoryMappedFile_2_openP40__pyx_obj_7pyarrow_3lib_MemoryMappedFileP7_objectS2_E18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_16MemoryMappedFile_2_openP40__pyx_obj_7pyarrow_3lib_MemoryMappedFileP7_objectS2_E23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_3_openP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_1createP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_16MemoryMappedFile_createP7_objectS0_E18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_16MemoryMappedFile_createP7_objectS0_E23__pyx_dict_cached_value_ZL49__pyx_pw_7pyarrow_3lib_16MemoryMappedFile_1createP7_objectPKS0_lS0_.cold_ZL44__pyx_f_7pyarrow_3lib_6OSFile__open_readableP30__pyx_obj_7pyarrow_3lib_OSFileNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPN5arrow10MemoryPoolE.cold_ZL53__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_3detachP7_objectPKS0_lS0__ZL53__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_3detachP7_objectPKS0_lS0_.cold_ZL44__pyx_f_7pyarrow_3lib_6OSFile__open_writableP30__pyx_obj_7pyarrow_3lib_OSFileNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEP51__pyx_opt_args_7pyarrow_3lib_6OSFile__open_writable.cold_ZL38__pyx_f_7pyarrow_3lib_get_input_streamP7_objectbPSt10shared_ptrIN5arrow2io11InputStreamEE.cold_ZL39__pyx_f_7pyarrow_3lib__get_input_streamP7_objectPSt10shared_ptrIN5arrow2io11InputStreamEE_ZZL39__pyx_f_7pyarrow_3lib__get_input_streamP7_objectPSt10shared_ptrIN5arrow2io11InputStreamEEE18__pyx_dict_version_ZZL39__pyx_f_7pyarrow_3lib__get_input_streamP7_objectPSt10shared_ptrIN5arrow2io11InputStreamEEE23__pyx_dict_cached_value_ZL51__pyx_pw_7pyarrow_3lib_13MessageReader_5open_streamP7_objectPKS0_lS0__ZL51__pyx_pw_7pyarrow_3lib_13MessageReader_5open_streamP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_21CompressedInputStream_1__init__P7_objectS0_S0__ZL56__pyx_pw_7pyarrow_3lib_21CompressedInputStream_1__init__P7_objectS0_S0_.cold_ZL57__pyx_pw_7pyarrow_3lib_22CompressedOutputStream_1__init__P7_objectS0_S0__ZL57__pyx_pw_7pyarrow_3lib_22CompressedOutputStream_1__init__P7_objectS0_S0_.cold_ZL54__pyx_pw_7pyarrow_3lib_19BufferedInputStream_1__init__P7_objectS0_S0__ZL54__pyx_pw_7pyarrow_3lib_19BufferedInputStream_1__init__P7_objectS0_S0_.cold_ZL55__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_1__init__P7_objectS0_S0__ZL55__pyx_pw_7pyarrow_3lib_20BufferedOutputStream_1__init__P7_objectS0_S0_.cold_ZL57__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_compressionP7_objectS0_Pv_ZL57__pyx_setprop_7pyarrow_3lib_15IpcWriteOptions_compressionP7_objectS0_Pv.cold_ZL39__pyx_pw_7pyarrow_3lib_5Codec_1__init__P7_objectS0_S0__ZL39__pyx_pw_7pyarrow_3lib_5Codec_1__init__P7_objectS0_S0_.cold_ZL58__pyx_pw_7pyarrow_3lib_13MessageReader_11read_next_messageP7_objectPKS0_lS0__ZL58__pyx_pw_7pyarrow_3lib_13MessageReader_11read_next_messageP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_251read_messageP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_251read_messageP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_22_RecordBatchFileWriter_1_openP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_22_RecordBatchFileWriter_1_openP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamWriter_5_openP7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamWriter_5_openP7_objectPKS0_lS0_.cold_ZL79__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_7get_batch_with_custom_metadataP7_objectPKS0_lS0__ZL79__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_7get_batch_with_custom_metadataP7_objectPKS0_lS0_.cold_ZL80__pyx_pf_7pyarrow_3lib_17RecordBatchReader_6read_next_batch_with_custom_metadataP41__pyx_obj_7pyarrow_3lib_RecordBatchReader_ZL80__pyx_pf_7pyarrow_3lib_17RecordBatchReader_6read_next_batch_with_custom_metadataP41__pyx_obj_7pyarrow_3lib_RecordBatchReader.cold_ZL80__pyx_pw_7pyarrow_3lib_17RecordBatchReader_7read_next_batch_with_custom_metadataP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_32from_batchesP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_17RecordBatchReader_32from_batchesP7_objectPKS0_lS0_.cold_ZL67__pyx_pw_7pyarrow_3lib_17RecordBatchReader_28_import_from_c_capsuleP7_objectPKS0_lS0__ZL67__pyx_pw_7pyarrow_3lib_17RecordBatchReader_28_import_from_c_capsuleP7_objectPKS0_lS0_.cold_ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_24_import_from_cP7_objectPKS0_lS0__ZL59__pyx_pw_7pyarrow_3lib_17RecordBatchReader_24_import_from_cP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_17RecordBatchReader_20castP7_objectPKS0_lS0__ZL49__pyx_pw_7pyarrow_3lib_17RecordBatchReader_20castP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamReader_3_openP7_objectPKS0_lS0__ZL56__pyx_pw_7pyarrow_3lib_24_RecordBatchStreamReader_3_openP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_3_openP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_22_RecordBatchFileReader_2_openP46__pyx_obj_7pyarrow_3lib__RecordBatchFileReaderP7_objectS2_P38__pyx_obj_7pyarrow_3lib_IpcReadOptionsP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_22_RecordBatchFileReader_2_openP46__pyx_obj_7pyarrow_3lib__RecordBatchFileReaderP7_objectS2_P38__pyx_obj_7pyarrow_3lib_IpcReadOptionsP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZL54__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_3_openP7_objectPKS0_lS0_.cold_ZL45__pyx_pw_7pyarrow_3lib_8MapArray_1from_arraysP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_0_ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_0_ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE18__pyx_dict_version_1_ZZL44__pyx_pf_7pyarrow_3lib_8MapArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolE23__pyx_dict_cached_value_1_ZL45__pyx_pw_7pyarrow_3lib_8MapArray_1from_arraysP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_18FixedSizeListArray_1from_arraysP7_objectPKS0_lS0__ZZL55__pyx_pf_7pyarrow_3lib_18FixedSizeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeS0_E18__pyx_dict_version_ZZL55__pyx_pf_7pyarrow_3lib_18FixedSizeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeS0_E23__pyx_dict_cached_value_ZL56__pyx_pw_7pyarrow_3lib_18FixedSizeListArray_1from_arraysP7_objectPKS0_lS0_.cold_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E18__pyx_dict_version_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E23__pyx_dict_cached_value_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E18__pyx_dict_version_0_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E23__pyx_dict_cached_value_0_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E18__pyx_dict_version_1_ZZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_E23__pyx_dict_cached_value_1_ZL53__pyx_f_7pyarrow_3lib_native_transcoding_input_streamSt10shared_ptrIN5arrow2io11InputStreamEEP7_objectS5_.cold_ZL46__pyx_pw_7pyarrow_3lib_9ListArray_1from_arraysP7_objectPKS0_lS0__ZZL45__pyx_pf_7pyarrow_3lib_9ListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_ZZL45__pyx_pf_7pyarrow_3lib_9ListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_ZZL45__pyx_pf_7pyarrow_3lib_9ListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_0_ZZL45__pyx_pf_7pyarrow_3lib_9ListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_0_ZL46__pyx_pw_7pyarrow_3lib_9ListArray_1from_arraysP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_14LargeListArray_1from_arraysP7_objectPKS0_lS0__ZZL51__pyx_pf_7pyarrow_3lib_14LargeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_ZZL51__pyx_pf_7pyarrow_3lib_14LargeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_ZZL51__pyx_pf_7pyarrow_3lib_14LargeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_0_ZZL51__pyx_pf_7pyarrow_3lib_14LargeListArray_from_arraysP7_objectS0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_0_ZL52__pyx_pw_7pyarrow_3lib_14LargeListArray_1from_arraysP7_objectPKS0_lS0_.cold_ZL51__pyx_pw_7pyarrow_3lib_13ListViewArray_1from_arraysP7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_0_ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_1_ZZL50__pyx_pf_7pyarrow_3lib_13ListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_1_ZL51__pyx_pw_7pyarrow_3lib_13ListViewArray_1from_arraysP7_objectPKS0_lS0_.cold_ZL56__pyx_pw_7pyarrow_3lib_18LargeListViewArray_1from_arraysP7_objectPKS0_lS0__ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_0_ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_0_ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E18__pyx_dict_version_1_ZZL55__pyx_pf_7pyarrow_3lib_18LargeListViewArray_from_arraysP7_objectS0_S0_P32__pyx_obj_7pyarrow_3lib_DataTypeP34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_E23__pyx_dict_cached_value_1_ZL56__pyx_pw_7pyarrow_3lib_18LargeListViewArray_1from_arraysP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_1_from_arraysP7_objectPKS0_lS0__ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_0_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_1_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_2_ZZL56__pyx_pf_7pyarrow_3lib_18RunEndEncodedArray__from_arraysP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_2_ZL57__pyx_pw_7pyarrow_3lib_18RunEndEncodedArray_1_from_arraysP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_11RecordBatch_39from_pandasP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_11RecordBatch_39from_pandasP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_5Table_27from_pandasP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_5Table_27from_pandasP7_objectPKS0_lS0_.cold_ZL55__pyx_pw_7pyarrow_3lib_16DictionaryScalar_1_reconstructP7_objectPKS0_lS0__ZZL54__pyx_pf_7pyarrow_3lib_16DictionaryScalar__reconstructP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL54__pyx_pf_7pyarrow_3lib_16DictionaryScalar__reconstructP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZZL54__pyx_pf_7pyarrow_3lib_16DictionaryScalar__reconstructP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL54__pyx_pf_7pyarrow_3lib_16DictionaryScalar__reconstructP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZL55__pyx_pw_7pyarrow_3lib_16DictionaryScalar_1_reconstructP7_objectPKS0_lS0_.cold_ZL30__pyx_pw_7pyarrow_3lib_61fieldP7_objectPKS0_lS0__ZZL30__pyx_pf_7pyarrow_3lib_60fieldP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL30__pyx_pf_7pyarrow_3lib_60fieldP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZL30__pyx_pw_7pyarrow_3lib_61fieldP7_objectPKS0_lS0_.cold_ZL50__pyx_pw_7pyarrow_3lib_17StringViewBuilder_3appendP7_objectPKS0_lS0__ZZL50__pyx_pf_7pyarrow_3lib_17StringViewBuilder_2appendP41__pyx_obj_7pyarrow_3lib_StringViewBuilderP7_objectE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_17StringViewBuilder_2appendP41__pyx_obj_7pyarrow_3lib_StringViewBuilderP7_objectE23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_17StringViewBuilder_2appendP41__pyx_obj_7pyarrow_3lib_StringViewBuilderP7_objectE18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_17StringViewBuilder_2appendP41__pyx_obj_7pyarrow_3lib_StringViewBuilderP7_objectE23__pyx_dict_cached_value_0_ZL50__pyx_pw_7pyarrow_3lib_17StringViewBuilder_3appendP7_objectPKS0_lS0_.cold_ZL40__pyx_pw_7pyarrow_3lib_5Array_72to_numpyP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_5Array_72to_numpyP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_44to_numpyP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_43to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_12ChunkedArray_43to_numpyP36__pyx_obj_7pyarrow_3lib_ChunkedArrayP7_objectE23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_12ChunkedArray_44to_numpyP7_objectPKS0_lS0_.cold_ZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2__ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E23__pyx_dict_cached_value_0_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E18__pyx_dict_version_1_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E23__pyx_dict_cached_value_1_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E18__pyx_dict_version_2_ZZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_E23__pyx_dict_cached_value_2_ZL50__pyx_pf_7pyarrow_3lib_16KeyValueMetadata___init__P40__pyx_obj_7pyarrow_3lib_KeyValueMetadataP7_objectS2_.cold_ZL51__pyx_pw_7pyarrow_3lib_16KeyValueMetadata_1__init__P7_objectS0_S0__ZL53__pyx_pw_7pyarrow_3lib_11RecordBatch_23rename_columnsP7_objectPKS0_lS0__ZZL53__pyx_pf_7pyarrow_3lib_11RecordBatch_22rename_columnsP35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectE18__pyx_dict_version_ZZL53__pyx_pf_7pyarrow_3lib_11RecordBatch_22rename_columnsP35__pyx_obj_7pyarrow_3lib_RecordBatchP7_objectE23__pyx_dict_cached_value_ZL53__pyx_pw_7pyarrow_3lib_11RecordBatch_23rename_columnsP7_objectPKS0_lS0_.cold_ZL46__pyx_pw_7pyarrow_3lib_5Table_55rename_columnsP7_objectPKS0_lS0__ZZL46__pyx_pf_7pyarrow_3lib_5Table_54rename_columnsP29__pyx_obj_7pyarrow_3lib_TableP7_objectE18__pyx_dict_version_ZZL46__pyx_pf_7pyarrow_3lib_5Table_54rename_columnsP29__pyx_obj_7pyarrow_3lib_TableP7_objectE23__pyx_dict_cached_value_ZL46__pyx_pw_7pyarrow_3lib_5Table_55rename_columnsP7_objectPKS0_lS0_.cold_ZL42__pyx_pw_7pyarrow_3lib_6Tensor_7from_numpyP7_objectPKS0_lS0__ZZL42__pyx_pf_7pyarrow_3lib_6Tensor_6from_numpyP7_objectS0_E18__pyx_dict_version_ZZL42__pyx_pf_7pyarrow_3lib_6Tensor_6from_numpyP7_objectS0_E23__pyx_dict_cached_value_ZL42__pyx_pw_7pyarrow_3lib_6Tensor_7from_numpyP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_59unify_schemasP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_59unify_schemasP7_objectPKS0_lS0_.cold_ZL32__pyx_pw_7pyarrow_3lib_139structP7_objectPKS0_lS0__ZZL32__pyx_pf_7pyarrow_3lib_138structP7_objectS0_E18__pyx_dict_version_ZZL32__pyx_pf_7pyarrow_3lib_138structP7_objectS0_E23__pyx_dict_cached_value_ZZL32__pyx_pf_7pyarrow_3lib_138structP7_objectS0_E18__pyx_dict_version_0_ZZL32__pyx_pf_7pyarrow_3lib_138structP7_objectS0_E23__pyx_dict_cached_value_0_ZL32__pyx_pw_7pyarrow_3lib_139structP7_objectPKS0_lS0_.cold_ZL43__pyx_f_7pyarrow_3lib__extract_union_paramsP7_objectS0_PSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS5_EEPS1_IaSaIaEE.constprop.0_ZL43__pyx_f_7pyarrow_3lib__extract_union_paramsP7_objectS0_PSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS5_EEPS1_IaSaIaEE.constprop.0.cold_ZL38__pyx_pw_7pyarrow_3lib_141sparse_unionP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_141sparse_unionP7_objectPKS0_lS0_.cold_ZL37__pyx_pw_7pyarrow_3lib_143dense_unionP7_objectPKS0_lS0__ZL37__pyx_pw_7pyarrow_3lib_143dense_unionP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_7from_numpyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_6from_numpyP7_objectS0_S0_S0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_6from_numpyP7_objectS0_S0_S0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_6from_numpyP7_objectS0_S0_S0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_6from_numpyP7_objectS0_S0_S0_E23__pyx_dict_cached_value_0_ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_7from_numpyP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_7from_numpyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_7from_numpyP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_7from_numpyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_6from_numpyP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_7from_numpyP7_objectPKS0_lS0_.cold_ZL61__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_11from_pydata_sparseP7_objectPKS0_lS0__ZZL61__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_10from_pydata_sparseP7_objectS0_E18__pyx_dict_version_ZZL61__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_10from_pydata_sparseP7_objectS0_E23__pyx_dict_cached_value_ZZL61__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_10from_pydata_sparseP7_objectS0_E18__pyx_dict_version_0_ZZL61__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_10from_pydata_sparseP7_objectS0_E23__pyx_dict_cached_value_0_ZL61__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_11from_pydata_sparseP7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_149fixed_shape_tensorP7_objectPKS0_lS0__ZZL44__pyx_pf_7pyarrow_3lib_148fixed_shape_tensorP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeS0_S0_S0_E18__pyx_dict_version_ZZL44__pyx_pf_7pyarrow_3lib_148fixed_shape_tensorP7_objectP32__pyx_obj_7pyarrow_3lib_DataTypeS0_S0_S0_E23__pyx_dict_cached_value_ZL44__pyx_pw_7pyarrow_3lib_149fixed_shape_tensorP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_9from_scipyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSRMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_1_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSRMatrix_9from_scipyP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_9from_scipyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSCMatrix_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_1_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSCMatrix_9from_scipyP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_7from_numpyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E18__pyx_dict_version_2_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCSFTensor_6from_numpyP7_objectS0_S0_S0_S0_S0_E23__pyx_dict_cached_value_2_ZL52__pyx_pw_7pyarrow_3lib_15SparseCSFTensor_7from_numpyP7_objectPKS0_lS0_.cold_ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_9from_scipyP7_objectPKS0_lS0__ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E18__pyx_dict_version_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E18__pyx_dict_version_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_0_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E18__pyx_dict_version_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_1_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E18__pyx_dict_version_2_ZZL52__pyx_pf_7pyarrow_3lib_15SparseCOOTensor_8from_scipyP7_objectS0_E23__pyx_dict_cached_value_2_ZL52__pyx_pw_7pyarrow_3lib_15SparseCOOTensor_9from_scipyP7_objectPKS0_lS0_.cold_ZL54__pyx_pw_7pyarrow_3lib_15DictionaryArray_5from_buffersP7_objectPKS0_lS0__ZL54__pyx_pw_7pyarrow_3lib_15DictionaryArray_5from_buffersP7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_5Array_23from_buffersP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_5Array_23from_buffersP7_objectPKS0_lS0_.cold_ZL49__pyx_pw_7pyarrow_3lib_11StructArray_7from_arraysP7_objectPKS0_lS0__ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_0_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_0_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_1_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_1_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_4_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_4_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_5_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_5_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_2_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_2_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E18__pyx_dict_version_3_ZZL49__pyx_pf_7pyarrow_3lib_11StructArray_6from_arraysP7_objectS0_S0_S0_S0_E23__pyx_dict_cached_value_3_ZL49__pyx_pw_7pyarrow_3lib_11StructArray_7from_arraysP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_191concat_arraysP7_objectPKS0_lS0__ZL39__pyx_pw_7pyarrow_3lib_191concat_arraysP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_195chunked_arrayP7_objectPKS0_lS0__ZZL39__pyx_pf_7pyarrow_3lib_194chunked_arrayP7_objectS0_S0_E18__pyx_dict_version_0_ZZL39__pyx_pf_7pyarrow_3lib_194chunked_arrayP7_objectS0_S0_E23__pyx_dict_cached_value_0_ZZL39__pyx_pf_7pyarrow_3lib_194chunked_arrayP7_objectS0_S0_E18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_194chunked_arrayP7_objectS0_S0_E23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_195chunked_arrayP7_objectPKS0_lS0_.cold_ZL44__pyx_pw_7pyarrow_3lib_5Table_35from_batchesP7_objectPKS0_lS0__ZL44__pyx_pw_7pyarrow_3lib_5Table_35from_batchesP7_objectPKS0_lS0_.cold_ZL39__pyx_pw_7pyarrow_3lib_207concat_tablesP7_objectPKS0_lS0__ZZL39__pyx_pf_7pyarrow_3lib_206concat_tablesP7_objectS0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_S0_E18__pyx_dict_version_ZZL39__pyx_pf_7pyarrow_3lib_206concat_tablesP7_objectS0_P34__pyx_obj_7pyarrow_3lib_MemoryPoolS0_S0_E23__pyx_dict_cached_value_ZL39__pyx_pw_7pyarrow_3lib_207concat_tablesP7_objectPKS0_lS0_.cold_ZL57__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_9read_allP7_objectPKS0_lS0__ZL57__pyx_pw_7pyarrow_3lib_22_RecordBatchFileReader_9read_allP7_objectPKS0_lS0_.cold_ZL38__pyx_pw_7pyarrow_3lib_5Table_13selectP7_objectPKS0_lS0__ZL38__pyx_pw_7pyarrow_3lib_5Table_13selectP7_objectPKS0_lS0_.cold_ZL32__pyx_convert_vector_from_py_intP7_object_ZL32__pyx_convert_vector_from_py_intP7_object.cold_ZL57__pyx_pw_7pyarrow_3lib_17SignalStopHandler_3_init_signalsP7_objectPKS0_lS0__ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_0_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_0_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_1_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_1_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_2_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_2_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_3_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_3_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_4_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_4_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE18__pyx_dict_version_5_ZZL57__pyx_pf_7pyarrow_3lib_17SignalStopHandler_2_init_signalsP41__pyx_obj_7pyarrow_3lib_SignalStopHandlerE23__pyx_dict_cached_value_5_ZL57__pyx_pw_7pyarrow_3lib_17SignalStopHandler_3_init_signalsP7_objectPKS0_lS0_.cold_ZL60__pyx_setprop_7pyarrow_3lib_14IpcReadOptions_included_fieldsP7_objectS0_Pv_ZL60__pyx_setprop_7pyarrow_3lib_14IpcReadOptions_included_fieldsP7_objectS0_Pv.cold_ZL45__pyx_pw_7pyarrow_3lib_11RecordBatch_33selectP7_objectPKS0_lS0__ZL45__pyx_pw_7pyarrow_3lib_11RecordBatch_33selectP7_objectPKS0_lS0_.cold_ZL45__pyx_f_7pyarrow_3lib__convert_pandas_optionsP7_object_ZZL45__pyx_f_7pyarrow_3lib__convert_pandas_optionsP7_objectE18__pyx_dict_version_ZZL45__pyx_f_7pyarrow_3lib__convert_pandas_optionsP7_objectE23__pyx_dict_cached_value_ZL45__pyx_f_7pyarrow_3lib__convert_pandas_optionsP7_object.cold_ZL43__pyx_f_7pyarrow_3lib__array_like_to_pandasP7_objectS0_S0__ZL43__pyx_f_7pyarrow_3lib__array_like_to_pandasP7_objectS0_S0_.cold_ZL50__pyx_pw_7pyarrow_3lib_12ChunkedArray_42_to_pandasP7_objectPKS0_lS0__ZL42__pyx_pw_7pyarrow_3lib_5Array_68_to_pandasP7_objectPKS0_lS0__ZL45__pyx_f_7pyarrow_3lib__reconstruct_array_dataP7_object_ZL45__pyx_f_7pyarrow_3lib__reconstruct_array_dataP7_object.cold_ZL40__pyx_pw_7pyarrow_3lib_189_restore_arrayP7_objectPKS0_lS0__ZL40__pyx_pw_7pyarrow_3lib_189_restore_arrayP7_objectPKS0_lS0_.cold_ZL60__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_dim_namesP7_objectPv_ZZL64__pyx_pf_7pyarrow_3lib_20FixedShapeTensorType_9dim_names___get__P44__pyx_obj_7pyarrow_3lib_FixedShapeTensorTypeE18__pyx_dict_version_ZZL64__pyx_pf_7pyarrow_3lib_20FixedShapeTensorType_9dim_names___get__P44__pyx_obj_7pyarrow_3lib_FixedShapeTensorTypeE23__pyx_dict_cached_value_ZL60__pyx_getprop_7pyarrow_3lib_20FixedShapeTensorType_dim_namesP7_objectPv.cold_ZL47__pyx_pw_7pyarrow_3lib_10UnionArray_5from_denseP7_objectPKS0_lS0__ZZL47__pyx_pf_7pyarrow_3lib_10UnionArray_4from_denseP29__pyx_obj_7pyarrow_3lib_ArrayS0_P7_objectS2_S2_E18__pyx_dict_version_ZZL47__pyx_pf_7pyarrow_3lib_10UnionArray_4from_denseP29__pyx_obj_7pyarrow_3lib_ArrayS0_P7_objectS2_S2_E23__pyx_dict_cached_value_ZL47__pyx_pw_7pyarrow_3lib_10UnionArray_5from_denseP7_objectPKS0_lS0_.cold_ZL48__pyx_pw_7pyarrow_3lib_10UnionArray_7from_sparseP7_objectPKS0_lS0__ZZL48__pyx_pf_7pyarrow_3lib_10UnionArray_6from_sparseP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_E18__pyx_dict_version_ZZL48__pyx_pf_7pyarrow_3lib_10UnionArray_6from_sparseP29__pyx_obj_7pyarrow_3lib_ArrayP7_objectS2_S2_E23__pyx_dict_cached_value_ZL48__pyx_pw_7pyarrow_3lib_10UnionArray_7from_sparseP7_objectPKS0_lS0_.cold_ZL53__pyx_convert_unordered_set_from_py_std_3a__3a_stringP7_object_ZL53__pyx_convert_unordered_set_from_py_std_3a__3a_stringP7_object.cold_ZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_.constprop.0_ZZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_E18__pyx_dict_version_ZZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_E23__pyx_dict_cached_value_ZZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_E18__pyx_dict_version_0_ZZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_E23__pyx_dict_cached_value_0_ZL41__pyx_pf_7pyarrow_3lib_198table_to_blocksP7_objectS0_P29__pyx_obj_7pyarrow_3lib_TableS0_S0_.constprop.0.cold_ZL41__pyx_pw_7pyarrow_3lib_199table_to_blocksP7_objectPKS0_lS0__ZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_object_ZZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_objectE18__pyx_dict_version_ZZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_objectE23__pyx_dict_cached_value_ZZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_objectE18__pyx_dict_version_0_ZZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_objectE23__pyx_dict_cached_value_0_ZL50__pyx_pf_7pyarrow_3lib_12StructScalar_9__getitem__P36__pyx_obj_7pyarrow_3lib_StructScalarP7_object.cold_ZL51__pyx_pw_7pyarrow_3lib_12StructScalar_10__getitem__P7_objectS0__ZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEE_ZZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEEE18__pyx_dict_version_ZZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEEE23__pyx_dict_cached_value_ZZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEEE18__pyx_dict_version_0_ZZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEEE23__pyx_dict_cached_value_0_ZL41__pyx_f_7pyarrow_3lib__schema_from_arraysP7_objectS0_S0_PSt10shared_ptrIN5arrow6SchemaEE.cold_ZL38__pyx_f_7pyarrow_3lib__sanitize_arraysP7_objectS0_S0_S0_PSt10shared_ptrIN5arrow6SchemaEE_ZZL38__pyx_f_7pyarrow_3lib__sanitize_arraysP7_objectS0_S0_S0_PSt10shared_ptrIN5arrow6SchemaEEE18__pyx_dict_version_ZZL38__pyx_f_7pyarrow_3lib__sanitize_arraysP7_objectS0_S0_S0_PSt10shared_ptrIN5arrow6SchemaEEE23__pyx_dict_cached_value_ZL38__pyx_f_7pyarrow_3lib__sanitize_arraysP7_objectS0_S0_S0_PSt10shared_ptrIN5arrow6SchemaEE.cold_ZL50__pyx_pw_7pyarrow_3lib_11RecordBatch_41from_arraysP7_objectPKS0_lS0__ZL50__pyx_pw_7pyarrow_3lib_11RecordBatch_41from_arraysP7_objectPKS0_lS0_.cold_ZL43__pyx_pw_7pyarrow_3lib_5Table_29from_arraysP7_objectPKS0_lS0__ZL43__pyx_pw_7pyarrow_3lib_5Table_29from_arraysP7_objectPKS0_lS0_.cold_ZL32__pyx_pw_7pyarrow_3lib_155schemaP7_objectPKS0_lS0__ZZL32__pyx_pf_7pyarrow_3lib_154schemaP7_objectS0_S0_E18__pyx_dict_version_ZZL32__pyx_pf_7pyarrow_3lib_154schemaP7_objectS0_S0_E23__pyx_dict_cached_value_ZZL32__pyx_pf_7pyarrow_3lib_154schemaP7_objectS0_S0_E18__pyx_dict_version_0_ZZL32__pyx_pf_7pyarrow_3lib_154schemaP7_objectS0_S0_E23__pyx_dict_cached_value_0_ZL32__pyx_pw_7pyarrow_3lib_155schemaP7_objectPKS0_lS0_.cold_ZL23__pyx_Generator_methods_ZL26__pyx_Generator_memberlist_ZL23__pyx_Generator_getsets_ZL24__pyx_CyFunction_methods_ZL24__pyx_CyFunction_members_ZL24__pyx_CyFunction_getsets_ZL13__pyx_methods_ZL21__pyx_moduledef_slots_ZL35__pyx_tp_as_sequence___Pyx_EnumMeta_ZL34__pyx_tp_as_mapping___Pyx_EnumMeta_ZL28__pyx_methods___Pyx_EnumMeta_ZL50__pyx_methods_7pyarrow_3lib__RecordBatchFileReader_ZL50__pyx_getsets_7pyarrow_3lib__RecordBatchFileReader_ZL55__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_2_open_ZL59__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_4get_batch_ZL80__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_6get_batch_with_custom_metadata_ZL58__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_8read_all_ZL60__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_10__enter___ZL59__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_12__exit___ZL68__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_14__reduce_cython___ZL70__pyx_doc_7pyarrow_3lib_22_RecordBatchFileReader_16__setstate_cython___ZL50__pyx_methods_7pyarrow_3lib__RecordBatchFileWriter_ZL54__pyx_doc_7pyarrow_3lib_22_RecordBatchFileWriter__open_ZL67__pyx_doc_7pyarrow_3lib_22_RecordBatchFileWriter_2__reduce_cython___ZL69__pyx_doc_7pyarrow_3lib_22_RecordBatchFileWriter_4__setstate_cython___ZL52__pyx_methods_7pyarrow_3lib__RecordBatchStreamReader_ZL52__pyx_getsets_7pyarrow_3lib__RecordBatchStreamReader_ZL57__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamReader_2_open_ZL69__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamReader_4__reduce_cython___ZL71__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamReader_6__setstate_cython___ZL52__pyx_methods_7pyarrow_3lib__RecordBatchStreamWriter_ZL52__pyx_getsets_7pyarrow_3lib__RecordBatchStreamWriter_ZL57__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamWriter_4_open_ZL69__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamWriter_6__reduce_cython___ZL71__pyx_doc_7pyarrow_3lib_24_RecordBatchStreamWriter_8__setstate_cython___ZL41__pyx_methods_7pyarrow_3lib_MessageReader_ZL52__pyx_doc_7pyarrow_3lib_13MessageReader_4open_stream_ZL59__pyx_doc_7pyarrow_3lib_13MessageReader_10read_next_message_ZL59__pyx_doc_7pyarrow_3lib_13MessageReader_12__reduce_cython___ZL61__pyx_doc_7pyarrow_3lib_13MessageReader_14__setstate_cython___ZL48__pyx_methods_7pyarrow_3lib_TransformInputStream_ZL65__pyx_doc_7pyarrow_3lib_20TransformInputStream_2__reduce_cython___ZL67__pyx_doc_7pyarrow_3lib_20TransformInputStream_4__setstate_cython___ZL40__pyx_methods_7pyarrow_3lib_BufferReader_ZL57__pyx_doc_7pyarrow_3lib_12BufferReader_4__reduce_cython___ZL59__pyx_doc_7pyarrow_3lib_12BufferReader_6__setstate_cython___ZL44__pyx_methods_7pyarrow_3lib_MockOutputStream_ZL48__pyx_doc_7pyarrow_3lib_16MockOutputStream_2size_ZL61__pyx_doc_7pyarrow_3lib_16MockOutputStream_4__reduce_cython___ZL63__pyx_doc_7pyarrow_3lib_16MockOutputStream_6__setstate_cython___ZL46__pyx_methods_7pyarrow_3lib_BufferOutputStream_ZL54__pyx_doc_7pyarrow_3lib_18BufferOutputStream_2getvalue_ZL63__pyx_doc_7pyarrow_3lib_18BufferOutputStream_4__reduce_cython___ZL65__pyx_doc_7pyarrow_3lib_18BufferOutputStream_6__setstate_cython___ZL49__pyx_methods_7pyarrow_3lib_FixedSizeBufferWriter_ZL68__pyx_doc_7pyarrow_3lib_21FixedSizeBufferWriter_2set_memcopy_threads_ZL70__pyx_doc_7pyarrow_3lib_21FixedSizeBufferWriter_4set_memcopy_blocksize_ZL70__pyx_doc_7pyarrow_3lib_21FixedSizeBufferWriter_6set_memcopy_threshold_ZL66__pyx_doc_7pyarrow_3lib_21FixedSizeBufferWriter_8__reduce_cython___ZL69__pyx_doc_7pyarrow_3lib_21FixedSizeBufferWriter_10__setstate_cython___ZL34__pyx_methods_7pyarrow_3lib_OSFile_ZL39__pyx_doc_7pyarrow_3lib_6OSFile_2fileno_ZL50__pyx_doc_7pyarrow_3lib_6OSFile_4__reduce_cython___ZL52__pyx_doc_7pyarrow_3lib_6OSFile_6__setstate_cython___ZL44__pyx_methods_7pyarrow_3lib_MemoryMappedFile_ZL49__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_create_ZL49__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_2_open_ZL50__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_4resize_ZL50__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_6fileno_ZL61__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_8__reduce_cython___ZL64__pyx_doc_7pyarrow_3lib_16MemoryMappedFile_10__setstate_cython___ZL38__pyx_methods_7pyarrow_3lib_PythonFile_ZL46__pyx_doc_7pyarrow_3lib_10PythonFile_2truncate_ZL46__pyx_doc_7pyarrow_3lib_10PythonFile_4readline_ZL47__pyx_doc_7pyarrow_3lib_10PythonFile_6readlines_ZL55__pyx_doc_7pyarrow_3lib_10PythonFile_8__reduce_cython___ZL58__pyx_doc_7pyarrow_3lib_10PythonFile_10__setstate_cython___ZL38__pyx_tp_as_sequence_StringViewBuilder_ZL37__pyx_tp_as_mapping_StringViewBuilder_ZL45__pyx_methods_7pyarrow_3lib_StringViewBuilder_ZL45__pyx_getsets_7pyarrow_3lib_StringViewBuilder_ZL51__pyx_doc_7pyarrow_3lib_17StringViewBuilder_2append_ZL58__pyx_doc_7pyarrow_3lib_17StringViewBuilder_4append_values_ZL51__pyx_doc_7pyarrow_3lib_17StringViewBuilder_6finish_ZL63__pyx_doc_7pyarrow_3lib_17StringViewBuilder_10__reduce_cython___ZL65__pyx_doc_7pyarrow_3lib_17StringViewBuilder_12__setstate_cython___ZL34__pyx_tp_as_sequence_StringBuilder_ZL33__pyx_tp_as_mapping_StringBuilder_ZL41__pyx_methods_7pyarrow_3lib_StringBuilder_ZL41__pyx_getsets_7pyarrow_3lib_StringBuilder_ZL47__pyx_doc_7pyarrow_3lib_13StringBuilder_2append_ZL54__pyx_doc_7pyarrow_3lib_13StringBuilder_4append_values_ZL47__pyx_doc_7pyarrow_3lib_13StringBuilder_6finish_ZL59__pyx_doc_7pyarrow_3lib_13StringBuilder_10__reduce_cython___ZL61__pyx_doc_7pyarrow_3lib_13StringBuilder_12__setstate_cython___ZL49__pyx_methods_7pyarrow_3lib_FixedShapeTensorArray_ZL64__pyx_doc_7pyarrow_3lib_21FixedShapeTensorArray_to_numpy_ndarray_ZL58__pyx_doc_7pyarrow_3lib_21FixedShapeTensorArray_2to_tensor_ZL67__pyx_doc_7pyarrow_3lib_21FixedShapeTensorArray_4from_numpy_ndarray_ZL46__pyx_methods_7pyarrow_3lib_RunEndEncodedArray_ZL46__pyx_getsets_7pyarrow_3lib_RunEndEncodedArray_ZL57__pyx_doc_7pyarrow_3lib_18RunEndEncodedArray__from_arrays_ZL57__pyx_doc_7pyarrow_3lib_18RunEndEncodedArray_2from_arrays_ZL58__pyx_doc_7pyarrow_3lib_18RunEndEncodedArray_4from_buffers_ZL66__pyx_doc_7pyarrow_3lib_18RunEndEncodedArray_6find_physical_offset_ZL66__pyx_doc_7pyarrow_3lib_18RunEndEncodedArray_8find_physical_length_ZL44__pyx_getsets_7pyarrow_3lib_LargeBinaryArray_ZL44__pyx_methods_7pyarrow_3lib_LargeStringArray_ZL55__pyx_doc_7pyarrow_3lib_16LargeStringArray_from_buffers_ZL50__pyx_methods_7pyarrow_3lib_FixedShapeTensorScalar_ZL57__pyx_doc_7pyarrow_3lib_22FixedShapeTensorScalar_to_numpy_ZL59__pyx_doc_7pyarrow_3lib_22FixedShapeTensorScalar_2to_tensor_ZL43__pyx_methods_7pyarrow_3lib_ExtensionScalar_ZL43__pyx_getsets_7pyarrow_3lib_ExtensionScalar_ZL47__pyx_doc_7pyarrow_3lib_15ExtensionScalar_as_py_ZL55__pyx_doc_7pyarrow_3lib_15ExtensionScalar_2from_storage_ZL39__pyx_methods_7pyarrow_3lib_UnionScalar_ZL39__pyx_getsets_7pyarrow_3lib_UnionScalar_ZL43__pyx_doc_7pyarrow_3lib_11UnionScalar_as_py_ZL47__pyx_methods_7pyarrow_3lib_RunEndEncodedScalar_ZL47__pyx_getsets_7pyarrow_3lib_RunEndEncodedScalar_ZL51__pyx_doc_7pyarrow_3lib_19RunEndEncodedScalar_as_py_ZL44__pyx_methods_7pyarrow_3lib_DictionaryScalar_ZL44__pyx_getsets_7pyarrow_3lib_DictionaryScalar_ZL55__pyx_doc_7pyarrow_3lib_16DictionaryScalar__reconstruct_ZL54__pyx_doc_7pyarrow_3lib_16DictionaryScalar_2__reduce___ZL49__pyx_doc_7pyarrow_3lib_16DictionaryScalar_4as_py_ZL30__pyx_tp_as_sequence_MapScalar_ZL29__pyx_tp_as_mapping_MapScalar_ZL37__pyx_methods_7pyarrow_3lib_MapScalar_ZL41__pyx_doc_7pyarrow_3lib_9MapScalar_5as_py_ZL33__pyx_tp_as_sequence_StructScalar_ZL32__pyx_tp_as_mapping_StructScalar_ZL40__pyx_methods_7pyarrow_3lib_StructScalar_ZL45__pyx_doc_7pyarrow_3lib_12StructScalar_5items_ZL46__pyx_doc_7pyarrow_3lib_12StructScalar_11as_py_ZL53__pyx_doc_7pyarrow_3lib_12StructScalar_13_as_py_tuple_ZL31__pyx_tp_as_sequence_ListScalar_ZL30__pyx_tp_as_mapping_ListScalar_ZL38__pyx_methods_7pyarrow_3lib_ListScalar_ZL38__pyx_getsets_7pyarrow_3lib_ListScalar_ZL43__pyx_doc_7pyarrow_3lib_10ListScalar_6as_py_ZL40__pyx_methods_7pyarrow_3lib_StringScalar_ZL44__pyx_doc_7pyarrow_3lib_12StringScalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_BinaryScalar_ZL48__pyx_doc_7pyarrow_3lib_12BinaryScalar_as_buffer_ZL45__pyx_doc_7pyarrow_3lib_12BinaryScalar_2as_py_ZL54__pyx_methods_7pyarrow_3lib_MonthDayNanoIntervalScalar_ZL54__pyx_getsets_7pyarrow_3lib_MonthDayNanoIntervalScalar_ZL58__pyx_doc_7pyarrow_3lib_26MonthDayNanoIntervalScalar_as_py_ZL42__pyx_methods_7pyarrow_3lib_DurationScalar_ZL42__pyx_getsets_7pyarrow_3lib_DurationScalar_ZL46__pyx_doc_7pyarrow_3lib_14DurationScalar_as_py_ZL43__pyx_methods_7pyarrow_3lib_TimestampScalar_ZL43__pyx_getsets_7pyarrow_3lib_TimestampScalar_ZL47__pyx_doc_7pyarrow_3lib_15TimestampScalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_Time64Scalar_ZL40__pyx_getsets_7pyarrow_3lib_Time64Scalar_ZL44__pyx_doc_7pyarrow_3lib_12Time64Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_Time32Scalar_ZL40__pyx_getsets_7pyarrow_3lib_Time32Scalar_ZL44__pyx_doc_7pyarrow_3lib_12Time32Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_Date64Scalar_ZL40__pyx_getsets_7pyarrow_3lib_Date64Scalar_ZL44__pyx_doc_7pyarrow_3lib_12Date64Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_Date32Scalar_ZL40__pyx_getsets_7pyarrow_3lib_Date32Scalar_ZL44__pyx_doc_7pyarrow_3lib_12Date32Scalar_as_py_ZL44__pyx_methods_7pyarrow_3lib_Decimal256Scalar_ZL48__pyx_doc_7pyarrow_3lib_16Decimal256Scalar_as_py_ZL44__pyx_methods_7pyarrow_3lib_Decimal128Scalar_ZL48__pyx_doc_7pyarrow_3lib_16Decimal128Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_DoubleScalar_ZL44__pyx_doc_7pyarrow_3lib_12DoubleScalar_as_py_ZL39__pyx_methods_7pyarrow_3lib_FloatScalar_ZL43__pyx_doc_7pyarrow_3lib_11FloatScalar_as_py_ZL43__pyx_methods_7pyarrow_3lib_HalfFloatScalar_ZL47__pyx_doc_7pyarrow_3lib_15HalfFloatScalar_as_py_ZL39__pyx_methods_7pyarrow_3lib_Int64Scalar_ZL43__pyx_doc_7pyarrow_3lib_11Int64Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_UInt64Scalar_ZL44__pyx_doc_7pyarrow_3lib_12UInt64Scalar_as_py_ZL39__pyx_methods_7pyarrow_3lib_Int32Scalar_ZL43__pyx_doc_7pyarrow_3lib_11Int32Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_UInt32Scalar_ZL44__pyx_doc_7pyarrow_3lib_12UInt32Scalar_as_py_ZL39__pyx_methods_7pyarrow_3lib_Int16Scalar_ZL43__pyx_doc_7pyarrow_3lib_11Int16Scalar_as_py_ZL40__pyx_methods_7pyarrow_3lib_UInt16Scalar_ZL44__pyx_doc_7pyarrow_3lib_12UInt16Scalar_as_py_ZL38__pyx_methods_7pyarrow_3lib_Int8Scalar_ZL42__pyx_doc_7pyarrow_3lib_10Int8Scalar_as_py_ZL39__pyx_methods_7pyarrow_3lib_UInt8Scalar_ZL43__pyx_doc_7pyarrow_3lib_11UInt8Scalar_as_py_ZL41__pyx_methods_7pyarrow_3lib_BooleanScalar_ZL45__pyx_doc_7pyarrow_3lib_13BooleanScalar_as_py_ZL38__pyx_methods_7pyarrow_3lib_NullScalar_ZL43__pyx_doc_7pyarrow_3lib_10NullScalar_4as_py_ZL51__pyx_methods_7pyarrow_3lib__ExtensionRegistryNanny_ZL67__pyx_doc_7pyarrow_3lib_23_ExtensionRegistryNanny_2release_registry_ZL68__pyx_doc_7pyarrow_3lib_23_ExtensionRegistryNanny_4__reduce_cython___ZL70__pyx_doc_7pyarrow_3lib_23_ExtensionRegistryNanny_6__setstate_cython___ZL48__pyx_methods_7pyarrow_3lib_UnknownExtensionType_ZL71__pyx_doc_7pyarrow_3lib_20UnknownExtensionType_2__arrow_ext_serialize___ZL30__pyx_tp_as_sequence_UnionType_ZL29__pyx_tp_as_mapping_UnionType_ZL37__pyx_methods_7pyarrow_3lib_UnionType_ZL37__pyx_getsets_7pyarrow_3lib_UnionType_ZL46__pyx_doc_7pyarrow_3lib_9UnionType_9__reduce___ZL43__pyx_methods_7pyarrow_3lib_ProxyMemoryPool_ZL60__pyx_doc_7pyarrow_3lib_15ProxyMemoryPool_2__reduce_cython___ZL62__pyx_doc_7pyarrow_3lib_15ProxyMemoryPool_4__setstate_cython___ZL45__pyx_methods_7pyarrow_3lib_LoggingMemoryPool_ZL62__pyx_doc_7pyarrow_3lib_17LoggingMemoryPool_2__reduce_cython___ZL64__pyx_doc_7pyarrow_3lib_17LoggingMemoryPool_4__setstate_cython___ZL42__pyx_methods_7pyarrow_3lib__PandasAPIShim_ZL42__pyx_getsets_7pyarrow_3lib__PandasAPIShim_ZL48__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_2series_ZL52__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_4data_frame_ZL48__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_10is_v1_ZL52__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_12is_ge_v21_ZL51__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_14is_ge_v3_ZL67__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_34get_rangeindex_attribute_ZL60__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_36__reduce_cython___ZL62__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_38__setstate_cython___ZL45__pyx_methods_7pyarrow_3lib_SignalStopHandler_ZL45__pyx_getsets_7pyarrow_3lib_SignalStopHandler_ZL58__pyx_doc_7pyarrow_3lib_17SignalStopHandler_2_init_signals_ZL54__pyx_doc_7pyarrow_3lib_17SignalStopHandler_4__enter___ZL53__pyx_doc_7pyarrow_3lib_17SignalStopHandler_6__exit___ZL63__pyx_doc_7pyarrow_3lib_17SignalStopHandler_10__reduce_cython___ZL65__pyx_doc_7pyarrow_3lib_17SignalStopHandler_12__setstate_cython___ZL37__pyx_methods_7pyarrow_3lib_StopToken_ZL52__pyx_doc_7pyarrow_3lib_9StopToken___reduce_cython___ZL55__pyx_doc_7pyarrow_3lib_9StopToken_2__setstate_cython___ZL33__pyx_methods_7pyarrow_3lib_Codec_ZL33__pyx_getsets_7pyarrow_3lib_Codec_ZL38__pyx_doc_7pyarrow_3lib_5Codec_2detect_ZL44__pyx_doc_7pyarrow_3lib_5Codec_4is_available_ZL58__pyx_doc_7pyarrow_3lib_5Codec_6supports_compression_level_ZL57__pyx_doc_7pyarrow_3lib_5Codec_8default_compression_level_ZL58__pyx_doc_7pyarrow_3lib_5Codec_10minimum_compression_level_ZL58__pyx_doc_7pyarrow_3lib_5Codec_12maximum_compression_level_ZL41__pyx_doc_7pyarrow_3lib_5Codec_14compress_ZL43__pyx_doc_7pyarrow_3lib_5Codec_16decompress_ZL50__pyx_doc_7pyarrow_3lib_5Codec_20__reduce_cython___ZL52__pyx_doc_7pyarrow_3lib_5Codec_22__setstate_cython___ZL40__pyx_methods_7pyarrow_3lib_CacheOptions_ZL40__pyx_getsets_7pyarrow_3lib_CacheOptions_ZL60__pyx_doc_7pyarrow_3lib_12CacheOptions_4from_network_metrics_ZL52__pyx_doc_7pyarrow_3lib_12CacheOptions_6_reconstruct_ZL50__pyx_doc_7pyarrow_3lib_12CacheOptions_8__reduce___ZL45__pyx_methods_7pyarrow_3lib_RecordBatchReader_ZL45__pyx_getsets_7pyarrow_3lib_RecordBatchReader_ZL60__pyx_doc_7pyarrow_3lib_17RecordBatchReader_4read_next_batch_ZL81__pyx_doc_7pyarrow_3lib_17RecordBatchReader_6read_next_batch_with_custom_metadata_ZL78__pyx_doc_7pyarrow_3lib_17RecordBatchReader_8iter_batches_with_custom_metadata_ZL54__pyx_doc_7pyarrow_3lib_17RecordBatchReader_11read_all_ZL51__pyx_doc_7pyarrow_3lib_17RecordBatchReader_13close_ZL55__pyx_doc_7pyarrow_3lib_17RecordBatchReader_15__enter___ZL54__pyx_doc_7pyarrow_3lib_17RecordBatchReader_17__exit___ZL50__pyx_doc_7pyarrow_3lib_17RecordBatchReader_19cast_ZL58__pyx_doc_7pyarrow_3lib_17RecordBatchReader_21_export_to_c_ZL60__pyx_doc_7pyarrow_3lib_17RecordBatchReader_23_import_from_c_ZL64__pyx_doc_7pyarrow_3lib_17RecordBatchReader_25__arrow_c_stream___ZL68__pyx_doc_7pyarrow_3lib_17RecordBatchReader_27_import_from_c_capsule_ZL57__pyx_doc_7pyarrow_3lib_17RecordBatchReader_29from_stream_ZL58__pyx_doc_7pyarrow_3lib_17RecordBatchReader_31from_batches_ZL63__pyx_doc_7pyarrow_3lib_17RecordBatchReader_33__reduce_cython___ZL65__pyx_doc_7pyarrow_3lib_17RecordBatchReader_35__setstate_cython___ZL47__pyx_methods_7pyarrow_3lib__CRecordBatchWriter_ZL47__pyx_getsets_7pyarrow_3lib__CRecordBatchWriter_ZL51__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_write_ZL58__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_2write_batch_ZL58__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_4write_table_ZL52__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_6close_ZL56__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_8__enter___ZL56__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_10__exit___ZL65__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_12__reduce_cython___ZL67__pyx_doc_7pyarrow_3lib_19_CRecordBatchWriter_14__setstate_cython___ZL50__pyx_methods_7pyarrow_3lib_CompressedOutputStream_ZL67__pyx_doc_7pyarrow_3lib_22CompressedOutputStream_2__reduce_cython___ZL69__pyx_doc_7pyarrow_3lib_22CompressedOutputStream_4__setstate_cython___ZL49__pyx_methods_7pyarrow_3lib_CompressedInputStream_ZL66__pyx_doc_7pyarrow_3lib_21CompressedInputStream_2__reduce_cython___ZL68__pyx_doc_7pyarrow_3lib_21CompressedInputStream_4__setstate_cython___ZL48__pyx_methods_7pyarrow_3lib_BufferedOutputStream_ZL54__pyx_doc_7pyarrow_3lib_20BufferedOutputStream_2detach_ZL65__pyx_doc_7pyarrow_3lib_20BufferedOutputStream_4__reduce_cython___ZL67__pyx_doc_7pyarrow_3lib_20BufferedOutputStream_6__setstate_cython___ZL47__pyx_methods_7pyarrow_3lib_BufferedInputStream_ZL53__pyx_doc_7pyarrow_3lib_19BufferedInputStream_2detach_ZL64__pyx_doc_7pyarrow_3lib_19BufferedInputStream_4__reduce_cython___ZL66__pyx_doc_7pyarrow_3lib_19BufferedInputStream_6__setstate_cython___ZL38__pyx_methods_7pyarrow_3lib_NativeFile_ZL38__pyx_getsets_7pyarrow_3lib_NativeFile_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_4__enter___ZL46__pyx_doc_7pyarrow_3lib_10NativeFile_6__exit___ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_10readable_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_12writable_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_14seekable_ZL45__pyx_doc_7pyarrow_3lib_10NativeFile_16isatty_ZL45__pyx_doc_7pyarrow_3lib_10NativeFile_18fileno_ZL44__pyx_doc_7pyarrow_3lib_10NativeFile_20close_ZL51__pyx_doc_7pyarrow_3lib_10NativeFile_22_assert_open_ZL55__pyx_doc_7pyarrow_3lib_10NativeFile_24_assert_readable_ZL55__pyx_doc_7pyarrow_3lib_10NativeFile_26_assert_writable_ZL55__pyx_doc_7pyarrow_3lib_10NativeFile_28_assert_seekable_ZL43__pyx_doc_7pyarrow_3lib_10NativeFile_30size_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_32metadata_ZL43__pyx_doc_7pyarrow_3lib_10NativeFile_34tell_ZL43__pyx_doc_7pyarrow_3lib_10NativeFile_36seek_ZL44__pyx_doc_7pyarrow_3lib_10NativeFile_38flush_ZL44__pyx_doc_7pyarrow_3lib_10NativeFile_40write_ZL43__pyx_doc_7pyarrow_3lib_10NativeFile_42read_ZL49__pyx_doc_7pyarrow_3lib_10NativeFile_44get_stream_ZL46__pyx_doc_7pyarrow_3lib_10NativeFile_46read_at_ZL44__pyx_doc_7pyarrow_3lib_10NativeFile_48read1_ZL46__pyx_doc_7pyarrow_3lib_10NativeFile_50readall_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_52readinto_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_54readline_ZL48__pyx_doc_7pyarrow_3lib_10NativeFile_56readlines_ZL50__pyx_doc_7pyarrow_3lib_10NativeFile_62read_buffer_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_64truncate_ZL49__pyx_doc_7pyarrow_3lib_10NativeFile_66writelines_ZL47__pyx_doc_7pyarrow_3lib_10NativeFile_68download_ZL45__pyx_doc_7pyarrow_3lib_10NativeFile_70upload_ZL56__pyx_doc_7pyarrow_3lib_10NativeFile_72__reduce_cython___ZL58__pyx_doc_7pyarrow_3lib_10NativeFile_74__setstate_cython___ZL43__pyx_methods_7pyarrow_3lib_ResizableBuffer_ZL48__pyx_doc_7pyarrow_3lib_15ResizableBuffer_resize_ZL27__pyx_tp_as_sequence_Buffer_ZL26__pyx_tp_as_mapping_Buffer_ZL25__pyx_tp_as_buffer_Buffer_ZL34__pyx_methods_7pyarrow_3lib_Buffer_ZL34__pyx_getsets_7pyarrow_3lib_Buffer_ZL36__pyx_doc_7pyarrow_3lib_6Buffer_8hex_ZL39__pyx_doc_7pyarrow_3lib_6Buffer_12slice_ZL40__pyx_doc_7pyarrow_3lib_6Buffer_14equals_ZL47__pyx_doc_7pyarrow_3lib_6Buffer_18__reduce_ex___ZL44__pyx_doc_7pyarrow_3lib_6Buffer_20to_pybytes_ZL39__pyx_methods_7pyarrow_3lib_RecordBatch_ZL39__pyx_getsets_7pyarrow_3lib_RecordBatch_ZL54__pyx_doc_7pyarrow_3lib_11RecordBatch_2_is_initialized_ZL49__pyx_doc_7pyarrow_3lib_11RecordBatch_4__reduce___ZL47__pyx_doc_7pyarrow_3lib_11RecordBatch_6validate_ZL62__pyx_doc_7pyarrow_3lib_11RecordBatch_8replace_schema_metadata_ZL47__pyx_doc_7pyarrow_3lib_11RecordBatch_10_column_ZL61__pyx_doc_7pyarrow_3lib_11RecordBatch_12get_total_buffer_size_ZL50__pyx_doc_7pyarrow_3lib_11RecordBatch_14__sizeof___ZL50__pyx_doc_7pyarrow_3lib_11RecordBatch_16add_column_ZL53__pyx_doc_7pyarrow_3lib_11RecordBatch_18remove_column_ZL50__pyx_doc_7pyarrow_3lib_11RecordBatch_20set_column_ZL54__pyx_doc_7pyarrow_3lib_11RecordBatch_22rename_columns_ZL49__pyx_doc_7pyarrow_3lib_11RecordBatch_24serialize_ZL45__pyx_doc_7pyarrow_3lib_11RecordBatch_26slice_ZL46__pyx_doc_7pyarrow_3lib_11RecordBatch_28filter_ZL46__pyx_doc_7pyarrow_3lib_11RecordBatch_30equals_ZL46__pyx_doc_7pyarrow_3lib_11RecordBatch_32select_ZL44__pyx_doc_7pyarrow_3lib_11RecordBatch_34cast_ZL50__pyx_doc_7pyarrow_3lib_11RecordBatch_36_to_pandas_ZL51__pyx_doc_7pyarrow_3lib_11RecordBatch_38from_pandas_ZL51__pyx_doc_7pyarrow_3lib_11RecordBatch_40from_arrays_ZL57__pyx_doc_7pyarrow_3lib_11RecordBatch_42from_struct_array_ZL55__pyx_doc_7pyarrow_3lib_11RecordBatch_44to_struct_array_ZL49__pyx_doc_7pyarrow_3lib_11RecordBatch_46to_tensor_ZL52__pyx_doc_7pyarrow_3lib_11RecordBatch_48_export_to_c_ZL54__pyx_doc_7pyarrow_3lib_11RecordBatch_50_import_from_c_ZL57__pyx_doc_7pyarrow_3lib_11RecordBatch_52__arrow_c_array___ZL58__pyx_doc_7pyarrow_3lib_11RecordBatch_54__arrow_c_stream___ZL62__pyx_doc_7pyarrow_3lib_11RecordBatch_56_import_from_c_capsule_ZL59__pyx_doc_7pyarrow_3lib_11RecordBatch_58_export_to_c_device_ZL61__pyx_doc_7pyarrow_3lib_11RecordBatch_60_import_from_c_device_ZL33__pyx_methods_7pyarrow_3lib_Table_ZL33__pyx_getsets_7pyarrow_3lib_Table_ZL47__pyx_doc_7pyarrow_3lib_5Table_2_is_initialized_ZL40__pyx_doc_7pyarrow_3lib_5Table_4validate_ZL42__pyx_doc_7pyarrow_3lib_5Table_6__reduce___ZL37__pyx_doc_7pyarrow_3lib_5Table_8slice_ZL39__pyx_doc_7pyarrow_3lib_5Table_10filter_ZL39__pyx_doc_7pyarrow_3lib_5Table_12select_ZL56__pyx_doc_7pyarrow_3lib_5Table_14replace_schema_metadata_ZL40__pyx_doc_7pyarrow_3lib_5Table_16flatten_ZL47__pyx_doc_7pyarrow_3lib_5Table_18combine_chunks_ZL51__pyx_doc_7pyarrow_3lib_5Table_20unify_dictionaries_ZL39__pyx_doc_7pyarrow_3lib_5Table_22equals_ZL37__pyx_doc_7pyarrow_3lib_5Table_24cast_ZL44__pyx_doc_7pyarrow_3lib_5Table_26from_pandas_ZL44__pyx_doc_7pyarrow_3lib_5Table_28from_arrays_ZL50__pyx_doc_7pyarrow_3lib_5Table_30from_struct_array_ZL48__pyx_doc_7pyarrow_3lib_5Table_32to_struct_array_ZL45__pyx_doc_7pyarrow_3lib_5Table_34from_batches_ZL43__pyx_doc_7pyarrow_3lib_5Table_36to_batches_ZL42__pyx_doc_7pyarrow_3lib_5Table_38to_reader_ZL43__pyx_doc_7pyarrow_3lib_5Table_40_to_pandas_ZL40__pyx_doc_7pyarrow_3lib_5Table_42_column_ZL54__pyx_doc_7pyarrow_3lib_5Table_44get_total_buffer_size_ZL43__pyx_doc_7pyarrow_3lib_5Table_46__sizeof___ZL43__pyx_doc_7pyarrow_3lib_5Table_48add_column_ZL46__pyx_doc_7pyarrow_3lib_5Table_50remove_column_ZL43__pyx_doc_7pyarrow_3lib_5Table_52set_column_ZL47__pyx_doc_7pyarrow_3lib_5Table_54rename_columns_ZL37__pyx_doc_7pyarrow_3lib_5Table_56drop_ZL41__pyx_doc_7pyarrow_3lib_5Table_58group_by_ZL37__pyx_doc_7pyarrow_3lib_5Table_60join_ZL42__pyx_doc_7pyarrow_3lib_5Table_62join_asof_ZL51__pyx_doc_7pyarrow_3lib_5Table_64__arrow_c_stream___ZL29__pyx_tp_as_sequence__Tabular_ZL28__pyx_tp_as_mapping__Tabular_ZL36__pyx_methods_7pyarrow_3lib__Tabular_ZL36__pyx_getsets_7pyarrow_3lib__Tabular_ZL44__pyx_doc_7pyarrow_3lib_8_Tabular_2__array___ZL48__pyx_doc_7pyarrow_3lib_8_Tabular_4__dataframe___ZL43__pyx_doc_7pyarrow_3lib_8_Tabular_14_column_ZL57__pyx_doc_7pyarrow_3lib_8_Tabular_16_ensure_integer_index_ZL51__pyx_doc_7pyarrow_3lib_8_Tabular_18_is_initialized_ZL42__pyx_doc_7pyarrow_3lib_8_Tabular_20column_ZL45__pyx_doc_7pyarrow_3lib_8_Tabular_22drop_null_ZL41__pyx_doc_7pyarrow_3lib_8_Tabular_24field_ZL47__pyx_doc_7pyarrow_3lib_8_Tabular_26from_pydict_ZL47__pyx_doc_7pyarrow_3lib_8_Tabular_28from_pylist_ZL47__pyx_doc_7pyarrow_3lib_8_Tabular_30itercolumns_ZL43__pyx_doc_7pyarrow_3lib_8_Tabular_33sort_by_ZL40__pyx_doc_7pyarrow_3lib_8_Tabular_35take_ZL45__pyx_doc_7pyarrow_3lib_8_Tabular_37to_pydict_ZL45__pyx_doc_7pyarrow_3lib_8_Tabular_39to_pylist_ZL45__pyx_doc_7pyarrow_3lib_8_Tabular_41to_string_ZL49__pyx_doc_7pyarrow_3lib_8_Tabular_43remove_column_ZL48__pyx_doc_7pyarrow_3lib_8_Tabular_45drop_columns_ZL46__pyx_doc_7pyarrow_3lib_8_Tabular_47add_column_ZL49__pyx_doc_7pyarrow_3lib_8_Tabular_49append_column_ZL53__pyx_doc_7pyarrow_3lib_8_Tabular_51__reduce_cython___ZL55__pyx_doc_7pyarrow_3lib_8_Tabular_53__setstate_cython___ZL33__pyx_tp_as_sequence_ChunkedArray_ZL32__pyx_tp_as_mapping_ChunkedArray_ZL40__pyx_methods_7pyarrow_3lib_ChunkedArray_ZL40__pyx_getsets_7pyarrow_3lib_ChunkedArray_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_4__reduce___ZL46__pyx_doc_7pyarrow_3lib_12ChunkedArray_6length_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_12to_string_ZL47__pyx_doc_7pyarrow_3lib_12ChunkedArray_14format_ZL49__pyx_doc_7pyarrow_3lib_12ChunkedArray_18validate_ZL62__pyx_doc_7pyarrow_3lib_12ChunkedArray_20get_total_buffer_size_ZL51__pyx_doc_7pyarrow_3lib_12ChunkedArray_22__sizeof___ZL48__pyx_doc_7pyarrow_3lib_12ChunkedArray_29is_null_ZL47__pyx_doc_7pyarrow_3lib_12ChunkedArray_31is_nan_ZL49__pyx_doc_7pyarrow_3lib_12ChunkedArray_33is_valid_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_37fill_null_ZL47__pyx_doc_7pyarrow_3lib_12ChunkedArray_39equals_ZL51__pyx_doc_7pyarrow_3lib_12ChunkedArray_41_to_pandas_ZL49__pyx_doc_7pyarrow_3lib_12ChunkedArray_43to_numpy_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_45__array___ZL45__pyx_doc_7pyarrow_3lib_12ChunkedArray_47cast_ZL58__pyx_doc_7pyarrow_3lib_12ChunkedArray_49dictionary_encode_ZL48__pyx_doc_7pyarrow_3lib_12ChunkedArray_51flatten_ZL55__pyx_doc_7pyarrow_3lib_12ChunkedArray_53combine_chunks_ZL47__pyx_doc_7pyarrow_3lib_12ChunkedArray_55unique_ZL53__pyx_doc_7pyarrow_3lib_12ChunkedArray_57value_counts_ZL46__pyx_doc_7pyarrow_3lib_12ChunkedArray_59slice_ZL47__pyx_doc_7pyarrow_3lib_12ChunkedArray_61filter_ZL46__pyx_doc_7pyarrow_3lib_12ChunkedArray_63index_ZL45__pyx_doc_7pyarrow_3lib_12ChunkedArray_65take_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_67drop_null_ZL45__pyx_doc_7pyarrow_3lib_12ChunkedArray_69sort_ZL59__pyx_doc_7pyarrow_3lib_12ChunkedArray_71unify_dictionaries_ZL46__pyx_doc_7pyarrow_3lib_12ChunkedArray_73chunk_ZL51__pyx_doc_7pyarrow_3lib_12ChunkedArray_75iterchunks_ZL50__pyx_doc_7pyarrow_3lib_12ChunkedArray_78to_pylist_ZL59__pyx_doc_7pyarrow_3lib_12ChunkedArray_80__arrow_c_stream___ZL63__pyx_doc_7pyarrow_3lib_12ChunkedArray_82_import_from_c_capsule_ZL53__pyx_methods_7pyarrow_3lib_MonthDayNanoIntervalArray_ZL61__pyx_doc_7pyarrow_3lib_25MonthDayNanoIntervalArray_to_pylist_ZL42__pyx_methods_7pyarrow_3lib_ExtensionArray_ZL42__pyx_getsets_7pyarrow_3lib_ExtensionArray_ZL53__pyx_doc_7pyarrow_3lib_14ExtensionArray_from_storage_ZL43__pyx_methods_7pyarrow_3lib_DictionaryArray_ZL43__pyx_getsets_7pyarrow_3lib_DictionaryArray_ZL59__pyx_doc_7pyarrow_3lib_15DictionaryArray_dictionary_encode_ZL60__pyx_doc_7pyarrow_3lib_15DictionaryArray_2dictionary_decode_ZL55__pyx_doc_7pyarrow_3lib_15DictionaryArray_4from_buffers_ZL54__pyx_doc_7pyarrow_3lib_15DictionaryArray_6from_arrays_ZL39__pyx_getsets_7pyarrow_3lib_BinaryArray_ZL39__pyx_methods_7pyarrow_3lib_StringArray_ZL50__pyx_doc_7pyarrow_3lib_11StringArray_from_buffers_ZL38__pyx_methods_7pyarrow_3lib_UnionArray_ZL38__pyx_getsets_7pyarrow_3lib_UnionArray_ZL42__pyx_doc_7pyarrow_3lib_10UnionArray_child_ZL43__pyx_doc_7pyarrow_3lib_10UnionArray_2field_ZL48__pyx_doc_7pyarrow_3lib_10UnionArray_4from_dense_ZL49__pyx_doc_7pyarrow_3lib_10UnionArray_6from_sparse_ZL46__pyx_methods_7pyarrow_3lib_FixedSizeListArray_ZL46__pyx_getsets_7pyarrow_3lib_FixedSizeListArray_ZL56__pyx_doc_7pyarrow_3lib_18FixedSizeListArray_from_arrays_ZL36__pyx_methods_7pyarrow_3lib_MapArray_ZL36__pyx_getsets_7pyarrow_3lib_MapArray_ZL45__pyx_doc_7pyarrow_3lib_8MapArray_from_arrays_ZL46__pyx_methods_7pyarrow_3lib_LargeListViewArray_ZL46__pyx_getsets_7pyarrow_3lib_LargeListViewArray_ZL56__pyx_doc_7pyarrow_3lib_18LargeListViewArray_from_arrays_ZL53__pyx_doc_7pyarrow_3lib_18LargeListViewArray_2flatten_ZL41__pyx_methods_7pyarrow_3lib_ListViewArray_ZL41__pyx_getsets_7pyarrow_3lib_ListViewArray_ZL51__pyx_doc_7pyarrow_3lib_13ListViewArray_from_arrays_ZL48__pyx_doc_7pyarrow_3lib_13ListViewArray_2flatten_ZL42__pyx_methods_7pyarrow_3lib_LargeListArray_ZL42__pyx_getsets_7pyarrow_3lib_LargeListArray_ZL52__pyx_doc_7pyarrow_3lib_14LargeListArray_from_arrays_ZL37__pyx_methods_7pyarrow_3lib_ListArray_ZL37__pyx_getsets_7pyarrow_3lib_ListArray_ZL46__pyx_doc_7pyarrow_3lib_9ListArray_from_arrays_ZL41__pyx_methods_7pyarrow_3lib_BaseListArray_ZL47__pyx_doc_7pyarrow_3lib_13BaseListArray_flatten_ZL61__pyx_doc_7pyarrow_3lib_13BaseListArray_2value_parent_indices_ZL54__pyx_doc_7pyarrow_3lib_13BaseListArray_4value_lengths_ZL39__pyx_methods_7pyarrow_3lib_StructArray_ZL43__pyx_doc_7pyarrow_3lib_11StructArray_field_ZL55__pyx_doc_7pyarrow_3lib_11StructArray_2_flattened_field_ZL46__pyx_doc_7pyarrow_3lib_11StructArray_4flatten_ZL50__pyx_doc_7pyarrow_3lib_11StructArray_6from_arrays_ZL43__pyx_doc_7pyarrow_3lib_11StructArray_8sort_ZL40__pyx_getsets_7pyarrow_3lib_BooleanArray_ZL43__pyx_methods_7pyarrow_3lib_SparseCSFTensor_ZL43__pyx_getsets_7pyarrow_3lib_SparseCSFTensor_ZL59__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_4from_dense_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_6from_numpy_ZL54__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_8from_tensor_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_10to_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_12to_tensor_ZL50__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_14equals_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_18dim_name_ZL61__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_20__reduce_cython___ZL63__pyx_doc_7pyarrow_3lib_15SparseCSFTensor_22__setstate_cython___ZL43__pyx_methods_7pyarrow_3lib_SparseCOOTensor_ZL43__pyx_getsets_7pyarrow_3lib_SparseCOOTensor_ZL59__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_4from_dense_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_6from_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_8from_scipy_ZL62__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_10from_pydata_sparse_ZL55__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_12from_tensor_ZL52__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_14to_numpy_ZL52__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_16to_scipy_ZL60__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_18to_pydata_sparse_ZL53__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_20to_tensor_ZL50__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_22equals_ZL52__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_26dim_name_ZL61__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_28__reduce_cython___ZL63__pyx_doc_7pyarrow_3lib_15SparseCOOTensor_30__setstate_cython___ZL43__pyx_methods_7pyarrow_3lib_SparseCSCMatrix_ZL43__pyx_getsets_7pyarrow_3lib_SparseCSCMatrix_ZL59__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_4from_dense_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_6from_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_8from_scipy_ZL55__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_10from_tensor_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_12to_numpy_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_14to_scipy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_16to_tensor_ZL50__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_18equals_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_22dim_name_ZL61__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_24__reduce_cython___ZL63__pyx_doc_7pyarrow_3lib_15SparseCSCMatrix_26__setstate_cython___ZL43__pyx_methods_7pyarrow_3lib_SparseCSRMatrix_ZL43__pyx_getsets_7pyarrow_3lib_SparseCSRMatrix_ZL59__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_4from_dense_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_6from_numpy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_8from_scipy_ZL55__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_10from_tensor_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_12to_numpy_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_14to_scipy_ZL53__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_16to_tensor_ZL50__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_18equals_ZL52__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_22dim_name_ZL61__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_24__reduce_cython___ZL63__pyx_doc_7pyarrow_3lib_15SparseCSRMatrix_26__setstate_cython___ZL25__pyx_tp_as_buffer_Tensor_ZL34__pyx_methods_7pyarrow_3lib_Tensor_ZL34__pyx_getsets_7pyarrow_3lib_Tensor_ZL62__pyx_doc_7pyarrow_3lib_6Tensor_2_make_shape_or_strides_buffer_ZL43__pyx_doc_7pyarrow_3lib_6Tensor_6from_numpy_ZL41__pyx_doc_7pyarrow_3lib_6Tensor_8to_numpy_ZL40__pyx_doc_7pyarrow_3lib_6Tensor_10equals_ZL42__pyx_doc_7pyarrow_3lib_6Tensor_14dim_name_ZL51__pyx_doc_7pyarrow_3lib_6Tensor_18__reduce_cython___ZL53__pyx_doc_7pyarrow_3lib_6Tensor_20__setstate_cython___ZL26__pyx_tp_as_sequence_Array_ZL25__pyx_tp_as_mapping_Array_ZL33__pyx_methods_7pyarrow_3lib_Array_ZL33__pyx_getsets_7pyarrow_3lib_Array_ZL44__pyx_doc_7pyarrow_3lib_5Array_2_debug_print_ZL36__pyx_doc_7pyarrow_3lib_5Array_4diff_ZL36__pyx_doc_7pyarrow_3lib_5Array_6cast_ZL36__pyx_doc_7pyarrow_3lib_5Array_8view_ZL36__pyx_doc_7pyarrow_3lib_5Array_10sum_ZL39__pyx_doc_7pyarrow_3lib_5Array_12unique_ZL50__pyx_doc_7pyarrow_3lib_5Array_14dictionary_encode_ZL45__pyx_doc_7pyarrow_3lib_5Array_16value_counts_ZL44__pyx_doc_7pyarrow_3lib_5Array_18from_pandas_ZL43__pyx_doc_7pyarrow_3lib_5Array_20__reduce___ZL45__pyx_doc_7pyarrow_3lib_5Array_22from_buffers_ZL54__pyx_doc_7pyarrow_3lib_5Array_24get_total_buffer_size_ZL43__pyx_doc_7pyarrow_3lib_5Array_26__sizeof___ZL42__pyx_doc_7pyarrow_3lib_5Array_33to_string_ZL39__pyx_doc_7pyarrow_3lib_5Array_35format_ZL39__pyx_doc_7pyarrow_3lib_5Array_41equals_ZL40__pyx_doc_7pyarrow_3lib_5Array_45is_null_ZL39__pyx_doc_7pyarrow_3lib_5Array_47is_nan_ZL41__pyx_doc_7pyarrow_3lib_5Array_49is_valid_ZL42__pyx_doc_7pyarrow_3lib_5Array_51fill_null_ZL38__pyx_doc_7pyarrow_3lib_5Array_55slice_ZL37__pyx_doc_7pyarrow_3lib_5Array_57take_ZL42__pyx_doc_7pyarrow_3lib_5Array_59drop_null_ZL39__pyx_doc_7pyarrow_3lib_5Array_61filter_ZL38__pyx_doc_7pyarrow_3lib_5Array_63index_ZL37__pyx_doc_7pyarrow_3lib_5Array_65sort_ZL43__pyx_doc_7pyarrow_3lib_5Array_67_to_pandas_ZL42__pyx_doc_7pyarrow_3lib_5Array_69__array___ZL41__pyx_doc_7pyarrow_3lib_5Array_71to_numpy_ZL42__pyx_doc_7pyarrow_3lib_5Array_73to_pylist_ZL39__pyx_doc_7pyarrow_3lib_5Array_75tolist_ZL41__pyx_doc_7pyarrow_3lib_5Array_77validate_ZL40__pyx_doc_7pyarrow_3lib_5Array_79buffers_ZL45__pyx_doc_7pyarrow_3lib_5Array_81_export_to_c_ZL47__pyx_doc_7pyarrow_3lib_5Array_83_import_from_c_ZL50__pyx_doc_7pyarrow_3lib_5Array_85__arrow_c_array___ZL55__pyx_doc_7pyarrow_3lib_5Array_87_import_from_c_capsule_ZL52__pyx_doc_7pyarrow_3lib_5Array_89_export_to_c_device_ZL54__pyx_doc_7pyarrow_3lib_5Array_91_import_from_c_device_ZL43__pyx_doc_7pyarrow_3lib_5Array_93__dlpack___ZL50__pyx_doc_7pyarrow_3lib_5Array_95__dlpack_device___ZL46__pyx_methods_7pyarrow_3lib__PandasConvertible_ZL54__pyx_doc_7pyarrow_3lib_18_PandasConvertible_to_pandas_ZL63__pyx_doc_7pyarrow_3lib_18_PandasConvertible_2__reduce_cython___ZL65__pyx_doc_7pyarrow_3lib_18_PandasConvertible_4__setstate_cython___ZL34__pyx_methods_7pyarrow_3lib_Scalar_ZL34__pyx_getsets_7pyarrow_3lib_Scalar_ZL37__pyx_doc_7pyarrow_3lib_6Scalar_2cast_ZL41__pyx_doc_7pyarrow_3lib_6Scalar_4validate_ZL40__pyx_doc_7pyarrow_3lib_6Scalar_10equals_ZL44__pyx_doc_7pyarrow_3lib_6Scalar_16__reduce___ZL39__pyx_doc_7pyarrow_3lib_6Scalar_18as_py_ZL27__pyx_tp_as_sequence_Schema_ZL26__pyx_tp_as_mapping_Schema_ZL34__pyx_methods_7pyarrow_3lib_Schema_ZL34__pyx_getsets_7pyarrow_3lib_Schema_ZL44__pyx_doc_7pyarrow_3lib_6Schema_11__reduce___ZL44__pyx_doc_7pyarrow_3lib_6Schema_15__sizeof___ZL45__pyx_doc_7pyarrow_3lib_6Schema_19empty_table_ZL40__pyx_doc_7pyarrow_3lib_6Schema_21equals_ZL45__pyx_doc_7pyarrow_3lib_6Schema_23from_pandas_ZL39__pyx_doc_7pyarrow_3lib_6Schema_25field_ZL40__pyx_doc_7pyarrow_3lib_6Schema_27_field_ZL47__pyx_doc_7pyarrow_3lib_6Schema_29field_by_name_ZL49__pyx_doc_7pyarrow_3lib_6Schema_31get_field_index_ZL55__pyx_doc_7pyarrow_3lib_6Schema_33get_all_field_indices_ZL40__pyx_doc_7pyarrow_3lib_6Schema_35append_ZL40__pyx_doc_7pyarrow_3lib_6Schema_37insert_ZL40__pyx_doc_7pyarrow_3lib_6Schema_39remove_ZL37__pyx_doc_7pyarrow_3lib_6Schema_41set_ZL46__pyx_doc_7pyarrow_3lib_6Schema_43add_metadata_ZL47__pyx_doc_7pyarrow_3lib_6Schema_45with_metadata_ZL43__pyx_doc_7pyarrow_3lib_6Schema_47serialize_ZL49__pyx_doc_7pyarrow_3lib_6Schema_49remove_metadata_ZL43__pyx_doc_7pyarrow_3lib_6Schema_51to_string_ZL46__pyx_doc_7pyarrow_3lib_6Schema_53_export_to_c_ZL48__pyx_doc_7pyarrow_3lib_6Schema_55_import_from_c_ZL52__pyx_doc_7pyarrow_3lib_6Schema_61__arrow_c_schema___ZL56__pyx_doc_7pyarrow_3lib_6Schema_63_import_from_c_capsule_ZL33__pyx_methods_7pyarrow_3lib_Field_ZL33__pyx_getsets_7pyarrow_3lib_Field_ZL38__pyx_doc_7pyarrow_3lib_5Field_4equals_ZL42__pyx_doc_7pyarrow_3lib_5Field_8__reduce___ZL46__pyx_doc_7pyarrow_3lib_5Field_16with_metadata_ZL48__pyx_doc_7pyarrow_3lib_5Field_18remove_metadata_ZL42__pyx_doc_7pyarrow_3lib_5Field_20with_type_ZL42__pyx_doc_7pyarrow_3lib_5Field_22with_name_ZL46__pyx_doc_7pyarrow_3lib_5Field_24with_nullable_ZL40__pyx_doc_7pyarrow_3lib_5Field_26flatten_ZL45__pyx_doc_7pyarrow_3lib_5Field_28_export_to_c_ZL47__pyx_doc_7pyarrow_3lib_5Field_30_import_from_c_ZL51__pyx_doc_7pyarrow_3lib_5Field_32__arrow_c_schema___ZL55__pyx_doc_7pyarrow_3lib_5Field_34_import_from_c_capsule_ZL37__pyx_tp_as_sequence_KeyValueMetadata_ZL36__pyx_tp_as_mapping_KeyValueMetadata_ZL44__pyx_methods_7pyarrow_3lib_KeyValueMetadata_ZL50__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_2equals_ZL55__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_18__reduce___ZL48__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_20key_ZL50__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_22value_ZL49__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_24keys_ZL51__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_27values_ZL50__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_30items_ZL52__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_33get_all_ZL52__pyx_doc_7pyarrow_3lib_16KeyValueMetadata_35to_dict_ZL43__pyx_methods_7pyarrow_3lib_PyExtensionType_ZL53__pyx_doc_7pyarrow_3lib_15PyExtensionType_4__reduce___ZL66__pyx_doc_7pyarrow_3lib_15PyExtensionType_6__arrow_ext_serialize___ZL68__pyx_doc_7pyarrow_3lib_15PyExtensionType_8__arrow_ext_deserialize___ZL57__pyx_doc_7pyarrow_3lib_15PyExtensionType_10set_auto_load_ZL48__pyx_methods_7pyarrow_3lib_FixedShapeTensorType_ZL48__pyx_getsets_7pyarrow_3lib_FixedShapeTensorType_ZL66__pyx_doc_7pyarrow_3lib_20FixedShapeTensorType___arrow_ext_class___ZL58__pyx_doc_7pyarrow_3lib_20FixedShapeTensorType_2__reduce___ZL74__pyx_doc_7pyarrow_3lib_20FixedShapeTensorType_4__arrow_ext_scalar_class___ZL41__pyx_methods_7pyarrow_3lib_ExtensionType_ZL64__pyx_doc_7pyarrow_3lib_13ExtensionType_8__arrow_ext_serialize___ZL67__pyx_doc_7pyarrow_3lib_13ExtensionType_10__arrow_ext_deserialize___ZL52__pyx_doc_7pyarrow_3lib_13ExtensionType_12__reduce___ZL61__pyx_doc_7pyarrow_3lib_13ExtensionType_14__arrow_ext_class___ZL68__pyx_doc_7pyarrow_3lib_13ExtensionType_16__arrow_ext_scalar_class___ZL45__pyx_methods_7pyarrow_3lib_BaseExtensionType_ZL45__pyx_getsets_7pyarrow_3lib_BaseExtensionType_ZL63__pyx_doc_7pyarrow_3lib_17BaseExtensionType___arrow_ext_class___ZL71__pyx_doc_7pyarrow_3lib_17BaseExtensionType_2__arrow_ext_scalar_class___ZL55__pyx_doc_7pyarrow_3lib_17BaseExtensionType_4wrap_array_ZL45__pyx_methods_7pyarrow_3lib_RunEndEncodedType_ZL45__pyx_getsets_7pyarrow_3lib_RunEndEncodedType_ZL54__pyx_doc_7pyarrow_3lib_17RunEndEncodedType___reduce___ZL42__pyx_methods_7pyarrow_3lib_Decimal256Type_ZL42__pyx_getsets_7pyarrow_3lib_Decimal256Type_ZL51__pyx_doc_7pyarrow_3lib_14Decimal256Type___reduce___ZL42__pyx_methods_7pyarrow_3lib_Decimal128Type_ZL42__pyx_getsets_7pyarrow_3lib_Decimal128Type_ZL51__pyx_doc_7pyarrow_3lib_14Decimal128Type___reduce___ZL47__pyx_methods_7pyarrow_3lib_FixedSizeBinaryType_ZL56__pyx_doc_7pyarrow_3lib_19FixedSizeBinaryType___reduce___ZL40__pyx_getsets_7pyarrow_3lib_DurationType_ZL38__pyx_getsets_7pyarrow_3lib_Time64Type_ZL38__pyx_getsets_7pyarrow_3lib_Time32Type_ZL41__pyx_methods_7pyarrow_3lib_TimestampType_ZL41__pyx_getsets_7pyarrow_3lib_TimestampType_ZL50__pyx_doc_7pyarrow_3lib_13TimestampType___reduce___ZL42__pyx_methods_7pyarrow_3lib_DictionaryType_ZL42__pyx_getsets_7pyarrow_3lib_DictionaryType_ZL51__pyx_doc_7pyarrow_3lib_14DictionaryType___reduce___ZL42__pyx_methods_7pyarrow_3lib_DictionaryMemo_ZL59__pyx_doc_7pyarrow_3lib_14DictionaryMemo_2__reduce_cython___ZL61__pyx_doc_7pyarrow_3lib_14DictionaryMemo_4__setstate_cython___ZL31__pyx_tp_as_sequence_StructType_ZL30__pyx_tp_as_mapping_StructType_ZL38__pyx_methods_7pyarrow_3lib_StructType_ZL52__pyx_doc_7pyarrow_3lib_10StructType_get_field_index_ZL59__pyx_doc_7pyarrow_3lib_10StructType_4get_all_field_indices_ZL49__pyx_doc_7pyarrow_3lib_10StructType_13__reduce___ZL45__pyx_methods_7pyarrow_3lib_FixedSizeListType_ZL45__pyx_getsets_7pyarrow_3lib_FixedSizeListType_ZL54__pyx_doc_7pyarrow_3lib_17FixedSizeListType___reduce___ZL35__pyx_methods_7pyarrow_3lib_MapType_ZL35__pyx_getsets_7pyarrow_3lib_MapType_ZL43__pyx_doc_7pyarrow_3lib_7MapType___reduce___ZL45__pyx_methods_7pyarrow_3lib_LargeListViewType_ZL45__pyx_getsets_7pyarrow_3lib_LargeListViewType_ZL54__pyx_doc_7pyarrow_3lib_17LargeListViewType___reduce___ZL40__pyx_methods_7pyarrow_3lib_ListViewType_ZL40__pyx_getsets_7pyarrow_3lib_ListViewType_ZL49__pyx_doc_7pyarrow_3lib_12ListViewType___reduce___ZL41__pyx_methods_7pyarrow_3lib_LargeListType_ZL41__pyx_getsets_7pyarrow_3lib_LargeListType_ZL50__pyx_doc_7pyarrow_3lib_13LargeListType___reduce___ZL36__pyx_methods_7pyarrow_3lib_ListType_ZL36__pyx_getsets_7pyarrow_3lib_ListType_ZL44__pyx_doc_7pyarrow_3lib_8ListType___reduce___ZL36__pyx_methods_7pyarrow_3lib_DataType_ZL36__pyx_getsets_7pyarrow_3lib_DataType_ZL46__pyx_doc_7pyarrow_3lib_8DataType_10__reduce___ZL42__pyx_doc_7pyarrow_3lib_8DataType_16equals_ZL51__pyx_doc_7pyarrow_3lib_8DataType_18to_pandas_dtype_ZL48__pyx_doc_7pyarrow_3lib_8DataType_20_export_to_c_ZL50__pyx_doc_7pyarrow_3lib_8DataType_22_import_from_c_ZL54__pyx_doc_7pyarrow_3lib_8DataType_24__arrow_c_schema___ZL58__pyx_doc_7pyarrow_3lib_8DataType_26_import_from_c_capsule_ZL38__pyx_methods_7pyarrow_3lib_MemoryPool_ZL38__pyx_getsets_7pyarrow_3lib_MemoryPool_ZL52__pyx_doc_7pyarrow_3lib_10MemoryPool_2release_unused_ZL53__pyx_doc_7pyarrow_3lib_10MemoryPool_4bytes_allocated_ZL48__pyx_doc_7pyarrow_3lib_10MemoryPool_6max_memory_ZL56__pyx_doc_7pyarrow_3lib_10MemoryPool_10__reduce_cython___ZL58__pyx_doc_7pyarrow_3lib_10MemoryPool_12__setstate_cython___ZL35__pyx_methods_7pyarrow_3lib_Message_ZL35__pyx_getsets_7pyarrow_3lib_Message_ZL40__pyx_doc_7pyarrow_3lib_7Message_4equals_ZL46__pyx_doc_7pyarrow_3lib_7Message_6serialize_to_ZL43__pyx_doc_7pyarrow_3lib_7Message_8serialize_ZL52__pyx_doc_7pyarrow_3lib_7Message_12__reduce_cython___ZL54__pyx_doc_7pyarrow_3lib_7Message_14__setstate_cython___ZL42__pyx_methods_7pyarrow_3lib_IpcReadOptions_ZL42__pyx_getsets_7pyarrow_3lib_IpcReadOptions_ZL59__pyx_doc_7pyarrow_3lib_14IpcReadOptions_2__reduce_cython___ZL61__pyx_doc_7pyarrow_3lib_14IpcReadOptions_4__setstate_cython___ZL43__pyx_methods_7pyarrow_3lib_IpcWriteOptions_ZL43__pyx_getsets_7pyarrow_3lib_IpcWriteOptions_ZL60__pyx_doc_7pyarrow_3lib_15IpcWriteOptions_2__reduce_cython___ZL62__pyx_doc_7pyarrow_3lib_15IpcWriteOptions_4__setstate_cython___ZL50__pyx_doc_7pyarrow_3lib_262__pyx_unpickle__Tabular_ZL60__pyx_doc_7pyarrow_3lib_260__pyx_unpickle__PandasConvertible_ZL56__pyx_doc_7pyarrow_3lib_258__pyx_unpickle__PandasAPIShim_ZL55__pyx_doc_7pyarrow_3lib_256benchmark_PandasObjectIsNull_ZL44__pyx_doc_7pyarrow_3lib_254read_record_batch_ZL38__pyx_doc_7pyarrow_3lib_252read_schema_ZL39__pyx_doc_7pyarrow_3lib_250read_message_ZL38__pyx_doc_7pyarrow_3lib_248read_tensor_ZL39__pyx_doc_7pyarrow_3lib_246write_tensor_ZL48__pyx_doc_7pyarrow_3lib_244get_record_batch_size_ZL42__pyx_doc_7pyarrow_3lib_242get_tensor_size_ZL54__pyx_doc_7pyarrow_3lib_16_ReadPandasMixin_read_pandas_ZL40__pyx_doc_7pyarrow_3lib_240output_stream_ZL39__pyx_doc_7pyarrow_3lib_238input_stream_ZL37__pyx_doc_7pyarrow_3lib_236decompress_ZL35__pyx_doc_7pyarrow_3lib_234compress_ZL46__pyx_doc_7pyarrow_3lib_232_detect_compression_ZL36__pyx_doc_7pyarrow_3lib_230as_buffer_ZL41__pyx_doc_7pyarrow_3lib_228foreign_buffer_ZL36__pyx_doc_7pyarrow_3lib_226py_buffer_ZL51__pyx_doc_7pyarrow_3lib_224transcoding_input_stream_ZL46__pyx_doc_7pyarrow_3lib_10Transcoder_2__call___ZL45__pyx_doc_7pyarrow_3lib_10Transcoder___init___ZL42__pyx_doc_7pyarrow_3lib_222allocate_buffer_ZL44__pyx_doc_7pyarrow_3lib_220create_memory_map_ZL37__pyx_doc_7pyarrow_3lib_218memory_map_ZL46__pyx_doc_7pyarrow_3lib_216set_io_thread_count_ZL42__pyx_doc_7pyarrow_3lib_214io_thread_count_ZL39__pyx_doc_7pyarrow_3lib_212have_libhdfs_ZL49__pyx_doc_7pyarrow_3lib_12TableGroupBy_2aggregate_ZL47__pyx_doc_7pyarrow_3lib_12TableGroupBy___init___ZL39__pyx_doc_7pyarrow_3lib_210_from_pylist_ZL39__pyx_doc_7pyarrow_3lib_208_from_pydict_ZL40__pyx_doc_7pyarrow_3lib_206concat_tables_ZL32__pyx_doc_7pyarrow_3lib_204table_ZL39__pyx_doc_7pyarrow_3lib_202record_batch_ZL45__pyx_doc_7pyarrow_3lib_200_reconstruct_table_ZL42__pyx_doc_7pyarrow_3lib_198table_to_blocks_ZL52__pyx_doc_7pyarrow_3lib_196_reconstruct_record_batch_ZL40__pyx_doc_7pyarrow_3lib_194chunked_array_ZL39__pyx_doc_7pyarrow_3lib_192_empty_array_ZL40__pyx_doc_7pyarrow_3lib_190concat_arrays_ZL41__pyx_doc_7pyarrow_3lib_188_restore_array_ZL43__pyx_doc_7pyarrow_3lib_186_normalize_slice_ZL37__pyx_doc_7pyarrow_3lib_184infer_type_ZL33__pyx_doc_7pyarrow_3lib_182repeat_ZL32__pyx_doc_7pyarrow_3lib_180nulls_ZL34__pyx_doc_7pyarrow_3lib_178asarray_ZL32__pyx_doc_7pyarrow_3lib_176array_ZL55__pyx_doc_7pyarrow_3lib_174_handle_arrow_array_protocol_ZL49__pyx_doc_7pyarrow_3lib_172_ndarray_to_arrow_type_ZL33__pyx_doc_7pyarrow_3lib_170scalar_ZL45__pyx_doc_7pyarrow_3lib_168_datetime_from_int_ZL57__pyx_doc_7pyarrow_3lib_166_unregister_py_extension_types_ZL54__pyx_doc_7pyarrow_3lib_164_register_py_extension_type_ZL41__pyx_doc_7pyarrow_3lib_162is_float_value_ZL43__pyx_doc_7pyarrow_3lib_160is_integer_value_ZL43__pyx_doc_7pyarrow_3lib_158is_boolean_value_ZL43__pyx_doc_7pyarrow_3lib_156from_numpy_dtype_ZL33__pyx_doc_7pyarrow_3lib_154schema_ZL38__pyx_doc_7pyarrow_3lib_152ensure_type_ZL41__pyx_doc_7pyarrow_3lib_150type_for_alias_ZL45__pyx_doc_7pyarrow_3lib_148fixed_shape_tensor_ZL42__pyx_doc_7pyarrow_3lib_146run_end_encoded_ZL32__pyx_doc_7pyarrow_3lib_144union_ZL38__pyx_doc_7pyarrow_3lib_142dense_union_ZL39__pyx_doc_7pyarrow_3lib_140sparse_union_ZL33__pyx_doc_7pyarrow_3lib_138struct_ZL37__pyx_doc_7pyarrow_3lib_136dictionary_ZL31__pyx_doc_7pyarrow_3lib_134map__ZL42__pyx_doc_7pyarrow_3lib_132large_list_view_ZL36__pyx_doc_7pyarrow_3lib_130list_view_ZL37__pyx_doc_7pyarrow_3lib_128large_list_ZL32__pyx_doc_7pyarrow_3lib_126list__ZL38__pyx_doc_7pyarrow_3lib_124string_view_ZL38__pyx_doc_7pyarrow_3lib_122binary_view_ZL37__pyx_doc_7pyarrow_3lib_120large_utf8_ZL39__pyx_doc_7pyarrow_3lib_118large_string_ZL39__pyx_doc_7pyarrow_3lib_116large_binary_ZL33__pyx_doc_7pyarrow_3lib_114binary_ZL31__pyx_doc_7pyarrow_3lib_112utf8_ZL33__pyx_doc_7pyarrow_3lib_110string_ZL37__pyx_doc_7pyarrow_3lib_108decimal256_ZL37__pyx_doc_7pyarrow_3lib_106decimal128_ZL34__pyx_doc_7pyarrow_3lib_104float64_ZL34__pyx_doc_7pyarrow_3lib_102float32_ZL34__pyx_doc_7pyarrow_3lib_100float16_ZL32__pyx_doc_7pyarrow_3lib_98date64_ZL32__pyx_doc_7pyarrow_3lib_96date32_ZL49__pyx_doc_7pyarrow_3lib_94month_day_nano_interval_ZL34__pyx_doc_7pyarrow_3lib_92duration_ZL32__pyx_doc_7pyarrow_3lib_90time64_ZL32__pyx_doc_7pyarrow_3lib_88time32_ZL35__pyx_doc_7pyarrow_3lib_86timestamp_ZL42__pyx_doc_7pyarrow_3lib_84string_to_tzinfo_ZL42__pyx_doc_7pyarrow_3lib_82tzinfo_to_string_ZL31__pyx_doc_7pyarrow_3lib_80int64_ZL32__pyx_doc_7pyarrow_3lib_78uint64_ZL31__pyx_doc_7pyarrow_3lib_76int32_ZL32__pyx_doc_7pyarrow_3lib_74uint32_ZL31__pyx_doc_7pyarrow_3lib_72int16_ZL32__pyx_doc_7pyarrow_3lib_70uint16_ZL30__pyx_doc_7pyarrow_3lib_68int8_ZL31__pyx_doc_7pyarrow_3lib_66uint8_ZL31__pyx_doc_7pyarrow_3lib_64bool__ZL30__pyx_doc_7pyarrow_3lib_62null_ZL31__pyx_doc_7pyarrow_3lib_60field_ZL39__pyx_doc_7pyarrow_3lib_58unify_schemas_ZL41__pyx_doc_7pyarrow_3lib_56ensure_metadata_ZL51__pyx_doc_7pyarrow_3lib_54unregister_extension_type_ZL49__pyx_doc_7pyarrow_3lib_52register_extension_type_ZL41__pyx_doc_7pyarrow_3lib_9UnionType_5field_ZL43__pyx_doc_7pyarrow_3lib_10StructType_2field_ZL40__pyx_doc_7pyarrow_3lib_8DataType_4field_ZL42__pyx_doc_7pyarrow_3lib_50_to_pandas_dtype_ZL45__pyx_doc_7pyarrow_3lib_48_get_pandas_tz_type_ZL42__pyx_doc_7pyarrow_3lib_46_get_pandas_type_ZL39__pyx_doc_7pyarrow_3lib_44_is_primitive_ZL51__pyx_doc_7pyarrow_3lib_42supported_memory_backends_ZL47__pyx_doc_7pyarrow_3lib_40jemalloc_set_decay_ms_ZL47__pyx_doc_7pyarrow_3lib_38total_allocated_bytes_ZL48__pyx_doc_7pyarrow_3lib_36log_memory_allocations_ZL41__pyx_doc_7pyarrow_3lib_34set_memory_pool_ZL46__pyx_doc_7pyarrow_3lib_32mimalloc_memory_pool_ZL46__pyx_doc_7pyarrow_3lib_30jemalloc_memory_pool_ZL44__pyx_doc_7pyarrow_3lib_28system_memory_pool_ZL45__pyx_doc_7pyarrow_3lib_26logging_memory_pool_ZL43__pyx_doc_7pyarrow_3lib_24proxy_memory_pool_ZL45__pyx_doc_7pyarrow_3lib_22default_memory_pool_ZL53__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_32get_values_ZL51__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_30is_index_ZL52__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_28is_series_ZL56__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_26is_data_frame_ZL52__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_24is_sparse_ZL67__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_22is_extension_array_dtype_ZL56__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_20is_datetimetz_ZL57__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_18is_categorical_ZL56__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_16is_array_like_ZL54__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_8pandas_dtype_ZL53__pyx_doc_7pyarrow_3lib_14_PandasAPIShim_6infer_dtype_ZL46__pyx_doc_7pyarrow_3lib_20set_timezone_db_path_ZL38__pyx_doc_7pyarrow_3lib_18runtime_info_ZL48__pyx_doc_7pyarrow_3lib_16enable_signal_handlers_ZL49__pyx_doc_7pyarrow_3lib_14ArrowCancelled___init___ZL47__pyx_doc_7pyarrow_3lib_13ArrowKeyError___str___ZL35__pyx_doc_7pyarrow_3lib_14frombytes_ZL33__pyx_doc_7pyarrow_3lib_12tobytes_ZL42__pyx_doc_7pyarrow_3lib_10encode_file_path_ZL42__pyx_doc_7pyarrow_3lib_8_gdb_test_session_ZL29__pyx_doc_7pyarrow_3lib_6_pac_ZL28__pyx_doc_7pyarrow_3lib_4_pc_ZL38__pyx_doc_7pyarrow_3lib_2set_cpu_count_ZL33__pyx_doc_7pyarrow_3lib_cpu_count_ZL31__pyx_k_month_day_nano_interval_ZL30__pyx_k_strings_to_categorical_ZL28__pyx_k_integer_object_nulls_ZL27__pyx_k_timestamp_as_object_ZL27__pyx_k_detected_simd_level_ZL27__pyx_k_deduplicate_objects_ZL26__pyx_k_num_record_batches_ZL25__pyx_k_dictionary_encode_ZL23__pyx_k_maps_as_pydicts_ZL23__pyx_k_git_description_ZL23__pyx_k_full_so_version_ZL22__pyx_k_zero_copy_only_ZL22__pyx_k_date_as_object_ZL21__pyx_k_self_destruct_ZL20__pyx_k_version_info_ZL20__pyx_k_value_counts_ZL20__pyx_k_split_blocks_ZL20__pyx_k_package_kind_ZL20__pyx_k_large_string_ZL20__pyx_k_large_binary_ZL19__pyx_k_use_threads_ZL19__pyx_k_string_view_ZL18__pyx_k_so_version_ZL18__pyx_k_simd_level_ZL18__pyx_k_large_utf8_ZL18__pyx_k_from_arrow_ZL18__pyx_k_dictionary_ZL15__pyx_k_version_ZL15__pyx_k_replace_ZL15__pyx_k_ordered_ZL15__pyx_k_is_null_ZL15__pyx_k_indices_ZL15__pyx_k_float64_ZL15__pyx_k_float32_ZL15__pyx_k_float16_ZL14__pyx_k_unique_ZL14__pyx_k_uint64_ZL14__pyx_k_uint32_ZL14__pyx_k_uint16_ZL14__pyx_k_string_ZL14__pyx_k_sparse_ZL14__pyx_k_pandas_ZL14__pyx_k_object_ZL14__pyx_k_is_nan_ZL14__pyx_k_git_id_ZL14__pyx_k_enable_ZL14__pyx_k_detect_ZL14__pyx_k_date64_ZL14__pyx_k_date32_ZL13__pyx_k_write_ZL13__pyx_k_value_ZL13__pyx_k_uint8_ZL13__pyx_k_int64_ZL13__pyx_k_int32_ZL13__pyx_k_int16_ZL12__pyx_k_utf8_ZL12__pyx_k_unit_ZL12__pyx_k_safe_ZL12__pyx_k_read_ZL12__pyx_k_pool_ZL12__pyx_k_null_ZL12__pyx_k_mask_ZL12__pyx_k_item_ZL12__pyx_k_int8_ZL12__pyx_k_drop_ZL11__pyx_k_sum_ZL11__pyx_k_key_ZL10__pyx_k_tz_ZL9__pyx_k_n_ZL9__pyx_k_i_ZL9__pyx_k_f_ZL9__pyx_k_ecrtstuff.cderegister_tm_clones__do_global_dtors_auxcompleted.0__do_global_dtors_aux_fini_array_entryframe_dummy__frame_dummy_init_array_entry__FRAME_END___ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow16DictionaryScalar9ValueTypeD1Ev_ZNSt14_Function_baseD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_2io19BufferedInputStreamEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow15ExtensionScalarD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_3ipc23RecordBatchStreamReaderEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt6vectorISt10shared_ptrINS_12ChunkedArrayEESaIS4_EEED2Ev_ZNSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE24_M_release_last_use_coldEv_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt18bad_variant_accessD2Ev_ZNSt12_Vector_baseIN5arrow8FieldRefESaIS1_EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt10shared_ptrINS_14LargeListArrayEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow6ResultISt10unique_ptrINS_15ResizableBufferESt14default_deleteIS2_EEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EED1Ev_ZN5arrow21PrettyPrintDelimitersC1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNK5arrow17StringViewBuilder4typeEv_ZN5arrow6ResultISt10shared_ptrINS_5FieldEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEvDW.ref._ZTISt16invalid_argument_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEEC1IS1_INS_11StructArrayEEvEEONS0_IT_EE_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_17RecordBatchReaderEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt6vectorISt10shared_ptrIN5arrow9ArrayDataEESaIS3_EED2Ev_ZNSt12_Vector_baseIlSaIlEED2Ev__GNU_EH_FRAME_HDR_ZN5arrow6ResultISt10shared_ptrINS_6SchemaEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow13StringBuilderD0Ev_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE16AppendEmptyValueEv_ZNSt8__detail9__variant15_Copy_ctor_baseILb0EJN5arrow9FieldPathENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorINS2_8FieldRefESaISB_EEEEC1ERKSE__ZN5arrow6StatusC1ERKS0__ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_5TableEEED2Ev_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow11RuntimeInfoD1Ev_ZNSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS3_EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_18LargeListViewArrayEEED2Ev_ZN5arrow5ArrayD0Ev_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow13BufferBuilder6FinishEPSt10shared_ptrINS_6BufferEEb_ZN5arrow4util12CodecOptionsD0EvDW.ref._ZTISt9bad_alloc_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt6vectorISt10shared_ptrIN5arrow12ChunkedArrayEESaIS3_EED2Ev_ZN5arrow6ResultISt10shared_ptrINS_2io12OutputStreamEEED2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_8DataTypeEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNK5arrow16DictionaryScalar4dataEv_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE5ResetEv_ZN5arrow6ResultISt10shared_ptrINS_3ipc17RecordBatchWriterEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_6ScalarEEED1Ev_ZN5arrow6ResultISt10unique_ptrINS_3ipc7MessageESt14default_deleteIS3_EEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow15ExtensionScalarD2Ev_ZN5arrow6ResultINS_23RecordBatchWithMetadataEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_18LargeListViewArrayEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EEC2ERKS5__ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ScalarD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt10unique_ptrIN5arrow4util5CodecESt14default_deleteIS2_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EED2EvDW.ref._ZTISt10bad_typeid_ZNSt12_Vector_baseIN5arrow8FieldRefESaIS1_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow4util12CodecOptionsD2Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE9push_backERKS5__ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEEC2ERKS4__ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt12_Vector_baseIiSaIiEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow2py13PandasOptionsD1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io12ReadableFileEEED2Ev_ZN5arrow6ResultISt10shared_ptrINS_9ListArrayEEED1Ev__dso_handle_ZNSt10shared_ptrIN5arrow12StatusDetailEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE14FinishInternalEPSt10shared_ptrINS_9ArrayDataEE_ZN5arrow6ResultISt10shared_ptrINS_5FieldEEED2Ev_ZN5arrow6StatusC2ERKS0__ZN5arrow16DictionaryScalarD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EED2EvDW.ref._ZTINSt8ios_base7failureB5cxx11E_ZN5arrow15DictionaryArrayD2Ev_ZN5arrow6ResultISt10shared_ptrINS_14LargeListArrayEEED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNKSt18bad_variant_access4whatEv_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS3_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_3ipc21RecordBatchFileReaderEEED2Ev_ZN5arrow6ResultINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEED2Ev_ZNSt6vectorISt10shared_ptrIN5arrow12ChunkedArrayEESaIS3_EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt17_Function_handlerIFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS5_EPS9_E9_M_invokeERKSt9_Any_dataOS1_S7_OS8__ZNSt17_Function_handlerIFN5arrow6ResultISt10shared_ptrINS0_13MemoryManagerEEEEilEPS6_E9_M_invokeERKSt9_Any_dataOiOl_ZN5arrow6ResultISt10shared_ptrINS_2io22CompressedOutputStreamEEED1EvDW.ref.__gxx_personality_v0_ZN5arrow10NullScalarD1Ev_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEED1Ev_ZN5arrow11RuntimeInfoD2EvDW.ref._ZTISt14overflow_error_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow6ResultISt10shared_ptrINS_17RecordBatchReaderEEED1Ev_ZN5arrow6ResultISt10unique_ptrINS_15ResizableBufferESt14default_deleteIS2_EEED1Ev_ZNSt14_Function_baseD2Ev_ZN5arrow6ResultISt10shared_ptrINS_2io11InputStreamEEED1Ev_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow17StringViewBuilderD0Ev_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEEC1ERKS4__ZN5arrow6ResultISt10shared_ptrINS_13ListViewArrayEEED2Ev_ZN5arrow6ResultISt10shared_ptrINS_2io16FileOutputStreamEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow18PrettyPrintOptionsD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE10_M_releaseEv_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt10shared_ptrIN5arrow12StatusDetailEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultINS_23RecordBatchWithMetadataEED2Ev_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEvDW.ref._ZTISt9exception_ZN5arrow12ArrayBuilder13CheckCapacityEl_ZNSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE15_M_add_ref_copyEv_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZStplIcSt11char_traitsIcESaIcEENSt7__cxx1112basic_stringIT_T0_T1_EEOS8_S9__ZN5arrow6ResultISt10shared_ptrINS_6BufferEEED2Ev_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE10AppendNullEv_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow13StringBuilderD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow17BinaryViewBuilder16AppendEmptyValueEv_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6Status11DeleteStateEv_ZN5arrow6ResultISt10shared_ptrINS_11StructArrayEEED2Ev_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt8__detail9__variant16_Variant_storageILb0EJN5arrow9FieldPathENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorINS2_8FieldRefESaISB_EEEE8_M_resetEv_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow21PrettyPrintDelimitersC2Ev_ZNSt12_Vector_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE6ResizeEl_ZN5arrow4util12CodecOptionsD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_3ipc21RecordBatchFileReaderEEED1Ev_ZN5arrow5ArrayD1Ev_ZN5arrow6ResultISt10shared_ptrINS_11RecordBatchEEED2Ev_ZN5arrow6ResultISt10shared_ptrINS_8DataTypeEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNK5arrow12ArrayBuilder6lengthEv_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow6ResultISt10shared_ptrINS_2io16MemoryMappedFileEEED2Ev_ZN5arrow12ChunkedArrayC1ESt10shared_ptrINS_5ArrayEE_ZN5arrow6ResultISt10shared_ptrINS_6BufferEEED1Ev_ZN5arrow13BufferBuilder6ResizeElb_ZN5arrow6ResultISt10shared_ptrINS_3ipc21RecordBatchFileReaderEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow6ResultISt10shared_ptrINS_2io22CompressedOutputStreamEEED2Ev_ZN5arrow6ResultINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEED1Ev_ZN5arrow6ResultISt10unique_ptrINS_6BufferESt14default_deleteIS2_EEED1Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_6ScalarEEED2Ev_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow16DictionaryScalar9ValueTypeD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow6ResultISt10shared_ptrINS_13ListViewArrayEEED1Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_2io16MemoryMappedFileEEED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow6ResultISt10shared_ptrINS_3ipc17RecordBatchWriterEEED1Ev_ZN5arrow6ScalarD0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt10unique_ptrIN5arrow4util5CodecESt14default_deleteIS2_EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_17RecordBatchReaderEEED2Ev_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt10shared_ptrINS_2io20BufferedOutputStreamEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_DYNAMIC__TMC_END___ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_3ipc17RecordBatchWriterEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt12_Vector_baseIiSaIiEED2Ev_ZN5arrow17LoggingMemoryPoolD1Ev_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED2EvDW.ref._ZTISt15underflow_error_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow5ArrayD2Ev_ZN5arrow16DictionaryScalarD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt17_Function_handlerIFN5arrow6ResultISt10shared_ptrINS0_13MemoryManagerEEEEilEPS6_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation_ZNSt12__shared_ptrIKN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE5resetIS1_EENSt9enable_ifIXsrSt21__sp_is_constructibleIS2_T_E5valueEvE4typeEPS9__ZN5arrow17BinaryViewBuilder6AppendEPKhl_ZN5arrow6ScalarD2Ev_ZN5arrow18PrettyPrintOptionsD2Ev_ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EED2Ev_ZN5arrow13StringBuilderD2Ev_ZN5arrow17BinaryViewBuilder6ResizeEl_ZNSt10unique_ptrIN5arrow6BufferESt14default_deleteIS1_EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNK5arrow4util5Codec17compression_levelEv_ZN5arrow6ResultISt10shared_ptrINS_9ListArrayEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow23RecordBatchWithMetadataD2Ev_ZN5arrow7compute11CastOptionsD0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_3ipc21RecordBatchFileReaderEEED2EvDW.ref._ZTISt11range_error_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow2py13PandasOptionsD2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow10NullScalarD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow6ResultISt10shared_ptrINS_6TensorEEED1Ev_ZN5arrow2py12PyReleaseGIL18unique_ptr_deleterEP3_ts_ZNSt6vectorISt10shared_ptrIN5arrow9ArrayDataEESaIS3_EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_12ChunkedArrayEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN5arrow6ResultISt10unique_ptrINS_6BufferESt14default_deleteIS2_EEED2Ev_ZN5arrow6ResultISt10shared_ptrINS_6TensorEEED2Ev_ZN5arrow17StringViewBuilderD1EvDW.ref._ZTISt12out_of_range_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEvDW.ref._ZTISt8bad_cast_ZN5arrow15ExtensionScalarD0Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow12ChunkedArrayC2ESt10shared_ptrINS_5ArrayEE_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_2io12OutputStreamEEED1Ev_ZNSt18bad_variant_accessD1Ev_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EEC1ERKS5__ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow17BinaryViewBuilder11AppendNullsEl_ZNK5arrow16DictionaryScalar4viewEv_ZNSt12_Vector_baseIaSaIaEED1Ev_ZN5arrow17LoggingMemoryPoolD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io20BufferedOutputStreamEEED1Ev_ZN5arrow6ResultISt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow15DictionaryArrayD1Ev_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE17AppendEmptyValuesEl_ZN5arrow16DictionaryScalarD0Ev_ZNSt10unique_ptrIN5arrow15ResizableBufferESt14default_deleteIS1_EED2Ev_ZN5arrow6ResultISt10shared_ptrINS_6SchemaEEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_11StructArrayEEED1Ev_ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_17RecordBatchReaderEEED2Ev_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io21CompressedInputStreamEEED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow17BinaryViewBuilder10AppendNullEv_ZNSt12_Vector_baseIlSaIlEED1Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt12__shared_ptrIN5arrow8DataTypeELN9__gnu_cxx12_Lock_policyE2EE5resetINS0_14DictionaryTypeEEENSt9enable_ifIXsrSt21__sp_is_constructibleIS1_T_E5valueEvE4typeEPS9_DW.ref._ZTISt12domain_error_ZNSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt6vectorISt10shared_ptrIN5arrow5ArrayEESaIS3_EED2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow10MemoryPool13ReleaseUnusedEv_ZN5arrow17BinaryViewBuilder17AppendEmptyValuesEl_ZNSt12__shared_ptrIN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE5resetIS1_EENSt9enable_ifIXsrSt21__sp_is_constructibleIS1_T_E5valueEvE4typeEPS8__ZNSt8__detail9__variant15_Copy_ctor_baseILb0EJN5arrow9FieldPathENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorINS2_8FieldRefESaISB_EEEEC2ERKSE__ZN5arrow15DictionaryArrayD0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow17RecordBatchReader5CloseEv_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE11AppendNullsEl_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt12_Vector_baseIaSaIaEED2Ev_ZN5arrow10NullScalarD0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNK5arrow13StringBuilder4typeEv_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow6ResultISt10unique_ptrINS_4util5CodecESt14default_deleteIS3_EEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ResultISt10unique_ptrINS_3ipc7MessageESt14default_deleteIS3_EEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io11InputStreamEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZN5arrow7compute11CastOptionsD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE21_M_deallocate_bucketsEv_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt10unique_ptrIN5arrow15ResizableBufferESt14default_deleteIS1_EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io19BufferedInputStreamEEED2Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt6vectorISt10shared_ptrINS_12ChunkedArrayEESaIS4_EEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_2io16FileOutputStreamEEED2Ev_ZN5arrow17LoggingMemoryPoolD0Ev_ZNSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow6ResultISt10shared_ptrINS_11RecordBatchEEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_18RunEndEncodedArrayEEED1Ev_ZN5arrow17StringViewBuilderD2Ev_ZNSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EED1Ev_ZN5arrow8internal17StringHeapBuilder7ReserveEl_ZN5arrow6ResultISt10unique_ptrINS_4util5CodecESt14default_deleteIS3_EEED1Ev_ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EED1Ev_ZN5arrow17RecordBatchReader8ReadNextEv_ZNSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt10shared_ptrIKNS_16KeyValueMetadataEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZNSt18bad_variant_accessD0Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZNSt10unique_ptrIN5arrow6BufferESt14default_deleteIS1_EED2Ev_ZNSt12_Vector_baseINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EED1Ev_ZN5arrow17BaseBinaryBuilderINS_10BinaryTypeEE16AppendArraySliceERKNS_9ArraySpanEll_ZNSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED2Ev_ZN5arrow6ResultISt10shared_ptrINS_3ipc23RecordBatchStreamReaderEEED2Ev_ZN5arrow6ResultISt10shared_ptrIKNS_16KeyValueMetadataEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow6ResultISt10shared_ptrINS_2io12ReadableFileEEED1Ev_ZNSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow6ResultISt10shared_ptrINS_5TableEEED1Ev_ZN5arrow6ResultISt10shared_ptrINS_5ArrayEEEC2IS1_INS_11StructArrayEEvEEONS0_IT_EE_GLOBAL_OFFSET_TABLE__ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow7compute11CastOptionsD1Ev_ZNSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZNSt6vectorISt10shared_ptrIN5arrow5ArrayEESaIS3_EED1Ev_ZNSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EED0Ev_ZN5arrow6ResultISt10shared_ptrINS_18RunEndEncodedArrayEEED2Ev_ZNSt17_Function_handlerIFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS5_EPS9_E10_M_managerERSt9_Any_dataRKSC_St18_Manager_operation_ZNSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE14_M_get_deleterERKSt9type_info_ZNSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE10_M_disposeEv_ZN5arrow6ResultISt10shared_ptrINS_2io21CompressedInputStreamEEED2Ev_ZNSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE10_M_destroyEv_ZN5arrow23RecordBatchWithMetadataD1Ev_ZN5arrow6ResultISt10shared_ptrINS_12ChunkedArrayEEED2Ev_ZN5arrow2py13SmartPtrNoGILISt10shared_ptrJNS_3ipc17RecordBatchWriterEEED2Ev_ZTVSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow11StructArray14GetFieldByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPyObject_CallFinalizerFromDealloc_ZNSt6vectorISt10shared_ptrIN5arrow6SchemaEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow23AllocateResizableBufferElPNS_10MemoryPoolE_Z32pyarrow_unwrap_sparse_csf_tensorP7_object_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_Z20pyarrow_unwrap_batchP7_objectPyObject_Hash_ZTIN5arrow8DataTypeE_ZNK5arrow8DataType6EqualsERKSt10shared_ptrIS0_Eb_ZTINSt8ios_base7failureB5cxx11E@GLIBCXX_3.4.21PyObject_ReprPyObject_SelfIter_ZNSt6vectorIlSaIlEE17_M_realloc_insertIJRKlEEEvN9__gnu_cxx17__normal_iteratorIPlS1_EEDpOT_PySequence_GetSlice_ZNK5arrow5Array4ViewERKSt10shared_ptrINS_8DataTypeEE_ZN5arrow2py20ConvertTableToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_5TableEEPP7_objectPyExc_ValueError_ZNK5arrow5Array9GetScalarEl_ZNK5arrow6Scalar6EqualsERKS0_RKNS_12EqualOptionsEPyUnicode_FromOrdinal__pyx_wrapperbase_7pyarrow_3lib_10StructType_11__getitem___ZTSSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EEPyLong_AsLongPyLong_FromSsize_tPy_OptimizeFlag_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EEPyTuple_GetSlice_ZTSN5arrow8internal20ArrayBuilderExtraOpsINS_17BaseBinaryBuilderINS_10BinaryTypeEEESt17basic_string_viewIcSt11char_traitsIcEEEEPyMem_Realloc_ZN5arrow9MakeArrayERKSt10shared_ptrINS_9ArrayDataEE_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_Z21pyarrow_unwrap_tensorP7_object_ZNK5arrow16KeyValueMetadata8ToStringB5cxx11Ev_ZNK5arrow6Schema10num_fieldsEv_ZN5arrow33UnregisterCancellingSignalHandlerEv_ZN5arrow7MapTypeC1ESt10shared_ptrINS_5FieldEES3_b_ZN5arrow2py14GetResultValueISt10shared_ptrINS_11RecordBatchEEEET_NS_6ResultIS5_EE_ZTSN5arrow15DictionaryArrayE_ZTIN5arrow5ArrayEPyBool_Type_Z30pyarrow_wrap_sparse_csf_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSFIndexEEEE_ZN5arrow9timestampENS_8TimeUnit4typeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPyObject_GetAttr_ZN5arrow2py24CastingRecordBatchReader4MakeESt10shared_ptrINS_17RecordBatchReaderEES2_INS_6SchemaEE_ZN5arrow18FixedSizeListArray10FromArraysERKSt10shared_ptrINS_5ArrayEEiS1_INS_6BufferEEl_ZSt11_Hash_bytesPKvmm@CXXABI_1.3.5_PyList_Extend_ZN5arrow23AllocateResizableBufferEllPNS_10MemoryPoolEPyDict_GetItemStringPyImport_AddModulePyObject_Call_ZTISt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EEPyExc_KeyError_ZN5arrow8internal18SendSignalToThreadEim_ZNK5arrow12ChunkedArray8ValidateEv_ZN5arrow2py9benchmark28Benchmark_PandasObjectIsNullEP7_object_Py_TrueStruct_ZN5arrow15ProxyMemoryPoolC1EPNS_10MemoryPoolE_ZNK5arrow6Tensor8dim_nameB5cxx11Ei_ZN5arrow2io18BufferOutputStreamC1ERKSt10shared_ptrINS_15ResizableBufferEEPyExc_IndexError_ZTIN5arrow2io12OutputStreamE_ZN5arrow14Decimal256TypeC1EiiPyTuple_Pack_ZTVSt18bad_variant_access_ZTSSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt14default_deleteIN5arrow15ResizableBufferEE_ZNK5arrow11RecordBatch13ToStructArrayEv_ZN5arrow19MakeArrayFromScalarERKNS_6ScalarElPNS_10MemoryPoolE_ZTIN5arrow14FixedWidthTypeE_Z24pyarrow_unwrap_data_typeP7_object_ZTIN5arrow4util18EqualityComparableINS_6ScalarEEEPyErr_NormalizeException_ZTISt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow4util5Codec26UseDefaultCompressionLevelEv_ZTVSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow6ScalarEPyObject_Init_ZNK5arrow6Status8ToStringB5cxx11Ev_ZTSSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZSt16__do_uninit_copyIN9__gnu_cxx17__normal_iteratorIPKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESt6vectorIS7_SaIS7_EEEEPS7_ET0_T_SG_SF__ZNSt6vectorISt10shared_ptrIN5arrow5TableEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT_PyTuple_TypePyGC_Disable_ZN5arrow2io20BufferedOutputStream6DetachEv_ZTVSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE_PyBytes_Resize_ZTISt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZSt9terminatev@GLIBCXX_3.4_ZNKSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE7compareEPKc@GLIBCXX_3.4.21_ZN5arrow3ipc14DictionaryMemoD1Ev_ZN5arrow6SchemaC1ESt6vectorISt10shared_ptrINS_5FieldEESaIS4_EES2_IKNS_16KeyValueMetadataEE_ZNK5arrow6Scalar8ValidateEv_ZTISt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTIN5arrow16DictionaryScalarE_ZN5arrow2py25UnregisterPyExtensionTypeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow3ipc13MessageReader4OpenERKSt10shared_ptrINS_2io11InputStreamEEPyObject_Str__pyx_wrapperbase_7pyarrow_3lib_12ChunkedArray_27__getitem___ZTSSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow3ipc15ReadRecordBatchERKNS0_7MessageERKSt10shared_ptrINS_6SchemaEEPKNS0_14DictionaryMemoERKNS0_14IpcReadOptionsE_PyUnicode_FastCopyCharacters_ZNSt6vectorISt10shared_ptrIN5arrow15ResizableBufferEESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT_PyList_Type_ZN5arrow15MakeArrayOfNullERKSt10shared_ptrINS_8DataTypeEElPNS_10MemoryPoolE_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_appendEPKcm@GLIBCXX_3.4.21PyObject_GetAttrStringPyCapsule_TypePyDescr_IsData__pyx_wrapperbase_7pyarrow_3lib_5Array_53__getitem___ZNK5arrow6Schema8AddFieldEiRKSt10shared_ptrINS_5FieldEE_ZN5arrow14ExtensionArrayC1ERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEE_ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EE17_M_default_appendEm_ZTIN5arrow2io16RandomAccessFileEarrow_init_numpy_ZN5arrow5Table17FromRecordBatchesESt10shared_ptrINS_6SchemaEERKSt6vectorIS1_INS_11RecordBatchEESaIS6_EE_ZNK5arrow3ipc7Message4typeEv_ZTSSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow13LargeListTypeE_Z18pyarrow_wrap_batchRKSt10shared_ptrIN5arrow11RecordBatchEE_ZNK5arrow12ChunkedArray5SliceElPyMem_Free_PyDict_SetItem_KnownHash_ZSt17__throw_bad_allocv@GLIBCXX_3.4_ZTVSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow13ListViewArray7FlattenEPNS_10MemoryPoolE_ZTISt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSN5arrow15ExtensionScalarEPyUnicode_New_ZTIN5arrow8internal19PrimitiveScalarBaseE_ZTIN5arrow6ScalarE_ZTSSt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEPyErr_Restore_ZN5arrow2py9IsPyFloatEP7_object_ZN5arrow6SchemaD0EvPyType_IsSubtype_ZN5arrow11dense_unionESt6vectorISt10shared_ptrINS_5FieldEESaIS3_EES0_IaSaIaEE_ZN5arrow4util6detail19StringStreamWrapperD1Ev_ZN5arrow17ImportRecordBatchEP10ArrowArrayP11ArrowSchema_ZN5arrow2py8internal24NewMonthDayNanoTupleTypeEv__cxa_begin_catch@CXXABI_1.3_ZNK5arrow6Schema8SetFieldEiRKSt10shared_ptrINS_5FieldEE_ZNK5arrow5Field8ToStringB5cxx11Eb_ZN5arrow14GetRuntimeInfoEvPyModule_GetDictPyTraceBack_Here_ZTSSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EEPyDict_SetItemPyModule_AddObject_ZN5arrow4util15TotalBufferSizeERKNS_12ChunkedArrayE_ZTSSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow5Table13SelectColumnsERKSt6vectorIiSaIiEE_ZN5arrow12ArrayBuilder5ResetEv__cxa_finalize@GLIBC_2.2.5_ZTIN5arrow15DictionaryArrayE_ZNK5arrow5Array8ValidateEvstrlen@GLIBC_2.2.5_ZTVN5arrow15DictionaryArrayEPyErr_WarnEx_ZTVSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EEPyObject_GC_IsFinalized_ZN5arrow2io16FileOutputStream4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEb_ZN5arrow2py25NdarraysToSparseCSCMatrixEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSCIndexEEEEmemcmp@GLIBC_2.2.5_ZN5arrow2py24MakeTransformInputStreamESt10shared_ptrINS_2io11InputStreamEENS0_26TransformInputStreamVTableEP7_object_ZN5arrow4util5Codec23MaximumCompressionLevelENS_11Compression4typeEPyDescr_NewClassMethod_ZN5arrow18ImportChunkedArrayEP16ArrowArrayStream_ZN5arrow3ipc14MakeFileWriterESt10shared_ptrINS_2io12OutputStreamEERKS1_INS_6SchemaEERKNS0_15IpcWriteOptionsERKS1_IKNS_16KeyValueMetadataEEPyList_AsTuple_ZN5arrow2py19PyRecordBatchReader4MakeESt10shared_ptrINS_6SchemaEEP7_object_ZTIN5arrow12ArrayBuilderEPyNumber_Index_ZN5arrow18system_memory_poolEvPyUnicode_InternFromStringPyTuple_NewPyObject_SetAttr_ZN5arrow2py23TensorToSparseCSFTensorERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSFIndexEEEEPyObject_IsInstance_ZN5arrow2py8IsPyBoolEP7_object_ZNK5arrow12ChunkedArray5SliceEll_ZTIN5arrow2io8WritableE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE9_M_assignIRKSI_NS7_17_ReuseOrAllocNodeISaINS7_10_Hash_nodeIS5_Lb1EEEEEEEEvOT_RKT0__ZN5arrow16TableBatchReader13set_chunksizeElPyEval_RestoreThread_ZN5arrow2py23RegisterPyExtensionTypeERKSt10shared_ptrINS_8DataTypeEE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow5Field14RemoveMetadataEv_ZN5arrow8internal18WinErrorFromStatusERKNS_6StatusEPySlice_TypePyErr_NoMemory_ZN5arrow3ipc7MessageD1Ev_ZTISt12domain_error@GLIBCXX_3.4PyMemoryView_Type_Py_NoneStruct_ZN5arrow11StructArray4MakeERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EERKS1_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES2_INS_6BufferEEllPyExc_AttributeErrorPyException_SetTraceback_ZN5arrow17DictionaryUnifier10UnifyTableERKNS_5TableEPNS_10MemoryPoolE_ZN5arrow9ListArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZN5arrow2py14PyReadableFileC1EP7_object_ZN5arrow4util5Codec11IsAvailableENS_11Compression4typeE_ZTSSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EEPyNumber_AddPyDict_SetItemString_ZSt20__throw_length_errorPKc@GLIBCXX_3.4_ZTSN5arrow10NullScalarE_ZTIN5arrow17BinaryViewBuilderE_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_assignERKS4_@GLIBCXX_3.4.21_ZN5arrow15DenseUnionArray4MakeERKNS_5ArrayES3_St6vectorISt10shared_ptrIS1_ESaIS6_EES4_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES4_IaSaIaEE_ZTIN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEE_ZN5arrow9utf8_viewEvPyWrapperDescr_Type__pyx_wrapperbase_7pyarrow_3lib_9MapScalar_2__iter___ZN5arrow2io23GetIOThreadPoolCapacityEvPyExc_IOErrorPyUnicode_FromFormat_ZN5arrow20mimalloc_memory_poolEPPNS_10MemoryPoolEmemset@GLIBC_2.2.5__pyx_wrapperbase_7pyarrow_3lib_12StructScalar_9__getitem__PyList_Append__pyx_wrapperbase_7pyarrow_3lib_9UnionType_7__getitem__PyInterpreterState_GetIDPyExc_MemoryError_ZN5arrow2py8PyBuffer12FromPyObjectEP7_object_ZTSSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EEPyType_TypePyAsyncGen_TypePyBytes_FromStringAndSize_Z19pyarrow_wrap_schemaRKSt10shared_ptrIN5arrow6SchemaEE_ZTIN5arrow13BinaryBuilderE_ZTISt14overflow_error@GLIBCXX_3.4_ZN5arrow4util20ReferencedBufferSizeERKNS_5TableE_ZTISt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTVSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2io16MemoryMappedFile4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEENS0_8FileMode4typeE_ZN5arrow2py9IsPyErrorERKNS_6StatusEPyThreadState_GetFrame_ZNK5arrow16KeyValueMetadata6EqualsERKS0__ZN5arrow11ExportFieldERKNS_5FieldEP11ArrowSchema_Z32pyarrow_unwrap_sparse_coo_tensorP7_object_ZN5arrow2py14GetResultValueISt10shared_ptrINS_5TableEEEET_NS_6ResultIS5_EE_ZN5arrow4nullEv_ZN5arrow2io11HaveLibHdfsEv_ZN5arrow3ipc10ReadTensorEPNS_2io11InputStreamE_PyStack_AsDict_Z20pyarrow_unwrap_arrayP7_objectPyImport_GetModuleDict_ZN5arrow23ImportDeviceRecordBatchEP16ArrowDeviceArrayP11ArrowSchemaRKSt8functionIFNS_6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEEPyDict_Contains_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEC1EOS4_@GLIBCXX_3.4.21PyStaticMethod_New_ZN5arrow12ArrayBuilder6ResizeEl_ZTISt16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EEPyByteArray_Type_ZN5arrow2py15get_memory_poolEv_ZNK5arrow6Schema12WithMetadataERKSt10shared_ptrIKNS_16KeyValueMetadataEE_ZN5arrow3ipc10ReadSchemaEPNS_2io11InputStreamEPNS0_14DictionaryMemoE_ZNK5arrow18LargeListViewArray5sizesEv_ZTISt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow10StructType18GetAllFieldIndicesERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTISt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow13ListViewArray5sizesEv_ZNK5arrow5Field12WithMetadataERKSt10shared_ptrIKNS_16KeyValueMetadataEE_ZN5arrow21ExtensionTypeRegistry17GetGlobalRegistryEv_Z21pyarrow_unwrap_schemaP7_objectPyCode_NewWithPosOnlyArgs_ZN5arrow3gdb11TestSessionEvPyDict_Next_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEED1Ev@GLIBCXX_3.4.21_ZN5arrow14Decimal128TypeC1EiiPyException_GetTracebackPyTuple_Size_ZNK5arrow16KeyValueMetadata8ContainsESt17basic_string_viewIcSt11char_traitsIcEE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE5clearEvPyErr_SetObject_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTVSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE__cxa_guard_release@CXXABI_1.3_ZN5arrow19SetSignalStopSourceEv_ZNK5arrow6Status4WarnEv_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EE17_M_realloc_insertIJS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow18LargeListViewArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_S6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZTSN5arrow7compute11CastOptionsE_ZN5arrow15DictionaryArray10FromArraysERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES9__ZTVSt15_Sp_counted_ptrIPN5arrow2io16MockOutputStreamELN9__gnu_cxx12_Lock_policyE2EEPyImport_GetModule_Z32pyarrow_unwrap_sparse_csr_matrixP7_objectPyObject_IsSubclass_ZNK5arrow6Scalar12ValidateFullEv_ZTVN5arrow16DictionaryScalarEPyBytes_FromStringPyObject_GetIter_ZNK5arrow11StructArray5fieldEi_ZN5arrow8internal14DieWithMessageERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_Z30pyarrow_wrap_sparse_csr_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSRIndexEEEE_ZGVZNK5arrow6Status7messageB5cxx11EvE10no_message_PyBytes_JoinPyErr_Format_ZN5arrow14MakeNullScalarESt10shared_ptrINS_8DataTypeEE_ZN5arrow11ImportArrayEP10ArrowArrayP11ArrowSchema_ZN5arrow2io16MemoryMappedFile6ResizeEl_ZNK5arrow6Schema14RemoveMetadataEv_PyObject_GenericGetAttrWithDictPyObject_CallObject_ZTSSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE_ZTVN10__cxxabiv117__class_type_infoE@CXXABI_1.3_ZN5arrow3ipc11ReadMessageEPNS_2io11InputStreamEPNS_10MemoryPoolE_ZTVN5arrow7compute11CastOptionsEPyFloat_FromDouble_ZN5arrow10StopSource5tokenEv_ZN5arrow21jemalloc_set_decay_msEi_ZN5arrow18LargeListViewArray10FromArraysERKNS_5ArrayES3_S3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZN5arrow2io16RandomAccessFile9GetStreamESt10shared_ptrIS1_Ell_fini_ZN5arrow11ImportFieldEP11ArrowSchema_ZN5arrow16KeyValueMetadataC1ESt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EES9__ZTSN5arrow13StringBuilderE_ZNK5arrow11RecordBatch6EqualsERKS0_bRKNS_12EqualOptionsEPyFloat_AsDoublePyVectorcall_Function_ZNK5arrow10StructType18GetAllFieldsByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow11ExportArrayERKNS_5ArrayEP10ArrowArrayP11ArrowSchema_ZNK5arrow10StructType13GetFieldIndexERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow3ipc21RecordBatchFileReader4OpenEPNS_2io16RandomAccessFileElRKNS0_14IpcReadOptionsEPyList_Reverse_ZN5arrow2py8internal12check_statusERKNS_6StatusE_ZN5arrow24SetCpuThreadPoolCapacityEi_ZTVSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow6Tensor13is_contiguousEvPySet_Type_ZN5arrow31RegisterCancellingSignalHandlerERKSt6vectorIiSaIiEE_ZN5arrow15large_list_viewESt10shared_ptrINS_5FieldEE_Z18pyarrow_wrap_fieldRKSt10shared_ptrIN5arrow5FieldEE_ZN5arrow6dlpack12ExportDeviceERKSt10shared_ptrINS_5ArrayEE_ZTSFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEPyType_Modified_ZN5arrow4util20ReferencedBufferSizeERKNS_5ArrayE_Py_Dealloc_ZN5arrow8internal16SignalFromStatusERKNS_6StatusEPyCFunction_Type_ZTSN5arrow6ScalarE_ZGVZNK5arrow6Status6detailEvE9no_detail_ZSt28__throw_bad_array_new_lengthv@GLIBCXX_3.4.29_ZNK5arrow6Buffer6EqualsERKS0__ZN5arrow8MapArray10FromArraysERKSt10shared_ptrINS_5ArrayEES5_S5_PNS_10MemoryPoolE_ZN5arrow17ImportDeviceArrayEP16ArrowDeviceArrayP11ArrowSchemaRKSt8functionIFNS_6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEE_ZN5arrow2py8internal33MonthDayNanoIntervalArrayToPyListERKNS_25MonthDayNanoIntervalArrayEPyExc_ModuleNotFoundError_ZN5arrow2py20ConvertArrayToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_5ArrayEEP7_objectPS8__ZTSSt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EEPyExc_OverflowError_Z29pyarrow_wrap_resizable_bufferRKSt10shared_ptrIN5arrow15ResizableBufferEEPyMem_Malloc_ZTSSt15_Sp_counted_ptrIPN5arrow13LargeListTypeELN9__gnu_cxx12_Lock_policyE2EEPyErr_ExceptionMatchesPy_LeaveRecursiveCall_ZTSSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow9StopToken4PollEv_Z26pyarrow_wrap_chunked_arrayRKSt10shared_ptrIN5arrow12ChunkedArrayEEPyTraceBack_Type_ZN5arrow2io23SetIOThreadPoolCapacityEi_ZN5arrow12UnifySchemasERKSt6vectorISt10shared_ptrINS_6SchemaEESaIS3_EENS_5Field12MergeOptionsE_ZTSSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow6Schema8metadataEv_ZN5arrow14AllocateBufferEllPNS_10MemoryPoolEPyList_NewPyImport_ImportModule_ZNK5arrow6Schema5fieldEi_ZNK5arrow12SparseTensor8dim_nameB5cxx11Ei_ZN5arrow21ResetSignalStopSourceEv_ZNK5arrow11RecordBatch11num_columnsEv_ZN5arrow12ArrayBuilder6FinishEPSt10shared_ptrINS_5ArrayEE_ZTISt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EEPyExc_GeneratorExit_PyObject_GetDictPtr_ZN5arrow2io21FixedSizeBufferWriterC1ERKSt10shared_ptrINS_6BufferEEPyExc_NameErrorPyUnicode_FromString_ZN5arrow15SliceBufferSafeERKSt10shared_ptrINS_6BufferEEl_ZTISt23enable_shared_from_thisIN5arrow6ScalarEE_ZNK5arrow12ChunkedArray9GetScalarEl_ZN5arrow2io12CacheOptions12LazyDefaultsEv_ZTISt15_Sp_counted_ptrIPN5arrow6SchemaELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow6Schema6EqualsERKS0_b__cxa_atexit@GLIBC_2.2.5_ZN5arrow6StatusC1ENS_10StatusCodeERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow3ipc23RecordBatchStreamReader4OpenERKSt10shared_ptrINS_2io11InputStreamEERKNS0_14IpcReadOptionsE_ZTIN5arrow13StringBuilderE_Z30pyarrow_wrap_sparse_csc_matrixRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCSCIndexEEEE_ZNK5arrow18LargeListViewArray7offsetsEvPyMethodDescr_Type_ZNK5arrow5Array12ValidateFullEv_ZTVSt15_Sp_counted_ptrIPN5arrow15DictionaryArrayELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2io21FixedSizeBufferWriter21set_memcopy_thresholdEl_ZNSt6vectorISt10shared_ptrIN5arrow11RecordBatchEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow4util15TotalBufferSizeERKNS_5TableE_ZTISt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTIN5arrow7compute15FunctionOptionsE_ZNK5arrow18FixedSizeListArray6valuesEvPyUnicode_Type_ZTVSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE__pthread_key_create_ZNK5arrow16KeyValueMetadata3keyB5cxx11El_ZNK5arrow15DictionaryArray10dictionaryEv_ZNK5arrow13ListViewArray7offsetsEv_ZN5arrow23ImportRecordBatchReaderEP16ArrowArrayStream_ZTVSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE__pyx_wrapperbase_7pyarrow_3lib_10StructType_6__len___ZTSN5arrow17StringViewBuilderE_ZNK5arrow3ipc7Message8metadataEvPyUnicode_Resize_ZN5arrow4util6detail19StringStreamWrapperC1Ev_ZZNK5arrow6Status6detailEvE9no_detail_ZN5arrow15SliceBufferSafeERKSt10shared_ptrINS_6BufferEEllPyExc_ArithmeticError_ZN5arrow2py24SparseCSRMatrixToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSRIndexEEEEP7_objectPS9_SA_SA_PyErr_PrintEx_ZNSo9_M_insertIlEERSoT_@GLIBCXX_3.4.9_ZTISt10bad_typeid@GLIBCXX_3.4_ZN5arrow6time64ENS_8TimeUnit4typeE_ZTSN5arrow16DictionaryScalarE_ZTVSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EE_ZTISt15underflow_error@GLIBCXX_3.4_Z19pyarrow_wrap_bufferRKSt10shared_ptrIN5arrow6BufferEE_ZN5arrow18RunEndEncodedArray4MakeERKSt10shared_ptrINS_8DataTypeEElRKS1_INS_5ArrayEES9_lPyNumber_FloorDivide_ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EES4_IS1_IS0_ESaIS9_EES9_llPyErr_ClearPyDict_DelItem_ZN5arrow2io21FixedSizeBufferWriter19set_memcopy_threadsEi_ZNK5arrow16KeyValueMetadata3GetB5cxx11ESt17basic_string_viewIcSt11char_traitsIcEE_Znwm@GLIBCXX_3.4_ZN5arrow17LoggingMemoryPoolC1EPNS_10MemoryPoolE_ZN5arrow4util5Codec23MinimumCompressionLevelENS_11Compression4typeE_ZNK5arrow3ipc7Message6EqualsERKS1__ZN5arrow2py15TensorToNdarrayERKSt10shared_ptrINS_6TensorEEP7_objectPS7__ZdlPvm@CXXABI_1.3.9PyModule_NewObject_ZN5arrow2io19BufferedInputStream6DetachEv_ZNK5arrow12StructScalar5fieldENS_8FieldRefE_ZN5arrow2py14import_pyarrowEvPyRun_StringFlags_ZN5arrow2py27ConvertChunkedArrayToPandasERKNS0_13PandasOptionsESt10shared_ptrINS_12ChunkedArrayEEP7_objectPS8__ZNK5arrow10Decimal2568ToStringB5cxx11Ei_ZN5arrow11RecordBatch15FromStructArrayERKSt10shared_ptrINS_5ArrayEEPNS_10MemoryPoolE_ZNK5arrow6Schema18GetAllFieldIndicesERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow12ArrayBuilder16UnsafeSetNotNullEl_ZNK5arrow12ChunkedArray12ValidateFullEv_ZTIN5arrow10NullScalarE_ZTVSt19_Sp_counted_deleterIPN5arrow6BufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt19_Sp_make_shared_tagPyGC_Enable_ZN5arrow2io12BufferReaderC1ESt10shared_ptrINS_6BufferEE_ZTIN5arrow4util12CodecOptionsE_Z19pyarrow_wrap_tensorRKSt10shared_ptrIN5arrow6TensorEEPy_Version_ZNSt6vectorISt10shared_ptrIN5arrow5ArrayEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZNK5arrow5Field8WithNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZNK5arrow12BooleanArray11false_countEv_ZTISt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EEPyModule_GetName_ZTSSt15_Sp_counted_ptrIPN5arrow2io18BufferOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2io22CompressedOutputStream4MakeEPNS_4util5CodecERKSt10shared_ptrINS0_12OutputStreamEEPNS_10MemoryPoolE_ZTVSt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow3ipc10ReadSchemaERKNS0_7MessageEPNS0_14DictionaryMemoE_ZN5arrow2py23set_default_memory_poolEPNS_10MemoryPoolE_ZNSt6vectorISt10shared_ptrIN5arrow9ArrayDataEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT_PyErr_SetInterruptPySet_Contains_ZN5arrow11PrettyPrintERKNS_5ArrayERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EEPyExc_RuntimeWarning_Z18pyarrow_wrap_tableRKSt10shared_ptrIN5arrow5TableEE_ZTVN5arrow13StringBuilderEstrrchr@GLIBC_2.2.5__stack_chk_fail@GLIBC_2.4_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py25NdarraysToSparseCSFTensorEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEES9_RKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSFIndexEEEE_ZN5arrow4util6detail19StringStreamWrapper3strB5cxx11EvPyOS_snprintf_ZNK5arrow12SparseTensor8ToTensorEPNS_10MemoryPoolE_ZN5arrow6Status8CopyFromERKS0_PyDict_New_ZN5arrow17DictionaryUnifier17UnifyChunkedArrayERKSt10shared_ptrINS_12ChunkedArrayEEPNS_10MemoryPoolE_ZNK5arrow9UnionType4modeEvPySet_Add_ZN5arrow2py14RestorePyErrorERKNS_6StatusEPyClassMethod_New_ZN5arrow17RecordBatchReader7ToTableEv__dynamic_cast@CXXABI_1.3PyErr_SetString_ZSt16__ostream_insertIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_PKS3_l@GLIBCXX_3.4.9_ZN5arrow10ExportTypeERKNS_8DataTypeEP11ArrowSchema_ZTVN5arrow4util12CodecOptionsE_ZN5arrow16TableBatchReaderC1ESt10shared_ptrINS_5TableEE_ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EEll_ZNK5arrow5Table13RenameColumnsERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EEPyExc_BufferError_ZN5arrow8internal15ErrnoFromStatusERKNS_6StatusE_ZN5arrow3ipc15SerializeSchemaERKNS_6SchemaEPNS_10MemoryPoolE__pyx_wrapperbase_7pyarrow_3lib_10ListScalar_2__getitem___ZN5arrow4util20ReferencedBufferSizeERKNS_11RecordBatchE_ZTVN5arrow8ListTypeEPySlice_NewPyExc_NotImplementedError_ZNK5arrow10UnionArray5fieldEi_ZTVN5arrow17FixedSizeListTypeE_ZN5arrow4util5Codec24SupportsCompressionLevelENS_11Compression4typeE_ZNK5arrow5Array5SliceEll_ZNK5arrow5Array4DiffB5cxx11ERKS0__ZNK5arrow2py15PyExtensionType11SetInstanceEP7_objectPyObject_VectorcallDictfree@GLIBC_2.2.5PyExc_Exception_ZSt16__do_uninit_copyIN9__gnu_cxx17__normal_iteratorIPKN5arrow8FieldRefESt6vectorIS3_SaIS3_EEEEPS3_ET0_T_SC_SB__ZTISt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEPyThreadState_Get_ZN5arrow27SupportedMemoryBackendNamesB5cxx11Ev_ZTVSt15_Sp_counted_ptrIPN5arrow16KeyValueMetadataELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow6Tensor4sizeEvPyObject_SetItem_ZN5arrow7compute11CastOptionsC1Eb_ZN5arrow12sparse_unionESt6vectorISt10shared_ptrINS_5FieldEESaIS3_EES0_IaSaIaEE_ZN5arrow2io21FixedSizeBufferWriter21set_memcopy_blocksizeEl_ZNSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS5_EE17_M_realloc_insertIJRKS5_EEEvN9__gnu_cxx17__normal_iteratorIPS5_S7_EEDpOT__Z32pyarrow_unwrap_sparse_csc_matrixP7_object__pyx_wrapperbase_7pyarrow_3lib_10StructType_8__iter___ZN5arrow2io12ReadableFile4OpenERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPNS_10MemoryPoolEPyExc_DeprecationWarning_ZTSN5arrow2io12OutputStreamE_ZN5arrow3ipc18GetRecordBatchSizeERKNS_11RecordBatchEPl_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE10_M_disposeEv@GLIBCXX_3.4.21_ZTVN10__cxxabiv119__pointer_type_infoE@CXXABI_1.3_ZN5arrow17ImportRecordBatchEP10ArrowArraySt10shared_ptrINS_6SchemaEEPyCapsule_New_ZTVSt15_Sp_counted_ptrIPN5arrow5FieldELN9__gnu_cxx12_Lock_policyE2EE_ZTSN5arrow4util18EqualityComparableINS_6ScalarEEEPyByteArray_FromStringAndSize_ZNK5arrow5Array6EqualsERKS0_RKNS_12EqualOptionsE_ZNK5arrow6Tensor6EqualsERKS0_RKNS_12EqualOptionsE_PyDict_GetItem_KnownHash_ZN5arrow17ConcatenateTablesERKSt6vectorISt10shared_ptrINS_5TableEESaIS3_EENS_24ConcatenateTablesOptionsEPNS_10MemoryPoolE_ZTIN5arrow15ExtensionScalarE_ZN5arrow18FixedSizeListArray10FromArraysERKSt10shared_ptrINS_5ArrayEES1_INS_8DataTypeEES1_INS_6BufferEEl_ZTSN5arrow7compute15FunctionOptionsE_ZN5arrow12BaseListTypeD2Ev_ZN5arrow2py15PyExtensionType9FromClassESt10shared_ptrINS_8DataTypeEENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEP7_objectPS2_INS_13ExtensionTypeEE_ZN5arrow2io12CacheOptions22MakeFromNetworkMetricsElldl_ZNK5arrow11RecordBatch13RenameColumnsERKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaIS7_EE_ZTISt9bad_alloc@GLIBCXX_3.4_ZTVN10__cxxabiv120__si_class_type_infoE@CXXABI_1.3_ZNK5arrow6Schema11RemoveFieldEi_ZNK5arrow2py15PyExtensionType11GetInstanceEv_ZTSFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_EPyObject_SetAttrString_ZTISt23_Sp_counted_ptr_inplaceIN5arrow16DictionaryScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSPFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_ZN5arrow2py17ConvertPySequenceEP7_objectS2_NS0_19PyConversionOptionsEPNS_10MemoryPoolE_ZTVN5arrow2io16MockOutputStreamE_ZTSSt11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EEPyGILState_Release_ZNK5arrow6Scalar4hashEvPyCapsule_GetPointerPyExc_RuntimeError_ZTSSt14default_deleteIN5arrow4util5CodecEE_ZNKSt8__detail20_Prime_rehash_policy14_M_need_rehashEmmm@GLIBCXX_3.4.18_ZN5arrow2py8internal14TzinfoToStringB5cxx11EP7_object__pyx_wrapperbase_7pyarrow_3lib_10ListScalar_4__iter___ZTSSt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14AllocateBufferElPNS_10MemoryPoolEPyExc_UnboundLocalError_ZN5arrow2io16MemoryMappedFile6CreateERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEElmalloc@GLIBC_2.2.5_ZN5arrow12ArrayBuilder12AppendScalarERKNS_6ScalarEl_ZTVN5arrow12ArrayBuilderE_ZN5arrow5Field12MergeOptions10PermissiveEv_ZN5arrow10InitializeERKNS_13GlobalOptionsE_PyGen_SetStopIterationValue_ZN5arrow9ArrayData4MakeESt10shared_ptrINS_8DataTypeEElSt6vectorIS1_INS_6BufferEESaIS6_EES4_IS1_IS0_ESaIS9_EEll_ZTVN5arrow17BinaryViewBuilderE_ZTSSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEPyBytes_AsString_ZN5arrow2py23MakeStreamTransformFuncENS0_26TransformInputStreamVTableEP7_object_ZNK5arrow18RunEndEncodedArray18FindPhysicalLengthEvPyCapsule_IsValidPyFrozenSet_New_ZTSN5arrow8internal19PrimitiveScalarBaseEPyUnicode_Format_ZN5arrow2py15NdarrayToTensorEPNS_10MemoryPoolEP7_objectRKSt6vectorINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISB_EEPSt10shared_ptrINS_6TensorEE_ZN5arrow2py15PyForeignBuffer4MakeEPKhlP7_objectPSt10shared_ptrINS_6BufferEE_ZTIN5arrow2io11InputStreamE_ZN5arrow4util5Codec6CreateENS_11Compression4typeEi_ZTVSt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EEPyObject_GC_Del_Z20pyarrow_unwrap_tableP7_object_ZZNK5arrow6Status7messageB5cxx11EvE10no_message_ZN5arrow4util15TotalBufferSizeERKNS_11RecordBatchE_ZNK5arrow3ipc7Message16metadata_versionEv_Py_NotImplementedStructPyUnicode_Decode_ZTISt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow3ipc7Message11SerializeToEPNS_2io12OutputStreamERKNS0_15IpcWriteOptionsEPl_ZN5arrow12ArrayBuilder13AppendScalarsERKSt6vectorISt10shared_ptrINS_6ScalarEESaIS4_EE_ZNK5arrow11StructArray7FlattenEPNS_10MemoryPoolE_ZNK5arrow5Field12WithNullableEb_ZN5arrow2py8internal14StringToTzinfoERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPyDict_GetItemWithError_ZN5arrow16SparseUnionArray4MakeERKNS_5ArrayESt6vectorISt10shared_ptrIS1_ESaIS6_EES4_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISE_EES4_IaSaIaEEPyObject_FreePyBaseObject_Type_ZNK5arrow12SparseTensor6EqualsERKS0_RKNS_12EqualOptionsEPySequence_List_ZTSN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEEPyExc_StopIterationPyObject_GetItem_ZN5arrow3ipc14IpcReadOptions8DefaultsEvstrcmp@GLIBC_2.2.5PyExc_TypeErrorPyCode_NewEmpty_ZTISt8bad_cast@GLIBCXX_3.4_ZN5arrow8internal10SendSignalEi_Z21pyarrow_unwrap_bufferP7_object_ZN5arrow10StructTypeC1ERKSt6vectorISt10shared_ptrINS_5FieldEESaIS4_EE_ZN5arrow17BinaryViewBuilder5ResetEv__pyx_wrapperbase_7pyarrow_3lib_9UnionType_2__iter___ZNK5arrow12BooleanArray10true_countEv_ZN5arrow13ExtensionType9WrapArrayERKSt10shared_ptrINS_8DataTypeEERKS1_INS_12ChunkedArrayEE_ZNSt6vectorIaSaIaEE17_M_realloc_insertIJRKaEEEvN9__gnu_cxx17__normal_iteratorIPaS1_EEDpOT__ZN5arrow9timestampENS_8TimeUnit4typeE_ZN5arrow13ListViewArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_S6_PNS_10MemoryPoolES1_INS_6BufferEEl_ZTSSt18bad_variant_access_ZNSt6vectorISt10shared_ptrIN5arrow5FieldEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT__ZN5arrow5FieldD0EvPyType_Ready_ZNK5arrow5Array5SliceEl_ZN5arrow25DefaultDeviceMemoryMapperEilPyObject_SizePyEval_SaveThread_ZNK5arrow11RecordBatch13SelectColumnsERKSt6vectorIiSaIiEEPyFrozenSet_Type_ZN5arrow9extension20FixedShapeTensorType4MakeERKSt10shared_ptrINS_8DataTypeEERKSt6vectorIlSaIlEESB_RKS7_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISH_EE_ZN5arrow2io20BufferedOutputStream6CreateElPNS_10MemoryPoolESt10shared_ptrINS0_12OutputStreamEE__pyx_wrapperbase_7pyarrow_3lib_9MapScalar___getitem___ZTTN5arrow2io16MockOutputStreamE_ZN5arrow12GetBuildInfoEv_ZNK5arrow11RecordBatch8ToTensorEbbPNS_10MemoryPoolEPyMethod_TypePyMethod_New_ZNK5arrow12ChunkedArray6EqualsERKS0_RKNS_12EqualOptionsE_ZN5arrow2py25NdarraysToSparseCSRMatrixEPNS_10MemoryPoolEP7_objectS4_S4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSRIndexEEEE__cxa_rethrow@CXXABI_1.3_ZN5arrow23ExportRecordBatchReaderESt10shared_ptrINS_17RecordBatchReaderEEP16ArrowArrayStream_ZTVSt15_Sp_counted_ptrIPN5arrow14DictionaryTypeELN9__gnu_cxx12_Lock_policyE2EEPyObject_ClearWeakRefs_ZN5arrow2py8internal36MonthDayNanoIntervalScalarToPyObjectERKNS_26MonthDayNanoIntervalScalarEPyErr_SetNone_ZTVN5arrow19FixedSizeBinaryTypeE_ZN5arrow2py23TensorToSparseCSCMatrixERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSCIndexEEEE_PyUnicode_Ready_ZN5arrow10ImportTypeEP11ArrowSchema_ZTSN5arrow13BinaryBuilderEPyInit_libPyLong_AsSsize_t_ZTIPFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZN5arrow12ChunkedArray4MakeESt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EES2_INS_8DataTypeEEPyObject_GC_UnTrack_ZTVSt15_Sp_counted_ptrIPN5arrow4util5CodecELN9__gnu_cxx12_Lock_policyE2EEPyNumber_Remainder_Py_FalseStructPyLong_FromLong_ZN5arrow15DictionaryArrayC1ERKSt10shared_ptrINS_9ArrayDataEE_ZN5arrow3ipc11WriteTensorERKNS_6TensorEPNS_2io12OutputStreamEPiPl_ZNK5arrow15DictionaryArray7indicesEv_ZNK5arrow9ListArray7offsetsEv_ZN5arrow2py24SparseCSCMatrixToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSCIndexEEEEP7_objectPS9_SA_SA_PyErr_WriteUnraisablePyObject_RichCompareBoolPyNumber_Or_ZN5arrow2py17NumPyDtypeToArrowEP7_object_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE9_M_rehashEmRKm_PyObject_GC_New_ZN5arrow17ExportRecordBatchERKNS_11RecordBatchEP10ArrowArrayP11ArrowSchemaPyObject_IsTrue_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE10_M_replaceEmmPKcm@GLIBCXX_3.4.21_ZNK5arrow10Decimal1288ToStringB5cxx11EiPyGILState_Check_ZNK5arrow5Table13CombineChunksEPNS_10MemoryPoolE_ZN5arrow23ImportDeviceRecordBatchEP16ArrowDeviceArraySt10shared_ptrINS_6SchemaEERKSt8functionIFNS_6ResultIS2_INS_13MemoryManagerEEEEilEE_ZN5arrow4util20ReferencedBufferSizeERKNS_12ChunkedArrayEPyErr_WarnFormat__pyx_wrapperbase_7pyarrow_3lib_9UnionType___len___ZTVSt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EEPy_EnterRecursiveCall_ZN5arrow2py15PyHalf_FromHalfEt_ZN5arrow12ImportSchemaEP11ArrowSchema_ZTVSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EEPyList_Sort_ZTISt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py24SparseCOOTensorToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCOOIndexEEEEP7_objectPS9_SA__ZNK5arrow11StructArray17GetFlattenedFieldEiPNS_10MemoryPoolE_ZN5arrow17ExportDeviceArrayERKNS_5ArrayESt10shared_ptrINS_6Device9SyncEventEEP16ArrowDeviceArrayP11ArrowSchema_ZTSSt15_Sp_counted_ptrIPN5arrow10StructTypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow12ChunkedArrayC1ESt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EES2_INS_8DataTypeEE_Z22pyarrow_wrap_data_typeRKSt10shared_ptrIN5arrow8DataTypeEE_ZN5arrow11RecordBatch4MakeESt10shared_ptrINS_6SchemaEElSt6vectorIS1_INS_5ArrayEESaIS6_EEPyList_SetSlice_Z19pyarrow_wrap_scalarRKSt10shared_ptrIN5arrow6ScalarEE_ZNSt6vectorIiSaIiEE17_M_realloc_insertIJiEEEvN9__gnu_cxx17__normal_iteratorIPiS1_EEDpOT__ZTSN5arrow5ArrayE_ZN5arrow6Buffer11ToHexStringB5cxx11EvPyLong_AsUnsignedLong_ZN5arrow9extension20FixedShapeTensorType10MakeTensorERKSt10shared_ptrINS_15ExtensionScalarEE_ZTVN10__cxxabiv120__function_type_infoE@CXXABI_1.3_ZNK5arrow5Field6EqualsERKS0_b_ZNK5arrow14LargeListArray7offsetsEv_ZTISt15_Sp_counted_ptrIPN5arrow14Decimal128TypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow14LargeListArray10FromArraysESt10shared_ptrINS_8DataTypeEERKNS_5ArrayES6_PNS_10MemoryPoolES1_INS_6BufferEElPyDict_TypePyLong_FromSize_t_ZN5arrow19default_memory_poolEv_ZNK5arrow3ipc7Message4bodyEv_ZN5arrow16TableBatchReaderC1ERKNS_5TableE_ZN5arrow2py23TensorToSparseCOOTensorERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCOOIndexEEEE_ZTIN5arrow17StringViewBuilderE_ZNK5arrow16DictionaryScalar15GetEncodedValueEv_ZN5arrow15run_end_encodedESt10shared_ptrINS_8DataTypeEES2__Z28pyarrow_unwrap_chunked_arrayP7_object_ZNK5arrow16KeyValueMetadata5valueB5cxx11ElPyNumber_NegativePyCoro_TypePyErr_Occurred_ZN5arrow2py24SparseCSFTensorToNdarrayERKSt10shared_ptrINS_16SparseTensorImplINS_14SparseCSFIndexEEEEP7_objectPS9_SA_SA_PyObject_GenericGetAttrmemmove@GLIBC_2.2.5_ZN5arrow2io21CompressedInputStream4MakeEPNS_4util5CodecERKSt10shared_ptrINS0_11InputStreamEEPNS_10MemoryPoolE_ZN5arrow11PrettyPrintERKNS_12ChunkedArrayERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_Z18pyarrow_wrap_arrayRKSt10shared_ptrIN5arrow5ArrayEE_ZNSt6vectorISt10shared_ptrIN5arrow12ChunkedArrayEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT___cxa_end_catch@CXXABI_1.3_ZTVN5arrow17BaseBinaryBuilderINS_10BinaryTypeEEE_ZTISt15_Sp_counted_ptrIPN5arrow19FixedSizeBinaryTypeELN9__gnu_cxx12_Lock_policyE2EEPyDict_CopyPyLong_TypePyExc_StopAsyncIteration_ZTIN5arrow2io13FileInterfaceE_ZZNSt19_Sp_make_shared_tag5_S_tiEvE5__tagPyErr_Fetch__gxx_personality_v0@CXXABI_1.3_ZN5arrow2py14InferArrowTypeEP7_objectS2_b_ZNK5arrow5Array10null_countEv_ZN5arrow14DictionaryTypeC1ERKSt10shared_ptrINS_8DataTypeEES5_b_ZN5arrow2py14PyOutputStreamC1EP7_object_ZTISt23_Sp_counted_ptr_inplaceIN5arrow15ExtensionScalarESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py14NdarrayToArrowEPNS_10MemoryPoolEP7_objectS4_bRKSt10shared_ptrINS_8DataTypeEERKNS_7compute11CastOptionsEPS5_INS_12ChunkedArrayEE_ZN5arrow6time32ENS_8TimeUnit4typeE_ZNK5arrow5Field8WithTypeERKSt10shared_ptrINS_8DataTypeEE_ZTVSt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow18RunEndEncodedArray18FindPhysicalOffsetEv_ZNK5arrow12ChunkedArray7FlattenEPNS_10MemoryPoolE_ZTSN5arrow4util18EqualityComparableINS_7compute15FunctionOptionsEEE_ZN5arrow18RunEndEncodedArray4MakeElRKSt10shared_ptrINS_5ArrayEES5_l_ZN5arrow3ipc14DictionaryMemoC1Ev_ZTISt11range_error@GLIBCXX_3.4_ZN5arrow12ArrayBuilder13UnsafeSetNullEl_ZN5arrow11PrettyPrintERKNS_6SchemaERKNS_18PrettyPrintOptionsEPNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow9list_viewESt10shared_ptrINS_5FieldEE_ZTSN5arrow4util12CodecOptionsE_ZTIN5arrow8internal20ArrayBuilderExtraOpsINS_17BaseBinaryBuilderINS_10BinaryTypeEEESt17basic_string_viewIcSt11char_traitsIcEEEE__pyx_module_is_main_pyarrow__lib_ZTIN5arrow2py15PyExtensionTypeE_ZN5arrow20jemalloc_memory_poolEPPNS_10MemoryPoolE_ZTISt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow17BinaryViewBuilder16AppendArraySliceERKNS_9ArraySpanEllPySequence_Contains_ZTVN5arrow17StringViewBuilderE_ZN5arrow11StructArray4MakeERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS4_EERKS1_IS2_INS_5FieldEESaISA_EES2_INS_6BufferEEll_ZN5arrow2py16GetPrimitiveTypeENS_4Type4typeE_PyThreadState_UncheckedGet_ZTIPFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilEPyImport_ImportModuleLevelObject_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow12ChunkedArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_Z30pyarrow_wrap_sparse_coo_tensorRKSt10shared_ptrIN5arrow16SparseTensorImplINS0_14SparseCOOIndexEEEEmemcpy@GLIBC_2.2.5_PyType_Lookup_ZN5arrow2py25NdarraysToSparseCOOTensorEPNS_10MemoryPoolEP7_objectS4_RKSt6vectorIlSaIlEERKS5_INSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEESaISF_EEPSt10shared_ptrINS_16SparseTensorImplINS_14SparseCOOIndexEEEEPyFrame_New_ZN5arrow3ipc17FormatMessageTypeB5cxx11ENS0_11MessageTypeE_ZN5arrow2py14GetResultValueISt10shared_ptrINS_5ArrayEEEET_NS_6ResultIS5_EE_ZN5arrow17BinaryViewBuilder14FinishInternalEPSt10shared_ptrINS_9ArrayDataEEPyDict_Size__pyx_wrapperbase_7pyarrow_3lib_10ListScalar___len___ZTSSt15_Sp_counted_ptrIPN5arrow10NullScalarELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow3ipc13GetTensorSizeERKNS_6TensorEPlPyFloat_Type__pyx_wrapperbase_7pyarrow_3lib_8_Tabular_8__getitem___ZNK5arrow5Field7FlattenEv_ZN5arrow18ExportChunkedArrayESt10shared_ptrINS_12ChunkedArrayEEP16ArrowArrayStream_Z20pyarrow_unwrap_fieldP7_object_ZTISt12out_of_range@GLIBCXX_3.4_ZN5arrow3ipc16MakeStreamWriterESt10shared_ptrINS_2io12OutputStreamEERKS1_INS_6SchemaEERKNS0_15IpcWriteOptionsE_ZTVN10__cxxabiv121__vmi_class_type_infoE@CXXABI_1.3_ITM_deregisterTMCloneTable_ZN5arrow3ipc15IpcWriteOptions8DefaultsEv_ZTSSt15_Sp_counted_ptrIPN5arrow2py14PyReadableFileELN9__gnu_cxx12_Lock_policyE2EE_ZNK5arrow8DataType6EqualsERKS0_b_ZN5arrow23ExportDeviceRecordBatchERKNS_11RecordBatchESt10shared_ptrINS_6Device9SyncEventEEP16ArrowDeviceArrayP11ArrowSchemaPyIter_Next_Unwind_Resume@GCC_3.0_ZN5arrow10DebugPrintERKNS_5ArrayEiPyObject_Format_ZTIN5arrow7compute11CastOptionsEPySet_New_ZN5arrow3ipc21RecordBatchFileReader4OpenEPNS_2io16RandomAccessFileERKNS0_14IpcReadOptionsE_ZTISt18bad_variant_access_ZTIN5arrow2io19BufferedInputStreamEPySequence_Tuple_ZTSSt23_Sp_counted_ptr_inplaceIN5arrow14ExtensionArrayESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow5FieldE_ZNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEE9_M_createERmm@GLIBCXX_3.4.21_ZN5arrow12ExportSchemaERKNS_6SchemaEP11ArrowSchemaPyLong_FromUnsignedLong_ZTSSt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EEPyErr_CheckSignals_ZTISt16invalid_argument@GLIBCXX_3.4_ZNK5arrow16KeyValueMetadata4sizeEv_ZN5arrow13ExtensionType9WrapArrayERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEEPyArg_UnpackTuplePyBytes_Type_ZTVSt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow3ipc14DictionaryMemoELN9__gnu_cxx12_Lock_policyE2EE__cxa_guard_acquire@CXXABI_1.3_ZN5arrow24GetCpuThreadPoolCapacityEv_ZN5arrow11ImportArrayEP10ArrowArraySt10shared_ptrINS_8DataTypeEEPyExc_SystemError_ZTIN5arrow4util18EqualityComparableINS_7compute15FunctionOptionsEEEPyExc_ImportError_ZNK5arrow18LargeListViewArray7FlattenEPNS_10MemoryPoolE_ZNK5arrow12SparseTensor4sizeEv_ZNSt6vectorISt10shared_ptrIN5arrow6BufferEESaIS3_EE17_M_realloc_insertIJRKS3_EEEvN9__gnu_cxx17__normal_iteratorIPS3_S5_EEDpOT_PyGen_Type_ZN5arrow2io19BufferedInputStream6CreateElPNS_10MemoryPoolESt10shared_ptrINS0_11InputStreamEElPyObject_RichCompare_ZNK5arrow9extension21FixedShapeTensorArray8ToTensorEvPyIter_Send_ZTSSt15_Sp_counted_ptrIPN5arrow14Decimal256TypeELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt14default_deleteIN5arrow6BufferEE_ZNSt9exceptionD2Ev@GLIBCXX_3.4_ZN5arrow8durationENS_8TimeUnit4typeE_ZNK5arrow5Table6EqualsERKS0_b_ZN5arrow4util15TotalBufferSizeERKNS_5ArrayE_ZTIN5arrow13ExtensionTypeE_PyDict_NewPresized_ZTSPFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZTSSt15_Sp_counted_ptrIPN5arrow2io21FixedSizeBufferWriterELN9__gnu_cxx12_Lock_policyE2EEPyUnicode_Compare_ZNK5arrow6Schema13GetFieldIndexERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE_ZN5arrow17ImportDeviceArrayEP16ArrowDeviceArraySt10shared_ptrINS_8DataTypeEERKSt8functionIFNS_6ResultIS2_INS_13MemoryManagerEEEEilEE_ZN5arrow8MapArray10FromArraysESt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES7_S7_PNS_10MemoryPoolE_ZTISt15_Sp_counted_ptrIPN5arrow17FixedSizeListTypeELN9__gnu_cxx12_Lock_policyE2EEPyGILState_Ensure_ZTISt19_Sp_counted_deleterIPN5arrow15ResizableBufferESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt15_Sp_counted_ptrIPN5arrow8ListTypeELN9__gnu_cxx12_Lock_policyE2EEPyCMethod_New_ZN5arrow3ipc20SerializeRecordBatchERKNS_11RecordBatchERKNS0_15IpcWriteOptionsE_ZTVN5arrow5ArrayE_ZTISt9exception@GLIBCXX_3.4PyObject_GC_Track__gmon_start___ZN5arrow2py14GetResultValueINS_23RecordBatchWithMetadataEEET_NS_6ResultIS3_EEPyCapsule_GetName_ZNK5arrow2io16MemoryMappedFile15file_descriptorEv_ZN5arrow14LargeListArray10FromArraysERKNS_5ArrayES3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZTISt15_Sp_counted_ptrIPN5arrow2io12BufferReaderELN9__gnu_cxx12_Lock_policyE2EEPyTuple_GetItem_ZN5arrow5Table4MakeESt10shared_ptrINS_6SchemaEESt6vectorIS1_INS_12ChunkedArrayEESaIS6_EEl__cxa_pure_virtual@CXXABI_1.3__pyx_wrapperbase_7pyarrow_3lib_13ExtensionType_2__init__PyUnicode_Concat_ZN5arrow4utf8Ev_ZTIFN5arrow6ResultISt10shared_ptrINS_13MemoryManagerEEEEilE_ZTIN5arrow2io20BufferedOutputStreamE_ITM_registerTMCloneTablePyException_SetCause_ZN5arrow9ListArray10FromArraysERKNS_5ArrayES3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEElPyModuleDef_Init_ZN5arrow13ListViewArray10FromArraysERKNS_5ArrayES3_S3_PNS_10MemoryPoolESt10shared_ptrINS_6BufferEEl_ZTVSt19_Sp_counted_deleterIPN5arrow4util5CodecESt14default_deleteIS2_ESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZN5arrow2py7IsPyIntEP7_object__pyx_wrapperbase_7pyarrow_3lib_15TimestampScalar_2__repr__PyErr_GivenExceptionMatchesPy_IsInitialized_ZTVN5arrow10NullScalarE_ZN5arrow4util5Codec16GetCodecAsStringB5cxx11ENS_11Compression4typeE_ZN5arrow2py23TensorToSparseCSRMatrixERKSt10shared_ptrINS_6TensorEEPS1_INS_16SparseTensorImplINS_14SparseCSRIndexEEEE_ZN5arrow15DictionaryArrayC1ERKSt10shared_ptrINS_8DataTypeEERKS1_INS_5ArrayEES9__ZTIFvP7_objectRKSt10shared_ptrIN5arrow6BufferEEPS4_E_ZN5arrow4util5Codec23DefaultCompressionLevelENS_11Compression4typeE_ZNSt10_HashtableINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES5_SaIS5_ENSt8__detail9_IdentityESt8equal_toIS5_ESt4hashIS5_ENS7_18_Mod_range_hashingENS7_20_Default_ranged_hashENS7_20_Prime_rehash_policyENS7_17_Hashtable_traitsILb1ELb1ELb1EEEE18_M_assign_elementsIRKSI_EEvOT__ZN5arrow4util5Codec6CreateENS_11Compression4typeERKNS0_12CodecOptionsEPyNumber_Subtract_ZN5arrow11ConcatenateERKSt6vectorISt10shared_ptrINS_5ArrayEESaIS3_EEPNS_10MemoryPoolE_ZNK5arrow6Schema18GetAllFieldsByNameERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEPyUnicode_FromStringAndSize_Z21pyarrow_unwrap_scalarP7_object_ZN5arrow6dlpack11ExportArrayERKSt10shared_ptrINS_5ArrayEE_ZTISt23_Sp_counted_ptr_inplaceIN5arrow16TableBatchReaderESaIvELN9__gnu_cxx12_Lock_policyE2EE_ZTSSt23enable_shared_from_thisIN5arrow6ScalarEE_ZTISt15_Sp_counted_ptrIPN5arrow2py14PyOutputStreamELN9__gnu_cxx12_Lock_policyE2EEPyUnicode_JoinPyBytes_AsStringAndSize_ZTVSt15_Sp_counted_ptrIPN5arrow7MapTypeELN9__gnu_cxx12_Lock_policyE2EE_ZTVN5arrow15ExtensionScalarEPyNumber_InPlaceAdd.symtab.strtab.shstrtab.gnu.hash.dynsym.dynstr.gnu.version.gnu.version_r.rela.dyn.init.plt.plt.got.text.fini.rodata.eh_frame_hdr.eh_frame.gcc_except_table.init_array.fini_array.data.rel.ro.dynamic.data.bss.commentDoH H % 8,8,V-؂؂5ox=x=8BoDDQEE[af  o00nC$u#(#( {0(0( 33P0?40?455Xd6Xd6`d6`d6hd6hd6Xu6Xu6jhw6hw666( 88 08Tظ8# c<JE