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@DD?HIIJJMMMMMMLNNOPPP LRhRR Sdims_).size() == ndims_byte() ref_.load() >= 1/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/core/lib/core/refcount.hNDIMS == new_sizes.size()/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/core/framework/tensor.hnew_num_elements == NumElements()Expected label # but got #---------- Prediction for ---------- - " ()"Loaded JPEG: xImage needs to have at least but only has Failed to load compute graph at ''tensorflow/examples/label_image/data/grace_hopper.jpgdata/tensorflow_inception_graph.pbexamples/label_image/data/imagenet_comp_graph_label_strings.txtMulsoftmaximage to be processedimagegraph to be executedgraphname of file containing labelslabelsresize image to this width in pixelsinput_widthresize image to this height in pixelsinput_heightscale pixel values to this meaninput_meanscale pixel values to this std deviationinput_stdname of input layerinput_layername of output layeroutput_layerrun a self testself_testinterpret image and graph file names relative to this directoryroot_dirUnknown argument CT_REPEAT_MAINRunning model failed: Running print failed: Running check failed: Self-test failed!/home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/map_entry_lite.hCHECK failed: default_instance_ != NULL: tensorflow.RunConfiguration.argumenttensorflow.EntryValue.string_valuetensorflow.CommitId.hashtensorflow.CommitId.snapshottensorflow.GPUInfo.modeltensorflow.GPUInfo.uuidtensorflow.GPUInfo.bus_idtensorflow.PlatformInfo.bitstensorflow.PlatformInfo.linkagetensorflow.PlatformInfo.machinetensorflow.PlatformInfo.releasetensorflow.PlatformInfo.systemtensorflow.PlatformInfo.versiontensorflow.AvailableDeviceInfo.nametensorflow.AvailableDeviceInfo.typetensorflow.AvailableDeviceInfo.physical_descriptiontensorflow.BuildConfiguration.modetensorflow.BuildConfiguration.cc_flagstensorflow.BuildConfiguration.optstensorflow.MachineConfiguration.hostnametensorflow.MachineConfiguration.serial_identifier/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/util/test_log.pb.cctensorflow.TestResults.targettensorflow.TestResults.name/home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/map.hProtocol Buffer map usage error: MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetStringValue type does not match Expected : Actual : tensorflow/core/util/test_log.protoCHECK failed: file != NULL: /home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/map_entry.hCHECK failed: (&from) != (this): /home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/stubs/hash.hShould never be called.tensorflow.BenchmarkEntry.nametensorflow.BenchmarkEntry.ExtrasEntry.keytensorflow.CPUInfo.cpu_infotensorflow.CPUInfo.cpu_governortensorflow.CPUInfo.CacheSizeEntry.key/home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/map_field_inl.hCHECK failed: MapFieldBase::repeated_field_ != NULL: CHECK failed: *MapFieldBase::entry_descriptor_ != NULL: CHECK failed: default_entry_ != NULL: tensorflow.SavedSliceMeta.nametensorflow.SavedSlice.name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/util/saved_tensor_slice.pb.cc -tensorflow/core/util/saved_tensor_slice.proto tensorflow,tensorflow/core/framework/tensor_shape.proto,tensorflow/core/framework/tensor_slice.proto&tensorflow/core/framework/tensor.proto%tensorflow/core/framework/types.proto(tensorflow/core/framework/versions.proto" SavedSliceMeta name ( + shape ( 2.tensorflow.TensorShapeProto" type (2.tensorflow.DataType+ slice ( 2.tensorflow.TensorSliceProto"l SavedTensorSliceMeta* tensor ( 2.tensorflow.SavedSliceMeta( versions ( 2.tensorflow.VersionDef"n SavedSlice name ( + slice ( 2.tensorflow.TensorSliceProto% data ( 2.tensorflow.TensorProto"i SavedTensorSlices. meta ( 2 .tensorflow.SavedTensorSliceMeta$ data ( 2.tensorflow.SavedSliceB2 org.tensorflow.utilBSavedTensorSliceProtosPbproto3tensorflow/core/util/saved_tensor_slice.prototensorflow.MemmappedFileSystemDirectoryElement.name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/util/memmapped_file_system.pb.cc 0tensorflow/core/util/memmapped_file_system.proto tensorflow"C #MemmappedFileSystemDirectoryElement offset ( name ( "` MemmappedFileSystemDirectory@ element ( 2/.tensorflow.MemmappedFileSystemDirectoryElementBbproto3tensorflow/core/util/memmapped_file_system.prototensorflow.TaggedRunMetadata.tagtensorflow.Event.file_versiontensorflow.LogMessage.messagetensorflow.SessionLog.checkpoint_pathtensorflow.SessionLog.msg/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/util/event.pb.cctensorflow/core/util/event.prototensorflow.ServerDef.job_nametensorflow.ServerDef.protocol/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/tensorflow_server.pb.cc 0tensorflow/core/protobuf/tensorflow_server.proto tensorflow%tensorflow/core/protobuf/config.proto"r JobDef name ( , tasks ( 2.tensorflow.JobDef.TasksEntry, TasksEntry key ( value ( :8"- ClusterDef job ( 2.tensorflow.JobDef" ServerDef' cluster ( 2.tensorflow.ClusterDef job_name (  task_index (7 default_session_config ( 2.tensorflow.ConfigProto protocol ( B/ org.tensorflow.distruntimeB ServerProtosPbproto3tensorflow/core/protobuf/tensorflow_server.protoMapKey::GetInt32Valuetensorflow.JobDef.nametensorflow.JobDef.TasksEntry.valuetensorflow.SaverDef.filename_tensor_nametensorflow.SaverDef.save_tensor_nametensorflow.SaverDef.restore_op_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/saver.pb.cctensorflow/core/protobuf/saver.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/queue_runner.pb.cctensorflow.QueueRunnerDef.queue_nametensorflow.QueueRunnerDef.enqueue_op_nametensorflow.QueueRunnerDef.close_op_nametensorflow.QueueRunnerDef.cancel_op_name +tensorflow/core/protobuf/queue_runner.proto tensorflow*tensorflow/core/lib/core/error_codes.proto" QueueRunnerDef queue_name (  enqueue_op_name (  close_op_name (  cancel_op_name ( < queue_closed_exception_types (2.tensorflow.error.CodeB2 org.tensorflow.frameworkBQueueRunnerProtosPbproto3tensorflow/core/protobuf/queue_runner.proto/home/fursin/CK/local/env/cee3fdac8c24b118/install/include/google/protobuf/repeated_field.hCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/named_tensor.pb.cctensorflow.NamedTensorProto.name +tensorflow/core/protobuf/named_tensor.proto tensorflow&tensorflow/core/framework/tensor.proto"I NamedTensorProto name ( ' tensor ( 2.tensorflow.TensorProtoB2 org.tensorflow.frameworkBNamedTensorProtosPbproto3tensorflow/core/protobuf/named_tensor.prototensorflow.CollectionDef.NodeList.valuetensorflow.AssetFileDef.filenametensorflow.MetaGraphDef.MetaInfoDef.meta_graph_versiontensorflow.MetaGraphDef.MetaInfoDef.tagstensorflow.MetaGraphDef.MetaInfoDef.tensorflow_versiontensorflow.MetaGraphDef.MetaInfoDef.tensorflow_git_version/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/meta_graph.pb.cctensorflow.TensorInfo.nametensorflow/core/protobuf/meta_graph.prototensorflow.MetaGraphDef.CollectionDefEntry.keytensorflow.MetaGraphDef.SignatureDefEntry.keytensorflow.SignatureDef.InputsEntry.keytensorflow.SignatureDef.OutputsEntry.keytensorflow.SignatureDef.method_nametensorflow.GPUOptions.allocator_typetensorflow.GPUOptions.visible_device_list/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/config.pb.cctensorflow/core/protobuf/config.prototensorflow.ConfigProto.DeviceCountEntry.keytensorflow.ConfigProto.device_filterstensorflow.DebugTensorWatch.node_nametensorflow.DebugTensorWatch.debug_opstensorflow.DebugTensorWatch.debug_urls/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/debug.pb.cc $tensorflow/core/protobuf/debug.proto tensorflow" DebugTensorWatch node_name (  output_slot ( debug_ops (  debug_urls ( + #tolerate_debug_op_creation_failures ("b DebugOptions= debug_tensor_watch_opts ( 2.tensorflow.DebugTensorWatch global_step (B, org.tensorflow.frameworkB DebugProtosPbproto3tensorflow/core/protobuf/debug.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/rewriter_config.pb.cc .tensorflow/core/protobuf/rewriter_config.proto tensorflow"O RewriterConfig optimize_tensor_layout ( disable_model_pruning (B5 org.tensorflow.frameworkBRewriterConfigProtosPbproto3tensorflow/core/protobuf/rewriter_config.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/protobuf/tensor_bundle.pb.cctensorflow/core/protobuf/tensor_bundle.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/lib/core/error_codes.pb.cctensorflow/core/lib/core/error_codes.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/versions.pb.cc (tensorflow/core/framework/versions.proto tensorflow"K VersionDef producer ( min_consumer ( bad_consumers (B/ org.tensorflow.frameworkBVersionsProtosPbproto3tensorflow/core/framework/versions.prototensorflow.VariableDef.variable_nametensorflow.VariableDef.initializer_nametensorflow.VariableDef.snapshot_nametensorflow.SaveSliceInfoDef.full_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/variable.pb.cc (tensorflow/core/framework/variable.proto tensorflow" VariableDef variable_name (  initializer_name (  snapshot_name ( 9 save_slice_info_def ( 2.tensorflow.SaveSliceInfoDef is_resource ("` SaveSliceInfoDef full_name (  full_shape ( var_offset ( var_shape (B/ org.tensorflow.frameworkBVariableProtosPbproto3tensorflow/core/framework/variable.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/types.pb.cctensorflow/core/framework/types.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/tensor_slice.pb.cctensorflow/core/framework/tensor_slice.prototensorflow.TensorShapeProto.Dim.name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/tensor_shape.pb.cc ,tensorflow/core/framework/tensor_shape.proto tensorflow"z TensorShapeProto- dim ( 2 .tensorflow.TensorShapeProto.Dim unknown_rank (! Dim size ( name ( B2 org.tensorflow.frameworkBTensorShapeProtosPbproto3tensorflow/core/framework/tensor_shape.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/tensor_description.pb.cc 2tensorflow/core/framework/tensor_description.proto tensorflow%tensorflow/core/framework/types.proto,tensorflow/core/framework/tensor_shape.proto6tensorflow/core/framework/allocation_description.proto" TensorDescription# dtype (2.tensorflow.DataType+ shape ( 2.tensorflow.TensorShapeProtoA allocation_description ( 2!.tensorflow.AllocationDescriptionB8 org.tensorflow.frameworkBTensorDescriptionProtosPbproto3tensorflow/core/framework/tensor_description.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/tensor.pb.cc &tensorflow/core/framework/tensor.proto tensorflow/tensorflow/core/framework/resource_handle.proto,tensorflow/core/framework/tensor_shape.proto%tensorflow/core/framework/types.proto" TensorProto# dtype (2.tensorflow.DataType2 tensor_shape ( 2.tensorflow.TensorShapeProto version_number ( tensor_content (  half_val (B float_val (B double_val (B int_val (B string_val (  scomplex_val (B int64_val (B bool_val (B dcomplex_val (B7 resource_handle_val ( 2.tensorflow.ResourceHandleB- org.tensorflow.frameworkB TensorProtosPbproto3tensorflow/core/framework/tensor.prototensorflow.SummaryDescription.type_hint/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/summary.pb.cctensorflow.Summary.Audio.content_typetensorflow.Summary.Value.tagtensorflow.Summary.Value.node_nametensorflow/core/framework/summary.prototensorflow.AllocatorMemoryUsed.allocator_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/step_stats.pb.cctensorflow.DeviceStepStats.devicetensorflow.NodeExecStats.node_nametensorflow.NodeExecStats.timeline_label *tensorflow/core/framework/step_stats.proto tensorflow6tensorflow/core/framework/allocation_description.proto2tensorflow/core/framework/tensor_description.proto"j AllocatorMemoryUsed allocator_name (  total_bytes ( peak_bytes ( live_bytes ("U NodeOutput slot (9 tensor_description ( 2.tensorflow.TensorDescription" MemoryStats host_temp_memory_size ( device_temp_memory_size (# host_persistent_memory_size (% device_persistent_memory_size (( host_persistent_tensor_alloc_ids (* "device_persistent_tensor_alloc_ids (" NodeExecStats node_name (  all_start_micros ( op_start_rel_micros ( op_end_rel_micros ( all_end_rel_micros (/ memory ( 2.tensorflow.AllocatorMemoryUsed& output ( 2.tensorflow.NodeOutput timeline_label (  scheduled_micros ( thread_id ( < referenced_tensor ( 2!.tensorflow.AllocationDescription- memory_stats ( 2.tensorflow.MemoryStats"P DeviceStepStats device ( - node_stats ( 2.tensorflow.NodeExecStats"; StepStats. dev_stats ( 2.tensorflow.DeviceStepStatsB0 org.tensorflow.frameworkBStepStatsProtosPbproto3tensorflow/core/framework/step_stats.prototensorflow.ResourceHandle.devicetensorflow.ResourceHandle.containertensorflow.ResourceHandle.nametensorflow.ResourceHandle.maybe_type_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/resource_handle.pb.cc /tensorflow/core/framework/resource_handle.proto tensorflow"m ResourceHandle device (  container (  name (  hash_code ( maybe_type_name ( B4 org.tensorflow.frameworkBResourceHandleProtoPbproto3tensorflow/core/framework/resource_handle.prototensorflow.RemoteFusedGraphExecuteInfo.graph_input_node_nametensorflow.RemoteFusedGraphExecuteInfo.graph_output_node_nametensorflow.RemoteFusedGraphExecuteInfo.executor_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/remote_fused_graph_execute_info.pb.cc ?tensorflow/core/framework/remote_fused_graph_execute_info.proto tensorflow(tensorflow/core/framework/node_def.proto,tensorflow/core/framework/tensor_shape.proto%tensorflow/core/framework/types.proto" RemoteFusedGraphExecuteInfo! node ( 2.tensorflow.NodeDef graph_input_node_name (  graph_output_node_name (  executor_name ( & serialized_executor_parameters ( f default_graph_input_tensor_shape ( 2<.tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProtog !default_graph_output_tensor_shape ( 2<.tensorflow.RemoteFusedGraphExecuteInfo.TensorShapeTypeProtoh TensorShapeTypeProto# dtype (2.tensorflow.DataType+ shape ( 2.tensorflow.TensorShapeProtoBA org.tensorflow.frameworkB RemoteFusedGraphExecuteInfoProtoPbproto3tensorflow/core/framework/remote_fused_graph_execute_info.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/reader_base.pb.cc +tensorflow/core/framework/reader_base.proto tensorflow"r ReaderBaseState work_started ( work_finished ( num_records_produced ( current_work ( B1 org.tensorflow.frameworkBReaderBaseProtosPbproto3tensorflow/core/framework/reader_base.prototensorflow.OpDef.ArgDef.nametensorflow.OpDef.ArgDef.descriptiontensorflow.OpDef.ArgDef.type_attrtensorflow.OpDef.ArgDef.number_attrtensorflow.OpDef.ArgDef.type_list_attrtensorflow.OpDef.nametensorflow.OpDef.summarytensorflow.OpDef.descriptiontensorflow.OpDef.AttrDef.nametensorflow.OpDef.AttrDef.typetensorflow.OpDef.AttrDef.descriptiontensorflow.OpDeprecation.explanation/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/op_def.pb.cc &tensorflow/core/framework/op_def.proto tensorflow*tensorflow/core/framework/attr_value.proto%tensorflow/core/framework/types.proto" OpDef name ( + input_arg ( 2.tensorflow.OpDef.ArgDef, output_arg ( 2.tensorflow.OpDef.ArgDef' attr ( 2.tensorflow.OpDef.AttrDef. deprecation ( 2.tensorflow.OpDeprecation summary (  description (  is_commutative ( is_aggregate ( is_stateful (" allows_uninitialized_input ( ArgDef name (  description ( " type (2.tensorflow.DataType type_attr (  number_attr (  type_list_attr (  is_ref ( AttrDef name (  type ( , default_value ( 2.tensorflow.AttrValue description (  has_minimum ( minimum (- allowed_values ( 2.tensorflow.AttrValue"5 OpDeprecation version ( explanation ( "' OpList op ( 2.tensorflow.OpDefB, org.tensorflow.frameworkB OpDefProtosPbproto3tensorflow/core/framework/op_def.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/node_def.pb.cc (tensorflow/core/framework/node_def.proto tensorflow*tensorflow/core/framework/attr_value.proto" NodeDef name (  op (  input (  device ( + attr ( 2.tensorflow.NodeDef.AttrEntryB AttrEntry key ( $ value ( 2.tensorflow.AttrValue:8B* org.tensorflow.frameworkB NodeProtoPbproto3tensorflow/core/framework/node_def.prototensorflow.NodeDef.nametensorflow.NodeDef.optensorflow.NodeDef.inputtensorflow.NodeDef.devicetensorflow.NodeDef.AttrEntry.keytensorflow.MemoryLogStep.handletensorflow.MemoryLogTensorDeallocation.allocator_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/log_memory.pb.cctensorflow.MemoryLogTensorOutput.kernel_nametensorflow.MemoryLogRawAllocation.operationtensorflow.MemoryLogRawAllocation.allocator_nametensorflow.MemoryLogRawDeallocation.operationtensorflow.MemoryLogRawDeallocation.allocator_nametensorflow.MemoryLogTensorAllocation.kernel_name *tensorflow/core/framework/log_memory.proto tensorflow2tensorflow/core/framework/tensor_description.proto"0 MemoryLogStep step_id ( handle ( "p MemoryLogTensorAllocation step_id ( kernel_name ( - tensor ( 2.tensorflow.TensorDescription"L MemoryLogTensorDeallocation allocation_id ( allocator_name ( "{ MemoryLogTensorOutput step_id ( kernel_name (  index (- tensor ( 2.tensorflow.TensorDescription" MemoryLogRawAllocation step_id ( operation (  num_bytes ( ptr ( allocation_id ( allocator_name ( " MemoryLogRawDeallocation step_id ( operation (  allocation_id ( allocator_name (  deferred (B0 org.tensorflow.frameworkBLogMemoryProtosPbproto3tensorflow/core/framework/log_memory.prototensorflow.KernelDef.optensorflow.KernelDef.device_typetensorflow.KernelDef.host_memory_argtensorflow.KernelDef.labeltensorflow.KernelDef.AttrConstraint.name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/kernel_def.pb.cc *tensorflow/core/framework/kernel_def.proto tensorflow*tensorflow/core/framework/attr_value.proto" KernelDef op (  device_type ( 8 constraint ( 2$.tensorflow.KernelDef.AttrConstraint host_memory_arg (  label ( M AttrConstraint name ( - allowed_values ( 2.tensorflow.AttrValueB0 org.tensorflow.frameworkBKernelDefProtosPbproto3tensorflow/core/framework/kernel_def.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/graph_transfer_info.pb.cctensorflow.GraphTransferInfo.NodeInfo.nametensorflow.GraphTransferInfo.NodeInfo.type_nametensorflow.GraphTransferInfo.GraphInputNodeInfo.nametensorflow.GraphTransferInfo.GraphOutputNodeInfo.nametensorflow.GraphTransferInfo.ConstNodeInfo.nametensorflow/core/framework/graph_transfer_info.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/graph.pb.cc %tensorflow/core/framework/graph.proto tensorflow(tensorflow/core/framework/node_def.proto(tensorflow/core/framework/function.proto(tensorflow/core/framework/versions.proto" GraphDef! node ( 2.tensorflow.NodeDef( versions ( 2.tensorflow.VersionDef version (B/ library ( 2.tensorflow.FunctionDefLibraryB, org.tensorflow.frameworkB GraphProtosPbproto3tensorflow/core/framework/graph.prototensorflow.GradientDef.function_nametensorflow.GradientDef.gradient_func/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/function.pb.cc (tensorflow/core/framework/function.proto tensorflow*tensorflow/core/framework/attr_value.proto(tensorflow/core/framework/node_def.proto&tensorflow/core/framework/op_def.proto"j FunctionDefLibrary) function ( 2.tensorflow.FunctionDef) gradient ( 2.tensorflow.GradientDef" FunctionDef$ signature ( 2.tensorflow.OpDef/ attr ( 2!.tensorflow.FunctionDef.AttrEntry% node_def ( 2.tensorflow.NodeDef- ret ( 2 .tensorflow.FunctionDef.RetEntryB AttrEntry key ( $ value ( 2.tensorflow.AttrValue:8* RetEntry key (  value ( :8"; GradientDef function_name (  gradient_func ( B/ org.tensorflow.frameworkBFunctionProtosPbproto3tensorflow/core/framework/function.prototensorflow.FunctionDef.RetEntry.keytensorflow.FunctionDef.RetEntry.valuetensorflow.FunctionDef.AttrEntry.keytensorflow.DeviceAttributes.nametensorflow.DeviceAttributes.device_typetensorflow.DeviceAttributes.physical_device_desc/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/device_attributes.pb.cc 1tensorflow/core/framework/device_attributes.proto tensorflow" DeviceLocality bus_id (" DeviceAttributes name (  device_type (  memory_limit (, locality ( 2.tensorflow.DeviceLocality incarnation ( physical_device_desc ( B7 org.tensorflow.frameworkBDeviceAttributesProtosPbproto3tensorflow/core/framework/device_attributes.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/cost_graph.pb.cctensorflow.CostGraphDef.Node.nametensorflow.CostGraphDef.Node.device *tensorflow/core/framework/cost_graph.proto tensorflow,tensorflow/core/framework/tensor_shape.proto%tensorflow/core/framework/types.proto" CostGraphDef+ node ( 2.tensorflow.CostGraphDef.Node Node name (  device (  id (; input_info ( 2'.tensorflow.CostGraphDef.Node.InputInfo= output_info ( 2(.tensorflow.CostGraphDef.Node.OutputInfo temporary_memory_size ( host_temp_memory_size ( device_temp_memory_size (# host_persistent_memory_size (% device_persistent_memory_size ( compute_cost ( compute_time ( memory_time ( is_final ( control_input (; InputInfo preceding_node ( preceding_port ( OutputInfo size ( alias_input_port (+ shape ( 2.tensorflow.TensorShapeProto# dtype (2.tensorflow.DataTypeB0 org.tensorflow.frameworkBCostGraphProtosPbproto3tensorflow/core/framework/cost_graph.prototensorflow.AttrValue.placeholder/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/attr_value.pb.cctensorflow/core/framework/attr_value.prototensorflow.NameAttrList.nametensorflow.NameAttrList.AttrEntry.keytensorflow.AllocationDescription.allocator_name/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/framework/allocation_description.pb.cc 6tensorflow/core/framework/allocation_description.proto tensorflow" AllocationDescription requested_bytes ( allocated_bytes ( allocator_name (  allocation_id ( has_single_reference ( ptr (B< org.tensorflow.frameworkBAllocationDescriptionProtosPbproto3tensorflow/core/framework/allocation_description.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/example/feature.pb.cctensorflow/core/example/feature.prototensorflow.Features.FeatureEntry.keytensorflow.FeatureLists.FeatureListEntry.key/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/example/example.pb.cc %tensorflow/core/example/example.proto tensorflow%tensorflow/core/example/feature.proto"1 Example& features ( 2.tensorflow.Features"i SequenceExample% context ( 2.tensorflow.Features/ feature_lists ( 2.tensorflow.FeatureListsB, org.tensorflow.exampleB ExampleProtosPbproto3tensorflow/core/example/example.proto/home/fursin/CK/local/env/7245d034f5e853a2/src/tensorflow/contrib/makefile/gen/proto/tensorflow/core/grappler/costs/op_performance_data.pb.cctensorflow.OpPerformance.node 8tensorflow/core/grappler/costs/op_performance_data.proto tensorflow,tensorflow/core/framework/tensor_shape.proto%tensorflow/core/framework/types.proto*tensorflow/core/framework/attr_value.proto" OpInfo op ( * attr ( 2.tensorflow.OpInfo.AttrEntry3 inputs ( 2#.tensorflow.OpInfo.TensorProperties3 device ( 2#.tensorflow.OpInfo.DevicePropertiesB AttrEntry key ( $ value ( 2.tensorflow.AttrValue:8d TensorProperties# dtype (2.tensorflow.DataType+ shape ( 2.tensorflow.TensorShapeProto DeviceProperties type (  vendor (  model (  frequency ( num_cores (I environment ( 24.tensorflow.OpInfo.DeviceProperties.EnvironmentEntry num_registers ( l1_cache_size ( l2_cache_size ( l3_cache_size (- %shared_memory_size_per_multiprocessor ( memory_size ( bandwidth (2 EnvironmentEntry key (  value ( :8" OpPerformance op ( 2.tensorflow.OpInfo node (  temporary_memory_size ( compute_cost ( compute_time ( memory_time ( compute_efficiency ( memory_efficiency (5 op_memory ( 2".tensorflow.OpPerformance.OpMemory OpMemory output_memory ( host_temp_memory ( device_temp_memory ( host_persistent_memory ( device_persistent_memory ("F OpPerformanceList1 op_performance ( 2.tensorflow.OpPerformanceBbproto3tensorflow/core/grappler/costs/op_performance_data.prototensorflow.OpInfo.DeviceProperties.typetensorflow.OpInfo.DeviceProperties.vendortensorflow.OpInfo.DeviceProperties.modeltensorflow.OpInfo.DeviceProperties.EnvironmentEntry.keytensorflow.OpInfo.DeviceProperties.EnvironmentEntry.valuetensorflow.OpInfo.optensorflow.OpInfo.AttrEntry.keytensorflow/core/common_runtime/allocator_retry.ccRequest to allocate 0 bytesBinForSize(bin_size) == BinFromIndex(b)tensorflow/core/common_runtime/bfc_allocator.ccBinForSize(bin_size + 255) == BinFromIndex(b)BinForSize(bin_size * 2 - 1) == BinFromIndex(b)BinForSize(bin_size * 2) != BinFromIndex(b)./tensorflow/core/framework/allocator.hCheck failed: false allocator doesn't track sizesCheck failed: !c->in_use() && (c->bin_num == kInvalidBinNum) Check failed: !c->in_use() && (c->bin_num != kInvalidBinNum) Check failed: count > 0 Could not find chunk in binCheck failed: h != kInvalidChunkHandle Asked for requested size of pointer we never allocated: Asked for allocated size of pointer we never allocated: Asked for allocation id of pointer we never allocated: start_location < resolutionend_location < resolution./tensorflow/core/common_runtime/bfc_allocator.hCould not find Region for Check failed: c2->prev == h1 Check failed: !c1->in_use() && !c2->in_use() Check failed: c->in_use() && (c->bin_num == kInvalidBinNum) tried to deallocate nullptrBin (): Total Chunks: , Chunks in use: allocated for chunks. client-requested for chunks. in use in bin. client-requested in use in bin.Bin for was , Chunk State: Size: | Requested Size: | in_use: , prev: , next: Summary of in-use Chunks by size: Chunk at of size Free at Chunks of size totalling Sum Total of in-use chunks: Stats: tried to allocate 0 bytesAllocator () ran out of memory trying to allocate . Current allocation summary follows.. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.Feed endpoints: Fetch endpoints: , Target nodes: __cf__ConstdtypevalueBeforenode_map.count(in) > size_t{0}tensorflow/core/common_runtime/constant_folding.cc <-Constant graph:Afteralready_added.count(node_map[in]) > size_t{0}tensorflow/core/common_runtime/copy_tensor.ccCheck failed: !non_cpu_src && !non_cpu_dst The cost model graph doesn't exist.COST_MODELDevice does not implement MakeTensorFromProto()tensorflow/core/common_runtime/device.ccCheck failed: DeviceNameUtils::ParseFullName(name(), &parsed_name_) Invalid device name: ./tensorflow/core/framework/device_base.hGetAllocator() is not implemented.CPUCPU Factory not registered. Did you link in threadpool_device?No CPU devices are available in this processtensorflow/core/common_runtime/device_factory.ccDuplicate registration of device factory for type with the same priority device_factory->CreateDevices(opt, name_prefix, &devices)devices.size() == size_t{1}vector::reserve -> unknown device.tensorflow/core/common_runtime/device_mgr.cc/_Reset()Close(const RunOptions& run_options) is not supported for this session.Create(const RunOptions& run_options, const GraphDef& graph) is not supported for this session.Extend(const RunOptions& run_options, const GraphDef& graph) is not supported for this session.Compute;Timed out waiting for notificationPRun cancellation__per_step_' is not a pre-defined fetch.The tensor returned for was not valid.Feed : not foundFetch can't be computed from the feeds that have been fed so far.DIRECT_SESSIONdirectDevice mapping: no known devices.Device mapping: %stensorflow/core/common_runtime/direct_session.ccDevice mapping: /job:localhost/replica:0/task:0A Graph has already been created for this session.Session has been closed../tensorflow/core/common_runtime/executor.hCheck failed: done_cb_ != nullptr ResourceHandleToInputTensor() received non-DT_RESOURCE Tensor: Invalid resource type hash code: (name: type: )' is not a pre-defined feed.Must run 'setup' before performing partial runs!The feed had already been fed.The fetch had already been fetched.An error unrelated to this prun has been detected. ,Creating a partition for which doesn't exist in the list of available devices. Available devices: Stateful placement mismatch. Current assignment of to does not match ->/Run call was cancelledInvalid inter_op_thread_pool: Session was not created with a graph before Run()!The handle '' created for this partial run is not unique.Session was not created with a graph before PRunSetup()!Output of type does not match declared output type for node Missing -th output from vector::_M_fill_insert[ %.1fMB %.1fMB] %.1fMB] tensor_nameGetNodeAttr(def, "tensor_name", &tensor_name)tensorflow/core/common_runtime/executor.ccrecv_deviceGetNodeAttr(def, "recv_device", &recv_device) = ( @send_deviceGetNodeAttr(def, "send_device", &send_device)Check failed: done_cb != nullptr uintptr_t(bytes_) % alignof(LargeCounts) == 0./tensorflow/core/common_runtime/pending_counts.his_constantframe_nameparallel_iterations-th input expects a ref type'op_kernel' Must be non NULLAttempting to use uninitialized value Check failed: node_offsets_ == nullptr Check failed: node_offsets_[id] == kuint32max reinterpret_cast(ptr) % kItemAlignment == 0e->src_output() <= ((int32)0x3FFFFFFF)ptr == space_ + total_bytesExecutor failed to create kernel. Check failed: item->kernel Bad send_device attr '' in node Bad recv_device attr 'Check failed: p.create_kernel != nullptr Check failed: p.delete_kernel != nullptr nAttr is not defined.No function library is provided.h < func_graphs_.size()tensorflow/core/common_runtime/function.ccrets->size() == ctx->num_outputs()_Retval_ArgindexGetNodeAttr(n->def(), "index", &index)0 <= indexindex < node_vec->size()Check failed: n->IsOp() fbody_->arg_types.size() == fbody_->arg_nodes.size()fbody_->ret_types.size() == fbody_->ret_nodes.size()'fbody' Must be non NULLFunction handle is not valid. Likely an internal error.FuncNoOpsIdentityT_ListToArray_ArrayToListRemoveListArrayConverter unexpected duplicated input: RemoveListArrayConverter unexpected missing input: Malformed graph node. multiple input edges: ^_noinlineExpandInlineFunctions Check failed: data.node != nullptr Inlining mismatch: vs. Check failed: gbody_ == nullptr AddSymbolicGradients(y_node_outputs, x_node_outputs, y_grad_node_outputs, &x_grad_node_outputs, g)No gradient is defined for 'f_body' Must be non NULLSymbolicGradientfSymbolicGradient is missing attr: fCan't take gradient of SymbolicGradientFunction InitialRemoveListArrayConverterConstFoldingOptimizeCSEReCopyRemoveDeadNodesRemoveIdentityNodesFixupSourceAndSinkEdgesExpandInlineFunctionsDid not find key Send of a dead tensorSend of an already sent tensorCannot find a device for GraphRunner./cpu:0/cpu:1EigenUnexpected memory type pair on an edge: Trying to get the memory type for 'th output of node that has only outputsMemory type mismatch () between : and : from _HostRecv_Recvedge__tensor_typesend_device_incarnationNodeBuilder(g->NewName("n"), host ? "_HostRecv" : "_Recv") .Attr("tensor_type", edge->src()->output_type(edge->src_output())) .Attr("tensor_name", tensor_name) .Attr("send_device", device_name) .Attr("send_device_incarnation", 0) .Attr("recv_device", device_name) .Finalize(g, &ret)tensorflow/core/common_runtime/memory_types.cc_HostSend_SendNodeBuilder(g->NewName("n"), host ? "_HostSend" : "_Send") .Input(edge->src(), edge->src_output()) .Attr("tensor_name", tensor_name) .Attr("send_device", device_name) .Attr("send_device_incarnation", 0) .Attr("recv_device", device_name) .Finalize(g, &ret)Parallel concat removal should happen before partitioning and a graph should be available.ParallelConcat_ParallelConcatStart/Internal_classshape_ParallelConcatUpdatelocNon-DMA-safe tensor may not be copied from/to a GPU.tensorflow/core/common_runtime/rendezvous_mgr.ccCheck failed: !s.ok() Read to unsafe read conversion should happen before partitioning and a graph should be available.ReadVariableOp_SINK_UnsafeReadVariableRun with options is not supported for this session.Partial run is not supported for this session.tensorflow/core/common_runtime/session.ccFailed to create session.target: "" config: Registered factories are {}.Multiple session factories registered for the given session options: {} Candidate factories are {}. No session factory registered for the given session options: {} tensorflow/core/common_runtime/session_factory.ccTwo session factories are being registered underThe tensor with handle '' is not in the session store.Failed to delete a tensor with handle '' in the session store.Failed to add a tensor with handle '' to the session store.Failed to add a tensor with name '' to the tensor store.TensorHandleCould not find context for output_port '' is out of range, node '' has Could not find mapping from old to new copy of destination node: Missing src contextShapePackPack input must be int32 or int64ConcatConcatV2No shape inference function exists for op '', did you forget to define it?Input ('') for '' was not previously added to ShapeRefiner.Attempted to prune a graph that has not been fully initialized.vector::_M_range_inserttf_graphMissing node shape or typeCHECK failed: it != end(): GraphDef argument to Extend includes node '%s', which was created by a previous call to Create or Extend in this session.Can't extend GraphDef at version with graph at version Colocation Debug Info: Colocation group had the following types and devices: : Cannot colocate nodes '' and '' because no device type supports both of those nodes and the other nodes colocated with them.Could not satisfy explicit device specification '' because no devices matching that specification are registered in this process; available devices: ' because no supported kernel for devices is available.' because the node was colocated with a group of nodes that required incompatible device 'No devices are registeredNode had no OpKernel registered to support this operation: Operation was and inputs were tensorflow/core/common_runtime/simple_placer.cc%s: (%s): %s Node id was not positive: Malformed assigned device 'Assigned device '' does not match any device' does not have registered OpKernel support for No OpKernel was registered to support Op '' with these attrs. Registered devices: [], Registered kernels: Malformed device specification 'member.parent >= 0 for node '' because the input edge from '' is a reference connection and already has a device field set to Nodes were connected by a reference connection (requiring them to be on the same device), but the two nodes were assigned two different devices: Cannot assign a device to node '': SizeRankIgnoring device specification tensorflow/core/common_runtime/step_stats_collector.ccCheck failed: step_stats_ lla:maerts/:upgCheck failed: strings::safe_strto32(ordered_capture, &gpu_id) Cannot parse tensor from proto: /cpu:cpuLimit: %20lld InUse: %20lld MaxInUse: %20lld NumAllocs: %20lld MaxAllocSize: %20lld DefaultCPUAllocatortensorflow/core/framework/allocator_registry.ccCheck failed: !name.empty() Need a valid name for Allocatorpriority >= 0Priority needs to be non-negativeCheck failed: !CheckForDuplicates(name, priority) Allocator with name: [] and priority: [] already registered'm_curr_allocator_' Must be non NULL"list(stringintfloatbooltensorfuncplaceholderlist { }ibtypelist(string)list(int)list(float)list(bool)list(type)list(shape)list(tensor)list(func)AttrValue had value with unexpected type 'placeholder'AttrValue missing value with expected type 'AttrValue has invalid DataType enum: AttrValue must not have reference type value of AttrValue has invalid DataTypeAttrValue contains invalid DataTypeAttrValue had value with type 'list(string)' when '' expectedAttrValue had value with type 'string' when 'AttrValue had value with type 'list(int)' when 'AttrValue had value with type 'int' when 'AttrValue had value with type 'list(float)' when 'AttrValue had value with type 'float' when 'AttrValue had value with type 'list(bool)' when 'AttrValue had value with type 'bool' when 'AttrValue had value with type 'list(type)' when 'AttrValue had value with type 'type' when 'AttrValue had value with type 'list(shape)' when 'AttrValue had value with type 'shape' when 'AttrValue had value with type 'list(tensor)' when 'AttrValue had value with type 'tensor' when 'AttrValue had value with type 'list(func)' when 'AttrValue had value with type 'func' when '=]truefalse$token < next_cancellation_token_tensorflow/core/framework/cancellation.ccInvalid cancellation tokenStride must be > 0, but got computed output size would be negativetranspose_atranspose_bDepthwiseConv2D requires the stride attribute to contain 4 values, but got: data_formatNCHWstridespaddingCan't concatenate scalars (use tf.stack instead)Number of elements in index () and values () do not match.Index rank () and shape rank (./tensorflow/core/util/tensor_format.hInvalid dimension: Invalid format: Conv2D requires the stride attribute to contain 4 values, but got: AvgPool requires the stride attribute to contain 4 values, but got: ksizeAvgPool requires the ksize attribute to contain 4 values, but got: MaxPool requires the stride attribute to contain 4 values, but got: MaxPool requires the ksize attribute to contain 4 values, but got: Conv3D requires the stride attribute to contain 5 values, but got: NCDHWPool3D ops require the stride attribute to contain 5 values, but got: Pool3D requires the ksize attribute to contain 5 values, but got: ./tensorflow/core/framework/tensor.hInvalid reduction dimension for input with dimensions.keep_dimsDevice does not implement attributes()Could not infer type for input 'No type or type_attr field in arg 'Could not infer length of input 'Could not infer list of types for input 'Ref(* GetArg is not within [0, Expects arguments, but is providedExpects arg[] to be but SetRetval is provided.Expects ret[, but Retval[] has already been set.] does not have valueGradient for function '' already exists.type attr not found: Duplicated arg name: Duplicated ret name: No gradient defined for op: get_function(fnode.op(), &fnode_sig)tensorflow/core/framework/function.ccAttempt to access beyond input size: >= too long for input ] expected type != , the type of input[] == '', is not found. is not found: Expected input[' to be a control input.GetNodeAttr(*x, "index", &xi)GetNodeAttr(*y, "index", &yi)Check failed: GetOpGradFactory()->insert({op, func}).second Duplicated gradient for is not found from for attr 'dtypes.size() >= size_t{1}In Failed to bind all placeholders in Somehow duplicated: Return missing. is not found.Invalid ret types : In function output }{ @ ) -> () { return } (2 == n->attr_size()1 == n->input_size()b.Finalize(&op_reg_data)Check failed: iter != ret_index.end() Node input '' in '' of op_def_registry->LookUpOpDef(n->op(), &op_def)Check failed: op_def != nullptr NameRangesForNode(*n, *op_def, nullptr, &output_names)o.second.second <= src.ret.size()Missing ret for output 'Return '' in Function with name: already exists in function library.AddFunctionDef(it.second->fdef)versions = ; Tried to add default attrs to GraphDef starting at offset with total nodes in graph: Attr '' missing in producer's OpDef: but found in node: kernel_def_->label() == ""tensorflow/core/framework/kernel_def_builder.ccTrying to set a kernel's label a second time: '' in: __LOG_MEMORY__.%s: __pos (which is %zu) > this->size() (which is %zu)basic_string::substrtensorflow/core/framework/log_memory.cc { A lookup table of size: Input key shape must end with the table's key shape Key must be type but got Value must be type Expected shape for value, got for default value, got ', 'HostMemory args '' not found in OpDef: _input_hostmem_output_hostmemMore Input() calls than the input_argsEmpty input node nameNon-control input starting with ^: Input '' passed expected expected ref typeInconsistent values for attr '' Single tensor passed to '', expected listList provided to input '' when single Tensor expected while building NodeDef '' using errors while building NodeDef ': inputs specified of inputs in OpIllegal op input name 'Illegal op name 'vector::_M_insert_aux_device="]( [[Node: ]]No attr named '' in NodeDef: has value out of range for an int32 that can't be converted to a TensorValue for number_attr() < 0Missing type or type_attr field in No type fields in Argument '' incorrectly specified in op definition: All control inputs must follow all data inputs 's 'Control input '' must not have ':' in NodeDef: Non-control input '' after control input in NodeDef: OpDef has duplicate attr name 'NodeDef mentions attr '' not in ; NodeDef: . (Check whether your GraphDef-interpreting binary is up to date with your GraphDef-generating binary.).; NodeDef missing attr' from NodeDef expected inputs '' do not match inputs specified; NodeDef op '' does not match loc:@Cannot over-write a valid watcher with another.Op type not registered 'Op with name RegisterAlreadyLocked(op_data_factory)tensorflow/core/framework/op.ccRegisterAlreadyLocked(deferred_[i])ValidateKernelRegistrations(*this) from Attr("") for Op OutputInput from ("Extra call to Doc() for Op Deprecated called twice for Op SetShapeFn called twice for Op Trouble parsing ':'listnumbertypenumerictypequantizedtyperealnumbertyperealnumerictypeTrouble unescaping "", got error: Trouble parsing type string at 'Unrecognized type string '>=Could not parse default value 'Extra '' unparsed at the endTrouble parsing allowed string at 'Could not parse integer lower limit after '>=', found '' insteadExpected , or } after strings in list, not: 'Expected , or } after types in list, not: 'Expected ) to close 'list(', not: 'Trouble parsing 'name:'RefTrouble parsing either a type or an attr name at 'Reference to unknown attr 'Reference to attr '' with type that isn't type or list(type)Did not find closing ')' for 'Ref(', instead found: 'No matching input/output/attr for name '' from Doc() for Op no restrictionno minimumOp= 0Can't have both number_attr and type_list_attrExactly one of type, type_attr must be setExactly one of type, type_attr, type_list_attr must be set' used as type_attr != type' used as type_list_attr != list(type)Illegal use of ref type ''. Use 'Ref(type)' insteadMissing typeInvalid name: (Did you use CamelCase?)Attr can't have name that matches a data type' at the end of attr 's type ' with list type must have a non-negative minimum, not ' with has_minimum = false but minimum not equal to default of 0' in Op ' in Op 'Unrecognized type '' in attr '' has minimum for unsupported type 'list(' is missing ')' in attr * Incompatible Op change: ' removed; old: ; new: ' has a stricter set of allowed values; from ' has a higher minimum; from Unexpected change in input ref lists.Unexpected change in output ref listsName mismatch' changed type '' -> 'Input signature mismatch '' vs. ' changed from non-ref to refOutput signature mismatch ' changed from ref to non-ref' added without defaultMissing attr '' in op: Can't change default value for attr ' in op: Missing default for attr 'Op is not available in GraphDef version . It has been removed in version . tensorflow/core/framework/op_def_util.cc is deprecated. It will cease to work in GraphDef version Signature mismatch, have: expected: Inputs to operation must have the same size and shape. Input 0: != input ; label=' in device='OOM when allocating temporary tensor with shapeOOM when allocating tensor with shapeUnknown input name: OpKernel used list-valued input name '' when single-valued input was expectedOpKernel used ref input name '' when non-ref input was expectedOpKernel used non-ref input name '' when ref input was expectedOpKernel used immutable input name 'Unknown output name: OpKernel used list-valued output name '' when single-valued output was expected_kernelKernelDef ' has constraint on attr '' with unsupported type: ' has constraint on attr '' that has value '' that does not have type 'type' or 'list(type)' in NodeDef 'OpKernel '' not in NodeDef '', KernelDef: 'Multiple OpKernel registrations match NodeDef '': 'No registered '' OpKernel for devices compatible with node (OpKernel was found, but attributes didn't match). Registered: when instantiating for node: tensorflow/core/framework/op_kernel.ccCheck failed: context Check failed: output_tensor->CopyFrom(*input.tensor, output_shape) OpKernel could not forward input '' to output 'Check failed: eigen_cpu_device_ != nullptr _no_registerOpKernel ('')') for unknown op: HostMemory arg 'Session tensorflow/core/framework/op_segment.ccCreate kernel failed: ?Shape has dimensions with values below -1 (where -1 means unknown)An unknown shape must not have any dimensions set.Dimension must be >= -1d.size() >= -1tensorflow/core/framework/partial_tensor_shape.ccdim >= -1size >= -1Check failed: result != this PartialTensorShape: Incompatible ranks during merge: PartialTensorShape: Incompatible shapes during merge: a readerReader SerializeStateReader RestoreStateReadManyLocked() for must set *at_end=true, *num_produced > 0 or return an error.ReadLocked() for must set *at_end=true, *produced=true, or return an error. set *produced=true *and* returned an error: Unexpected negative value when restoring in Inconsistent work started vs. finished when restoring in Expected single component queueExpected queue with single string componentExpected to dequeue a one-element string tensorInvalid rendezvous key: tensorflow/core/framework/rendezvous.ccCheck failed: !status.ok() Duplicated recv: Check failed: table_.insert({key_hash, item}).second Duplicated send: Trying to access resource located in device from device localhostprivatepublicDuplicate hash code found for type Container does not exist.Resource %-20s | %-40s | %-40s | %-stensorflow/core/framework/resource_mgr.ccCheck failed: base != nullptr Check failed: b != nullptr Check failed: rmgr containercontainer contains invalid characters: shared_nameshared_name cannot start with '_':InferenceContext for node: Dimensions must be equal, but are for '' (op: '') with input shapes: ] = and with computed input tensors: and with input tensors computed as partial shapes: ] = <Dimension must be but is Negative dimension size caused by subtracting Divisor must be positive but is Dimension size must be evenly divisible by Input must be scalar but has rank Scalar input for dim size must be int32 or int64Dimension size, given by scalar input , must be non-negative but is Dimension size overflow from adding Negative dimension size caused by overflow when multiplying Shape must be rank but is rank Shape must be at least rank Shape must be at most rank Subshape start out of bounds: , for shape with rank Subshape end out of bounds: Subshape must have computed start <= end, but is (computed from start and end over shape with rank Out of range dim_index for shape with dimensionsMust have exactly shapes.Wrong number of handle shapes passed; expected got Wrong number of handle dtypes passed; expected Wrong number of inputs passed: while expected based on NodeDefinput_tensors_.size() <= inputs_.size()tensorflow/core/framework/shape_inference.cc in both shapes must be equal, but are Merge(Dim(s0, i), Dim(s1, i), &dims[i])Shapes must be equal rank, but are Input tensor must be rank 1, but was rank Invalid value in tensor used for shape: Input tensor must be int32 or int64, but was 'node_def' Must be non NULLdtype() == expected_dtypetensorflow/core/framework/tensor.ccCheck failed: IsAligned() 1 == NumElements()Must have a one element tensorshape.num_elements() == other.NumElements()in_size != 0out_size != 0shape.num_elements() * out_size == other.shape().num_elements() * in_sizeroot_->base() <= this->base()this->base() <= root_limitthis->base() + n <= root_limitn > 0CHECK failed: (&other) != (this): Type not setUnexpected type: Check failed: buf_ null buf_ with non-zero shape size uninitialized Tensor of elements of type ...Tensor= 10 <= startstart <= limitlimit <= dim0_sizeInput size was and expected Unknown (from Proto) has too many dimensions has negative dimensions is too large (more than entries)NDIMS == dims()tensorflow/core/framework/tensor_shape.ccAsking for tensor of from a tensor of NDIMS >= dims()Asking for tensor of at least dimensions from a tensor of 0 <= nn <= kMaxElementssize >= 0nd < MaxDimensions()Too many dimensions in tensor0 <= num_elements_num_elements_ <= kMaxElementsToo many dimensions must be >= 0Negative number of dimensions s < capacity()./tensorflow/core/lib/gtl/inlined_vector.hd >= 0d <= dims()dims() < MaxDimensions()d < dims()-Mismatching ranks: shape = , slice = Extent in dimension out of bounds: shape = Expected a pair of numbers or '-' but got '': string = Expected non-negative start and positive length but got start = , length = : string = DT_STRING == other.dtype()tensorflow/core/framework/tensor_util.ccCannot concatenate zero tensorsCannot concatenate a zero-dimensional tensorCannot concatenate tensors that have different data typesoffset + from_data.size() <= to_data.size()Unexpected data typeoffset + tensor.NumElements() <= result->NumElements()Cannot split a zero-dimensional tensorThe values in 'sizes' do not sum to the zeroth-dimension size of 'tensor'offset + to_data.size() <= from_data.size()offset + split.NumElements() <= tensor.NumElements()ref_ >= 1tensorflow/core/framework/tracking_allocator.ccallocated_ >= allocated_bytes_reffloat32doublefloat64int32uint8int8complex64complex128int64qint8quint8qint16quint16bfloat16halffloat16resourceuint16int16qint32INVALIDtensorflow/core/framework/types.ccUnrecognized DataType enum value unknown dtype enum (SYCLGPU version check has consumer < min_producer producer version below min producer supported by TensorFlow 1.1.0-rc0. Please regenerate your min consumer version above current version for TensorFlow . Please upgrade TensorFlow. disallows consumer version . Please upgrade TensorFlow: this version is likely buggy.#F15854#5DA5DA#FAA43A#60BD68#F17CB0#B2912F#B276B2#DECF3F#4D4D4DThe Enter node must have a frame name.The node '' has inputs from different frames. The input '' is in frame ''. The input ''.map::atvector::_M_fill_inserttensorflow/core/graph/costmodel.ccCheck failed: is_global_ Check failed: !cm.is_global() num_slots == slot_bytes_[global_id].size()true == cm.is_global()num_slots == slot_bytes_[i].size()id < slot_bytes_.size()slot < perslot->size()Check failed: static_cast(n->id()) < slot_bytes_.size() : no size estimate for perslot[i] >= Bytes(0): no size estimate for output# of Check failed: static_cast(n->id()) < time_.size() && time_[n->id()] >= Microseconds(0) : no time estimate for num_outputs == perslot->size()Cannot resize slot_bytes, node= min_count_=Node count total time avg time Actual node name '' is not expected 'Node named '' has op '' that is not expected '' has device '' has inputs '' that don't match expected '' has input ' that doesn't match expected '' missing expected control input '' has unexpected control input '' missing expected attr '' with value: ' has attr ' that does not match expected: ' has unexpected attr 'Did not find expected node 'Found unexpected node '' not in expected graph: y_node_outputs_.size() == y_grad_node_outputs.size()tensorflow/core/graph/gradients.cc'src.node' Must be non NULLCheck failed: !ready_.empty() Check failed: iter != backprops_.end() ZerosLikeAddNN1 == num_ynum_y == grads.size()ndef.input_size() == num_x + num_yTinTout{name:'' id: source} sink} op device: def:{}}Invalid input_edge index: , Node only has inputs.Could not find input edge for Duplicate edge input number: Missing edge input number: Invalid edge input number SwitchRefSwitchtensorflow/core/graph/graph.ccCheck failed: class_ == NC_UNINITIALIZED MergeRefMergeEnterRefEnterExitRefExitNextIterationRefNextIterationLoopCondControlTriggerHostConstVariableVariableV2RefIdentityGetSessionHandleGetSessionHandleV2GetSessionTensorDeleteSessionTensorCheck failed: source->out_edges_.insert(e).second Check failed: dest->in_edges_.insert(e).second e->src_->out_edges_.erase(e) == size_t{1}e->dst_->in_edges_.erase(e) == size_t{1}e == edges_[e->id_]edge_set_.erase(e) == size_t{1}Check failed: inputs[edge->dst_input()] == nullptr Edge with dst_input and had pre-existing input edge _SOURCEstatussource->id() == kSourceIdsink->id() == kSinkIdCheck failed: s.ok() of node was passed incompatible with expected _output_shapesNode ' outputs but the attribute specifies shapes for has an invalid attribute (shape # error:'RandomShuffleQueuePaddingFIFOQueueFIFOQueuePriorityQueueQueueSizeStackBarrierBarrierReadySizeBarrierIncompleteSizeHashTableMutableHashTableMutableHashTableOfTensorsMutexCuckooTableIndexTableWholeFileReaderTextLineReaderFixedLengthRecordReaderTFRecordReaderIdentityReaderConditionalAccumulatorSparseConditionalAccumulatorTable' has an attribute inconsistent with the GraphDef for output #': Connecting to invalid output of source node which has nodes in a cycle' expects to be colocated with unknown node '' had a back edge, but only Merge nodes can have back edges.cannot resolve input_map because multiple nodes exist with name 'cannot resolve control_dependencies because multiple nodes exist with name '' already exists in the GraphImported node name prefix '' would lead to invalid node names' in input_map does not exist in graph (input_map entry: input_map entry between control edge and non-control edge' in control_dependencies does not exist in graph': Node name contains invalid characters' is not unique' does not specify an operation': Control dependencies must come after regular dependencies': Unknown input node 'Requested return node '' not found in graph defInvalid return output of node '', which has GraphDefImport node name prefix conflicts with names of nodes already in the Graph, such as '' is missing a device specificationreturn_tensors argument to ImportNodeDef() must be non-null if opts.return_tensors is non-emptyreturn_tensors argument to ImportNodeDef() should be empty (has size Importing GraphDefs containing functions not yet implementedtensorflow/core/graph/graph_constructor.ccCheck failed: n->IsSource() || n->IsSink() *dest must be emptytensorflow/core/graph/graph_def_builder.cc_cloopclient_terminated_start_timesynch__HostCastCastDstT$$A cross-device loop must have a pivot predicate: dummyIncomplete graph, missing inputs for Attempt to add nullptr Node to node with type Attempt to add output not in range [0, ) to node with type Relu6SigmoidTanhReshapeMaxPoolAvgPoolMaxPool3DAvgPool3DReluMatMulConv2D/Variable/IsInitializedIsVariableInitialized/Switch/EMA/OneConst/DecayComplementSub/ValueDiff/UpdateValue/EMAValue/Merge/AssignValueAssign/ReductionAxes/RangeStart/RangeDelta/InputRankRange/InputMin/InputMax/Decay/MinMin/MaxMax/QuantizeAndDequantizeV2QuantizeAndDequantizeV2signed_inputnum_bitsrange_givenCannot accept empty graph pointer.num_bits should be in range [1, 63] but is: gradientsFound an unknown op: with type: ; Unknown ops are considered as model input for now and only inputs are supported currently.Invalid input graphInvalid output graphFetchOutputs node Tried to fetch data for '', which produces no output. To run to a node but not fetch any data, pass '' as an argument to the 'target_node_names' argument of the Session::Run API.FetchOutputs : output index too large, must be < _send_FeedInputs: unable to find feed output FeedInputs: should have output index < _recv_: size of _output_shapes attribute does not match the number of node outputsPlaceholderPlaceholderV2Graph::kControlSlot == e->src_output()tensorflow/core/graph/subgraph.ccEndpoint "" fed more than once. is both fed and fetched.PruneForTargets: Some target nodes not found: Must specify at least one target to fetch or execute.tensorflow/core/grappler/clusters/cluster.ccCheck failed: !already_created Check failed: already_created ctx->num_inputs() == node->num_inputs()tensorflow/core/grappler/costs/graph_properties.ccctx->num_outputs() == node->num_outputs()not setcpu_instruction_setARM NEONeigentensorflow/core/grappler/costs/utils.ccCheck failed: !input_name.empty() UNKNOWNtensorflow/core/grappler/devices.ccNumber of eligible GPUs (core count >= 8): tensorflow/core/grappler/grappler_item.ccCheck failed: node Check failed: in tensorflow/core/grappler/grappler_item_builder.ccid must be non-empty.train_opInvalid fetch node name , skipping this inputWill use fetch node Failed to detect the fetch node(s), skipping this inputUnknown type for placeholder Unknown shape for placeholder table_initializerqueue_runnersCould parse queue_runners, skipping this inputQueue without a cancel op, skipping this inputasset_filepathsCan't access one or more of the asset files, skipping this inputvariableslocal_variablesmodel_variablestrainable_variablestensorflow/core/grappler/optimizers/graph_rewriter.ccCheck failed: itr != nodes_.end() layoutMissing attribute squeeze_dimsNHWCAvgPoolGradConv2DBackpropFilterConv2DBackpropInputBiasAddBiasAddGradFusedBatchNormFusedBatchNormGradMaxPoolGradAddFloorNegRealDivReluGradSliceSquaredDifferenceSqueezeSumtensorflow/core/grappler/optimizers/layout_optimizer.ccFailed to parse TensorProto.ConcatOffsetFloorModExpect either Const or FloorMod for the input 1 of ConcatOffsetCheck failed: outputs.size() == 1 There is always only a single output for a Transpose node, due to the way it is added by NodeProcessor.TransposeTpermTshape-inputIndexGatherTindicesTparamsvalidate_indicesNumber of nodes for original graph: Number of nodes after Collapse: Number of nodes after Expand: meta_optimizermodel_prunerStopGradienttensorflow/core/grappler/optimizers/model_pruner.ccPruned nodes from the graph. The graph now contains nodes.tensorflow/core/grappler/utils.ccCheck failed: strings::safe_strto32(remaining.substr(1), position) tensorflow/core/lib/core/arena.ccCheck failed: SatisfyAlignment(kDefaultAlignment) adjusted_alignment <= static_cast(1 << 20)Alignment on boundaries greater than 1MB not supported.Check failed: NULL != block->mem block_size= adjusted_block_size= alignment= adjusted_alignment=Check failed: SatisfyAlignment(alignment) Check failed: alignment > 0 && 0 == (alignment & (alignment - 1)) size <= remaining_CancelledInvalid argumentDeadline exceededNot foundAlready existsPermission deniedUnauthenticatedResource exhaustedFailed preconditionAbortedOut of rangeUnimplementedInternalUnavailableData lossOKUnknown code(%d)Non-OK-status: status: num_threads >= 1tensorflow/core/lib/core/threadpool.cctf_Check failed: fn != nullptr total >= 0total == (int64)(Eigen::Index)totalCount: %.0f Average: %.4f StdDev: %.2f Min: %.4f Median: %.4f Max: %.4f ------------------------------------------------------ [ %10.2g, %10.2g ) %7.0f %7.3f%% %7.3f%% bad block contentsbad entry in blockCan't read a negative number of bytes: basic_string::insertCan only skip forward, not Seeking to a negative position: bad block handlenot an sstable (bad magic number)truncated block readblock checksum mismatchcorrupted compressed block contentsbad block typeStored data is too large to be a varint32.Can't skip a negative number of bytes://Cannot read negative number of bytesreached end of filefile is too short to be an sstabletensorflow/core/lib/monitoring/collection_registry.ccCheck failed: collection_function Requires collection_function to contain an implementation.Cannot register 2 metrics with the same name: /dev/urandomtensorflow/core/lib/random/simple_philox.ccCheck failed: 0 <= max_log && max_log <= 32 N >= 0tensorflow/core/lib/random/weighted_picker.ccindex < N_position <= level_[num_levels_ - 1][index]new_size >= 0Invalid character found in base64.'encoded' cannot be nullptr.'decoded' cannot be nullptr.Base64 string length cannot be 1 modulo 4.%016llx%llx%c0123456789abcdef%0.3g us%0.3g ms%0.3g s%0.3g min%0.3g h%0.3g days%0.3g months%0.3g yearsinf+inf-infinfinity+infinity-infinitynan+nan-nan0x-0x%.*g%lld%0.3Gtensorflow/core/lib/strings/numbers.ccCheck failed: unit < units + TF_ARRAYSIZE(units) %.2f%c%s%.1f%ciB%s%.2f%ciB-8E%s%lldBFalseTrue\n\r\t\"\'\\\String cannot end with \Value of \ exceeds 0xffString cannot end with \x\x cannot be followed by a non-hex digitUnknown escape sequence: \basic_string::eraseaudio is nullwav_string is nullsample_rate must be in (0, 2^32), got: num_channels must be in (0, 2^16), got: num_frames must be positive.basic_string::at: __n (which is %zu) >= this->size() (which is %zu)RIFFWAVEfmt datatensorflow/core/platform/cpu_feature_guard.ccThe TensorFlow library wasn't compiled to use instructions, but these are available on your machine and could speed up CPU computations.SSESSE2SSE3SSE4.1SSE4.2AVXAVX2AVX512FFMATF_CPP_MIN_VLOG_LEVELchar value signed char value unsigned char value nullptrCheck failed: basic_string::_S_construct null not validnativenative : TF_CPP_MIN_LOG_LEVEL::google::protobuf::uint64::google::protobuf::int64File system scheme not implementedRenaming File changed while reading: Unable to convert proto to text.Can't parse as binary proto as text protovector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)File factory for already registered/proc/self/exe-1 != readlink("/proc/self/exe", exe_path, sizeof(exe_path) - 1)tensorflow/core/platform/env.ccOperation not neededNot a directory*?[\ForEachtensorflow/core/platform/file_system.cc'undeleted_files' Must be non NULL'undeleted_dirs' Must be non NULLHDFS does not support ReadOnlyMemoryRegionRead less bytes than requestedfileviewfsfs.defaultFSviewfs is only supported as a fs.defaultFS.defaultKERB_TICKET_CACHE_PATH not found.hdfsBuilderConnecthdfsNewBuilderhdfsBuilderSetNameNodehdfsConfGetStrhdfsBuilderSetKerbTicketCachePathhdfsCloseFilehdfsPreadhdfsWritehdfsHFlushhdfsHSynchdfsOpenFilehdfsExistshdfsListDirectoryhdfsFreeFileInfohdfsDeletehdfsCreateDirectoryhdfsGetPathInfohdfsRenameCannot delete a non-empty directory.HADOOP_HDFS_HOMEEnvironment variable HADOOP_HDFS_HOME not setliblibhdfs.sohdfstensorflow/core/platform/posix/env.ccEnv::Default() must not be destroyed.so*port >= 0tensorflow/core/platform/posix/net.cc*port <= 65535socket() failed: setsockopt() failed: bind(port=) failed: getsockname() failed: addr_len <= sizeof(addr)actual_port > 0*port == actual_porttrial <= kMaximumTrialsFailed to pick an unused port for testing.port > 0can't determine number of CPU cores: assuming %d aw..tensorflow/core/platform/posix/subprocess.ccSetProgram called after the process was started.SetProgram failed to allocate file string.SetProgram failed to allocate command argument.SetChannelAction called after the process was started.SetChannelAction called with invalid channel: SetChannelAction called with invalid action: Start called after the process was started.Start called without setting a program.Start cannot create pipe: Start cannot make pipe non-blocking: Start cannot make pipe close-on-exec: Start cannot fork() child process: /dev/nullCommunicate cannot get SIGPIPE handler: Communicate cannot ignore SIGPIPE: Communicate cannot poll(): Communicate cannot poll(): timeout not possibleCommunicate called without a running process./tmpTEST_TMPDIRTMPTMPDIRtensorflow/core/platform/profile_utils/clock_cycle_profiler.ccCheck failed: !IsStarted() : average = us ( cycles), count = tensorflow/core/platform/profile_utils/cpu_utils.cccpu_utils_helper_instance_ is already instantiated.ScheduleClosureRunClosureEventCategoryMaxALLtensorflow/core/platform/tracing.ccCan't parse event mask name x_i >= 0tensorflow/core/util/bcast.ccy_i >= 0usage: %s Flags: usage: %s --%s=%d--%s=%lld--%s=%s--%s="%s" %-33s %s %s --tensorflow/core/util/command_line_flags.ccCouldn't interpret value for flag =true=false%d%c%lld%c--helpU == log_alpha->rows()tensorflow/core/util/ctc/ctc_loss_calculator.ccU == log_beta->rows()No valid path found./job:/replica:/task:/device:/CPU:/gpu:/GPU:Cannot merge devices with incompatible jobs: 'Cannot merge devices with incompatible replicas: 'Cannot merge devices with incompatible tasks: 'Cannot merge devices with incompatible types: 'Cannot merge devices with incompatible ids: 'tensorflow/core/util/device_name_utils.ccCheck failed: IsJobName(job) 0 <= replica0 <= taskCheck failed: !type.empty() 0 <= idCheck failed: name.has_job && name.has_replica && name.has_task && name.has_type && name.has_id Check failed: ParseFullName(fullname, &x) 01Failed to parse the env-var ${} into bool: . Use the default value: } into int64: Invalid config dtype: Name: , Key: , Index: . Number of values is not a multiple of stride length. Saw values but output shape is: values != expected. Values size: but output shape: Unsuported datatype.Could not parse example input, value: 'tensorflow/core/util/example_proto_fast_parsing.ccData loss! Feature '' in present in multiple concatenated tf.Examples. Ignoring all but last one.Data types don't match. Data type: but expected type: Can't parse serialized Example.bytesShould not happen., Feature: (data type: is required but could not be found.Data types don't match. Expected type: ./tensorflow/core/util/presized_cuckoo_map.hCuckoo path finding failed: Table too small?Collision found. This should happen only if you have around 2^32 entries in your config.Could not avoid collision. This should not happen.initial_count >= 0./tensorflow/core/lib/core/blocking_counter.hReceived input dtype: Invalid input dtype: len(sparse_keys) != len(sparse_types)len(dense_keys) != len(dense_types)len(dense_keys) != len(dense_shapes)num_dense_ too largelen(context_sparse_keys) != len(context_sparse_types)len(context_dense_keys) != len(context_dense_types)len(context_dense_keys) != len(context_dense_shapes)len(feature_list_sparse_keys) != len(feature_list_sparse_types)len(feature_list_dense_keys) != len(feature_list_dense_types). Number of int64 values != expected. values size: . Number of float values != expected. values size: , Key . Number of bytes values != expected. Values size: tensorflow/core/util/example_proto_helper.ccnot supposed to be here. dtype requested: dtype == values->dtype()Not supposed to be here. Saw dtype: . Data types don't match. Feature is: Expected len(names) == len(examples), but got: tensorflow/core/util/guarded_philox_random.ccCheck failed: !initialized_ seedseed2Check failed: initialized_ Read after file endmemmapped format doesn't support writingmemmapped format doesn't support GetChildrenmemmapped format doesn't support DeleteFilememmapped format doesn't support CreateDirmemmapped format doesn't support DeleteDirmemmapped format doesn't support RenameFileMemmappedEnv is not initialized from a file.MemmappedEnv is not initializedRegion is not foundCorrupted memmapped model file: Invalid package size Invalid directory offset Can't parse its internal directory Invalid offset of internal component Duplicate name of internal component MemmappedEnvWritter: flushing into not opened fileMemmappedEnvWritter: saving tensor into not opened fileMemmappedEnvWritter: element_name is invalid: must have memmapped package prefix and include [A-Za-z0-9_.]MemmappedEnvWritter: saving tensor with 0 sizeMemmappedEnvWritter: saving protobuf into not opened fileMemmappedEnvWritter: element_name is invalid: must have memmapped package prefix REFLECTSYMMETRIC is not an allowed padding mode.mode: {'REFLECT', 'SYMMETRIC'}SAMEVALID is not an allowed padding typepadding: {'SAME', 'VALID'}Failed to parse the leading number: src = The leading number should always be 0 for any valid key: src = Failed to parse the tensor name: src = Failed to parse the tensor rank: src = Expecting positive rank of the tensor, got , src = Too many elements Failed to parse start: src = Failed to parse length: src = tensorflow/core/util/saved_tensor_slice_util.ccCheck failed: !shape_and_slice.empty() Need least two elements in shape_and_slice specification: Non numerical dimension in shape_and_slice: rhs.iter_ == iter_tensorflow/core/util/sparse/group_iterator.ccCan't compare steps from different iterators Timings (microseconds): count=0(all same) std= nodes observedcount= curr= first= min= max= avg=Memory (bytes): ============================== ==============================[node type][start][first][avg ms][%][cdf%][mem KB][Name]%tensorflow/core/util/stat_summarizer.ccNumber of outputs changed between runs for '' - was , now Bad output slot '' for 'Output tensor changed between runs for '/stream/memcpy<>============================== Summary by node type ==============================Number of nodes executed: [Node type][count][avg %][cdf %]Run OrderTop by Computation TimeTop by Memory Use============ Node output tensor sizes in run order ========Expected begin, end, and strides to be 1D equal size tensors, but got shapes , and instead.but got shape for strides.Multiple ellipses in slice spec not allowedIndex out of range using input dim ; input has only dimstensorflow/core/util/strided_slice_op.ccbegin must be either int32 or int64strides[] must be non-zeroonly stride 1 allowed on non-range indexing.slice index of dimension out of bounds.%.*s.index%.*s.data-%05d-of-%05dEntry for key not parseable..tempstateDT_STRING == val.dtype()tensorflow/core/util/tensor_bundle/tensor_bundle.ccshape.dims() == dims()./tensorflow/core/framework/tensor_slice.hIncompatible dimensions between shape slices: shape = Asking for a -dim slice from a slice of dimension Invalid size in bundle entry: key ; stored size ; expected size ; expected size is at least Requested bytes but read bytes.bytes_read == sizeParsed past limit.String lengths longer than expected offset %08uThe length checksum does not match: expected but actual is Checksum does not match: stored vs. calculated on the restored bytes status_Key not found in checkpointInvaid tensor shape: Check failed: entry.ParseFromArray(value().data(), value().size()) ) Check failed: out_ == nullptr Adding duplicate key: Check failed: DataTypeCanUseMemcpy(val.dtype()) key != kHeaderEntryKeyfull_entry->dtype() == slice_tensor.dtype()Check failed: TensorShape(full_entry->shape()) == full_tensor_shape full_tensor_key != kHeaderEntryKeyBundleWriter is closedCheck failed: iter_->Valid() File: , iterator status: ParseEntryProto(iter_->key(), iter_->value(), &header)CheckpointcheckpointReading a bundle with different endianness from the readerCheck failed: iter->Valid() ParseEntryProto(iter->key(), iter->value(), &header)header.num_shards() >= 0Merging bundles with conflicting endianness; inputs corrupted?Duplicate tensor keyed by ; attempting to merge in a non-slice bundle entryexisting_entry.dtype() == to_merge_entry.dtype()Check failed: TensorShape(existing_entry.shape()) == TensorShape(to_merge_entry.shape()) Merging bundles with different format versions: merged vs. curr encountered, when merging prefix: shape.dims() <= kTensorSliceMaxRank./tensorflow/core/util/tensor_slice_util.hOnly tensors of size up to are supportedtss != nullptrDoes not have sufficient slices for partitioned tensor to restore in slice_spec: Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Check failed: CopyDataFromTensorSliceToTensorSlice( full_shape, stored_slice, slice_spec, stored_slice_tensor.flat().data(), val->flat().data()) Dtype not supported.Check failed: val != nullptr data_format: { 'NHWC', 'NCHW' } = 'NHWC' data_format: { 'NDHWC', 'NCDHW' } = 'NDHWC' NDHWCtensorflow/core/util/tensor_format.ccInvalid Format: slices: perhaps your file is in a different file format and you need to use a different restore operator?tensorflow/core/util/tensor_slice_reader.ccCould not open shard < sss_.size()Unable to open table file Failed to find the saved tensor slices at the beginning of the checkpoint file: Unsuccessful TensorSliceReader constructor: Failed to get matching files on Unsuccessful TensorSliceReader constructor: Failed to find any matching files for idx >= 0./tensorflow/core/util/tensor_slice_reader.hFailed to find the index for filename Check failed: sss_[idx]->Get(key, &value) Failed to seek to the record for tensor , slice : computed key = Check failed: ParseProtoUnlimited(&sts, value) Failed to parse the record for tensor not found in checkpoint fileSliced checkpoints are not supportedData type not supportedtensorflow/core/util/tensor_slice_reader_cache.ccCaching disabled because the open function is a lambda or RTTI is not enabled in this build.size_t{1} == still_opening_.erase(filepattern)Caching disabled because the checkpoint file is being opened with two different open functions: Overlapping slices: existing slice = , new slice = Incompatible tensor shapes detected for tensor : existing = , new = Incompatible tensor types detected for tensor tensorflow/core/util/tensor_slice_set.ccError writing (tmp) checkpoint file: tensorflow/core/util/tensor_slice_writer.ccFailed to rename file MaxBytesPerElement not implemented for dtype: Tensor slice is too large to serialize (conservative estimate: bytes)TF_USE_CUDNNtensorflow/core/util/use_cudnn.ccTF_CUDNN_USE_AUTOTUNETF_AVGPOOL_USE_CUDNNwindow >= 1tensorflow/core/util/util.ccv1.1.0-rc0-268-g879eeb54.9.x 20150123 (prerelease)tensorflow/core/util/work_sharder.ccblock_size > 0logits and labels must be same size: logits_size= labels_size=logits must be 2-dimensionalSoftmaxCrossEntropyWithLogitsSoftmaxXentWithLogitsOpWhereWhereOpWhereOp : Unhandled input dimensions: WhereOp: Race condition between counting the number of true elements and writing them. When counting, saw elements; but when writing their indices, saw elements.var_namelhs input needs to be a ref typeMissing var_name attributeVariableOpTemporaryVariableTemporaryVariableOpDestroyTemporaryVariableDestroyTemporaryVariableOpIsVariableInitializedOp./tensorflow/core/framework/resource_mgr.hCheck failed: resource != nullptr No per-step resource manager../tensorflow/core/kernels/variable_ops.hCheck failed: IsRefType(context->input_dtype(0)) axisUnpackUnpackOpUnpackOpInput shape axis must equal , got shape output size must fit in Eigen DenseIndexaxis = not in [tensorflow/core/kernels/unpack_op.ccCheck failed: output.CopyFrom(input.Slice(i, i + 1), output_shape) perm must be a vector, not Transposing a tensor of rank is not implemented.transpose expects a vector of size . But input(1) is a vector of size is out of range [0 .. is missing from {Error reshaping Tensor.invert_permutation expects a 1D vector.permutation of nonnegative int32s must have <= int32 max elements is not between 0 and is duplicated in the input.InvertPermutationInvertPermutationOpypermTransposeCpuOpin.dims() >= 2tensorflow/core/kernels/transpose_functor_cpu.ccin.dims() == out->dims()in.dims() == perm.size()in.dtype() == out->dtype()Unsupported dtype on CPU: ApplyGradientDescentApplyGradientDescentOpResourceApplyGradientDescentvarApplyAdadeltaApplyAdadeltaOpResourceApplyAdadeltaaccumaccum_updateSparseApplyAdadeltaSparseApplyAdadeltaOpResourceSparseApplyAdadeltaSparseApplyAdadeltaOpApplyProximalGradientDescentApplyProximalGradientDescentOpResourceApplyProximalGradientDescentApplyProximalGradientDescentOpSparseApplyProximalGradientDescentSparseApplyProximalGradientDescentOpResourceSparseApplyProximalGradientDescentSparseApplyProximalGradientDescentOpSparseApplyProximalGradientDescentOpSparseApplyProximalGradientDescentOpApplyAdagradApplyAdagradOpResourceApplyAdagradApplyProximalAdagradApplyProximalAdagradOpResourceApplyProximalAdagradApplyProximalAdagradOpSparseApplyAdagradSparseApplyAdagradOpResourceSparseApplyAdagradSparseApplyAdagradOpSparseApplyProximalAdagradSparseApplyProximalAdagradOpResourceSparseApplyProximalAdagradSparseApplyProximalAdagradOpSparseApplyProximalAdagradOpSparseApplyProximalAdagradOpApplyAdagradDAApplyAdagradDAOpResourceApplyAdagradDAgradient_accumulatorgradient_squared_accumulatorApplyAdagradDAOpSparseApplyAdagradDASparseApplyAdagradDAOpResourceSparseApplyAdagradDASparseApplyAdagradDAOpSparseApplyAdagradDAOpSparseApplyAdagradDAOpApplyFtrlApplyFtrlOpResourceApplyFtrllinearSparseApplyFtrlSparseApplyFtrlOpResourceSparseApplyFtrlSparseApplyFtrlOpApplyMomentumApplyMomentumOpResourceApplyMomentumSparseApplyMomentumSparseApplyMomentumOpResourceSparseApplyMomentumSparseApplyMomentumOpApplyAdamApplyAdamOpResourceApplyAdammvApplyRMSPropApplyRMSPropOpApplyCenteredRMSPropApplyCenteredRMSPropOpResourceApplyRMSPropmsmomResourceApplyCenteredRMSPropmgSparseApplyRMSPropSparseApplyRMSPropOpSparseApplyCenteredRMSPropSparseApplyCenteredRMSPropOpResourceSparseApplyRMSPropResourceSparseApplyCenteredRMSPropSparseApplyRMSPropOpSparseApplyCenteredRMSPropOpSparseApplyRMSPropOpSparseApplyCenteredRMSPropOpSparseApplyRMSPropOpSparseApplyCenteredRMSPropOpSparseApplyRMSPropOpSparseApplyCenteredRMSPropOpSparseApplyRMSPropOpSparseApplyCenteredRMSPropOpuse_lockinguse_nesterovTrying to access resource using the wrong type. Expected Invalid variable reference.Attempting to use uninitialized variables: lr is not a scalar: rho is not a scalar: epsilon is not a scalar: var and accum do not have the same shapevar and grad do not have the same shapevar and delta do not have the same shapealpha is not a scalar: l1 regularization strength is not a scalar: l2 regularization strength is not a scalar: lr is not a positive scalar: l1 regularization strength is not a non-negative scalar: l2 regularization strength is not a non-negative scalar: global_step is not a scalar: var and linear do not have the same shapelr_power is not a non-positive scalar: momentum is not a scalar: beta1_power is not a scalar: beta2_power is not a scalar: lr is not a scalar : beta1 is not a scalar: beta2 is not a scalar: var and m do not have the same shapevar and v do not have the same shapevar and ms do not have the same shapevar and mom do not have the same shapevar and mg do not have the same shapevar and accum_grad do not have the same shapevar and accum_update do not have the same shapevar must be at least 1 dimensionalindices must be one-dimensionalvar and grad must match in dimension grad must be the same size as indices in the first dimension.Index at offset in indices is out of rangeInner dimension should be greater than zero.sortedkTopKTopK<::tensorflow::int32>TopKV2TopKk must be scalar, got shape Need k >= 0, got input must be >= 1-D, got shape input must have at least k columnsTilemultiplesTmultiplesTileOpTileGradTileGradientOpExpected multiples argument to be a vector of length but got length Expected multiples[] >= 0, but got Expected multiples to be 1-D, but got shape TileOp : Unhandled input dimensions, DT : , dims : ] > 0, but got Expected input_dim[] to be divisible by multiples[], but % != 0TileGradientOp : Unhandled input dimensions, DT : TensorArrayTensorArrayOpTensorArrayV2TensorArrayV3TensorArrayGradTensorArrayGradOpTensorArrayGradV2TensorArrayGradV3handlegrad_handleTensorArrayWriteTensorArrayWriteOpTensorArrayWriteV2TensorArrayWriteV3TensorArrayWriteOpTensorArrayReadTensorArrayReadOpTensorArrayReadV2TensorArrayReadV3TensorArrayReadOpTensorArrayPackTensorArrayPackOrGatherOpTensorArrayGatherTensorArrayPackOrGatherOpTensorArrayGatherV2TensorArrayGatherV3TensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayPackOrGatherOpTensorArrayConcatlengthsTensorArrayConcatOpTensorArrayConcatV2TensorArrayConcatV3TensorArrayConcatOpTensorArrayConcatOpTensorArrayConcatOpTensorArrayConcatOpTensorArrayConcatOpTensorArrayUnpackTensorArrayUnpackOrScatterOpTensorArrayScatterTensorArrayUnpackOrScatterOpTensorArrayScatterV2TensorArrayScatterV3TensorArrayUnpackOrScatterOpTensorArrayUnpackOrScatterOpTensorArraySplitTensorArraySplitOpTensorArraySplitV2TensorArraySplitV3TensorArraySplitOpTensorArraySizeTensorArraySizeOpTensorArraySizeV2TensorArraySizeV3sizeTensorArrayCloseTensorArrayCloseOpTensorArrayCloseV2TensorArrayCloseV3element_shapesourceelement_shape_except0dynamic_sizeclear_after_readtensor_array_nameflow_inflow_outTensorArray has already been closed.: Could not read from TensorArray index because it has not yet been written to.: Could not read index twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).Tried to read from index but array size is: tensor_array::TensorSetZero type not supported: : Could not write to TensorArray index because the value dtype is but TensorArray dtype is because the value shape is which is incompatible with the TensorArray's element shape: because it has already been read. because it has already been written to.: Could not aggregate to TensorArray index because the existing shape is but the new input shape is : Tried to write to index but array is not resizeable and size is: Tensor array handle must be 2-element vector, but had shape: ./tensorflow/core/kernels/tensor_array.hCheck failed: !closed_ TensorArray[TensorArray size must be scalar, but had shape: Size should be >= 0._tensor_arraysNo resource manager.TensorArray index must be scalar, but had shape: TensorArray dtype is but Op is trying to write dtype but Op requested dtype TensorArray has size zero, but element shape is not fully defined. Currently only static shapes are supported when packing zero-size TensorArrays.TensorArray was passed element_shape which does not match the Tensor at index 0: TensorArray has inconsistent shapes. Index 0 has shape: but index has shape: indicesExpected indices to be a vector, but received shape: TensorArray has size zero, but element_shape_except0 is not fully defined. Currently only static shapes are supported when concatenating zero-size TensorArrays.Concat saw a scalar shape at index but requires at least vectors. Did you mean to call pack?TensorArray was passed element_shape_except0 but index 0 has (excepting dimension 0) shape: which does not match.TensorArray has inconsistent shapes. Index 0 has (excepting dimension 0) shape: has (excepting dimension 0) shape: tensor dim0 too large to unpackInput value for unpack must be at least a vector but received shape: Input value must have first dimension equal to the array size (Expected len(indices) == values.shape[0], but saw: Max scatter index must be <= array size (Expected lengths to be a vector, received shape: Expected lengths to have < max int32 entriesExpected value to be at least a vector, but received shape: Expected sum of lengths to be equal to values.shape[0], but sum of lengths is and value's shape is: TensorArray's size is not equal to the size of lengths (), and the TensorArray is not marked as dynamically resizeableInput container should be '_tensor_arrays', but received '_tensor_array_grads@ArraySize should be >= 0.Unable to create a gradients TensorArray for . Perhaps you used the multiple_writes_aggregate flag on a previous write? Gradient calculation is impossible when multiple writes are performed to the same index.Wrong input container. TensorArray sizes do not match during CopyShapesFrom: has size but rhs begin_maskend_maskellipsis_masknew_axis_maskshrink_axis_masksliced l-value shape does not match r-value shape . Automatic broadcasting not yet implemented.Unhandled input dimensions StridedSlicebeginendStridedSliceOpStridedSliceGradStridedSliceGradOpStridedSliceAssignStridedSliceAssignOpStridedSliceOpStridedSliceGradOpStridedSliceAssignOpStridedSliceOpStridedSliceGradOpStridedSliceAssignOpinput.dims() >= 1tensorflow/core/kernels/strided_slice_op.ccCheck failed: tmp.CopyFrom(input, final_shape) Check failed: tmp.CopyFrom(input.Slice(begin[0], end[0]), final_shape) shape must be 1-D, got shape.shape = shape must have type int32 or int64.shape of dy was instead of Check failed: result->CopyFrom(in, processing_shape) swap_memoryelem_typestack_nameStack[] has already been closed.StackOpStackPushStackPushOpStackPushOpelemoutputStackPopStackPopOpStackCloseStackCloseOpStack handle must have two elements, but had shape: Must have type ] is empty when calling Pop()._stacksSplitsplit_dimSplitOpCPU<::tensorflow::int32>SplitOpCPUSplitOpCPUNumber of ways to split should be > 0, but got Number of ways to split should evenly divide the split dimension, but got split_dim (size = and num_split Split requires input size < 0 <= split_dim < number of input dimensions (), but got SplitVTlensize_splitsSplitVOpCPU<::tensorflow::int32, int32>SplitVOpCPU<::tensorflow::int32, int64>SplitVOpCPUSplitVOpCPUSplitVOpCPUSplitVOpCPUThere can only be one -1 in the input.Determined shape must either match input shape along split_dim exactly if fully specified, or be less than the size of the input along split_dim if not fully specified. Got: size of the split_tensor must be 1-D and have the same elements as outputs got -D and elementsindices[] = [ is out of bounds: need 0 <= index < is out of order is repeatedOrder was not provided. Provide an order at construction time or run ReorderInPlaceSparseToDenseSparseToDense<::tensorflow::int32, int32>SparseToDense<::tensorflow::int32, int64>SparseToDenseSparseToDenseSparseToDenseSparseToDenseSparseToDenseSparseToDense./tensorflow/core/util/sparse/sparse_tensor.hCheck failed: TensorShapeUtils::IsMatrix(ix.shape()) indices must be a matrix, but got: ix.dtype() == DT_INT64indices must be type int64 but got: Check failed: TensorShapeUtils::IsVector(vals.shape()) vals must be a vec, but got: ix.shape().dim_size(0) == vals.shape().dim_size(0)indices and values rows (indexing dimension) must match.order.size() == dims_Order length must be SparseTensor rank.shape.dims() == dims_Shape rank must be SparseTensor rank.DataTypeToEnum::v() == dtype()ToDense requested with the wrong datatypeout->shape().dims() == dims_Incompatible dimensions between SparseTensor and outputout->dtype() == DataTypeToEnum::v()Output must be type: but got: sparse_indices should be a scalar, vector, or matrix, got shape output_shape has incorrect number of elements: should be: default_value should be a scalar.Indices are not valid (out of bounds). Shape: output_shape should be a vector, got shape sparse_values has incorrect shape , should be [] or [tensorflow/core/kernels/sparse_to_dense_op.ccCheck failed: indices_shaped.CopyFrom(indices, ix_shape) a_is_sparseb_is_sparseSparseMatMulTaTbSparseMatMulOpSparseMatMulOpSparseMatMulOpSparseMatMulOp*KR % *KL == 0tensorflow/core/kernels/sparse_matmul_op.ccCheck failed: cpu_worker_threads_ != nullptr a is not a matrixb is not a matrixMatrix size incompatible: a: , b: SoftsignSoftsignOpSoftsignGradSoftsignGradOpSoftsignOpSoftsignGradOpg and a must be the same sizeWe only handle up to Tensor::dims() up to 8, not SoftplusSoftplusOpSoftplusGradSoftplusGradOpSoftplusOpSoftplusGradOpLogSoftmaxSoftmaxOpLogSoftmaxSliceOpSliceOpSliceOptensorflow/core/kernels/slice_op.ccExpected begin and size arguments to be 1-D tensors of size , but got shapes Expected begin[] == 0 (got ) and size[] == 0 (got ) when input.dim_size() == 0Expected size[] in [0, got ], but got SliceOp : Unhandled input dimensionsTried to explicitly squeeze dimension but dimension was not 1: Tried to squeeze dim index for tensor with Could not squeeze input with shape and output shape Number of elements was larger than representable by 32-bit output typeShape output type is 32-bit but dim is ShapeN output type is 32-bit but shape dim out_typeShapeOpShapeOpShapeNShapeNOpShapeNOpRankOpSizeOpSizeOpExpandDimsdimTdimExpandDimsOpSqueezeOpTried to expand dim index Could not expand dimension with input shape GetSessionHandleOpGetSessionTensorOpDeleteSessionTensorOpstartlimitdeltaTidxRangeOpRangeOp<::tensorflow::int32>LinSpacestopnumLinSpaceOpRequires delta != 0: Requires start <= limit when delta > 0: Requires start >= limit when delta < 0: start must be a scalar, not shape limit must be a scalar, not shape delta must be a scalar, not shape stop must be a scalar, not shape num must be a scalar, not shape Requires num > 0: Op kernel context needs to provide a rendezvous.SendOpRecvOpNull ref for paramsparams must be at least 1-D, got shape Must have updates.shape = indices.shape + params.shape[1:], got updates.shape , indices.shape , params.shape ScatterAddScatterUpdateOpScatterUpdateOpScatterDivScatterUpdateOpScatterUpdateOpScatterMulScatterUpdateOpScatterUpdateOpScatterSubScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateScatterUpdateOpScatterUpdateOpScatterUpdateOpScatterUpdateOpindices has too many elements for indexing: > params.shape[0] too large for is not in [0, Must have updates.shape = indices.shape[:batch_dim] + params_shape[slice_dim:], got updates.shape: , indices.shape: , params_shape: , slice_dim: , and batch_dim: Output must be at least 1-D, got shape: The outermost dimension of updates and indices must match. Got indices.shape , updates.shape Indices and updates specified for empty output shapeparams_shape[0] too large for slice size is too large for indexing: ScatterNdAddScatterNdUpdateOpScatterNdUpdateOpScatterNdSubScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdUpdateOpScatterNdScatterNdOpScatterNdOpScatterNdOpScatterNdOpOnly indices.shape[-1] values between 1 and 5 are currently supported. Requested rank: Invalid indices: = [] is not in [0, Shape must be a vector] does not index into Shape in shape_and_slice spec does not match the shape stored in checkpoint: Expected dtype does not equal restored dtype Incompatible tensor shape and slice: shape = ./tensorflow/core/util/tensor_slice_writer.hMismatching shapes: existing tensor = , trying to add name , shape = Mismatching types: existing type = , type = Error writing Tensor. Possible size overflow.name == ssm.name()Input 0 (filename) must be a string scalar; got a tensor of elementsToo many inputs to SaveTensorsExpected elements for the tensor shapes and slices but got Expected totally inputs as input #1 (which is a string tensor of saved names) contains names, but received inputsSlice in shape_and_slice specification does not match the shape of the tensor to save: , tensor: Saving data type not yet supportedInput 0 (file_pattern) must be a string scalar; got a tensor of Input 1 (tensor_name) must be a string scalar; got a tensor of Expected 1 element for the tensor shape and slice but got Tensor name "" not found in checkpoint files Expected to restore a tensor of type , got a tensor of type instead: tensor_name = Shape in shape_and_slice spec does not match the shape in the save file: , save file shape: Restoring data type dtypesdelete_old_dirsInput prefix should have a single element, got tensor_names and shape_and_slices have different number of elements: Too many inputs to the op elements in shapes_and_slices, but got Got tensor names but tensors.Expected a total of Input tensor_names and shape_and_slices should be an 1-D tensors, got tensor names, but expected dtypes.SaveV2RestoreV2MergeV2CheckpointsInput checkpoint_prefixes should be an 1-D tensor, got Input destination_prefix should be a scalar tensor, got %s-%05d-of-%05d must be a scalar, got shape %s-?????-of-%05dSaveSaveOpSaveSlicesSaveSlicesOpShardedFilenameShardedFilenameOpShardedFilespecShardedFilespecOpbasenamenum_shardsshardseparatorStringJoinStringJoinOpinputsInput shapes do not match: batch_dimseq_dimbatch_dim == seq_dim == seq_dim must be < input.dims()( batch_dim must be < input.dims()len(seq_lens) != input.dims(), seq_lens() < 0) > input.dims(seq_lens input must be 1-dim, not ReverseSequenceOp : Unhandled input dimensions: ReverseSequenceReverseSequenceOpReverseSequenceOpReverseSequenceOpReverseSequenceOp'dims' must be 1-dimension, not 'dims' must have the same number of values as 'input' has dimensions. 'input' has 'dims' has valuesreverse is not implemented for tensors of rank > 8.ReversedimsReverseOpReverseV2ReverseV2OpReverseOpReverseV2Op'axis'[ is out of valid range [axis specified more than once.preferred_shardAttribute 'preferred_shard' must be greater or equal to -1RestoreRestoreOpRestoreSliceRestoreSliceOpalign_cornersinput must be 4-dimensionalshape_t must be 1-dimensionalshape_t must have two elementsoutput dimensions must be positiveimage must have at least one channelinput image height scale would cause an overflowinput image width scale would cause an overflownearest neighbor requires max height & width of 2^24input sizes must be between 0 and max int32input image must be of non-zero sizeshape_t's elements must be positiveResizeNearestNeighborResizeNearestNeighborOpResizeNearestNeighborGradResizeNearestNeighborOpGradResizeNearestNeighborOpResizeNearestNeighborOpGradinput_grad must be 4-dimensionalinput_grad must be of type floatoriginal_image must be 4-dimensionaloriginal sizes must be between 0 and max int32ResizeBilinearResizeBilinearOpResizeBilinearOpResizeBilinearGradResizeBilinearOpGradReshapeOponly one input size may be -1, not both size must be non-negative, not Reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zeroInput to reshape is a tensor with values, but the requested shape requires a multiple of values, but the requested shape has sizes input must be 1-D, not shape ./tensorflow/core/kernels/reshape_op.hCheck failed: output.CopyFrom(input, shape) ReluOpReluGradOpRelu6OpRelu6GradRelu6GradOpReluOpReluGradOpRelu6OpRelu6GradOpEluEluOpEluGradEluGradOpReductionOp>ReductionOp>helper.ndims() >= 0./tensorflow/core/kernels/reduction_ops_common.hError during reduction copy.Check failed: data_reshaped.CopyFrom(data, helper.data_reshape()) ProdReductionOp>ReductionOp>ReductionOp>ReductionOp>MeanReductionOp>ReductionOp>ReductionOp>ReductionOp>Invalid reduction dimension ( dimension(s)Anyreduction_indicesReductionOpcomponentstimeout_msTimeout not supported yet.cancel_pending_enqueuesQueueEnqueueEnqueueOpQueueEnqueueV2QueueEnqueueManyEnqueueManyOpQueueEnqueueManyV2QueueDequeueDequeueOpQueueDequeueV2QueueDequeueManyDequeueManyOpQueueDequeueManyV2QueueDequeueUpToDequeueUpToOpQueueDequeueUpToV2QueueCloseQueueCloseOpQueueCloseV2QueueSizeOpQueueSizeV2FakeQueueFakeQueueOpResource handle must have 2 elements, but had shape: DequeueManyOp requested < 0 elementsDequeueUpToOp requested A Queue of size: Queue '' is already closed.Shared queue '' has type '' that does not match type of Node 'capacity' has capacity but requested capacity was Wrong number of components in tuple. Expected , got Type mismatch in tuple component . Expected Shape mismatch in tuple component All input tensors must have the same size in the 0th dimension. Component has , and should have component_types' has component types but requested component types were shapes' has component shapes but requested component shapes were tensorflow/core/kernels/queue_base.cc: Skipping cancelled enqueue attempt with queue not closed: Skipping cancelled dequeue attempt with queue not closedEnqueue operation was cancelledDequeue operation was cancelledHandleSliceToElement Cannot copy slice: number of elements does not match. Shapes are: [element]: , [parent slice]: CopySliceToElement Unhandled data type: HandleElementToSlice Cannot copy slice: number of elements does not match. Shapes are: [element]: CopyElementToSlice Unhandled data type: Check failed: index >= 0 && index < tensor_shape.dims() Invalid index from the dimension: Unknown format Check failed: dim >= 0 && dim < num_dims - 2 Check failed: index >= 0 && index < attributes.size() tensor_in must be 4-dimensionalMaxPooling supports exactly one of pooling across depth or pooling across width/height.Depthwise max pooling requires the depth window to evenly divide the input depthDepthwise max pooling requires the depth window to equal the depth strideDepthwise max pooling is currently only implemented for CPU devices.shape must have known rank.PaddingFIFOQueueOpPaddingFIFOQueueV2./tensorflow/core/framework/resource_op_kernel.hCheck failed: (*ret)->Unref() HandleElementToLargerSlice Cannot copy slice: number of entries in element is greater than number of elements in parent slice. Shapes are: [element]: Expected PaddingFIFOQueue, found Empty component types for queue Different number of component types. Types: , Shapes: Shapes must be provided for all components, but received dtypes and Mismatched ranks. Element's rank is: but element is meant to be a slice in output Tensor having rank: (should be: HandleElementToLargerSliceWithRank Unhandled data type: CopyElementToLargerSlice Unhandled rank: SetElementZero Unhandled data type: Failed to restore element from partially-dequeued batch to PaddingFIFOQueue: PaddingFIFOQueue '' is closed and has insufficient elements (requested , current size PadpaddingsPadOpPadOpDims == paddings.dimension(0)tensorflow/core/kernels/pad_op.cc2 == paddings.dimension(1)inputs rank not in []: The first dimension of paddings must be the rank of inputsPaddings must be non-negative: paddings must be a matrix with 2 columns: Check failed: out.CopyFrom(in0, output_shape) PackOpPackOpPackOpvaluesShapes of all inputs must match: values[0].shape = != values[].shape = tensorflow/core/kernels/pack_op.ccCheck failed: output.CopyFrom(values[0], output_shape) stride must be less than or equal to kernel sizeindex * stride must be less than or equal to input sizeOneHotTIdepthOneHotOpOneHotOpOneHotOpOneHotOpOneHotOpOneHotOpExpected axis to be -1 or between [0, ). But received: depth must be a scalar, but got: on_value must be a scalar, but got: off_value must be a scalar, but got: depth must be non-negative, got: OneHot result would exceed the maximum number of elements (iou_thresholdNonMaxSuppressionNonMaxSuppressionOpiou_threshold must be in [0, 1]max_output_size must be 0-D, got shape boxes must be 2-Dboxes must have 4 columnsscores must be 1-Dscores has incompatible shapemodemode must be either REFLECT or SYMMETRIC.paddings must be no greater than the output dimension size: greater than paddings must be less than the output dimension size: not less than Unsupported rank: MirrorPadTpaddingsMirrorPadOpMirrorPadOpMirrorPadGradMirrorPadGradOpMirrorPadGradOppaddings must be non-negative: paddings must be no greater than the dimension size: paddings must be less than the dimension size: tensorflow/core/kernels/mirror_pad_op.ccInvalid data formatDefault MaxPoolingOp only supports NHWC.Sliding window ksize field must specify 4 dimensionsSliding window stride field must specify 4 dimensionsPooling is not yet supported on the batch dimension.Default MaxPoolingGradOp only supports NHWC on device type Sliding window strides field must specify 4 dimensionsMaxPoolingGrad is not yet supported on the depth dimension.Default MaxPoolingGradGradOp only supports NHWC MaxPoolingGradGrad is not yet supported on the depth dimension.MaxPoolingOpMaxPoolingOpMaxPoolingGradOpMaxPoolGradGradMaxPoolingGradGradOpMaxPoolingGradOpMaxPoolingGradGradOpDepthwise max pooling requires the depth window to evenly divide the input depth.Depthwise max pooling requires the depth window to equal the depth stride.tensor_out must be 4-dimensionalout_backprop must be 4-dimensionalout_grad_backprop must be 4-dimensionaltensorflow/core/kernels/maxpooling_op.ccCheck failed: input_backprop_index >= in_start && input_backprop_index < in_end Invalid input backprop index: MatMulOpMatMulOpIn[0] is not a matrixIn[1] is not a matrixMatrix size-incompatible: In[0]: , In[1]: depth_radiusdepth_radius = larger than int maxbiasalphabetaLRNLRNOpin must be 4-dimensionalargument to LRN too largeassertion failed: In[0] should be a scalar: summarizemessagefirst_nAssertAssertOpPrintPrintOptensorflow/core/kernels/logging_ops.ccFound instance of parallel_stack which could not be properly replaced.ParallelConcatUpdateParallelConcatStartParallelConcatStartFailureKernelvalue.dtype() == output->dtype()tensorflow/core/kernels/inplace_ops.ccUnsupported data type: value and update shape doesn't match: value and update shape doesn't match update shape doesn't match: predictions must be 2-dimensionaltargets must be 1-dimensionalFirst dimension of predictions must match length of targets targets[] is out of rangeInTopKInTopKInTopKMemmappedTensorAllocatorReadonly memory region has wrong alignmentReadonly memory region has wrong length () when allocating memory_region_nameImmutableConstImmutableConstantOptensorflow/core/kernels/immutable_constant_op.ccDeallocating not allocated region for readonly memory regionIdentityOpPreventGradientPlaceholderWithDefaultGatherOpGatherOpGatherOpGatherOpparams must be at least 1 dimensionalepsilonis_trainingFusedBatchNormOpFusedBatchNormGradOptensorflow/core/kernels/fused_batch_norm_op.ccCheck failed: tensor_format == FORMAT_NHWC The CPU implementation of FusedBatchNorm only supports NHWC tensor format for now.scale must be 1-dimensionaloffset must be 1-dimensionalestimated_mean must be 1-dimensionalestimated_variance must be 1-dimensionalestimated_mean empty for trainingestimated_variance must be empty for trainingThe CPU implementation of FusedBatchNorm only support saved mean must be 1-dimensionalsaved variance must be 1-dimensionalFIFOQueue 'FIFOQueueV2Expected FIFOQueue, found FIFOQueue's DequeueMany and DequeueUpTo require the components to have specified shapes.' is closed.Failed to restore element from partially-dequeued batch to FIFOQueue: min has to be smaller than max, was: minmaxFakeQuantWithMinMaxArgsFakeQuantWithMinMaxArgsOpFakeQuantWithMinMaxArgsGradientFakeQuantWithMinMaxArgsGradientOpFakeQuantWithMinMaxVarsFakeQuantWithMinMaxVarsOpFakeQuantWithMinMaxVarsGradientFakeQuantWithMinMaxVarsGradientOpFakeQuantWithMinMaxVarsPerChannelFakeQuantWithMinMaxVarsPerChannelOpFakeQuantWithMinMaxVarsPerChannelGradientFakeQuantWithMinMaxVarsPerChannelGradientOpgradient and input must be the same size3 == context->num_inputs()tensorflow/core/kernels/fake_quant_ops.cc4 == context->num_inputs()min has incorrect size, expected max has incorrect size, expected Only inputs of dimensions 1, 2 or 4 supported, was: context_sparse_typesNcontext_denseNfeature_list_denseNcontext_sparseTcontext_densefeature_list_sparse_typesfeature_list_dense_typesNfeature_list_sparsecontext_dense_shapesfeature_list_dense_shapessparse_typesdense_shapes[] has unknown rank or unknown inner dimensions: NdenseNsparseTdensedense_shapesParseExampleExampleParserOpParseSingleSequenceExampleSingleSequenceExampleParserOpnamesdense_keys.size() == attrs_.num_densetensorflow/core/kernels/example_parsing_ops.ccsparse_keys.size() == attrs_.num_sparseExpected names to be a vector, got shape: Expected len(names) == len(serialized), but got: Expected serialized to be a vector, got shape: Expected len(dense_defaults) == len(dense_keys) but got: dense_shape[] is a variable length shape: , therefore def_value[] must contain a single element (the padding element). But its shape is: def_value[].shape() == is not compatible with dense_shapes_[] == dense_defaults[].dtype() == != dense_types_[dense_valuesserializeddense_keyssparse_keysdense_defaultssparse_indicessparse_valuessparse_shapesdebug_namecontext_dense_keys.size() == attrs_.num_context_densecontext_sparse_keys.size() == attrs_.num_context_sparsefeature_list_dense_keys.size() == attrs_.num_feature_list_densefeature_list_sparse_keys.size() == attrs_.num_feature_list_sparseExpected context_dense_keys[] to be a vector, got shape: Expected context_sparse_keys[Expected feature_list_dense_keys[Expected feature_list_sparse_keys[Expected feature_list_dense_missing_assumed_empty to be a vector, got shape: Expected debug_name to be a scalar, got shape: Expected serialized to be a scalar, got shape: Expected len(context_dense_defaults) == len(context_dense_keys) but got: != context_dense_shapes_[context_dense_defaults[ != context_dense_types_[context_sparse_indices, Context feature: , Feature list '' is required but could not be found. Did you mean to include it in feature_list_dense_missing_assumed_empty or feature_list_dense_defaults?, Feature list: feature_list_dense_missing_assumed_emptycontext_dense_keysfeature_list_dense_keyscontext_sparse_keysfeature_list_sparse_keyscontext_dense_defaultscontext_sparse_valuescontext_sparse_shapescontext_dense_valuesfeature_list_sparse_indicesfeature_list_sparse_valuesfeature_list_sparse_shapesfeature_list_dense_values, Context feature '' is required but could not be found., Feature List: DynamicStitchOp: Must have some inputsDynamicStitchOp: Must have even number of argumentsDynamicStitchDynamicStitchOp<::tensorflow::int32>DynamicStitchOpdata[ does not start with indices[Need data[0].shape[:] = data[].shape[:], got data[0].shape = , data[, indices[0].shape = , indices[num_partitionsDynamicPartitionDynamicPartitionOp<::tensorflow::int32>DynamicPartitionOpdata.shape must start with partitions.shape, got data.shape = , partitions.shape = outputspartitionsout_vec[] size: is not LTE output_index[] : ] has been asynchronously overwitten and is no longer in range!Size of output_index: is no longer in range.block_sizeBlock size should be > 1: Input rank should be: instead of: Input depth dimension should be divisible by: DepthToSpaceDepthToSpaceOpDepthToSpaceOpImage width and height should be divisible by block_size: SpaceToDepthSpaceToDepthOpSpaceToDepthOpvalidate_shapeAssignOpTAssignOpTAssignAddDenseUpdateOpAssignSubDenseUpdateOpDenseUpdateOpDenseUpdateOpAttempting to use uninitialized parameters: Parameters and update must be the same sizeAssign requires shapes of both tensors to match. lhs shape= rhs shape= ./tensorflow/core/kernels/assign_op.hCheck failed: reshaped_old_lhs.CopyFrom(old_lhs, rhs.shape()) TF_USE_DEEP_CONV2Drows > 0./tensorflow/core/kernels/winograd_transform.hcols > 0DivModFloorDivInteger division by zeroUnexpected error in binary operator (only integer div and mod should have errors)Incompatible shapes: Broadcast between is not supported yet.UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>TanhGradSimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>PowBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>vec.size() == NDIMS./tensorflow/core/util/bcast.hBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>zSquareUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>SqrtUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>SqrtGradSimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>SigmoidGradSimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SignUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>SelectSelectOpSelectOp'then' and 'else' must have the same size. but received: te'cond' must be a vector, but saw shape: cond vector larger than flat outer dims dim 1 size >= 'then' must be at least a vector, but saw shape: Number of batches of 'then' must match size of 'cond', but saw: conditionRsqrtUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>RsqrtGradSimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>InvUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>InvGradSimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>SimpleBinaryOp>ReciprocalReciprocalGradUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>MinimumBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>MaximumBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>LogicalNotUnaryOpLogicalAndBinaryOpUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>LessBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>IsFiniteUnaryOp>UnaryOp>UnaryOp>GreaterEqualBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>GreaterBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>ExpUnaryOp>UnaryOp>UnaryOp>UnaryOp>UnaryOp>EqualBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>toleranceBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>ApproximateEqualApproximateEqualOpApproximateEqualOpx and y must be of the same shape. x shape: . y shape: BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>TruncateDivBinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>BinaryOp>merge_repeatedbeam_widthtop_pathsCTCGreedyDecoderCTCGreedyDecoderOpCTCBeamSearchDecoderCTCBeamSearchDecoderOpbatch_s.size() == top_paths_tensorflow/core/kernels/ctc_decoder_ops.ccsequence_lengthinputs is not a 3-Tensormax_time is 0sequence_length is not a vectorlen(sequence_length) != batch_size. len(sequence_length): batch_size: sequence_length() <= log_probabilitydecoded_indicesdecoded_valuesdecoded_shapenum_classes cannot exceed max int0 < m.dimension(1)./tensorflow/core/util/ctc/ctc_beam_entry.hCheck failed: !HasChildren() Check failed: HasChildren() ./tensorflow/core/lib/gtl/top_n.hCheck failed: !empty() num_classes_ == input.size()./tensorflow/core/util/ctc/ctc_beam_search.h'paths' Must be non NULL'log_probs' Must be non NULLrequested more paths than the beam width.Less leaves in the beam search than requested.output needs to be of size at least (top_n, batch_size).top_n == beam_log_probabilities.size()beams.size() == beam_log_probabilities.size()scores needs to be of size at least (batch_size, top_n).box_ind has values outside [0, batch)methodbilinearmethod must be 'bilinear'extrapolation_valuegrads image must be 4-Dgrads dimensions must be positiveinput image must be 4-Dimage dimensions must be positiveimage, grads depth differbox_ind must be 1-Dbox_ind has incompatible shapeboxes and grads have incompatible shapeFailed launch CropAndResizeBackpropBoxesKernel.image_size must be 1-Dimage_size must have 4 elementsimage_size and grads are incompatibleFailed launch CropAndResizeBackpropImageKernel.crop_size must be 1-Dcrop_size must have two elementscrop dimensions must be positiveFailed launch CropAndResizeKernel.CropAndResizecrop_sizeCropAndResizeOpCropAndResizeGradImageimage_sizeCropAndResizeGradImageOpCropAndResizeGradBoxesCropAndResizeGradBoxesOpIm2ColBufferResourceFusedResizeAndPadConv2DFusedResizeConv2DUsingGemmOp< float, FusedResizeAndPadConvFunctor, BILINEAR>, true>FusedPadConv2DFusedResizeConv2DUsingGemmOp< float, FusedResizeAndPadConvFunctor, NEAREST>, false>resize_align_cornersCurrent implementation does not yet support strides in the batch and depth dimensions.Input tensor can't be emptyThe first dimension of paddings must be the rank of inputs: Fused mirror padding only supports four-dimensional inputs, but requestedfilter must be 4-dimensional: filter too largeinput and filter must have the same depth: vs Input rows too largeInput cols too largebatch is too largeOutput tensor can't be emptytensorflow/core/kernels/conv_ops_fused.ccConv2D was called with bad input dimensions: Conv2D was called with bad filter dimensions: Conv2D was called with bad output width or height: Im2Col patch too large for bufferConv2dim2col_bufferInput too large for resize cacheresize_cacheFused mirror padding only support spatial padding, not batches: Fused mirror padding only support spatial padding, not channels: Conv2DOptensorflow/core/kernels/conv_ops.ccCheck failed: data_format == FORMAT_NHWC Generic conv implementation only supports NHWC tensor format for now.Check failed: index >= 0 && index < tensor.dims() use_cudnn_on_gpuConv2DFastBackpropFilterOp only supports NHWC.Conv2DCustomBackpropFilterOp only supports NHWC.Conv2DBackpropFilter: filter_sizes input must be 1-dim, not Conv2DCustomBackpropFilterOpcustomeigen_tensorConv2DFastBackpropFilterOpConv2DCustomBackpropFilter: filter_sizes input must be 1-dim, not Conv2DCustomBackpropFilterEigen Conv2DFastBackpropInputOp only supports NHWC.Conv2DCustomBackpropInputOp only supports NHWC.Conv2DBackpropInput: input_sizes input must be 1-dim, not Conv2DFastBackpropInputConv2DCustomBackpropInputOpConv2DFastBackpropInputOpConv2DCustomBackpropInput: Size of out_backprop doesn't match computed: actual = , computed = : input must be -dimensional: filter must be : out_backprop must be : input and out_backprop must have the same batch size: input and filter must have the same depth: filter and out_backprop must have the same out_depthpredSwitchOpoutput_falseoutput_trueRefSelectRefSelectOpMergeOpvalue_indexEnterOpExitOpNextIterationOpLoopCondOpinputControlTriggerOpAbortAbortOpMerge can not have more than one valid input.error_msgexit_without_errorThe second input must be a scalar, but it has shape Index must be a scalar, but it has shape Index must be in the range [0, ) but got tensorflow/core/kernels/control_flow_ops.ccAbort_op intentional failure; Exiting the process: Type mismatch between value () and dtype (You must feed a value for placeholder tensor '' with dtype and shape ConstantOpFillFillOpFillOpFillOpZerosLikeOpZerosLikeOpOnesLikeOnesLikeOpOnesLikeOpPlaceholderOpdims must be a vector of int32, got shape value must be a scalar, got shape should be a vector, but got shape Concat dim is out of range: should contain elements, but got All dimensions except must match. Input has shape [] and doesn't match input 0 with shape [].Concat dim tensor should be a scalar integer, but got shape concat_dimConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOpConcatV2OpConcatOffsetOpoffsetConcatOp : Dimensions of inputs should match: shape[0] = vs. shape[ tensor should be a scalar integer, but got shape ConcatOp : Expected concatenating dimensions in the range [ConcatOp : Ranks of all input tensors should match: shape[0] = ./tensorflow/core/kernels/concat_lib_cpu.hCheck failed: out >= out_start Check failed: out < out_end CheckNumericsCheckNumericsOpInf and NaNInfNaN : Tensor had Cast is not supportedSrcTCpuCastOpCPU BiasGradOp only supports NHWC.CPU BiasOp only supports NHWC.Input tensor must be at least 2D: Biases must be 1D: Must provide as many biases as the last dimension of the input tensor: Only ranks up to 5 supported: BiasOpBiasAddV1BiasOpBiasGradOpBiasGradOpBiasGrad requires tensor size <= int32 maxBroadcastArgss0s1r0BCastArgsOpBroadcastGradientArgsr1BCastGradArgsOpBroadcast for n-ary operations (n > 2)In[] must be a vector.Incompatible shapes: [] vs. [variance_epsilonscale_after_normalizationmean must be 1-dimensionalvar must be 1-dimensionalbeta must be 1-dimensionalgamma must be 1-dimensionalBatchNormWithGlobalNormalizationBatchNormOpBatchNormWithGlobalNormalizationGradBatchNormGradOpDefault AvgPoolingOp only supports NHWC.Default AvgPoolingGradOp only supports NHWC.AvgPoolingOpAvgPoolingOporig_input_shapeAvgPoolingGradOpNon-spatial pooling is not yet supported. Volunteers? :)out_backprop must be 1-dimensional and 4 elementsdim must be a scalar, but received tensor of shape: Expected dimension in the range [Reduction axis is empty in shape ArgOp : Unhandled input dimensions: ArgMaxdimensionArgMaxOpArgMinArgMinOpArgMaxOpArgMinOpAddNOpAddNOpDepthwiseConv2dNativeDepthwiseConv2dNativeOpDepthwise convolution on CPU is only supported for NHWC formatCurrent implementation only supports equal length strides in the row and column dimensions.total number of outputs should be within the range of int which is used in the GPU kernelMIN_COMBINEDMIN_FIRSTMode string must be 'MIN_COMBINED' or 'MIN_FIRST', is 'DequantizeDequantizeOpDequantizeOpDequantizeOpDequantizeOpDequantizeOpMetaGemmScratchResourceFATAL: dispatch1D failed: ran out of cases.FATAL: dispatch3DStage3 failed: ran out of cases.FATAL: dispatch3DStage2 failed: ran out of cases.FATAL: dispatch3DStage1 failed: ran out of cases.MetaGemmScratchBuffer./tensorflow/core/kernels/quantization_utils.hCheck failed: workers_ != nullptr Check failed: task != nullptr tensorflow/core/kernels/meta_support.cc'GetScratch(tf_context)' Must be non NULLQuantizeDownAndShrinkRangeTinputQuantizeDownAndShrinkRangeOpQuantizeV2QuantizeV2OpQuantizeV2OpQuantizeV2OpQuantizeV2OpQuantizeV2Opinput_max_range must be larger than input_min_range.QuantizedReluQuantizedReluOpQuantizedReluOpQuantizedRelu6QuantizedRelu6OpQuantizedRelu6OpQuantizedBatchNormWithGlobalNormalizationQuantizedBatchNormOpQuantizedBiasAddT1T2QuantizedBiasAddOpQuantizedBiasAddOpQuantizedConcatQuantizedConcatOpQuantizedConcatOpinput_minsQuantizedConcatOp : Expected mins input list length to equal values length input_maxesQuantizedConcatOp : Expected maxes input list length NEON, 12x4, depth 2NEON intrinsics, 4Nx1, depth 2gemmlowp error: %s allocation failureQuantizedConv2DTfilterQuantizedConv2DOptensorflow/core/kernels/quantized_conv_ops.ccFor kernel '' from input '': Zero is not representable in the quantized range used by the input. This means QuantizedConv2d has to fall back to a slow implementation, since the border of zero values can't be represented easily. You should try to construct graphs that avoid this situation.output_width > 0output_height > 0batch > 0out_rows > 0out_cols > 0out_depth > 0given_y_mingiven_y_min must be less than given_y_max : min_separationoutput_range_givengiven_y_maxQuantizedInstanceNorminput_min must be less than input_max : QuantizedMatMulToutputQuantizedMatMulOpmax_a must be larger than min_a.max_b must be larger than min_b.Matrix size-compatible: In[0]: tensorflow/core/kernels/quantized_matmul_op.ccCheck failed: c QuantizedMulQuantizedMulOpmax_x must be larger than min_a.max_x must be larger than min_b.0 == tensor_num_elements % vector_num_elementstensorflow/core/kernels/quantized_mul_op.ccndims=bcast.x_reshape()=bcast.y_reshape()=bcast.x_bcast()=bcast.y_bcast()=QuantizedAvgPoolQuantizedAvgPoolingOpQuantizedMaxPoolQuantizedMaxPoolingOpQuantizedReshapeQuantizedReshapeOpRequantizationRangeRequantizationRangeOpRequantizeRequantizeOprequested_output_min must be <= 0, but got requested_output_max must be >= requested_output_min, but got var: Ref(T)alpha: Tdelta: Tout: Ref(T)T: numbertypeuse_locking: bool = false Update '*var' by subtracting 'alpha' * 'delta' from it. var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. delta: The change. out: Same as "var". use_locking: If `True`, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. var: resource Update '*var' by subtracting 'alpha' * 'delta' from it. var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. delta: The change. use_locking: If `True`, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. l1: Tl2: T Update '*var' as FOBOS algorithm with fixed learning rate. prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. delta: The change. out: Same as "var". use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. grad: Tindices: TindicesTindices: {int32, int64} Sparse update '*var' as FOBOS algorithm with fixed learning rate. That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. out: Same as "var". use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' as FOBOS algorithm with fixed learning rate. prox_v = var - alpha * delta var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. delta: The change. use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Sparse update '*var' as FOBOS algorithm with fixed learning rate. That is for rows we have grad for, we update var as follows: prox_v = var - alpha * grad var = sign(prox_v)/(1+alpha*l2) * max{|prox_v|-alpha*l1,0} var: Should be from a Variable(). alpha: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. accum: Ref(T)accum_update: Ref(T)lr: Trho: Tepsilon: T Update '*var' according to the adadelta scheme. accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update; var: Should be from a Variable(). accum: Should be from a Variable(). accum_update: Should be from a Variable(). lr: Scaling factor. Must be a scalar. rho: Decay factor. Must be a scalar. epsilon: Constant factor. Must be a scalar. grad: The gradient. out: Same as "var". use_locking: If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. var: Should be from a Variable(). accum: Should be from a Variable(). accum_update:: Should be from a Variable(). lr: Learning rate. Must be a scalar. rho: Decay factor. Must be a scalar. epsilon: Constant factor. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. out: Same as "var". use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. accum: resourceaccum_update: resource Update '*var' according to the adadelta scheme. accum = rho() * accum + (1 - rho()) * grad.square(); update = (update_accum + epsilon).sqrt() * (accum + epsilon()).rsqrt() * grad; update_accum = rho() * update_accum + (1 - rho()) * update.square(); var -= update; var: Should be from a Variable(). accum: Should be from a Variable(). accum_update: Should be from a Variable(). lr: Scaling factor. Must be a scalar. rho: Decay factor. Must be a scalar. epsilon: Constant factor. Must be a scalar. grad: The gradient. use_locking: If True, updating of the var, accum and update_accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. var: Should be from a Variable(). accum: Should be from a Variable(). accum_update:: Should be from a Variable(). lr: Learning rate. Must be a scalar. rho: Decay factor. Must be a scalar. epsilon: Constant factor. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the adagrad scheme. accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) var: Should be from a Variable(). accum: Should be from a Variable(). lr: Scaling factor. Must be a scalar. grad: The gradient. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the adagrad scheme. accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) var: Should be from a Variable(). accum: Should be from a Variable(). lr: Scaling factor. Must be a scalar. grad: The gradient. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} var: Should be from a Variable(). accum: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. out: Same as "var". use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' and '*accum' according to FOBOS with Adagrad learning rate. accum += grad * grad prox_v = var - lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} var: Should be from a Variable(). accum: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update relevant entries in '*var' and '*accum' according to the adagrad scheme. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update relevant entries in '*var' and '*accum' according to the adagrad scheme. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad var -= lr * grad * (1 / sqrt(accum)) var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. gradient_accumulator: Ref(T)gradient_squared_accumulator: Ref(T)global_step: int64 Update '*var' according to the proximal adagrad scheme. var: Should be from a Variable(). gradient_accumulator: Should be from a Variable(). gradient_squared_accumulator: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. global_step: Training step number. Must be a scalar. out: Same as "var". use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update entries in '*var' and '*accum' according to the proximal adagrad scheme. var: Should be from a Variable(). gradient_accumulator: Should be from a Variable(). gradient_squared_accumulator: Should be from a Variable(). grad: The gradient. indices: A vector of indices into the first dimension of var and accum. lr: Learning rate. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. global_step: Training step number. Must be a scalar. out: Same as "var". use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad prox_v = var prox_v -= lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. out: Same as "var". use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. gradient_accumulator: resourcegradient_squared_accumulator: resource Update '*var' according to the proximal adagrad scheme. var: Should be from a Variable(). gradient_accumulator: Should be from a Variable(). gradient_squared_accumulator: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. global_step: Training step number. Must be a scalar. use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update entries in '*var' and '*accum' according to the proximal adagrad scheme. var: Should be from a Variable(). gradient_accumulator: Should be from a Variable(). gradient_squared_accumulator: Should be from a Variable(). grad: The gradient. indices: A vector of indices into the first dimension of var and accum. lr: Learning rate. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. global_step: Training step number. Must be a scalar. use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Sparse update entries in '*var' and '*accum' according to FOBOS algorithm. That is for rows we have grad for, we update var and accum as follows: accum += grad * grad prox_v = var prox_v -= lr * grad * (1 / sqrt(accum)) var = sign(prox_v)/(1+lr*l2) * max{|prox_v|-lr*l1,0} var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. use_locking: If True, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. linear: Ref(T)lr_power: T Update '*var' according to the Ftrl-proximal scheme. accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new var: Should be from a Variable(). accum: Should be from a Variable(). linear: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regulariation. Must be a scalar. l2: L2 regulariation. Must be a scalar. lr_power: Scaling factor. Must be a scalar. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new var: Should be from a Variable(). accum: Should be from a Variable(). linear: Should be from a Variable(). grad: The gradient. indices: A vector of indices into the first dimension of var and accum. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. lr_power: Scaling factor. Must be a scalar. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. linear: resource Update '*var' according to the Ftrl-proximal scheme. accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new var: Should be from a Variable(). accum: Should be from a Variable(). linear: Should be from a Variable(). grad: The gradient. lr: Scaling factor. Must be a scalar. l1: L1 regulariation. Must be a scalar. l2: L2 regulariation. Must be a scalar. lr_power: Scaling factor. Must be a scalar. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update relevant entries in '*var' according to the Ftrl-proximal scheme. That is for rows we have grad for, we update var, accum and linear as follows: accum_new = accum + grad * grad linear += grad + (accum_new^(-lr_power) - accum^(-lr_power)) / lr * var quadratic = 1.0 / (accum_new^(lr_power) * lr) + 2 * l2 var = (sign(linear) * l1 - linear) / quadratic if |linear| > l1 else 0.0 accum = accum_new var: Should be from a Variable(). accum: Should be from a Variable(). linear: Should be from a Variable(). grad: The gradient. indices: A vector of indices into the first dimension of var and accum. lr: Scaling factor. Must be a scalar. l1: L1 regularization. Must be a scalar. l2: L2 regularization. Must be a scalar. lr_power: Scaling factor. Must be a scalar. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. momentum: Tuse_nesterov: bool = false Update '*var' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum + grad var -= lr * accum var: Should be from a Variable(). accum: Should be from a Variable(). lr: Scaling factor. Must be a scalar. grad: The gradient. momentum: Momentum. Must be a scalar. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. Update relevant entries in '*var' and '*accum' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows: accum = accum * momentum + grad var -= lr * accum var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. momentum: Momentum. Must be a scalar. out: Same as "var". use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. Update '*var' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. accum = accum * momentum + grad var -= lr * accum var: Should be from a Variable(). accum: Should be from a Variable(). lr: Scaling factor. Must be a scalar. grad: The gradient. momentum: Momentum. Must be a scalar. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. Update relevant entries in '*var' and '*accum' according to the momentum scheme. Set use_nesterov = True if you want to use Nesterov momentum. That is for rows we have grad for, we update var and accum as follows: accum = accum * momentum + grad var -= lr * accum var: Should be from a Variable(). accum: Should be from a Variable(). lr: Learning rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var and accum. momentum: Momentum. Must be a scalar. use_locking: If `True`, updating of the var and accum tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. use_nesterov: If `True`, the tensor passed to compute grad will be var - lr * momentum * accum, so in the end, the var you get is actually var - lr * momentum * accum. m: Ref(T)v: Ref(T)beta1_power: Tbeta2_power: Tbeta1: Tbeta2: T Update '*var' according to the Adam algorithm. lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon) var: Should be from a Variable(). m: Should be from a Variable(). v: Should be from a Variable(). beta1_power: Must be a scalar. beta2_power: Must be a scalar. lr: Scaling factor. Must be a scalar. beta1: Momentum factor. Must be a scalar. beta2: Momentum factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. grad: The gradient. out: Same as "var". use_locking: If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. m: resourcev: resource Update '*var' according to the Adam algorithm. lr_t <- learning_rate * sqrt(1 - beta2^t) / (1 - beta1^t) m_t <- beta1 * m_{t-1} + (1 - beta1) * g_t v_t <- beta2 * v_{t-1} + (1 - beta2) * g_t * g_t variable <- variable - lr_t * m_t / (sqrt(v_t) + epsilon) var: Should be from a Variable(). m: Should be from a Variable(). v: Should be from a Variable(). beta1_power: Must be a scalar. beta2_power: Must be a scalar. lr: Scaling factor. Must be a scalar. beta1: Momentum factor. Must be a scalar. beta2: Momentum factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. grad: The gradient. use_locking: If `True`, updating of the var, m, and v tensors will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. ms: Ref(T)mom: Ref(T) Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. out: Same as "var". use_locking: If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. mg: Ref(T) Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom var: Should be from a Variable(). mg: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. out: Same as "var". use_locking: If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var, ms and mom. out: Same as "var". use_locking: If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). mg: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var, ms and mom. out: Same as "var". use_locking: If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. ms: resourcemom: resource Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. use_locking: If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. mg: resource Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) mg <- rho * mg_{t-1} + (1-rho) * grad ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms - mg * mg + epsilon) var <- var - mom var: Should be from a Variable(). mg: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. use_locking: If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the RMSProp algorithm. Note that in dense implementation of this algorithm, ms and mom will update even if the grad is zero, but in this sparse implementation, ms and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 Delta = learning_rate * gradient / sqrt(mean_square + epsilon) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var, ms and mom. use_locking: If `True`, updating of the var, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Update '*var' according to the centered RMSProp algorithm. The centered RMSProp algorithm uses an estimate of the centered second moment (i.e., the variance) for normalization, as opposed to regular RMSProp, which uses the (uncentered) second moment. This often helps with training, but is slightly more expensive in terms of computation and memory. Note that in dense implementation of this algorithm, mg, ms, and mom will update even if the grad is zero, but in this sparse implementation, mg, ms, and mom will not update in iterations during which the grad is zero. mean_square = decay * mean_square + (1-decay) * gradient ** 2 mean_grad = decay * mean_grad + (1-decay) * gradient Delta = learning_rate * gradient / sqrt(mean_square + epsilon - mean_grad ** 2) ms <- rho * ms_{t-1} + (1-rho) * grad * grad mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon) var <- var - mom var: Should be from a Variable(). mg: Should be from a Variable(). ms: Should be from a Variable(). mom: Should be from a Variable(). lr: Scaling factor. Must be a scalar. epsilon: Ridge term. Must be a scalar. rho: Decay rate. Must be a scalar. grad: The gradient. indices: A vector of indices into the first dimension of var, ms and mom. use_locking: If `True`, updating of the var, mg, ms, and mom tensors is protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. pos and len shapes must match: StringToHashBucketFastinput: stringoutput: int64num_buckets: int >= 1 Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process and will never change. However, it is not suitable for cryptography. This function may be used when CPU time is scarce and inputs are trusted or unimportant. There is a risk of adversaries constructing inputs that all hash to the same bucket. To prevent this problem, use a strong hash function with `tf.string_to_hash_bucket_strong`. input: The strings to assign a hash bucket. num_buckets: The number of buckets. output: A Tensor of the same shape as the input `string_tensor`. StringToHashBucketStrongkey: list(int) Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process. The hash function is a keyed hash function, where attribute `key` defines the key of the hash function. `key` is an array of 2 elements. A strong hash is important when inputs may be malicious, e.g. URLs with additional components. Adversaries could try to make their inputs hash to the same bucket for a denial-of-service attack or to skew the results. A strong hash prevents this by making it difficult, if not infeasible, to compute inputs that hash to the same bucket. This comes at a cost of roughly 4x higher compute time than `tf.string_to_hash_bucket_fast`. input: The strings to assign a hash bucket. num_buckets: The number of buckets. key: The key for the keyed hash function passed as a list of two uint64 elements. output: A Tensor of the same shape as the input `string_tensor`. StringToHashBucketstring_tensor: string Converts each string in the input Tensor to its hash mod by a number of buckets. The hash function is deterministic on the content of the string within the process. Note that the hash function may change from time to time. This functionality will be deprecated and it's recommended to use `tf.string_to_hash_bucket_fast()` or `tf.string_to_hash_bucket_strong()`. num_buckets: The number of buckets. output: A Tensor of the same shape as the input `string_tensor`. ReduceJoininputs: stringreduction_indices: int32keep_dims: bool = falseseparator: string = ''output: string Joins a string Tensor across the given dimensions. Computes the string join across dimensions in the given string Tensor of shape `[d_0, d_1, ..., d_n-1]`. Returns a new Tensor created by joining the input strings with the given separator (default: empty string). Negative indices are counted backwards from the end, with `-1` being equivalent to `n - 1`. For example: ```python # tensor `a` is [["a", "b"], ["c", "d"]] tf.reduce_join(a, 0) ==> ["ac", "bd"] tf.reduce_join(a, 1) ==> ["ab", "cd"] tf.reduce_join(a, -2) = tf.reduce_join(a, 0) ==> ["ac", "bd"] tf.reduce_join(a, -1) = tf.reduce_join(a, 1) ==> ["ab", "cd"] tf.reduce_join(a, 0, keep_dims=True) ==> [["ac", "bd"]] tf.reduce_join(a, 1, keep_dims=True) ==> [["ab"], ["cd"]] tf.reduce_join(a, 0, separator=".") ==> ["a.c", "b.d"] tf.reduce_join(a, [0, 1]) ==> ["acbd"] tf.reduce_join(a, [1, 0]) ==> ["abcd"] tf.reduce_join(a, []) ==> ["abcd"] ``` inputs: The input to be joined. All reduced indices must have non-zero size. reduction_indices: The dimensions to reduce over. Dimensions are reduced in the order specified. Omitting `reduction_indices` is equivalent to passing `[n-1, n-2, ..., 0]`. Negative indices from `-n` to `-1` are supported. keep_dims: If `True`, retain reduced dimensions with length `1`. separator: The separator to use when joining. output: Has shape equal to that of the input with reduced dimensions removed or set to `1` depending on `keep_dims`. AsStringinput: TT: {int32, int64, complex64, float, double, bool, int8}precision: int = -1scientific: bool = falseshortest: bool = falsewidth: int = -1fill: string = '' Converts each entry in the given tensor to strings. Supports many numeric types and boolean. precision: The post-decimal precision to use for floating point numbers. Only used if precision > -1. scientific: Use scientific notation for floating point numbers. shortest: Use shortest representation (either scientific or standard) for floating point numbers. width: Pad pre-decimal numbers to this width. Applies to both floating point and integer numbers. Only used if width > -1. fill: The value to pad if width > -1. If empty, pads with spaces. Another typical value is '0'. String cannot be longer than 1 character. inputs: N * stringN: int Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator). inputs: A list of string tensors. The tensors must all have the same shape, or be scalars. Scalars may be mixed in; these will be broadcast to the shape of non-scalar inputs. separator: string, an optional join separator. StringSplitdelimiter: stringindices: int64values: stringshape: int64 Split elements of `input` based on `delimiter` into a `SparseTensor`. Let N be the size of source (typically N will be the batch size). Split each element of `input` based on `delimiter` and return a `SparseTensor` containing the splitted tokens. Empty tokens are ignored. `delimiter` can be empty, or a string of split characters. If `delimiter` is an empty string, each element of `input` is split into individual single-byte character strings, including splitting of UTF-8 multibyte sequences. Otherwise every character of `delimiter` is a potential split point. For example: N = 2, input[0] is 'hello world' and input[1] is 'a b c', then the output will be indices = [0, 0; 0, 1; 1, 0; 1, 1; 1, 2] shape = [2, 3] values = ['hello', 'world', 'a', 'b', 'c'] input: 1-D. Strings to split. delimiter: 0-D. Delimiter characters (bytes), or empty string. indices: A dense matrix of int64 representing the indices of the sparse tensor. values: A vector of strings corresponding to the splited values. shape: a length-2 vector of int64 representing the shape of the sparse tensor, where the first value is N and the second value is the maximum number of tokens in a single input entry. EncodeBase64pad: bool = false Encode strings into web-safe base64 format. Refer to the following article for more information on base64 format: en.wikipedia.org/wiki/Base64. Base64 strings may have padding with '=' at the end so that the encoded has length multiple of 4. See Padding section of the link above. Web-safe means that the encoder uses - and _ instead of + and /. input: Strings to be encoded. output: Input strings encoded in base64. pad: Bool whether padding is applied at the ends. DecodeBase64 Decode web-safe base64-encoded strings. Input may or may not have padding at the end. See EncodeBase64 for padding. Web-safe means that input must use - and _ instead of + and /. input: Base64 strings to decode. output: Decoded strings. Substrpos: Tlen: TT: {int32, int64} Return substrings from `Tensor` of strings. For each string in the input `Tensor`, creates a substring starting at index `pos` with a total length of `len`. If `len` defines a substring that would extend beyond the length of the input string, then as many characters as possible are used. If `pos` is negative or specifies a character index larger than any of the input strings, then an `InvalidArgumentError` is thrown. `pos` and `len` must have the same shape, otherwise a `ValueError` is thrown on Op creation. *NOTE*: `Substr` supports broadcasting up to two dimensions. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) --- Examples Using scalar `pos` and `len`: ```python input = [b'Hello', b'World'] position = 1 length = 3 output = [b'ell', b'orl'] ``` Using `pos` and `len` with same shape as `input`: ```python input = [[b'ten', b'eleven', b'twelve'], [b'thirteen', b'fourteen', b'fifteen'], [b'sixteen', b'seventeen', b'eighteen']] position = [[1, 2, 3], [1, 2, 3], [1, 2, 3]] length = [[2, 3, 4], [4, 3, 2], [5, 5, 5]] output = [[b'en', b'eve', b'lve'], [b'hirt', b'urt', b'te'], [b'ixtee', b'vente', b'hteen']] ``` Broadcasting `pos` and `len` onto `input`: ``` input = [[b'ten', b'eleven', b'twelve'], [b'thirteen', b'fourteen', b'fifteen'], [b'sixteen', b'seventeen', b'eighteen'], [b'nineteen', b'twenty', b'twentyone']] position = [1, 2, 3] length = [1, 2, 3] output = [[b'e', b'ev', b'lve'], [b'h', b'ur', b'tee'], [b'i', b've', b'hte'], [b'i', b'en', b'nty']] ``` Broadcasting `input` onto `pos` and `len`: ``` input = b'thirteen' position = [1, 5, 7] length = [3, 2, 1] output = [b'hir', b'ee', b'n"] ``` input: Tensor of strings pos: Scalar defining the position of first character in each substring len: Scalar defining the number of characters to include in each substring output: Tensor of substrings The outer dimensions of indices.shape=must match the outer dimensions of updates.shape=The inner dimensions of ref.shape=must match the inner ref: Ref(dtype)shape: shapedtype: typecontainer: string = ''shared_name: string = '' Holds state in the form of a tensor that persists across steps. Outputs a ref to the tensor state so it may be read or modified. TODO(zhifengc/mrry): Adds a pointer to a more detail document about sharing states in tensorflow. ref: A reference to the variable tensor. shape: The shape of the variable tensor. dtype: The type of elements in the variable tensor. container: If non-empty, this variable is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this variable is named in the given bucket with this shared_name. Otherwise, the node name is used instead. Use VariableV2 instead.is_initialized: bool Checks whether a tensor has been initialized. Outputs boolean scalar indicating whether the tensor has been initialized. ref: Should be from a `Variable` node. May be uninitialized. dtype: The type of elements in the variable tensor. var_name: string = '' Returns a tensor that may be mutated, but only persists within a single step. This is an experimental op for internal use only and it is possible to use this op in unsafe ways. DO NOT USE unless you fully understand the risks. It is the caller's responsibility to ensure that 'ref' is eventually passed to a matching 'DestroyTemporaryVariable' op after all other uses have completed. Outputs a ref to the tensor state so it may be read or modified. E.g. var = state_ops._temporary_variable([1, 2], types.float_) var_name = var.op.name var = state_ops.assign(var, [[4.0, 5.0]]) var = state_ops.assign_add(var, [[6.0, 7.0]]) final = state_ops._destroy_temporary_variable(var, var_name=var_name) ref: A reference to the variable tensor. shape: The shape of the variable tensor. dtype: The type of elements in the variable tensor. var_name: Overrides the name used for the temporary variable resource. Default value is the name of the 'TemporaryVariable' op (which is guaranteed unique). ref: Ref(T)value: TT: typevar_name: string Destroys the temporary variable and returns its final value. Sets output to the value of the Tensor pointed to by 'ref', then destroys the temporary variable called 'var_name'. All other uses of 'ref' *must* have executed before this op. This is typically achieved by chaining the ref through each assign op, or by using control dependencies. Outputs the final value of the tensor pointed to by 'ref'. ref: A reference to the temporary variable tensor. var_name: Name of the temporary variable, usually the name of the matching 'TemporaryVariable' op. output_ref: Ref(T)validate_shape: bool = trueuse_locking: bool = true Update 'ref' by assigning 'value' to it. This operation outputs "ref" after the assignment is done. This makes it easier to chain operations that need to use the reset value. ref: Should be from a `Variable` node. May be uninitialized. value: The value to be assigned to the variable. validate_shape: If true, the operation will validate that the shape of 'value' matches the shape of the Tensor being assigned to. If false, 'ref' will take on the shape of 'value'. use_locking: If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref:= Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been reset. Update 'ref' by adding 'value' to it. This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value. ref: Should be from a `Variable` node. value: The value to be added to the variable. use_locking: If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref:= Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated. Update 'ref' by subtracting 'value' from it. This operation outputs "ref" after the update is done. This makes it easier to chain operations that need to use the reset value. ref: Should be from a `Variable` node. value: The value to be subtracted to the variable. use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref:= Same as "ref". Returned as a convenience for operations that want to use the new value after the variable has been updated. updates: T Applies sparse updates to a variable reference. This operation computes ```python # Scalar indices ref[indices, ...] = updates[...] # Vector indices (for each i) ref[indices[i], ...] = updates[i, ...] # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...] = updates[i, ..., j, ...] ``` This operation outputs `ref` after the update is done. This makes it easier to chain operations that need to use the reset value. If values in `ref` is to be updated more than once, because there are duplicate entries in `indices`, the order at which the updates happen for each value is undefined. Requires `updates.shape = indices.shape + ref.shape[1:]`.

ref: Should be from a `Variable` node. indices: A tensor of indices into the first dimension of `ref`. updates: A tensor of updated values to store in `ref`. output_ref:= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. use_locking: If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Adds sparse updates to a variable reference. This operation computes # Scalar indices ref[indices, ...] += updates[...] # Vector indices (for each i) ref[indices[i], ...] += updates[i, ...] # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...] += updates[i, ..., j, ...] This operation outputs `ref` after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple `indices` reference the same location, their contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`.
ref: Should be from a `Variable` node. indices: A tensor of indices into the first dimension of `ref`. updates: A tensor of updated values to add to `ref`. output_ref:= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. use_locking: If True, the addition will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Subtracts sparse updates to a variable reference. ```python # Scalar indices ref[indices, ...] -= updates[...] # Vector indices (for each i) ref[indices[i], ...] -= updates[i, ...] # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...] -= updates[i, ..., j, ...] ``` This operation outputs `ref` after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple `indices` reference the same location, their (negated) contributions add. Requires `updates.shape = indices.shape + ref.shape[1:]`.
ref: Should be from a `Variable` node. indices: A tensor of indices into the first dimension of `ref`. updates: A tensor of updated values to subtract from `ref`. output_ref:= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. use_locking: If True, the subtraction will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Multiplies sparse updates into a variable reference. This operation computes ```python # Scalar indices ref[indices, ...] *= updates[...] # Vector indices (for each i) ref[indices[i], ...] *= updates[i, ...] # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...] *= updates[i, ..., j, ...] ``` This operation outputs `ref` after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple `indices` reference the same location, their contributions multiply. Requires `updates.shape = indices.shape + ref.shape[1:]`. ref: Should be from a `Variable` node. indices: A tensor of indices into the first dimension of `ref`. updates: A tensor of updated values to multiply to `ref`. output_ref:= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. use_locking: If True, the operation will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Divides a variable reference by sparse updates. This operation computes ```python # Scalar indices ref[indices, ...] /= updates[...] # Vector indices (for each i) ref[indices[i], ...] /= updates[i, ...] # High rank indices (for each i, ..., j) ref[indices[i, ..., j], ...] /= updates[i, ..., j, ...] ``` This operation outputs `ref` after the update is done. This makes it easier to chain operations that need to use the reset value. Duplicate entries are handled correctly: if multiple `indices` reference the same location, their contributions divide. Requires `updates.shape = indices.shape + ref.shape[1:]`. ref: Should be from a `Variable` node. indices: A tensor of indices into the first dimension of `ref`. updates: A tensor of values that `ref` is divided by. output_ref:= Same as `ref`. Returned as a convenience for operations that want to use the updated values after the update is done. use_locking: If True, the operation will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. Applies sparse `updates` to individual values or slices within a given variable according to `indices`. `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `ref`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `ref`. `updates` is `Tensor` of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ``` For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ```python ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) update = tf.scatter_nd_update(ref, indices, updates) with tf.Session() as sess: print sess.run(update) ``` The resulting update to ref would look like this: [1, 11, 3, 10, 9, 6, 7, 12] See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to slices. ref: A mutable Tensor. Should be from a Variable node. indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref. updates: A Tensor. Must have the same type as ref. A tensor of updated values to add to ref. use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref: Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done. Applies sparse addition between `updates` and individual values or slices within a given variable according to `indices`. `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `ref`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `ref`. `updates` is `Tensor` of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ``` For example, say we want to add 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that addition would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11, 12]) add = tf.scatter_nd_add(ref, indices, updates) with tf.Session() as sess: print sess.run(add) The resulting update to ref would look like this: [1, 13, 3, 14, 14, 6, 7, 20] See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to slices. ref: A mutable Tensor. Should be from a Variable node. indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref. updates: A Tensor. Must have the same type as ref. A tensor of updated values to add to ref. use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref: Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done. Applies sparse subtraction between `updates` and individual values or slices within a given variable according to `indices`. `ref` is a `Tensor` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `ref`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `ref`. `updates` is `Tensor` of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, ref.shape[K], ..., ref.shape[P-1]]. ``` For example, say we want to subtract 4 scattered elements from a rank-1 tensor with 8 elements. In Python, that subtraction would look like this: ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8]) indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11, 12]) sub = tf.scatter_nd_sub(ref, indices, updates) with tf.Session() as sess: print sess.run(sub) The resulting update to ref would look like this: [1, -9, 3, -6, -4, 6, 7, -4] See [tf.scatter_nd](#scatter_nd) for more details about how to make updates to slices. ref: A mutable Tensor. Should be from a Variable node. indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref. updates: A Tensor. Must have the same type as ref. A tensor of updated values to subtract from ref. use_locking: An optional bool. Defaults to True. If True, the assignment will be protected by a lock; otherwise the behavior is undefined, but may exhibit less contention. output_ref: Same as ref. Returned as a convenience for operations that want to use the updated values after the update is done. CountUpTooutput: Tlimit: int Increments 'ref' until it reaches 'limit'. ref: Should be from a scalar `Variable` node. limit: If incrementing ref would bring it above limit, instead generates an 'OutOfRange' error. output: A copy of the input before increment. If nothing else modifies the input, the values produced will all be distinct. adjoint_aadjoint_bSparseAddGradbackprop_val_grad: Ta_indices: int64b_indices: int64sum_indices: int64a_val_grad: Tb_val_grad: T The gradient operator for the SparseAdd op. The SparseAdd op calculates A + B, where A, B, and the sum are all represented as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. non-empty values of the sum, and outputs the gradients w.r.t. the non-empty values of A and B. backprop_val_grad: 1-D with shape `[nnz(sum)]`. The gradient with respect to the non-empty values of the sum. a_indices: 2-D. The `indices` of the `SparseTensor` A, size `[nnz(A), ndims]`. b_indices: 2-D. The `indices` of the `SparseTensor` B, size `[nnz(B), ndims]`. sum_indices: 2-D. The `indices` of the sum `SparseTensor`, size `[nnz(sum), ndims]`. a_val_grad: 1-D with shape `[nnz(A)]`. The gradient with respect to the non-empty values of A. b_val_grad: 1-D with shape `[nnz(B)]`. The gradient with respect to the non-empty values of B. SparseAdda_values: Ta_shape: int64b_values: Tb_shape: int64thresh: Trealsum_values: Tsum_shape: int64Treal: realnumbertype Adds two `SparseTensor` objects to produce another `SparseTensor`. The input `SparseTensor` objects' indices are assumed ordered in standard lexicographic order. If this is not the case, before this step run `SparseReorder` to restore index ordering. By default, if two values sum to zero at some index, the output `SparseTensor` would still include that particular location in its index, storing a zero in the corresponding value slot. To override this, callers can specify `thresh`, indicating that if the sum has a magnitude strictly smaller than `thresh`, its corresponding value and index would then not be included. In particular, `thresh == 0` (default) means everything is kept and actual thresholding happens only for a positive value. In the following shapes, `nnz` is the count after taking `thresh` into account. a_indices: 2-D. The `indices` of the first `SparseTensor`, size `[nnz, ndims]` Matrix. a_values: 1-D. The `values` of the first `SparseTensor`, size `[nnz]` Vector. a_shape: 1-D. The `shape` of the first `SparseTensor`, size `[ndims]` Vector. b_indices: 2-D. The `indices` of the second `SparseTensor`, size `[nnz, ndims]` Matrix. b_values: 1-D. The `values` of the second `SparseTensor`, size `[nnz]` Vector. b_shape: 1-D. The `shape` of the second `SparseTensor`, size `[ndims]` Vector. thresh: 0-D. The magnitude threshold that determines if an output value/index pair takes space. SparseTensorDenseMatMulb: Tproduct: Tadjoint_a: bool = falseadjoint_b: bool = false Multiply SparseTensor (of rank 2) "A" by dense matrix "B". No validity checking is performed on the indices of A. However, the following input format is recommended for optimal behavior: if adjoint_a == false: A should be sorted in lexicographically increasing order. Use SparseReorder if you're not sure. if adjoint_a == true: A should be sorted in order of increasing dimension 1 (i.e., "column major" order instead of "row major" order). a_indices: 2-D. The `indices` of the `SparseTensor`, size `[nnz, 2]` Matrix. a_values: 1-D. The `values` of the `SparseTensor`, size `[nnz]` Vector. a_shape: 1-D. The `shape` of the `SparseTensor`, size `[2]` Vector. b: 2-D. A dense Matrix. adjoint_a: Use the adjoint of A in the matrix multiply. If A is complex, this is transpose(conj(A)). Otherwise it's transpose(A). adjoint_b: Use the adjoint of B in the matrix multiply. If B is complex, this is transpose(conj(B)). Otherwise it's transpose(B). SerializeSparsesparse_indices: int64sparse_values: Tsparse_shape: int64serialized_sparse: string Serialize a `SparseTensor` into a string 3-vector (1-D `Tensor`) object. sparse_indices: 2-D. The `indices` of the `SparseTensor`. sparse_values: 1-D. The `values` of the `SparseTensor`. sparse_shape: 1-D. The `shape` of the `SparseTensor`. SerializeManySparse Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` string `Tensor`. The `SparseTensor` must have rank `R` greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the `SparseTensor` must be sorted in increasing order of this first dimension. The serialized `SparseTensor` objects going into each row of `serialized_sparse` will have rank `R-1`. The minibatch size `N` is extracted from `sparse_shape[0]`. sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. DeserializeManySparsesparse_values: dtype Deserialize and concatenate `SparseTensors` from a serialized minibatch. The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where `N` is the minibatch size and the rows correspond to packed outputs of `SerializeSparse`. The ranks of the original `SparseTensor` objects must all match. When the final `SparseTensor` is created, it has rank one higher than the ranks of the incoming `SparseTensor` objects (they have been concatenated along a new row dimension). The output `SparseTensor` object's shape values for all dimensions but the first are the max across the input `SparseTensor` objects' shape values for the corresponding dimensions. Its first shape value is `N`, the minibatch size. The input `SparseTensor` objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run `SparseReorder` to restore index ordering. For example, if the serialized input is a `[2 x 3]` matrix representing two original `SparseTensor` objects: index = [ 0] [10] [20] values = [1, 2, 3] shape = [50] and index = [ 2] [10] values = [4, 5] shape = [30] then the final deserialized `SparseTensor` will be: index = [0 0] [0 10] [0 20] [1 2] [1 10] values = [1, 2, 3, 4, 5] shape = [2 50] serialized_sparse: 2-D, The `N` serialized `SparseTensor` objects. Must have 3 columns. dtype: The `dtype` of the serialized `SparseTensor` objects. sparse_indices: Tindicesoutput_shape: Tindicesdefault_value: Tvalidate_indices: bool = truedense: T Converts a sparse representation into a dense tensor. Builds an array `dense` with shape `output_shape` such that ```prettyprint # If sparse_indices is scalar dense[i] = (i == sparse_indices ? sparse_values : default_value) # If sparse_indices is a vector, then for each i dense[sparse_indices[i]] = sparse_values[i] # If sparse_indices is an n by d matrix, then for each i in [0, n) dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i] ``` All other values in `dense` are set to `default_value`. If `sparse_values` is a scalar, all sparse indices are set to this single value. Indices should be sorted in lexicographic order, and indices must not contain any repeats. If `validate_indices` is true, these properties are checked during execution. sparse_indices: 0-D, 1-D, or 2-D. `sparse_indices[i]` contains the complete index where `sparse_values[i]` will be placed. output_shape: 1-D. Shape of the dense output tensor. sparse_values: 1-D. Values corresponding to each row of `sparse_indices`, or a scalar value to be used for all sparse indices. default_value: Scalar value to set for indices not specified in `sparse_indices`. validate_indices: If true, indices are checked to make sure they are sorted in lexicographic order and that there are no repeats. dense: Dense output tensor of shape `output_shape`. SparseConcatindices: N * int64values: N * Tshapes: N * int64output_indices: int64output_values: Toutput_shape: int64concat_dim: intN: int >= 2 Concatenates a list of `SparseTensor` along the specified dimension. Concatenation is with respect to the dense versions of these sparse tensors. It is assumed that each input is a `SparseTensor` whose elements are ordered along increasing dimension number. All inputs' shapes must match, except for the concat dimension. The `indices`, `values`, and `shapes` lists must have the same length. The output shape is identical to the inputs', except along the concat dimension, where it is the sum of the inputs' sizes along that dimension. The output elements will be resorted to preserve the sort order along increasing dimension number. This op runs in `O(M log M)` time, where `M` is the total number of non-empty values across all inputs. This is due to the need for an internal sort in order to concatenate efficiently across an arbitrary dimension. For example, if `concat_dim = 1` and the inputs are sp_inputs[0]: shape = [2, 3] [0, 2]: "a" [1, 0]: "b" [1, 1]: "c" sp_inputs[1]: shape = [2, 4] [0, 1]: "d" [0, 2]: "e" then the output will be shape = [2, 7] [0, 2]: "a" [0, 4]: "d" [0, 5]: "e" [1, 0]: "b" [1, 1]: "c" Graphically this is equivalent to doing [ a] concat [ d e ] = [ a d e ] [b c ] [ ] [b c ] indices: 2-D. Indices of each input `SparseTensor`. values: 1-D. Non-empty values of each `SparseTensor`. shapes: 1-D. Shapes of each `SparseTensor`. output_indices: 2-D. Indices of the concatenated `SparseTensor`. output_values: 1-D. Non-empty values of the concatenated `SparseTensor`. output_shape: 1-D. Shape of the concatenated `SparseTensor`. concat_dim: Dimension to concatenate along. Must be in range [-rank, rank), where rank is the number of dimensions in each input `SparseTensor`. SparseSplitsplit_dim: int64values: Toutput_indices: num_split * int64output_values: num_split * Toutput_shape: num_split * int64num_split: int >= 1 Split a `SparseTensor` into `num_split` tensors along one dimension. If the `shape[split_dim]` is not an integer multiple of `num_split`. Slices `[0 : shape[split_dim] % num_split]` gets one extra dimension. For example, if `split_dim = 1` and `num_split = 2` and the input is input_tensor = shape = [2, 7] [ a d e ] [b c ] Graphically the output tensors are: output_tensor[0] = shape = [2, 4] [ a ] [b c ] output_tensor[1] = shape = [2, 3] [ d e ] [ ] split_dim: 0-D. The dimension along which to split. Must be in the range `[0, rank(shape))`. num_split: The number of ways to split. indices: 2-D tensor represents the indices of the sparse tensor. values: 1-D tensor represents the values of the sparse tensor. shape: 1-D. tensor represents the shape of the sparse tensor. output indices: A list of 1-D tensors represents the indices of the output sparse tensors. output_values: A list of 1-D tensors represents the values of the output sparse tensors. output_shape: A list of 1-D tensors represents the shape of the output sparse tensors. SparseReorderinput_indices: int64input_values: Tinput_shape: int64 Reorders a SparseTensor into the canonical, row-major ordering. Note that by convention, all sparse ops preserve the canonical ordering along increasing dimension number. The only time ordering can be violated is during manual manipulation of the indices and values vectors to add entries. Reordering does not affect the shape of the SparseTensor. If the tensor has rank `R` and `N` non-empty values, `input_indices` has shape `[N, R]`, input_values has length `N`, and input_shape has length `R`. input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. input_values: 1-D. `N` non-empty values corresponding to `input_indices`. input_shape: 1-D. Shape of the input SparseTensor. output_indices: 2-D. `N x R` matrix with the same indices as input_indices, but in canonical row-major ordering. output_values: 1-D. `N` non-empty values corresponding to `output_indices`. SparseReshapenew_shape: int64 Reshapes a SparseTensor to represent values in a new dense shape. This operation has the same semantics as reshape on the represented dense tensor. The `input_indices` are recomputed based on the requested `new_shape`. If one component of `new_shape` is the special value -1, the size of that dimension is computed so that the total dense size remains constant. At most one component of `new_shape` can be -1. The number of dense elements implied by `new_shape` must be the same as the number of dense elements originally implied by `input_shape`. Reshaping does not affect the order of values in the SparseTensor. If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape` has length `R_out`, then `input_indices` has shape `[N, R_in]`, `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and `output_shape` has length `R_out`. input_indices: 2-D. `N x R_in` matrix with the indices of non-empty values in a SparseTensor. input_shape: 1-D. `R_in` vector with the input SparseTensor's dense shape. new_shape: 1-D. `R_out` vector with the requested new dense shape. output_indices: 2-D. `N x R_out` matrix with the updated indices of non-empty values in the output SparseTensor. output_shape: 1-D. `R_out` vector with the full dense shape of the output SparseTensor. This is the same as `new_shape` but with any -1 dimensions filled in. SparseTensorDenseAdda_indices: Tindicesa_shape: Tindices Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`. This Op does not require `a_indices` be sorted in standard lexicographic order. a_indices: 2-D. The `indices` of the `SparseTensor`, with shape `[nnz, ndims]`. a_values: 1-D. The `values` of the `SparseTensor`, with shape `[nnz]`. a_shape: 1-D. The `shape` of the `SparseTensor`, with shape `[ndims]`. b: `ndims`-D Tensor. With shape `a_shape`. SparseReduceSumreduction_axes: int32keep_dims: bool = False Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to `tf.reduce_sum()`. In particular, this Op also returns a dense `Tensor` instead of a sparse one. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python. input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. input_values: 1-D. `N` non-empty values corresponding to `input_indices`. input_shape: 1-D. Shape of the input SparseTensor. reduction_axes: 1-D. Length-`K` vector containing the reduction axes. keep_dims: If true, retain reduced dimensions with length 1. output: `R-K`-D. The reduced Tensor. SparseReduceSumSparse Computes the sum of elements across dimensions of a SparseTensor. This Op takes a SparseTensor and is the sparse counterpart to `tf.reduce_sum()`. In contrast to SparseReduceSum, this Op returns a SparseTensor. Reduces `sp_input` along the dimensions given in `reduction_axes`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_axes`. If `keep_dims` is true, the reduced dimensions are retained with length 1. If `reduction_axes` has no entries, all dimensions are reduced, and a tensor with a single element is returned. Additionally, the axes can be negative, which are interpreted according to the indexing rules in Python. input_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. input_values: 1-D. `N` non-empty values corresponding to `input_indices`. input_shape: 1-D. Shape of the input SparseTensor. reduction_axes: 1-D. Length-`K` vector containing the reduction axes. keep_dims: If true, retain reduced dimensions with length 1. SparseDenseCwiseMulsp_indices: int64sp_values: Tsp_shape: int64 Component-wise multiplies a SparseTensor by a dense Tensor. The output locations corresponding to the implicitly zero elements in the sparse tensor will be zero (i.e., will not take up storage space), regardless of the contents of the dense tensor (even if it's +/-INF and that INF*0 == NaN). *Limitation*: this Op only broadcasts the dense side to the sparse side, but not the other direction. sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. sp_shape: 1-D. Shape of the input SparseTensor. dense: `R`-D. The dense Tensor operand. output: 1-D. The `N` values that are operated on. SparseDenseCwiseDiv Component-wise divides a SparseTensor by a dense Tensor. *Limitation*: this Op only broadcasts the dense side to the sparse side, but not the other direction. sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. sp_shape: 1-D. Shape of the input SparseTensor. dense: `R`-D. The dense Tensor operand. output: 1-D. The `N` values that are operated on. SparseDenseCwiseAdd Adds up a SparseTensor and a dense Tensor, using these special rules: (1) Broadcasts the dense side to have the same shape as the sparse side, if eligible; (2) Then, only the dense values pointed to by the indices of the SparseTensor participate in the cwise addition. By these rules, the result is a logical SparseTensor with exactly the same indices and shape, but possibly with different non-zero values. The output of this Op is the resultant non-zero values. sp_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, possibly not in canonical ordering. sp_values: 1-D. `N` non-empty values corresponding to `sp_indices`. sp_shape: 1-D. Shape of the input SparseTensor. dense: `R`-D. The dense Tensor operand. output: 1-D. The `N` values that are operated on. SparseSoftmaxT: {float, double} Applies softmax to a batched N-D `SparseTensor`. The inputs represent an N-D SparseTensor with logical shape `[..., B, C]` (where `N >= 2`), and with indices sorted in the canonical lexicographic order. This op is equivalent to applying the normal `tf.nn.softmax()` to each innermost logical submatrix with shape `[B, C]`, but with the catch that *the implicitly zero elements do not participate*. Specifically, the algorithm is equivalent to the following: (1) Applies `tf.nn.softmax()` to a densified view of each innermost submatrix with shape `[B, C]`, along the size-C dimension; (2) Masks out the original implicitly-zero locations; (3) Renormalizes the remaining elements. Hence, the `SparseTensor` result has exactly the same non-zero indices and shape. sp_indices: 2-D. `NNZ x R` matrix with the indices of non-empty values in a SparseTensor, in canonical ordering. sp_values: 1-D. `NNZ` non-empty values corresponding to `sp_indices`. sp_shape: 1-D. Shape of the input SparseTensor. output: 1-D. The `NNZ` values for the result `SparseTensor`. SparseSparseMaximumT: realnumbertype Returns the element-wise max of two SparseTensors. Assumes the two SparseTensors have the same shape, i.e., no broadcasting. a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, in the canonical lexicographic ordering. a_values: 1-D. `N` non-empty values corresponding to `a_indices`. a_shape: 1-D. Shape of the input SparseTensor. b_indices: counterpart to `a_indices` for the other operand. b_values: counterpart to `a_values` for the other operand; must be of the same dtype. b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. output_indices: 2-D. The indices of the output SparseTensor. output_values: 1-D. The values of the output SparseTensor. SparseSparseMinimum Returns the element-wise min of two SparseTensors. Assumes the two SparseTensors have the same shape, i.e., no broadcasting. a_indices: 2-D. `N x R` matrix with the indices of non-empty values in a SparseTensor, in the canonical lexicographic ordering. a_values: 1-D. `N` non-empty values corresponding to `a_indices`. a_shape: 1-D. Shape of the input SparseTensor. b_indices: counterpart to `a_indices` for the other operand. b_values: counterpart to `a_values` for the other operand; must be of the same dtype. b_shape: counterpart to `a_shape` for the other operand; the two shapes must be equal. output_indices: 2-D. The indices of the output SparseTensor. output_values: 1-D. The values of the output SparseTensor. AddSparseToTensorsMapsparse_handle: int64 Add a `SparseTensor` to a `SparseTensorsMap` return its handle. A `SparseTensor` is represented by three tensors: `sparse_indices`, `sparse_values`, and `sparse_shape`. This operator takes the given `SparseTensor` and adds it to a container object (a `SparseTensorsMap`). A unique key within this container is generated in the form of an `int64`, and this is the value that is returned. The `SparseTensor` can then be read out as part of a minibatch by passing the key as a vector element to `TakeManySparseFromTensorsMap`. To ensure the correct `SparseTensorsMap` is accessed, ensure that the same `container` and `shared_name` are passed to that Op. If no `shared_name` is provided here, instead use the *name* of the Operation created by calling `AddSparseToTensorsMap` as the `shared_name` passed to `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. sparse_indices: 2-D. The `indices` of the `SparseTensor`. sparse_values: 1-D. The `values` of the `SparseTensor`. sparse_shape: 1-D. The `shape` of the `SparseTensor`. sparse_handle: 0-D. The handle of the `SparseTensor` now stored in the `SparseTensorsMap`. container: The container name for the `SparseTensorsMap` created by this op. shared_name: The shared name for the `SparseTensorsMap` created by this op. If blank, the new Operation's unique name is used. AddManySparseToTensorsMapsparse_handles: int64 Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles. A `SparseTensor` of rank `R` is represented by three tensors: `sparse_indices`, `sparse_values`, and `sparse_shape`, where ```sparse_indices.shape[1] == sparse_shape.shape[0] == R``` An `N`-minibatch of `SparseTensor` objects is represented as a `SparseTensor` having a first `sparse_indices` column taking values between `[0, N)`, where the minibatch size `N == sparse_shape[0]`. The input `SparseTensor` must have rank `R` greater than 1, and the first dimension is treated as the minibatch dimension. Elements of the `SparseTensor` must be sorted in increasing order of this first dimension. The stored `SparseTensor` objects pointed to by each row of the output `sparse_handles` will have rank `R-1`. The `SparseTensor` values can then be read out as part of a minibatch by passing the given keys as vector elements to `TakeManySparseFromTensorsMap`. To ensure the correct `SparseTensorsMap` is accessed, ensure that the same `container` and `shared_name` are passed to that Op. If no `shared_name` is provided here, instead use the *name* of the Operation created by calling `AddManySparseToTensorsMap` as the `shared_name` passed to `TakeManySparseFromTensorsMap`. Ensure the Operations are colocated. sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. `sparse_indices[:, 0]` must be ordered values in `[0, N)`. sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. The minibatch size `N == sparse_shape[0]`. sparse_handles: 1-D. The handles of the `SparseTensor` now stored in the `SparseTensorsMap`. Shape: `[N]`. container: The container name for the `SparseTensorsMap` created by this op. shared_name: The shared name for the `SparseTensorsMap` created by this op. If blank, the new Operation's unique name is used. TakeManySparseFromTensorsMap Read `SparseTensors` from a `SparseTensorsMap` and concatenate them. The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where `N` is the minibatch size and the rows correspond to the output handles of `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the original `SparseTensor` objects that went into the given input ops must all match. When the final `SparseTensor` is created, it has rank one higher than the ranks of the incoming `SparseTensor` objects (they have been concatenated along a new row dimension on the left). The output `SparseTensor` object's shape values for all dimensions but the first are the max across the input `SparseTensor` objects' shape values for the corresponding dimensions. Its first shape value is `N`, the minibatch size. The input `SparseTensor` objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run `SparseReorder` to restore index ordering. For example, if the handles represent an input, which is a `[2, 3]` matrix representing two original `SparseTensor` objects: ``` index = [ 0] [10] [20] values = [1, 2, 3] shape = [50] ``` and ``` index = [ 2] [10] values = [4, 5] shape = [30] ``` then the final `SparseTensor` will be: ``` index = [0 0] [0 10] [0 20] [1 2] [1 10] values = [1, 2, 3, 4, 5] shape = [2 50] ``` sparse_handles: 1-D, The `N` serialized `SparseTensor` objects. Shape: `[N]`. sparse_indices: 2-D. The `indices` of the minibatch `SparseTensor`. sparse_values: 1-D. The `values` of the minibatch `SparseTensor`. sparse_shape: 1-D. The `shape` of the minibatch `SparseTensor`. dtype: The `dtype` of the `SparseTensor` objects stored in the `SparseTensorsMap`. container: The container name for the `SparseTensorsMap` read by this op. shared_name: The shared name for the `SparseTensorsMap` read by this op. It should not be blank; rather the `shared_name` or unique Operation name of the Op that created the original `SparseTensorsMap` should be used. tensor: Ttensor_name: stringsend_device: stringsend_device_incarnation: intrecv_device: stringclient_terminated: bool = false Sends the named tensor from send_device to recv_device. tensor: The tensor to send. tensor_name: The name of the tensor to send. send_device: The name of the device sending the tensor. send_device_incarnation: The current incarnation of send_device. recv_device: The name of the device receiving the tensor. client_terminated: If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller. tensor: tensor_typetensor_type: type Receives the named tensor from send_device on recv_device. tensor: The tensor to receive. tensor_name: The name of the tensor to receive. send_device: The name of the device sending the tensor. send_device_incarnation: The current incarnation of send_device. recv_device: The name of the device receiving the tensor. client_terminated: If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller. Sends the named tensor from send_device to recv_device. _HostSend requires its input on host memory whereas _Send requires its input on device memory. tensor: The tensor to send. tensor_name: The name of the tensor to send. send_device: The name of the device sending the tensor. send_device_incarnation: The current incarnation of send_device. recv_device: The name of the device receiving the tensor. client_terminated: If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller. Receives the named tensor from send_device on recv_device. _HostRecv requires its input on host memory whereas _Recv requires its input on device memory. tensor: The tensor to receive. tensor_name: The name of the tensor to receive. send_device: The name of the device sending the tensor. send_device_incarnation: The current incarnation of send_device. recv_device: The name of the device receiving the tensor. client_terminated: If set to true, this indicates that the node was added to the graph as a result of a client-side feed or fetch of Tensor data, in which case the corresponding send or recv is expected to be managed locally by the caller. PyFuncinput: Tinoutput: Touttoken: stringTin: list(type) >= 0Tout: list(type) >=0 Invokes a python function to compute func(input)->output. This operation is considered stateful. For a stateless version, see PyFuncStateless. token: A token representing a registered python function in this address space. input: List of Tensors that will provide input to the Op. output: The outputs from the Op. Tin: Data types of the inputs to the op. Tout: Data types of the outputs from the op. The length of the list specifies the number of outputs. PyFuncStatelessTout: list(type) >= 0 A stateless version of PyFunc. RandomUniformshape: Toutput: dtypeseed: int = 0seed2: int = 0dtype: {half,float,double} Outputs random values from a uniform distribution. The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. shape: The shape of the output tensor. dtype: The type of the output. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor of the specified shape filled with uniform random values. RandomUniformIntminval: Toutmaxval: ToutTout: {int32, int64} Outputs random integers from a uniform distribution. The generated values are uniform integers in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. The random integers are slightly biased unless `maxval - minval` is an exact power of two. The bias is small for values of `maxval - minval` significantly smaller than the range of the output (either `2^32` or `2^64`). shape: The shape of the output tensor. minval: 0-D. Inclusive lower bound on the generated integers. maxval: 0-D. Exclusive upper bound on the generated integers. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor of the specified shape filled with uniform random integers. RandomStandardNormal Outputs random values from a normal distribution. The generated values will have mean 0 and standard deviation 1. shape: The shape of the output tensor. dtype: The type of the output. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor of the specified shape filled with random normal values. ParameterizedTruncatedNormalmeans: dtypestdevs: dtypeminvals: dtypemaxvals: dtype Outputs random values from a normal distribution. The parameters may each be a scalar which applies to the entire output, or a vector of length shape[0] which stores the parameters for each batch. shape: The shape of the output tensor. Batches are indexed by the 0th dimension. means: The mean parameter of each batch. stdevs: The standard deviation parameter of each batch. Must be greater than 0. minvals: The minimum cutoff. May be -infinity. maxvals: The maximum cutoff. May be +infinity, and must be more than the minval for each batch. dtype: The type of the output. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A matrix of shape num_batches x samples_per_batch, filled with random truncated normal values using the parameters for each row. TruncatedNormal Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. shape: The shape of the output tensor. dtype: The type of the output. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor of the specified shape filled with random truncated normal values. RandomShuffle Randomly shuffles a tensor along its first dimension. The tensor is shuffled along dimension 0, such that each `value[j]` is mapped to one and only one `output[i]`. For example, a mapping that might occur for a 3x2 tensor is: ```prettyprint [[1, 2], [[5, 6], [3, 4], ==> [1, 2], [5, 6]] [3, 4]] ``` value: The tensor to be shuffled. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor of same shape and type as `value`, shuffled along its first dimension. Multinomiallogits: Tnum_samples: int32 Draws samples from a multinomial distribution. logits: 2-D Tensor with shape `[batch_size, num_classes]`. Each slice `[i, :]` represents the unnormalized log probabilities for all classes. num_samples: 0-D. Number of independent samples to draw for each row slice. seed: If either seed or seed2 is set to be non-zero, the internal random number generator is seeded by the given seed. Otherwise, a random seed is used. seed2: A second seed to avoid seed collision. output: 2-D Tensor with shape `[batch_size, num_samples]`. Each slice `[i, :]` contains the drawn class labels with range `[0, num_classes)`. RandomGammashape: SS: {int32, int64}T: {half, float, double} Outputs random values from the Gamma distribution(s) described by alpha. This op uses the algorithm by Marsaglia et al. to acquire samples via transformation-rejection from pairs of uniform and normal random variables. See http://dl.acm.org/citation.cfm?id=358414 shape: 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in alpha. alpha: A tensor in which each scalar is a "shape" parameter describing the associated gamma distribution. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor with shape `shape + shape(alpha)`. Each slice `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha. RandomPoissonrate: dtypedtype: {half, float, double} Outputs random values from the Poisson distribution(s) described by rate. This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974. Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley shape: 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate. rate: A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution. seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. output: A tensor with shape `shape + shape(rate)`. Each slice `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for `rate[i0, i1, ...iN]`. The dtype of the output matches the dtype of rate. Shape of a default must be a length-0 or length-1 vectorDecodeRawbytes: stringoutput: out_typeout_type: {half,float,double,int32,uint8,int16,int8,int64}little_endian: bool = true Reinterpret the bytes of a string as a vector of numbers. bytes: All the elements must have the same length. little_endian: Whether the input `bytes` are in little-endian order. Ignored for `out_type` values that are stored in a single byte like `uint8`. output: A Tensor with one more dimension than the input `bytes`. The added dimension will have size equal to the length of the elements of `bytes` divided by the number of bytes to represent `out_type`. serialized: stringnames: stringsparse_keys: Nsparse * stringdense_keys: Ndense * stringdense_defaults: Tdensesparse_indices: Nsparse * int64sparse_values: sparse_typessparse_shapes: Nsparse * int64dense_values: TdenseNsparse: int >= 0Ndense: int >= 0sparse_types: list({float,int64,string}) >= 0Tdense: list({float,int64,string}) >= 0dense_shapes: list(shape) >= 0 Transforms a vector of brain.Example protos (as strings) into typed tensors. serialized: A vector containing a batch of binary serialized Example protos. names: A vector containing the names of the serialized protos. May contain, for example, table key (descriptive) names for the corresponding serialized protos. These are purely useful for debugging purposes, and the presence of values here has no effect on the output. May also be an empty vector if no names are available. If non-empty, this vector must be the same length as "serialized". dense_keys: A list of Ndense string Tensors (scalars). The keys expected in the Examples' features associated with dense values. dense_defaults: A list of Ndense Tensors (some may be empty). dense_defaults[j] provides default values when the example's feature_map lacks dense_key[j]. If an empty Tensor is provided for dense_defaults[j], then the Feature dense_keys[j] is required. The input type is inferred from dense_defaults[j], even when it's empty. If dense_defaults[j] is not empty, and dense_shapes[j] is fully defined, then the shape of dense_defaults[j] must match that of dense_shapes[j]. If dense_shapes[j] has an undefined major dimension (variable strides dense feature), dense_defaults[j] must contain a single element: the padding element. dense_shapes: A list of Ndense shapes; the shapes of data in each Feature given in dense_keys. The number of elements in the Feature corresponding to dense_key[j] must always equal dense_shapes[j].NumEntries(). If dense_shapes[j] == (D0, D1, ..., DN) then the shape of output Tensor dense_values[j] will be (|serialized|, D0, D1, ..., DN): The dense outputs are just the inputs row-stacked by batch. This works for dense_shapes[j] = (-1, D1, ..., DN). In this case the shape of the output Tensor dense_values[j] will be (|serialized|, M, D1, .., DN), where M is the maximum number of blocks of elements of length D1 * .... * DN, across all minibatch entries in the input. Any minibatch entry with less than M blocks of elements of length D1 * ... * DN will be padded with the corresponding default_value scalar element along the second dimension. sparse_keys: A list of Nsparse string Tensors (scalars). The keys expected in the Examples' features associated with sparse values. sparse_types: A list of Nsparse types; the data types of data in each Feature given in sparse_keys. Currently the ParseExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList). feature_list_dense_missing_assumed_empty: stringcontext_sparse_keys: Ncontext_sparse * stringcontext_dense_keys: Ncontext_dense * stringfeature_list_sparse_keys: Nfeature_list_sparse * stringfeature_list_dense_keys: Nfeature_list_dense * stringcontext_dense_defaults: Tcontext_densedebug_name: stringcontext_sparse_indices: Ncontext_sparse * int64context_sparse_values: context_sparse_typescontext_sparse_shapes: Ncontext_sparse * int64context_dense_values: Tcontext_densefeature_list_sparse_indices: Nfeature_list_sparse * int64feature_list_sparse_values: feature_list_sparse_typesfeature_list_sparse_shapes: Nfeature_list_sparse * int64feature_list_dense_values: feature_list_dense_typesNcontext_sparse: int >= 0 = 0Ncontext_dense: int >= 0 = 0Nfeature_list_sparse: int >= 0 = 0Nfeature_list_dense: int >= 0 = 0context_sparse_types: list({float,int64,string}) >= 0 = []Tcontext_dense: list({float,int64,string}) >= 0 = []feature_list_dense_types: list({float,int64,string}) >= 0 = []context_dense_shapes: list(shape) >= 0 = []feature_list_sparse_types: list({float,int64,string}) >= 0 = []feature_list_dense_shapes: list(shape) >= 0 = [] Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors. serialized: A scalar containing a binary serialized SequenceExample proto. feature_list_dense_missing_assumed_empty: A vector listing the FeatureList keys which may be missing from the SequenceExample. If the associated FeatureList is missing, it is treated as empty. By default, any FeatureList not listed in this vector must exist in the SequenceExample. context_dense_keys: A list of Ncontext_dense string Tensors (scalars). The keys expected in the SequenceExamples' context features associated with dense values. feature_list_dense_keys: A list of Nfeature_list_dense string Tensors (scalars). The keys expected in the SequenceExamples' feature_lists associated with lists of dense values. context_dense_defaults: A list of Ncontext_dense Tensors (some may be empty). context_dense_defaults[j] provides default values when the SequenceExample's context map lacks context_dense_key[j]. If an empty Tensor is provided for context_dense_defaults[j], then the Feature context_dense_keys[j] is required. The input type is inferred from context_dense_defaults[j], even when it's empty. If context_dense_defaults[j] is not empty, its shape must match context_dense_shapes[j]. debug_name: A scalar containing the name of the serialized proto. May contain, for example, table key (descriptive) name for the corresponding serialized proto. This is purely useful for debugging purposes, and the presence of values here has no effect on the output. May also be an empty scalar if no name is available. context_dense_shapes: A list of Ncontext_dense shapes; the shapes of data in each context Feature given in context_dense_keys. The number of elements in the Feature corresponding to context_dense_key[j] must always equal context_dense_shapes[j].NumEntries(). The shape of context_dense_values[j] will match context_dense_shapes[j]. feature_list_dense_shapes: A list of Nfeature_list_dense shapes; the shapes of data in each FeatureList given in feature_list_dense_keys. The shape of each Feature in the FeatureList corresponding to feature_list_dense_key[j] must always equal feature_list_dense_shapes[j].NumEntries(). context_sparse_keys: A list of Ncontext_sparse string Tensors (scalars). The keys expected in the Examples' features associated with context_sparse values. context_sparse_types: A list of Ncontext_sparse types; the data types of data in each context Feature given in context_sparse_keys. Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList). feature_list_sparse_keys: A list of Nfeature_list_sparse string Tensors (scalars). The keys expected in the FeatureLists associated with sparse values. feature_list_sparse_types: A list of Nfeature_list_sparse types; the data types of data in each FeatureList given in feature_list_sparse_keys. Currently the ParseSingleSequenceExample supports DT_FLOAT (FloatList), DT_INT64 (Int64List), and DT_STRING (BytesList). ParseTensorout_type: type Transforms a serialized tensorflow.TensorProto proto into a Tensor. serialized: A scalar string containing a serialized TensorProto proto. out_type: The type of the serialized tensor. The provided type must match the type of the serialized tensor and no implicit conversion will take place. output: A Tensor of type `out_type`. DecodeJSONExamplejson_examples: stringbinary_examples: string Convert JSON-encoded Example records to binary protocol buffer strings. This op translates a tensor containing Example records, encoded using the [standard JSON mapping](https://developers.google.com/protocol-buffers/docs/proto3#json), into a tensor containing the same records encoded as binary protocol buffers. The resulting tensor can then be fed to any of the other Example-parsing ops. json_examples: Each string is a JSON object serialized according to the JSON mapping of the Example proto. binary_examples: Each string is a binary Example protocol buffer corresponding to the respective element of `json_examples`. DecodeCSVrecords: stringrecord_defaults: OUT_TYPEoutput: OUT_TYPEOUT_TYPE: list({float,int32,int64,string})field_delim: string = ',' Convert CSV records to tensors. Each column maps to one tensor. RFC 4180 format is expected for the CSV records. (https://tools.ietf.org/html/rfc4180) Note that we allow leading and trailing spaces with int or float field. records: Each string is a record/row in the csv and all records should have the same format. record_defaults: One tensor per column of the input record, with either a scalar default value for that column or empty if the column is required. field_delim: delimiter to separate fields in a record. output: Each tensor will have the same shape as records. StringToNumberout_type: {float, double, int32, int64} = DT_FLOAT Converts each string in the input Tensor to the specified numeric type. (Note that int32 overflow results in an error while float overflow results in a rounded value.) out_type: The numeric type to interpret each string in `string_tensor` as. output: A Tensor of the same shape as the input `string_tensor`. Does nothing. Only useful as a placeholder for control edges.Dilation2D requires the stride attribute to contain 4 values, but got: ratesDilation2D requires the rates attribute to contain 4 values, but got: input must have last dimension >= k = pooling_ratio field must specify 4 dimensionsSize computed for dim is negative: pooling_ratioPaddings must be non-negativeOperation requires the stride attribute to contain 4 values, but got: ksize: list(int) >= 4strides: list(int) >= 4 Performs average pooling on the input. Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. value: 4-D with shape `[batch, height, width, channels]`. ksize: The size of the sliding window for each dimension of `value`. strides: The stride of the sliding window for each dimension of `value`. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. output: The average pooled output tensor. orig_input_shape: int32 Computes gradients of the average pooling function. orig_input_shape: 1-D. Shape of the original input to `avg_pool`. grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `avg_pool`. ksize: The size of the sliding window for each dimension of the input. strides: The stride of the sliding window for each dimension of the input. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. output: 4-D. Gradients w.r.t. the input of `avg_pool`. t: Tm: Tv: Tbeta: Tgamma: Tresult: Tvariance_epsilon: floatscale_after_normalization: boolUse tf.nn.batch_normalization() Batch normalization. This op is deprecated. Prefer `tf.nn.batch_normalization`. t: A 4D input Tensor. m: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. v: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. beta: A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor. gamma: A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor. variance_epsilon: A small float number to avoid dividing by 0. scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma. backprop: Tdx: Tdm: Tdv: Tdb: Tdg: T Gradients for batch normalization. This op is deprecated. See `tf.nn.batch_normalization`. t: A 4D input Tensor. m: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. v: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. gamma: A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this Tensor will be multiplied with the normalized Tensor. backprop: 4D backprop Tensor. variance_epsilon: A small float number to avoid dividing by 0. scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma. dx: 4D backprop tensor for input. dm: 1D backprop tensor for mean. dv: 1D backprop tensor for variance. db: 1D backprop tensor for beta. dg: 1D backprop tensor for gamma. x: Tscale: Toffset: Tmean: Tvariance: Ty: Tbatch_mean: Tbatch_variance: Treserve_space_1: Treserve_space_2: Tepsilon: float = 0.0001data_format: string = 'NHWC'is_training: bool = true Batch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. x: A 4D Tensor for input data. scale: A 1D Tensor for scaling factor, to scale the normalized x. offset: A 1D Tensor for offset, to shift to the normalized x. mean: A 1D Tensor for population mean. Used for inference only; must be empty for training. variance: A 1D Tensor for population variance. Used for inference only; must be empty for training. y: A 4D Tensor for output data. batch_mean: A 1D Tensor for the computed batch mean, to be used by TensorFlow to compute the running mean. batch_variance: A 1D Tensor for the computed batch variance, to be used by TensorFlow to compute the running variance. reserve_space_1: A 1D Tensor for the computed batch mean, to be reused in the gradient computation. reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance in the cuDNN case), to be used in the gradient computation. T: The data type for the elements of input and output Tensors. epsilon: A small float number added to the variance of x. data_format: The data format for x and y. Either "NHWC" (default) or "NCHW". is_training: A bool value to indicate the operation is for training (default) or inference. y_backprop: Tx_backprop: Tscale_backprop: Toffset_backprop: Treserve_space_3: Treserve_space_4: T Gradient for batch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. y_backprop: A 4D Tensor for the gradient with respect to y. x: A 4D Tensor for input data. scale: A 1D Tensor for scaling factor, to scale the normalized x. reserve_space_1: A 1D Tensor for the computed batch mean, to be reused in the gradient computation. reserve_space_2: A 1D Tensor for the computed batch variance (inverted variance in the cuDNN case), to be used in the gradient computation. x_backprop: A 4D Tensor for the gradient with respect to x. scale_backprop: A 1D Tensor for the gradient with respect to scale. offset_backprop: A 1D Tensor for the gradient with respect to offset. reserve_space_3: Unused placeholder to match the mean input in FusedBatchNorm. reserve_space_4: Unused placeholder to match the variance input in FusedBatchNorm. T: The data type for the elements of input and output Tensors. epsilon: A small float number added to the variance of x. data_format: The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW". is_training: A bool value to indicate the operation is for training (default) or inference. bias: T Adds `bias` to `value`. This is a special case of `tf.add` where `bias` is restricted to be 1-D. Broadcasting is supported, so `value` may have any number of dimensions. value: Any number of dimensions. bias: 1-D with size the last dimension of `value`. data_format: Specify the data format of the input and output data. With the default format "NHWC", the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. The tensor will be added to "in_channels", the third-to-the-last dimension. output: Broadcasted sum of `value` and `bias`. out_backprop: T The backward operation for "BiasAdd" on the "bias" tensor. It accumulates all the values from out_backprop into the feature dimension. For NHWC data format, the feature dimension is the last. For NCHW data format, the feature dimension is the third-to-last. out_backprop: Any number of dimensions. output: 1-D with size the feature dimension of `out_backprop`. data_format: Specify the data format of the input and output data. With the default format "NHWC", the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. The tensor will be added to "in_channels", the third-to-the-last dimension. Adds `bias` to `value`. This is a deprecated version of BiasAdd and will be soon removed. This is a special case of `tf.add` where `bias` is restricted to be 1-D. Broadcasting is supported, so `value` may have any number of dimensions. value: Any number of dimensions. bias: 1-D with size the last dimension of `value`. output: Broadcasted sum of `value` and `bias`. filter: Tstrides: list(int)use_cudnn_on_gpu: bool = true Computes a 2-D convolution given 4-D `input` and `filter` tensors. Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following: 1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`. input: A 4-D tensor. The dimension order is interpreted according to the value of `data_format`, see below for details. filter: A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]` output: A 4-D tensor. The dimension order is determined by the value of `data_format`, see below for details. strides: 1-D tensor of length 4. The stride of the sliding window for each dimension of `input`. The dimension order is determined by the value of `data_format`, see below for details. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. input_sizes: int32 Computes the gradients of convolution with respect to the input. input_sizes: An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor. filter: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: The type of padding algorithm to use. output: 4-D with shape `[batch, in_height, in_width, in_channels]`. Gradient w.r.t. the input of the convolution. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. filter_sizes: int32 Computes the gradients of convolution with respect to the filter. input: 4-D with shape `[batch, in_height, in_width, in_channels]`. filter_sizes: An integer vector representing the tensor shape of `filter`, where `filter` is a 4-D `[filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: The type of padding algorithm to use. output: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. the `filter` input of the convolution. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. size: int32paddings: int32resize_align_corners: bool = false Performs a resize and padding as a preprocess during a convolution. It's often possible to do spatial transformations more efficiently as part of the packing stage of a convolution, so this op allows for an optimized implementation where these stages are fused together. This prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and defaults to 'NHWC' order. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage. input: 4-D with shape `[batch, in_height, in_width, in_channels]`. size: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of `input`. filter: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. resize_align_corners: If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Treat similarly the width dimension. strides: 1-D of length 4. The stride of the sliding window for each dimension of `input`. Must be in the same order as the dimension specified with format. padding: The type of padding algorithm to use. Performs a padding as a preprocess during a convolution. Similar to FusedResizeAndPadConv2d, this op allows for an optimized implementation where the spatial padding transformation stage is fused with the im2col lookup, but in this case without the bilinear filtering required for resizing. Fusing the padding prevents the need to write out the intermediate results as whole tensors, reducing memory pressure, and we can get some latency gains by merging the transformation calculations. The data_format attribute for Conv2D isn't supported by this op, and 'NHWC' order is used instead. Internally this op uses a single per-graph scratch buffer, which means that it will block if multiple versions are being run in parallel. This is because this operator is primarily an optimization to minimize memory usage. input: 4-D with shape `[batch, in_height, in_width, in_channels]`. paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of `input`. filter: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. strides: 1-D of length 4. The stride of the sliding window for each dimension of `input`. Must be in the same order as the dimension specified with format. padding: The type of padding algorithm to use. Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors. Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, channel_multiplier]`, containing `in_channels` convolutional filters of depth 1, `depthwise_conv2d` applies a different filter to each input channel (expanding from 1 channel to `channel_multiplier` channels for each), then concatenates the results together. Thus, the output has `in_channels * channel_multiplier` channels. for k in 0..in_channels-1 for q in 0..channel_multiplier-1 output[b, i, j, k * channel_multiplier + q] = sum_{di, dj} input[b, strides[1] * i + di, strides[2] * j + dj, k] * filter[di, dj, k, q] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`. strides: 1-D of length 4. The stride of the sliding window for each dimension of `input`. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. DepthwiseConv2dNativeBackpropInput Computes the gradients of depthwise convolution with respect to the input. input_sizes: An integer vector representing the shape of `input`, based on `data_format`. For example, if `data_format` is 'NHWC' then `input` is a 4-D `[batch, height, width, channels]` tensor. filter: 4-D with shape `[filter_height, filter_width, in_channels, depthwise_multiplier]`. out_backprop: 4-D with shape based on `data_format`. For example, if `data_format` is 'NHWC' then out_backprop shape is `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: The stride of the sliding window for each dimension of the input of the convolution. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. output: 4-D with shape according to `data_format`. For example, if `data_format` is 'NHWC', output shape is `[batch, in_height, in_width, in_channels]`. Gradient w.r.t. the input of the convolution. DepthwiseConv2dNativeBackpropFilter Computes the gradients of depthwise convolution with respect to the filter. input: 4-D with shape based on `data_format`. For example, if `data_format` is 'NHWC' then `input` is a 4-D `[batch, in_height, in_width, in_channels]` tensor. filter_sizes: An integer vector representing the tensor shape of `filter`, where `filter` is a 4-D `[filter_height, filter_width, in_channels, depthwise_multiplier]` tensor. out_backprop: 4-D with shape based on `data_format`. For example, if `data_format` is 'NHWC' then out_backprop shape is `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: The stride of the sliding window for each dimension of the input of the convolution. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. output: 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. Gradient w.r.t. the `filter` input of the convolution. Conv3Dstrides: list(int) >= 5 Computes a 3-D convolution given 5-D `input` and `filter` tensors. In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product. Our Conv3D implements a form of cross-correlation. input: Shape `[batch, in_depth, in_height, in_width, in_channels]`. filter: Shape `[filter_depth, filter_height, filter_width, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. Conv3DBackpropInputUse Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input. input: Shape `[batch, depth, rows, cols, in_channels]`. filter: Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. Conv3DBackpropFilterUse Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter. input: Shape `[batch, depth, rows, cols, in_channels]`. filter: Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. Conv3DBackpropInputV2 Computes the gradients of 3-D convolution with respect to the input. input_sizes: An integer vector representing the tensor shape of `input`, where `input` is a 5-D `[batch, depth, rows, cols, in_channels]` tensor. filter: Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. Conv3DBackpropFilterV2 Computes the gradients of 3-D convolution with respect to the filter. input: Shape `[batch, depth, rows, cols, in_channels]`. filter_sizes: An integer vector representing the tensor shape of `filter`, where `filter` is a 5-D `[filter_depth, filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. ksize: list(int) >= 5 Performs 3D average pooling on the input. ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. output: The average pooled output tensor. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. AvgPool3DGrad Computes gradients of average pooling function. ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. orig_input_shape: The original input dimensions. grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. output: The backprop for input. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. Performs 3D max pooling on the input. ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. input: Shape `[batch, depth, rows, cols, channels]` tensor to pool over. output: The max pooled output tensor. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. MaxPool3DGradorig_input: TInputorig_output: TInputksize: list(int) >= 5 T: numbertype = DT_FLOATTInput: numbertype = DT_FLOAT Computes gradients of max pooling function. ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. orig_input: The original input tensor. orig_output: The original output tensor. grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. MaxPool3DGradGradorig_input: Torig_output: T Computes second-order gradients of the maxpooling function. ksize: 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: The type of padding algorithm to use. orig_input: The original input tensor. orig_output: The original output tensor. grad: Output backprop of shape `[batch, depth, rows, cols, channels]`. output: Gradients of gradients w.r.t. the input to `max_pool`. data_format: The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. L2Loss L2 Loss. Computes half the L2 norm of a tensor without the `sqrt`: output = sum(t ** 2) / 2 t: Typically 2-D, but may have any dimensions. output: 0-D. depth_radius: int = 5bias: float = 1.0alpha: float = 1.0beta: float = 0.5T: {float, half} = DT_FLOAT Local Response Normalization. The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) output = input / (bias + alpha * sqr_sum) ** beta For details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). input: 4-D. depth_radius: 0-D. Half-width of the 1-D normalization window. bias: An offset (usually positive to avoid dividing by 0). alpha: A scale factor, usually positive. beta: An exponent. LRNGradinput_grads: Tinput_image: Toutput_image: T Gradients for Local Response Normalization. input_grads: 4-D with shape `[batch, height, width, channels]`. input_image: 4-D with shape `[batch, height, width, channels]`. output_image: 4-D with shape `[batch, height, width, channels]`. depth_radius: A depth radius. bias: An offset (usually > 0 to avoid dividing by 0). alpha: A scale factor, usually positive. beta: An exponent. output: The gradients for LRN. T: realnumbertype = DT_FLOAT Performs max pooling on the input. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. input: 4-D input to pool over. output: The max pooled output tensor. Computes gradients of the maxpooling function. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. orig_input: The original input tensor. orig_output: The original output tensor. grad: 4-D. Gradients w.r.t. the output of `max_pool`. output: Gradients w.r.t. the input to `max_pool`. Computes second-order gradients of the maxpooling function. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. orig_input: The original input tensor. orig_output: The original output tensor. grad: 4-D. Gradients of gradients w.r.t. the input of `max_pool`. output: Gradients of gradients w.r.t. the input to `max_pool`. MaxPoolWithArgmaxTargmax: {int32, int64} = DT_INT64argmax: Targmax Performs max pooling on the input and outputs both max values and indices. The indices in `argmax` are flattened, so that a maximum value at position `[b, y, x, c]` becomes flattened index `((b * height + y) * width + x) * channels + c`. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. input: 4-D with shape `[batch, height, width, channels]`. Input to pool over. output: The max pooled output tensor. argmax: 4-D. The flattened indices of the max values chosen for each output. MaxPoolGradWithArgmaxTargmax: {int32, int64} Computes gradients of the maxpooling function. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. input: The original input. grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `max_pool`. argmax: The indices of the maximum values chosen for each output of `max_pool`. output: Gradients w.r.t. the input of `max_pool`. MaxPoolGradGradWithArgmax Computes second-order gradients of the maxpooling function. ksize: The size of the window for each dimension of the input tensor. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. input: The original input. grad: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the input of `max_pool`. argmax: The indices of the maximum values chosen for each output of `max_pool`. output: Gradients of gradients w.r.t. the input of `max_pool`. Dilation2Drates: list(int) >= 4 Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. The `input` tensor has shape `[batch, in_height, in_width, depth]` and the `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default "NHWC" `data_format`. In detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with `conv2d`, we use unmirrored filters): output[b, y, x, c] = max_{dy, dx} input[b, strides[1] * y + rates[1] * dy, strides[2] * x + rates[2] * dx, c] + filter[dy, dx, c] Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros. Note on duality: The dilation of `input` by the `filter` is equal to the negation of the erosion of `-input` by the reflected `filter`. input: 4-D with shape `[batch, in_height, in_width, depth]`. filter: 3-D with shape `[filter_height, filter_width, depth]`. strides: The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: The type of padding algorithm to use. output: 4-D with shape `[batch, out_height, out_width, depth]`. Dilation2DBackpropInputin_backprop: T Computes the gradient of morphological 2-D dilation with respect to the input. input: 4-D with shape `[batch, in_height, in_width, depth]`. filter: 3-D with shape `[filter_height, filter_width, depth]`. out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. in_backprop: 4-D with shape `[batch, in_height, in_width, depth]`. strides: 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: The type of padding algorithm to use. Dilation2DBackpropFilterfilter_backprop: T Computes the gradient of morphological 2-D dilation with respect to the filter. input: 4-D with shape `[batch, in_height, in_width, depth]`. filter: 3-D with shape `[filter_height, filter_width, depth]`. out_backprop: 4-D with shape `[batch, out_height, out_width, depth]`. filter_backprop: 3-D with shape `[filter_height, filter_width, depth]`. strides: 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: The type of padding algorithm to use. features: Tactivations: T Computes rectified linear: `max(features, 0)`. gradients: Tbackprops: T Computes rectified linear gradients for a Relu operation. gradients: The backpropagated gradients to the corresponding Relu operation. features: The features passed as input to the corresponding Relu operation, OR the outputs of that operation (both work equivalently). backprops: `gradients * (features > 0)`. Computes rectified linear 6: `min(max(features, 0), 6)`. Computes rectified linear 6 gradients for a Relu6 operation. gradients: The backpropagated gradients to the corresponding Relu6 operation. features: The features passed as input to the corresponding Relu6 operation. backprops: The gradients: `gradients * (features > 0) * (features < 6)`. Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise. See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) ](http://arxiv.org/abs/1511.07289) outputs: T Computes gradients for the exponential linear (Elu) operation. gradients: The backpropagated gradients to the corresponding Elu operation. outputs: The outputs of the corresponding Elu operation. backprops: The gradients: `gradients * (outputs + 1)` if outputs < 0, `gradients` otherwise. Computes softplus: `log(exp(features) + 1)`. Computes softplus gradients for a softplus operation. gradients: The backpropagated gradients to the corresponding softplus operation. features: The features passed as input to the corresponding softplus operation. backprops: The gradients: `gradients / (1 + exp(-features))`. Computes softsign: `features / (abs(features) + 1)`. Computes softsign gradients for a softsign operation. gradients: The backpropagated gradients to the corresponding softsign operation. features: The features passed as input to the corresponding softsign operation. backprops: The gradients: `gradients / (1 + abs(-features)) ** 2`. softmax: T Computes softmax activations. For each batch `i` and class `j` we have softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j])) logits: 2-D with shape `[batch_size, num_classes]`. softmax: Same shape as `logits`. logsoftmax: T Computes log softmax activations. For each batch `i` and class `j` we have logsoftmax[i, j] = logits[i, j] - log(sum(exp(logits[i]))) logits: 2-D with shape `[batch_size, num_classes]`. logsoftmax: Same shape as `logits`. labels: Tloss: T Computes softmax cross entropy cost and gradients to backpropagate. Inputs are the logits, not probabilities. features: batch_size x num_classes matrix labels: batch_size x num_classes matrix The caller must ensure that each batch of labels represents a valid probability distribution. loss: Per example loss (batch_size vector). backprop: backpropagated gradients (batch_size x num_classes matrix). SparseSoftmaxCrossEntropyWithLogitslabels: TlabelsTlabels: {int32, int64} = DT_INT64 Computes softmax cross entropy cost and gradients to backpropagate. Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept a matrix of label probabilities, but rather a single label per row of features. This label is considered to have probability 1.0 for the given row. Inputs are the logits, not probabilities. features: batch_size x num_classes matrix labels: batch_size vector with values in [0, num_classes). This is the label for the given minibatch entry. loss: Per example loss (batch_size vector). backprop: backpropagated gradients (batch_size x num_classes matrix). predictions: floattargets: Tprecision: boolk: intT: {int32, int64} = DT_INT32 Says whether the targets are in the top `K` predictions. This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the prediction for the target class is among the top `k` predictions among all predictions for example `i`. Note that the behavior of `InTopK` differs from the `TopK` op in its handling of ties; if multiple classes have the same prediction value and straddle the top-`k` boundary, all of those classes are considered to be in the top `k`. More formally, let \\(predictions_i\\) be the predictions for all classes for example `i`, \\(targets_i\\) be the target class for example `i`, \\(out_i\\) be the output for example `i`, $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ predictions: A `batch_size` x `classes` tensor. targets: A `batch_size` vector of class ids. k: Number of top elements to look at for computing precision. precision: Computed Precision at `k` as a `bool Tensor`. indices: int32k: int >= 0sorted: bool = trueUse TopKV2 instead Finds values and indices of the `k` largest elements for the last dimension. If the input is a vector (rank-1), finds the `k` largest entries in the vector and outputs their values and indices as vectors. Thus `values[j]` is the `j`-th largest entry in `input`, and its index is `indices[j]`. For matrices (resp. higher rank input), computes the top `k` entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k] If two elements are equal, the lower-index element appears first. If `k` varies dynamically, use `TopKV2` below. input: 1-D or higher with last dimension at least `k`. k: Number of top elements to look for along the last dimension (along each row for matrices). sorted: If true the resulting `k` elements will be sorted by the values in descending order. values: The `k` largest elements along each last dimensional slice. indices: The indices of `values` within the last dimension of `input`. k: int32 Finds values and indices of the `k` largest elements for the last dimension. If the input is a vector (rank-1), finds the `k` largest entries in the vector and outputs their values and indices as vectors. Thus `values[j]` is the `j`-th largest entry in `input`, and its index is `indices[j]`. For matrices (resp. higher rank input), computes the top `k` entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k] If two elements are equal, the lower-index element appears first. input: 1-D or higher with last dimension at least `k`. k: 0-D. Number of top elements to look for along the last dimension (along each row for matrices). sorted: If true the resulting `k` elements will be sorted by the values in descending order. values: The `k` largest elements along each last dimensional slice. indices: The indices of `values` within the last dimension of `input`. FractionalMaxPoolrow_pooling_sequence: int64col_pooling_sequence: int64pooling_ratio: list(float) >=4pseudo_random: bool = falseoverlapping: bool = falsedeterministic: bool = falseT: {float, double, int32, int64} Performs fractional max pooling on the input. Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following: 1. input_row_length : the number of rows from the input set 2. output_row_length : which will be smaller than the input 3. alpha = input_row_length / output_row_length : our reduction ratio 4. K = floor(alpha) 5. row_pooling_sequence : this is the result list of pool boundary rows Then, row_pooling_sequence should satisfy: 1. a[0] = 0 : the first value of the sequence is 0 2. a[end] = input_row_length : the last value of the sequence is the size 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size 4. length(row_pooling_sequence) = output_row_length+1 For more details on fractional max pooling, see this paper: [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) value: 4-D with shape `[batch, height, width, channels]`. pooling_ratio: Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. deterministic: When set to True, a fixed pooling region will be used when iterating over a FractionalMaxPool node in the computation graph. Mainly used in unit test to make FractionalMaxPool deterministic. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. output: output tensor after fractional max pooling. row_pooling_sequence: row pooling sequence, needed to calculate gradient. col_pooling_sequence: column pooling sequence, needed to calculate gradient. FractionalMaxPoolGrad Computes gradient of the FractionalMaxPool function. orig_input: Original input for `fractional_max_pool` orig_output: Original output for `fractional_max_pool` out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_max_pool`. row_pooling_sequence: row pooling sequence, form pooling region with col_pooling_sequence. col_pooling_sequence: column pooling sequence, form pooling region with row_pooling sequence. overlapping: When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. output: 4-D. Gradients w.r.t. the input of `fractional_max_pool`. FractionalAvgPool Performs fractional average pooling on the input. Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region. value: 4-D with shape `[batch, height, width, channels]`. pooling_ratio: Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. deterministic: When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. output: output tensor after fractional avg pooling. row_pooling_sequence: row pooling sequence, needed to calculate gradient. col_pooling_sequence: column pooling sequence, needed to calculate gradient. FractionalAvgPoolGradorig_input_tensor_shape: int64 Computes gradient of the FractionalAvgPool function. Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor. orig_input_tensor_shape: Original input tensor shape for `fractional_avg_pool` out_backprop: 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_avg_pool`. row_pooling_sequence: row pooling sequence, form pooling region with col_pooling_sequence. col_pooling_sequence: column pooling sequence, form pooling region with row_pooling sequence. overlapping: When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. output: 4-D. Gradients w.r.t. the input of `fractional_avg_pool`. min_input: floatmax_input: floatmin_output: floatmax_output: floatT: quantizedtypeksize: list(int) Produces the average pool of the input tensor for quantized types. input: 4-D with shape `[batch, height, width, channels]`. ksize: The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. strides: The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. padding: The type of padding algorithm to use. min_input: The float value that the lowest quantized input value represents. max_input: The float value that the highest quantized input value represents. min_output: The float value that the lowest quantized output value represents. max_output: The float value that the highest quantized output value represents. input: T1bias: T2min_bias: floatmax_bias: floatmin_out: floatmax_out: floatT1: quantizedtypeT2: quantizedtypeout_type: quantizedtype Adds Tensor 'bias' to Tensor 'input' for Quantized types. Broadcasts the values of bias on dimensions 0..N-2 of 'input'. bias: A 1D bias Tensor with size matching the last dimension of 'input'. min_input: The float value that the lowest quantized input value represents. max_input: The float value that the highest quantized input value represents. min_bias: The float value that the lowest quantized bias value represents. max_bias: The float value that the highest quantized bias value represents. min_out: The float value that the lowest quantized output value represents. max_out: The float value that the highest quantized output value represents. input: Tinputfilter: Tfiltermin_filter: floatmax_filter: floatTinput: quantizedtypeTfilter: quantizedtypeout_type: quantizedtype = DT_QINT32 Computes a 2D convolution given quantized 4D input and filter tensors. The inputs are quantized tensors where the lowest value represents the real number of the associated minimum, and the highest represents the maximum. This means that you can only interpret the quantized output in the same way, by taking the returned minimum and maximum values into account. filter: filter's input_depth dimension must match input's depth dimensions. strides: The stride of the sliding window for each dimension of the input tensor. padding: The type of padding algorithm to use. min_input: The float value that the lowest quantized input value represents. max_input: The float value that the highest quantized input value represents. min_filter: The float value that the lowest quantized filter value represents. max_filter: The float value that the highest quantized filter value represents. min_output: The float value that the lowest quantized output value represents. max_output: The float value that the highest quantized output value represents. Produces the max pool of the input tensor for quantized types. input: The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. ksize: The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. strides: The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. padding: The type of padding algorithm to use. min_input: The float value that the lowest quantized input value represents. max_input: The float value that the highest quantized input value represents. min_output: The float value that the lowest quantized output value represents. max_output: The float value that the highest quantized output value represents. features: Tinputmin_features: floatmax_features: floatactivations: out_typemin_activations: floatmax_activations: floatout_type: quantizedtype = DT_QUINT8 Computes Quantized Rectified Linear: `max(features, 0)` activations: Has the same output shape as "features". min_features: The float value that the lowest quantized value represents. max_features: The float value that the highest quantized value represents. min_activations: The float value that the lowest quantized value represents. max_activations: The float value that the highest quantized value represents. Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)` activations: Has the same output shape as "features". min_features: The float value that the lowest quantized value represents. max_features: The float value that the highest quantized value represents. min_activations: The float value that the lowest quantized value represents. max_activations: The float value that the highest quantized value represents. QuantizedReluXmax_value: float Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)` activations: Has the same output shape as "features". min_features: The float value that the lowest quantized value represents. max_features: The float value that the highest quantized value represents. min_activations: The float value that the lowest quantized value represents. max_activations: The float value that the highest quantized value represents. t: Tinputt_min: floatt_max: floatm: Tinputm_min: floatm_max: floatv: Tinputv_min: floatv_max: floatbeta: Tinputbeta_min: floatbeta_max: floatgamma: Tinputgamma_min: floatgamma_max: floatresult: out_typeresult_min: floatresult_max: float Quantized Batch normalization. This op is deprecated and will be removed in the future. Prefer `tf.nn.batch_normalization`. t: A 4D input Tensor. t_min: The value represented by the lowest quantized input. t_max: The value represented by the highest quantized input. m: A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. m_min: The value represented by the lowest quantized mean. m_max: The value represented by the highest quantized mean. v: A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. v_min: The value represented by the lowest quantized variance. v_max: The value represented by the highest quantized variance. beta: A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor. beta_min: The value represented by the lowest quantized offset. beta_max: The value represented by the highest quantized offset. gamma: A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor. gamma_min: The value represented by the lowest quantized gamma. gamma_max: The value represented by the highest quantized gamma. variance_epsilon: A small float number to avoid dividing by 0. scale_after_normalization: A bool indicating whether the resulted tensor needs to be multiplied with gamma. CrossEntropySoftmaxGradgrad_softmax: Tgrad_x: T$Tn0grad_softmaxn1newshapen2n3grad_xdy: Tdxdyinput_grad: Tfilter_grad: Ti_shapeinput_gradfiltergrad$strides$padding$data_format$use_cudnn_on_gpuf_shapefilter_gradmaxpool$ksizedcost_dloss: Tdonotcare: Tdcost_dfeatures: Tdcost_dlabels: Tdonotcare_lossdloss_dfeaturesfeaturesneg1dcost_dloss_matdcost_dlossdcost_dfeaturesdcost_dlabelsFrom merging shape with other shapes.Cannot specify a negative value for output_dim0 for 'start' for 'stop' for 'num' for 'limit' for 'delta'Requires delta != 0adj_xadj_yTrying to merge handles pointing to different dtypes.Dimension () must be in the range [), where is the number of dimensions in the input.inputs: N * Tsum: TN: int >= 1 Add all input tensors element wise. inputs: Must all be the same size and shape. BatchMatMulT: {half, float, double, int32, complex64, complex128}adj_x: bool = falseadj_y: bool = false Multiplies slices of two tensors in batches. Multiplies all slices of `Tensor` `x` and `y` (each slice can be viewed as an element of a batch), and arranges the individual results in a single output tensor of the same batch size. Each of the individual slices can optionally be adjointed (to adjoint a matrix means to transpose and conjugate it) before multiplication by setting the `adj_x` or `adj_y` flag to `True`, which are by default `False`. The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]` and `[..., r_y, c_y]`. The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: r_o = c_x if adj_x else r_x c_o = r_y if adj_y else c_y It is computed as: output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :]) x: 2-D or higher with shape `[..., r_x, c_x]`. y: 2-D or higher with shape `[..., r_y, c_y]`. output: 3-D or higher with shape `[..., r_o, c_o]` adj_x: If `True`, adjoint the slices of `x`. Defaults to `False`. adj_y: If `True`, adjoint the slices of `y`. Defaults to `False`. x: SrcTy: DstTSrcT: typeDstT: type Cast x of type SrcT to y of DstT. Cast x of type SrcT to y of DstT. _HostCast requires its input and produces its output in host memory. AbsT: {half, float, double, int32, int64} Computes the absolute value of a tensor. Given a tensor `x`, this operation returns a tensor containing the absolute value of each element in `x`. For example, if x is an input element and y is an output element, this operation computes \\(y = |x|\\). ComplexAbsy: ToutT: {complex64, complex128} = DT_COMPLEX64Tout: {float, double} = DT_FLOAT Computes the complex absolute value of a tensor. Given a tensor `x` of complex numbers, this operation returns a tensor of type `float` or `double` that is the absolute value of each element in `x`. All elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute value is computed as \\( \sqrt{a^2 + b^2}\\). T: {half, float, double, int32, int64, complex64, complex128} Computes numerical negative value element-wise. I.e., \\(y = -x\\). Computes the reciprocal of x element-wise. I.e., \\(y = 1 / x\\). Use Reciprocalz: TT: {half, float, double, complex64, complex128} Computes the gradient for the inverse of `x` wrt its input. Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy` is the corresponding input gradient. Use ReciprocalGrad Computes square of x element-wise. I.e., \\(y = x * x = x^2\\). Computes square root of x element-wise. I.e., \\(y = \sqrt{x} = x^{1/2}\\). Computes the gradient for the sqrt of `x` wrt its input. Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy` is the corresponding input gradient. Computes reciprocal of square root of x element-wise. I.e., \\(y = 1 / \sqrt{x}\\). Round Rounds the values of a tensor to the nearest integer, element-wise. Rounds half to even. Also known as bankers rounding. If you want to round according to the current system rounding mode use std::cint. Computes the gradient for the rsqrt of `x` wrt its input. Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy` is the corresponding input gradient. Computes exponential of x element-wise. \\(y = e^x\\). Expm1 Computes exponential of x - 1 element-wise. I.e., \\(y = (\exp x) - 1\\). Computes natural logarithm of x element-wise. I.e., \\(y = \log_e x\\). Log1p Computes natural logarithm of (1 + x) element-wise. I.e., \\(y = \log_e (1 + x)\\). Computes hyperbolic tangent of `x` element-wise. Computes the gradient for the tanh of `x` wrt its input. Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy` is the corresponding input gradient. Lgamma Computes the log of the absolute value of `Gamma(x)` element-wise. Digamma Computes Psi, the derivative of Lgamma (the log of the absolute value of `Gamma(x)`), element-wise. Erf Computes the Gauss error function of `x` element-wise. Erfc Computes the complementary error function of `x` element-wise. Computes sigmoid of `x` element-wise. Specifically, `y = 1 / (1 + exp(-x))`. Computes the gradient of the sigmoid of `x` wrt its input. Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and `dy` is the corresponding input gradient. Sin Computes sin of x element-wise. Cos Computes cos of x element-wise. Tan Computes tan of x element-wise. Asin Computes asin of x element-wise. Acos Computes acos of x element-wise. Atan Computes atan of x element-wise. IsNany: bool Returns which elements of x are NaN. @compatibility(numpy) Equivalent to np.isnan @end_compatibility IsInf Returns which elements of x are Inf. @compatibility(numpy) Equivalent to np.isinf @end_compatibility Returns which elements of x are finite. @compatibility(numpy) Equivalent to np.isfinite @end_compatibility Returns an element-wise indication of the sign of a number. `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`. For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`. Returns element-wise largest integer not greater than x. Ceil Returns element-wise smallest integer in not less than x. Rint Returns element-wise integer closest to x. If the result is midway between two representable values, the even representable is chosen. For example: ``` rint(-1.5) ==> -2.0 rint(0.5000001) ==> 1.0 rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.] ``` T: {half, float, double, uint8, int8, int16, int32, int64, complex64, complex128, string} Returns x + y element-wise. *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns x - y element-wise. *NOTE*: `Sub` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) T: {half, float, double, uint8, int8, uint16, int16, int32, int64, complex64, complex128} Returns x * y element-wise. *NOTE*: `Mul` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns x / y element-wise. *NOTE*: `Div` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns x // y element-wise. *NOTE*: `FloorDiv` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns x / y element-wise for integer types. Truncation designates that negative numbers will round fractional quantities toward zero. I.e. -7 / 5 = 1. This matches C semantics but it is different than Python semantics. See `FloorDiv` for a division function that matches Python Semantics. *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns x / y element-wise for real types. If `x` and `y` are reals, this will return the floating-point division. *NOTE*: `Div` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns (x - y)(x - y) element-wise. *NOTE*: `SquaredDifference` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns the max of x and y (i.e. x > y ? x : y) element-wise. *NOTE*: `Maximum` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns the min of x and y (i.e. x < y ? x : y) element-wise. *NOTE*: `Minimum` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) T: {int32, int64, float, double} Returns element-wise remainder of division. *NOTE*: `Mod` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns element-wise remainder of division. When `x < 0` xor `y < 0` is true, this follows Python semantics in that the result here is consistent with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. *NOTE*: `FloorMod` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) TruncateMod Returns element-wise remainder of division. This emulates C semantics where true, this follows C semantics in that the result here is consistent with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`. *NOTE*: `Mod` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Computes the power of one value to another. Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for corresponding elements in `x` and `y`. For example: ``` # tensor 'x' is [[2, 2]], [3, 3]] # tensor 'y' is [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]] ``` Igammaca: T Compute the upper regularized incomplete Gamma function `Q(a, x)`. The upper regularized incomplete Gamma function is defined as: ``` Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x) ``` where ``` Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt ``` is the upper incomplete Gama function. Note, above `P(a, x)` (`Igamma`) is the lower regularized complete Gamma function. Igamma Compute the lower regularized incomplete Gamma function `Q(a, x)`. The lower regularized incomplete Gamma function is defined as: ``` P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x) ``` where ``` gamma(a, x) = int_{0}^{x} t^{a-1} exp(-t) dt ``` is the lower incomplete Gamma function. Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete Gamma function. Zetaq: T Compute the Hurwitz zeta function \\(\zeta(x, q)\\). The Hurwitz zeta function is defined as: ``` \zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x} ``` Polygamma Compute the polygamma function \\(\psi^{(n)}(x)\\). The polygamma function is defined as: ``` \psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x) ``` where \\(\psi(x)\\) is the digamma function. Betainc Compute the regularized incomplete beta integral \\(I_x(a, b)\\). The regularized incomplete beta integral is defined as: ``` I_x(a, b) = \frac{B(x; a, b)}{B(a, b)} ``` where ``` B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt ``` is the incomplete beta function and \\(B(a, b)\\) is the *complete* beta function. z: bool Returns the truth value of (x < y) element-wise. *NOTE*: `Less` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) LessEqual Returns the truth value of (x <= y) element-wise. *NOTE*: `LessEqual` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns the truth value of (x > y) element-wise. *NOTE*: `Greater` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Returns the truth value of (x >= y) element-wise. *NOTE*: `GreaterEqual` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) T: {half, float, double, uint8, int8, int16, int32, int64, complex64, quint8, qint8, qint32, string, bool, complex128} Returns the truth value of (x == y) element-wise. *NOTE*: `Equal` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) NotEqual Returns the truth value of (x != y) element-wise. *NOTE*: `NotEqual` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) tolerance: float = 0.00001 Returns the truth value of abs(x-y) < tolerance element-wise. x: bool Returns the truth value of NOT x element-wise. Returns the truth value of x AND y element-wise. *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) LogicalOr Returns the truth value of x OR y element-wise. *NOTE*: `LogicalOr` supports broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) condition: boole: T Selects elements from `t` or `e`, depending on `condition`. The `t`, and `e` tensors must all have the same shape, and the output will also have that shape. The `condition` tensor must be a scalar if `t` and `e` are scalars. If `t` and `e` are vectors or higher rank, then `condition` must be either a scalar, a vector with size matching the first dimension of `t`, or must have the same shape as `t`. The `condition` tensor acts as a mask that chooses, based on the value at each element, whether the corresponding element / row in the output should be taken from `t` (if true) or `e` (if false). If `condition` is a vector and `t` and `e` are higher rank matrices, then it chooses which row (outer dimension) to copy from `t` and `e`. If `condition` has the same shape as `t` and `e`, then it chooses which element to copy from `t` and `e`. For example: ```prettyprint # 'condition' tensor is [[True, False] # [False, True]] # 't' is [[1, 2], # [3, 4]] # 'e' is [[5, 6], # [7, 8]] select(condition, t, e) ==> [[1, 6], [7, 4]] # 'condition' tensor is [True, False] # 't' is [[1, 2], # [3, 4]] # 'e' is [[5, 6], # [7, 8]] select(condition, t, e) ==> [[1, 2], [7, 8]] ``` t:= A `Tensor` which may have the same shape as `condition`. If `condition` is rank 1, `t` may have higher rank, but its first dimension must match the size of `condition`. e:= A `Tensor` with the same type and shape as `t`. output:= A `Tensor` with the same type and shape as `t` and `e`. transpose_a: bool = falsetranspose_b: bool = false Multiply the matrix "a" by the matrix "b". The inputs must be two-dimensional matrices and the inner dimension of "a" (after being transposed if transpose_a is true) must match the outer dimension of "b" (after being transposed if transposed_b is true). *Note*: The default kernel implementation for MatMul on GPUs uses cublas. transpose_a: If true, "a" is transposed before multiplication. transpose_b: If true, "b" is transposed before multiplication. a: Tab: Tbproduct: floata_is_sparse: bool = falseb_is_sparse: bool = falseTa: {float, bfloat16} = DT_FLOATTb: {float, bfloat16} = DT_FLOAT Multiply matrix "a" by matrix "b". The inputs must be two-dimensional matrices and the inner dimension of "a" must match the outer dimension of "b". This op is optimized for the case where at least one of "a" or "b" is sparse. The breakeven for using this versus a dense matrix multiply on one platform was 30% zero values in the sparse matrix. reduction_indices: TidxTidx: {int32, int64} = DT_INT32 Computes the sum of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. Computes the mean of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. Computes the product of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. Computes the minimum of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. Computes the maximum of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. dimension: Tidx Returns the index with the largest value across dimensions of a tensor. dimension: int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. Returns the index with the smallest value across dimensions of a tensor. dimension: int32, 0 <= dimension < rank(input). Describes which dimension of the input Tensor to reduce across. For vectors, use dimension = 0. SegmentSumdata: Tsegment_ids: TindicesTindices: {int32,int64} Computes the sum along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that \\(output_i = \sum_j data_j\\) where sum is over `j` such that `segment_ids[j] == i`. If the sum is empty for a given segment ID `i`, `output[i] = 0`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SegmentMean Computes the mean along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is over `j` such that `segment_ids[j] == i` and `N` is the total number of values summed. If the mean is empty for a given segment ID `i`, `output[i] = 0`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SegmentProd Computes the product along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that \\(output_i = \prod_j data_j\\) where the product is over `j` such that `segment_ids[j] == i`. If the product is empty for a given segment ID `i`, `output[i] = 1`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SegmentMin Computes the minimum along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that \\(output_i = \min_j(data_j)\\) where `min` is over `j` such that `segment_ids[j] == i`. If the min is empty for a given segment ID `i`, `output[i] = 0`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SegmentMax Computes the maximum along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that \\(output_i = \max_j(data_j)\\) where `max` is over `j` such that `segment_ids[j] == i`. If the max is empty for a given segment ID `i`, `output[i] = 0`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. UnsortedSegmentSumnum_segments: int32 Computes the sum along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Computes a tensor such that `(output[i] = sum_{j...} data[j...]` where the sum is over tuples `j...` such that `segment_ids[j...] == i`. Unlike `SegmentSum`, `segment_ids` need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID `i`, `output[i] = 0`. `num_segments` should equal the number of distinct segment IDs.
segment_ids: A tensor whose shape is a prefix of `data.shape`. output: Has same shape as data, except for the first `segment_ids.rank` dimensions, which are replaced with a single dimension which has size `num_segments`. UnsortedSegmentMax Computes the Max along segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. This operator is similar to the [unsorted segment sum operator](../../api_docs/python/math_ops.md#UnsortedSegmentSum). Instead of computing the sum over segments, it computes the maximum such that: \\(output_i = \max_j data_j\\) where max is over `j` such that `segment_ids[j] == i`. If the maximum is empty for a given segment ID `i`, it outputs the smallest possible value for specific numeric type, `output[i] = numeric_limits::min()`.
segment_ids: A 1-D tensor whose rank is equal to the rank of `data`'s first dimension. output: Has same shape as data, except for dimension 0 which has size `num_segments`. SparseSegmentSumindices: Tidxsegment_ids: int32 Computes the sum along sparse segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first dimension, selecting a subset of dimension 0, specified by `indices`. For example: ```prettyprint c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]) # Select two rows, one segment. tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0])) ==> [[0 0 0 0]] # Select two rows, two segment. tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1])) ==> [[ 1 2 3 4] [-1 -2 -3 -4]] # Select all rows, two segments. tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1])) ==> [[0 0 0 0] [5 6 7 8]] # Which is equivalent to: tf.segment_sum(c, tf.constant([0, 0, 1])) ``` indices: A 1-D tensor. Has same rank as `segment_ids`. segment_ids: A 1-D tensor. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SparseSegmentMean Computes the mean along sparse segments of a tensor. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first dimension, selecting a subset of dimension 0, specified by `indices`. indices: A 1-D tensor. Has same rank as `segment_ids`. segment_ids: A 1-D tensor. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SparseSegmentMeanGradoutput_dim0: int32 Computes gradients for SparseSegmentMean. Returns tensor "output" with same shape as grad, except for dimension 0 whose value is output_dim0. grad: gradient propagated to the SparseSegmentMean op. indices: indices passed to the corresponding SparseSegmentMean op. segment_ids: segment_ids passed to the corresponding SparseSegmentMean op. output_dim0: dimension 0 of "data" passed to SparseSegmentMean op. SparseSegmentSqrtN Computes the sum along sparse segments of a tensor divided by the sqrt of N. N is the size of the segment being reduced. Read [the section on Segmentation](../../api_docs/python/math_ops.md#segmentation) for an explanation of segments. indices: A 1-D tensor. Has same rank as `segment_ids`. segment_ids: A 1-D tensor. Values should be sorted and can be repeated. output: Has same shape as data, except for dimension 0 which has size `k`, the number of segments. SparseSegmentSqrtNGrad Computes gradients for SparseSegmentSqrtN. Returns tensor "output" with same shape as grad, except for dimension 0 whose value is output_dim0. grad: gradient propagated to the SparseSegmentSqrtN op. indices: indices passed to the corresponding SparseSegmentSqrtN op. segment_ids: segment_ids passed to the corresponding SparseSegmentSqrtN op. output_dim0: dimension 0 of "data" passed to SparseSegmentSqrtN op. Allinput: booloutput: bool Computes the "logical and" of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. Computes the "logical or" of elements across dimensions of a tensor. Reduces `input` along the dimensions given in `reduction_indices`. Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions are retained with length 1. input: The tensor to reduce. reduction_indices: The dimensions to reduce. keep_dims: If true, retain reduced dimensions with length 1. output: The reduced tensor. start: Tidxlimit: Tidxdelta: Tidxoutput: TidxTidx: {float, double, int32, int64} = DT_INT32 Creates a sequence of numbers. This operation creates a sequence of numbers that begins at `start` and extends by increments of `delta` up to but not including `limit`. For example: ``` # 'start' is 3 # 'limit' is 18 # 'delta' is 3 tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15] ``` start: 0-D (scalar). First entry in the sequence. limit: 0-D (scalar). Upper limit of sequence, exclusive. delta: 0-D (scalar). Optional. Default is 1. Number that increments `start`. output: 1-D. start: Tstop: Tnum: Tidx Generates values in an interval. A sequence of `num` evenly-spaced values are generated beginning at `start`. If `num > 1`, the values in the sequence increase by `stop - start / num - 1`, so that the last one is exactly `stop`. For example: ``` tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0 11.0 12.0] ``` start: First entry in the range. stop: Last entry in the range. num: Number of values to generate. output: 1-D. The generated values. Complexreal: Timag: Tout: ToutT: {float, double} = DT_FLOATTout: {complex64, complex128} = DT_COMPLEX64 Converts two real numbers to a complex number. Given a tensor `real` representing the real part of a complex number, and a tensor `imag` representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form \\(a + bj\\), where *a* represents the `real` part and *b* represents the `imag` part. The input tensors `real` and `imag` must have the same shape. For example: ``` # tensor 'real' is [2.25, 3.25] # tensor `imag` is [4.75, 5.75] tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]] ``` Real Returns the real part of a complex number. Given a tensor `input` of complex numbers, this operation returns a tensor of type `float` that is the real part of each element in `input`. All elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real part returned by this operation and *b* is the imaginary part. For example: ``` # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.real(input) ==> [-2.25, 3.25] ``` Imag Returns the imaginary part of a complex number. Given a tensor `input` of complex numbers, this operation returns a tensor of type `float` that is the imaginary part of each element in `input`. All elements in `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real part and *b* is the imaginary part returned by this operation. For example: ``` # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.imag(input) ==> [4.75, 5.75] ``` Conj Returns the complex conjugate of a complex number. Given a tensor `input` of complex numbers, this operation returns a tensor of complex numbers that are the complex conjugate of each element in `input`. The complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the real part and *b* is the imaginary part. The complex conjugate returned by this operation is of the form \\(a - bj\\). For example: ``` # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j] tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j] ``` Cross Compute the pairwise cross product. `a` and `b` must be the same shape; they can either be simple 3-element vectors, or any shape where the innermost dimension is 3. In the latter case, each pair of corresponding 3-element vectors is cross-multiplied independently. a: A tensor containing 3-element vectors. b: Another tensor, of same type and shape as `a`. product: Pairwise cross product of the vectors in `a` and `b`. Bincountarr: int32weights: TT: {int32, int64, float32, float64}bins: T Counts the number of occurrences of each value in an integer array. Outputs a vector with length `size` and the same dtype as `weights`. If `weights` are empty, then index `i` stores the number of times the value `i` is counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of the value in `weights` at each index where the corresponding value in `arr` is `i`. Values in `arr` outside of the range [0, size) are ignored. arr: int32 `Tensor`. size: non-negative int32 scalar `Tensor`. weights: is an int32, int64, float32, or float64 `Tensor` with the same shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights equal to 1. bins: 1D `Tensor` with length equal to `size`. The counts or summed weights for each value in the range [0, size). Cumsumaxis: Tidxexclusive: bool = falsereverse: bool = falseout: T Compute the cumulative sum of the tensor `x` along `axis`. By default, this op performs an inclusive cumsum, which means that the first element of the input is identical to the first element of the output: ```prettyprint tf.cumsum([a, b, c]) ==> [a, a + b, a + b + c] ``` By setting the `exclusive` kwarg to `True`, an exclusive cumsum is performed instead: ```prettyprint tf.cumsum([a, b, c], exclusive=True) ==> [0, a, a + b] ``` By setting the `reverse` kwarg to `True`, the cumsum is performed in the opposite direction: ```prettyprint tf.cumsum([a, b, c], reverse=True) ==> [a + b + c, b + c, c] ``` This is more efficient than using separate `tf.reverse` ops. The `reverse` and `exclusive` kwargs can also be combined: ```prettyprint tf.cumsum([a, b, c], exclusive=True, reverse=True) ==> [b + c, c, 0] ``` Cumprod Compute the cumulative product of the tensor `x` along `axis`. By default, this op performs an inclusive cumprod, which means that the first element of the input is identical to the first element of the output: ```prettyprint tf.cumprod([a, b, c]) ==> [a, a * b, a * b * c] ``` By setting the `exclusive` kwarg to `True`, an exclusive cumprod is performed instead: ```prettyprint tf.cumprod([a, b, c], exclusive=True) ==> [1, a, a * b] ``` By setting the `reverse` kwarg to `True`, the cumprod is performed in the opposite direction: ```prettyprint tf.cumprod([a, b, c], reverse=True) ==> [a * b * c, b * c, c] ``` This is more efficient than using separate `tf.reverse` ops. The `reverse` and `exclusive` kwargs can also be combined: ```prettyprint tf.cumprod([a, b, c], exclusive=True, reverse=True) ==> [b * c, c, 1] ``` a: T1b: T2min_a: floatmax_a: floatmin_b: floatmax_b: floatout: ToutputToutput: quantizedtype = DT_QINT32Tactivation: quantizedtype = DT_QUINT8 Perform a quantized matrix multiplication of `a` by the matrix `b`. The inputs must be two-dimensional matrices and the inner dimension of `a` (after being transposed if `transpose_a` is non-zero) must match the outer dimension of `b` (after being transposed if `transposed_b` is non-zero). a: Must be a two-dimensional tensor. b: Must be a two-dimensional tensor. transpose_a: If true, `a` is transposed before multiplication. transpose_b: If true, `b` is transposed before multiplication. min_a: The float value that the lowest quantized `a` value represents. max_a: The float value that the highest quantized `a` value represents. min_b: The float value that the lowest quantized `b` value represents. max_b: The float value that the highest quantized `b` value represents. min_out: The float value that the lowest quantized output value represents. max_out: The float value that the highest quantized output value represents. Tactivation: The type of output produced by activation function following this operation. x: T1y: T2min_x: floatmax_x: floatmin_y: floatmax_y: floatz: Toutputmin_z: floatmax_z: float Returns x * y element-wise, working on quantized buffers. min_x: The float value that the lowest quantized `x` value represents. max_x: The float value that the highest quantized `x` value represents. min_y: The float value that the lowest quantized `y` value represents. max_y: The float value that the highest quantized `y` value represents. min_z: The float value that the lowest quantized output value represents. max_z: The float value that the highest quantized output value represents. *NOTE*: `QuantizedMul` supports limited forms of broadcasting. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) input_min: floatinput_max: floatoutput_min: floatoutput_max: float Convert the quantized 'input' tensor into a lower-precision 'output', using the actual distribution of the values to maximize the usage of the lower bit depth and adjusting the output min and max ranges accordingly. [input_min, input_max] are scalar floats that specify the range for the float interpretation of the 'input' data. For example, if input_min is -1.0f and input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. This operator tries to squeeze as much precision as possible into an output with a lower bit depth by calculating the actual min and max values found in the data. For example, maybe that quint16 input has no values lower than 16,384 and none higher than 49,152. That means only half the range is actually needed, all the float interpretations are between -0.5f and 0.5f, so if we want to compress the data into a quint8 output, we can use that range rather than the theoretical -1.0f to 1.0f that is suggested by the input min and max. In practice, this is most useful for taking output from operations like QuantizedMatMul that can produce higher bit-depth outputs than their inputs and may have large potential output ranges, but in practice have a distribution of input values that only uses a small fraction of the possible range. By feeding that output into this operator, we can reduce it from 32 bits down to 8 with minimal loss of accuracy. input_min: The float value that the minimum quantized input value represents. input_max: The float value that the maximum quantized input value represents. Tinput: The type of the input. output_min: The float value that the minimum quantized output value represents. output_max: The float value that the maximum quantized output value represents. out_type: The type of the output. Should be a lower bit depth than Tinput. requested_output_min: floatrequested_output_max: float Convert the quantized 'input' tensor into a lower-precision 'output', using the output range specified with 'requested_output_min' and 'requested_output_max'. [input_min, input_max] are scalar floats that specify the range for the float interpretation of the 'input' data. For example, if input_min is -1.0f and input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0 value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f. input_min: The float value that the minimum quantized input value represents. input_max: The float value that the maximum quantized input value represents. Tinput: The type of the input. requested_output_min: The float value that the minimum quantized output value represents. requested_output_max: The float value that the maximum quantized output value represents. output_min: The requested_output_min value is copied into this output. output_max: The requested_output_max value is copied into this output. out_type: The type of the output. Should be a lower bit depth than Tinput. Given a quantized tensor described by (input, input_min, input_max), outputs a range that covers the actual values present in that tensor. This op is typically used to produce the requested_output_min and requested_output_max for Requantize. input_min: The float value that the minimum quantized input value represents. input_max: The float value that the maximum quantized input value represents. Tinput: The type of the input. output_min: The computed min output. output_max: the computed max output. c:boolx:Ty:Tdz:Tdc:booldx:Tdy:Tdccdzzerosi:int32di:int32maskmask_castmask_sumnorm_dysynorm_dy_reshapeddidz: Tsigny2y2_negx_invcossinnegcosxsecxsecx2twox2y_invconstzeroneghalfonevalinvsxrxrysum_gxgxsum_gygycycxnxnx_y2const_zeroconst_onet0t1t2unsafe_lognz_xsafe_logpos_xt4x_shapex_rankstitch_val1i_shape:output:0one:output:0y_shapestitch_idx0:output:0x_shape:output:0stitch_val1:output:0tile_scalingy_shape:merged:0stitch_idx0zero:output:0x_rank:output:0dx:output:0di:y:0dy_reshapeddy_reshaped:output:0tile_scaling:z:0factorfactor_Tfactor:output:0dy_scaledfactor_T:y:0dy_scaled:z:0MatMul gradient for complex is not supported yet.T: list(type)summarize: int = 3 Asserts that the given condition is true. If `condition` evaluates to false, print the list of tensors in `data`. `summarize` determines how many entries of the tensors to print. condition: The condition to evaluate. data: The tensors to print out when condition is false. summarize: Print this many entries of each tensor. data: UU: list(type)message: string = ''first_n: int = -1 Prints a list of tensors. Passes `input` through to `output` and prints `data` when evaluating. input: The tensor passed to `output` data: A list of tensors to print out when op is evaluated. output:= The unmodified `input` tensor message: A string, prefix of the error message. first_n: Only log `first_n` number of times. -1 disables logging. summarize: Only print this many entries of each tensor. TensorSummarysummary: stringdescription: string = ''labels: list(string) = []display_name: string = '' Outputs a `Summary` protocol buffer with a tensor. tensor: A tensor to serialize. description: A json-encoded SummaryDescription proto. labels: An unused list of strings. display_name: An unused string. ScalarSummarytags: string Outputs a `Summary` protocol buffer with scalar values. The input `tags` and `values` must have the same shape. The generated summary has a summary value for each tag-value pair in `tags` and `values`. tags: Tags for the summary. values: Same shape as `tags. Values for the summary. summary: Scalar. Serialized `Summary` protocol buffer. HistogramSummarytag: string Outputs a `Summary` protocol buffer with a histogram. The generated [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) has one summary value containing a histogram for `values`. This op reports an `InvalidArgument` error if any value is not finite. tag: Scalar. Tag to use for the `Summary.Value`. values: Any shape. Values to use to build the histogram. summary: Scalar. Serialized `Summary` protocol buffer. ImageSummarymax_images: int >= 1 = 3T: {uint8, float, half} = DT_FLOATbad_color: tensor = { dtype: DT_UINT8 tensor_shape: { dim { size: 4 } } int_val: 255 int_val: 0 int_val: 0 int_val: 255 } Outputs a `Summary` protocol buffer with images. The summary has up to `max_images` summary values containing images. The images are built from `tensor` which must be 4-D with shape `[batch_size, height, width, channels]` and where `channels` can be: * 1: `tensor` is interpreted as Grayscale. * 3: `tensor` is interpreted as RGB. * 4: `tensor` is interpreted as RGBA. The images have the same number of channels as the input tensor. For float input, the values are normalized one image at a time to fit in the range `[0, 255]`. `uint8` values are unchanged. The op uses two different normalization algorithms: * If the input values are all positive, they are rescaled so the largest one is 255. * If any input value is negative, the values are shifted so input value 0.0 is at 127. They are then rescaled so that either the smallest value is 0, or the largest one is 255. The `tag` argument is a scalar `Tensor` of type `string`. It is used to build the `tag` of the summary values: * If `max_images` is 1, the summary value tag is '*tag*/image'. * If `max_images` is greater than 1, the summary value tags are generated sequentially as '*tag*/image/0', '*tag*/image/1', etc. The `bad_color` argument is the color to use in the generated images for non-finite input values. It is a `unit8` 1-D tensor of length `channels`. Each element must be in the range `[0, 255]` (It represents the value of a pixel in the output image). Non-finite values in the input tensor are replaced by this tensor in the output image. The default value is the color red. tag: Scalar. Used to build the `tag` attribute of the summary values. tensor: 4-D of shape `[batch_size, height, width, channels]` where `channels` is 1, 3, or 4. max_images: Max number of batch elements to generate images for. bad_color: Color to use for pixels with non-finite values. summary: Scalar. Serialized `Summary` protocol buffer. AudioSummaryV2tensor: floatsample_rate: floatmax_outputs: int >= 1 = 3 Outputs a `Summary` protocol buffer with audio. The summary has up to `max_outputs` summary values containing audio. The audio is built from `tensor` which must be 3-D with shape `[batch_size, frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`. The `tag` argument is a scalar `Tensor` of type `string`. It is used to build the `tag` of the summary values: * If `max_outputs` is 1, the summary value tag is '*tag*/audio'. * If `max_outputs` is greater than 1, the summary value tags are generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc. tag: Scalar. Used to build the `tag` attribute of the summary values. tensor: 2-D of shape `[batch_size, frames]`. sample_rate: The sample rate of the signal in hertz. max_outputs: Max number of batch elements to generate audio for. summary: Scalar. Serialized `Summary` protocol buffer. AudioSummaryUse AudioSummaryV2.MergeSummaryN : int >= 1 Merges summaries. This op creates a [`Summary`](https://www.tensorflow.org/code/tensorflow/core/framework/summary.proto) protocol buffer that contains the union of all the values in the input summaries. When the Op is run, it reports an `InvalidArgument` error if multiple values in the summaries to merge use the same tag. inputs: Can be of any shape. Each must contain serialized `Summary` protocol buffers. summary: Scalar. Serialized `Summary` protocol buffer. compute_vfull_matricescompute_uvMatrixDeterminant Computes the determinant of one ore more square matrices. The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices. The output is a tensor containing the determinants for all input submatrices `[..., :, :]`. input: Shape is `[..., M, M]`. output: Shape is `[...]`. MatrixInverseadjoint: bool = FalseT: {double, float} Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes). The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices. The output is a tensor of the same shape as the input containing the inverse for all input submatrices `[..., :, :]`. The op uses LU decomposition with partial pivoting to compute the inverses. If a matrix is not invertible there is no guarantee what the op does. It may detect the condition and raise an exception or it may simply return a garbage result. input: Shape is `[..., M, M]`. output: Shape is `[..., M, M]`. @compatibility(numpy) Equivalent to np.linalg.inv @end_compatibility Cholesky Computes the Cholesky decomposition of one or more square matrices. The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices, with the same constraints as the single matrix Cholesky decomposition above. The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices `[..., :, :]`. input: Shape is `[..., M, M]`. output: Shape is `[..., M, M]`. CholeskyGradl: T Computes the reverse mode backpropagated gradient of the Cholesky algorithm. For an explanation see "Differentiation of the Cholesky algorithm" by Iain Murray http://arxiv.org/abs/1602.07527. l: Output of batch Cholesky algorithm l = cholesky(A). Shape is `[..., M, M]`. Algorithm depends only on lower triangular part of the innermost matrices of this tensor. grad: df/dl where f is some scalar function. Shape is `[..., M, M]`. Algorithm depends only on lower triangular part of the innermost matrices of this tensor. output: Symmetrized version of df/dA . Shape is `[..., M, M]` SelfAdjointEigUse SelfAdjointEigV2 instead. Computes the Eigen Decomposition of a batch of square self-adjoint matrices. The input is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices, with the same constraints as the single matrix SelfAdjointEig. The result is a [..., M+1, M] matrix with [..., 0,:] containing the eigenvalues, and subsequent [...,1:, :] containing the eigenvectors. input: Shape is `[..., M, M]`. output: Shape is `[..., M+1, M]`. SelfAdjointEigV2compute_v: bool = True Computes the eigen decomposition of one or more square self-adjoint matrices. Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in `input` such that `input[..., :, :] = v[..., :, :] * diag(e[..., :])`. ```prettyprint # a is a tensor. # e is a tensor of eigenvalues. # v is a tensor of eigenvectors. e, v = self_adjoint_eig(a) e = self_adjoint_eig(a, compute_v=False) ``` input: `Tensor` input of shape `[N, N]`. compute_v: If `True` then eigenvectors will be computed and returned in `v`. Otherwise, only the eigenvalues will be computed. e: Eigenvalues. Shape is `[N]`. v: Eigenvectors. Shape is `[N, N]`. MatrixSolvematrix: Trhs: TT: {double, float, complex64, complex128} Solves systems of linear equations. `Matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices. `Rhs` is a tensor of shape `[..., M, K]`. The `output` is a tensor shape `[..., M, K]`. If `adjoint` is `False` then each output matrix satisfies `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. If `adjoint` is `True` then each output matrix satisfies `adjoint(matrix[..., :, :]) * output[..., :, :] = rhs[..., :, :]`. matrix: Shape is `[..., M, M]`. rhs: Shape is `[..., M, K]`. output: Shape is `[..., M, K]`. adjoint: Boolean indicating whether to solve with `matrix` or its (block-wise) adjoint. MatrixTriangularSolvelower: bool = True Solves systems of linear equations with upper or lower triangular matrices by backsubstitution. `matrix` is a tensor of shape `[..., M, M]` whose inner-most 2 dimensions form square matrices. If `lower` is `True` then the strictly upper triangular part of each inner-most matrix is assumed to be zero and not accessed. If `lower` is False then the strictly lower triangular part of each inner-most matrix is assumed to be zero and not accessed. `rhs` is a tensor of shape `[..., M, K]`. The output is a tensor of shape `[..., M, K]`. If `adjoint` is `True` then the innermost matrices in output` satisfy matrix equations `matrix[..., :, :] * output[..., :, :] = rhs[..., :, :]`. If `adjoint` is `False` then the strictly then the innermost matrices in `output` satisfy matrix equations `adjoint(matrix[..., i, k]) * output[..., k, j] = rhs[..., i, j]`. matrix: Shape is `[..., M, M]`. rhs: Shape is `[..., M, K]`. output: Shape is `[..., M, K]`. lower: Boolean indicating whether the innermost matrices in `matrix` are lower or upper triangular. adjoint: Boolean indicating whether to solve with `matrix` or its (block-wise) adjoint. @compatibility(numpy) Equivalent to np.linalg.triangular_solve @end_compatibility MatrixSolveLsl2_regularizer: doublefast: bool = True Solves one or more linear least-squares problems. `matrix` is a tensor of shape `[..., M, N]` whose inner-most 2 dimensions form matrices of size `[M, N]`. Rhs is a tensor of shape `[..., M, K]`. The output is a tensor shape `[..., N, K]` where each output matrix solves each of the equations matrix[..., :, :] * output[..., :, :] = rhs[..., :, :] in the least squares sense. matrix and right-hand sides in the batch: `matrix`=\\(A \in \Re^{m \times n}\\), `rhs`=\\(B \in \Re^{m \times k}\\), `output`=\\(X \in \Re^{n \times k}\\), `l2_regularizer`=\\(\lambda\\). If `fast` is `True`, then the solution is computed by solving the normal equations using Cholesky decomposition. Specifically, if \\(m \ge n\\) then \\(X = (A^T A + \lambda I)^{-1} A^T B\\), which solves the least-squares problem \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||A Z - B||_F^2 + \lambda ||Z||_F^2\\). If \\(m \lt n\\) then `output` is computed as \\(X = A^T (A A^T + \lambda I)^{-1} B\\), which (for \\(\lambda = 0\\)) is the minimum-norm solution to the under-determined linear system, i.e. \\(X = \mathrm{argmin}_{Z \in \Re^{n \times k} } ||Z||_F^2 \\), subject to \\(A Z = B\\). Notice that the fast path is only numerically stable when \\(A\\) is numerically full rank and has a condition number \\(\mathrm{cond}(A) \lt \frac{1}{\sqrt{\epsilon_{mach} } }\\) or\\(\lambda\\) is sufficiently large. If `fast` is `False` an algorithm based on the numerically robust complete orthogonal decomposition is used. This computes the minimum-norm least-squares solution, even when \\(A\\) is rank deficient. This path is typically 6-7 times slower than the fast path. If `fast` is `False` then `l2_regularizer` is ignored. matrix: Shape is `[..., M, N]`. rhs: Shape is `[..., M, K]`. output: Shape is `[..., N, K]`. l2_regularizer: Scalar tensor. @compatibility(numpy) Equivalent to np.linalg.lstsq @end_compatibility Qrr: Tfull_matrices: bool = False Computes the QR decompositions of one or more matrices. Computes the QR decomposition of each inner matrix in `tensor` such that `tensor[..., :, :] = q[..., :, :] * r[..., :,:])` ```prettyprint # a is a tensor. # q is a tensor of orthonormal matrices. # r is a tensor of upper triangular matrices. q, r = qr(a) q_full, r_full = qr(a, full_matrices=True) ``` input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`. q: Orthonormal basis for range of `a`. If `full_matrices` is `False` then shape is `[..., M, P]`; if `full_matrices` is `True` then shape is `[..., M, M]`. r: Triangular factor. If `full_matrices` is `False` then shape is `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`. full_matrices: If true, compute full-sized `q` and `r`. If false (the default), compute only the leading `P` columns of `q`. Svds: Tu: Tcompute_uv: bool = True Computes the singular value decompositions of one or more matrices. Computes the SVD of each inner matrix in `input` such that `input[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :])` ```prettyprint # a is a tensor containing a batch of matrices. # s is a tensor of singular values for each matrix. # u is the tensor containing of left singular vectors for each matrix. # v is the tensor containing of right singular vectors for each matrix. s, u, v = svd(a) s, _, _ = svd(a, compute_uv=False) ``` input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`. s: Singular values. Shape is `[..., P]`. u: Left singular vectors. If `full_matrices` is `False` then shape is `[..., M, P]`; if `full_matrices` is `True` then shape is `[..., M, M]`. Undefined if `compute_uv` is `False`. v: Left singular vectors. If `full_matrices` is `False` then shape is `[..., N, P]`. If `full_matrices` is `True` then shape is `[..., N, N]`. Undefined if `compute_uv` is false. compute_uv: If true, left and right singular vectors will be computed and returned in `u` and `v`, respectively. If false, `u` and `v` are not set and should never referenced. full_matrices: If true, compute full-sized `u` and `v`. If false (the default), compute only the leading `P` singular vectors. Ignored if `compute_uv` is `False`. BatchSelfAdjointEigBatchMatrixDeterminantUse MatrixDeterminant instead.BatchMatrixInverseUse MatrixInverse instead.BatchCholeskyUse Cholesky instead.BatchCholeskyGradUse CholeskyGrad instead.BatchSelfAdjointEigV2BatchMatrixSolveUse MatrixSolve instead.BatchMatrixTriangularSolveUse MatrixTriangularSolve instead.BatchMatrixSolveLsUse MatrixSolveLs instead.BatchSvdUse Svd instead.prefix: stringtensor_names: stringshape_and_slices: stringtensors: dtypesdtypes: list(type) Saves tensors in V2 checkpoint format. By default, saves the named tensors in full. If the caller wishes to save specific slices of full tensors, "shape_and_slices" should be non-empty strings and correspondingly well-formed. prefix: Must have a single element. The prefix of the V2 checkpoint to which we write the tensors. tensor_names: shape {N}. The names of the tensors to be saved. shape_and_slices: shape {N}. The slice specs of the tensors to be saved. Empty strings indicate that they are non-partitioned tensors. tensors: `N` tensors to save. Restores tensors from a V2 checkpoint. For backward compatibility with the V1 format, this Op currently allows restoring from a V1 checkpoint as well: - This Op first attempts to find the V2 index file pointed to by "prefix", and if found proceed to read it as a V2 checkpoint; - Otherwise the V1 read path is invoked. Relying on this behavior is not recommended, as the ability to fall back to read V1 might be deprecated and eventually removed. By default, restores the named tensors in full. If the caller wishes to restore specific slices of stored tensors, "shape_and_slices" should be non-empty strings and correspondingly well-formed. Callers must ensure all the named tensors are indeed stored in the checkpoint. prefix: Must have a single element. The prefix of a V2 checkpoint. tensor_names: shape {N}. The names of the tensors to be restored. shape_and_slices: shape {N}. The slice specs of the tensors to be restored. Empty strings indicate that they are non-partitioned tensors. dtypes: shape {N}. The list of expected dtype for the tensors. Must match those stored in the checkpoint. tensors: shape {N}. The restored tensors, whose shapes are read from the checkpoint directly. checkpoint_prefixes: stringdestination_prefix: stringdelete_old_dirs: bool = true V2 format specific: merges the metadata files of sharded checkpoints. The result is one logical checkpoint, with one physical metadata file and renamed data files. Intended for "grouping" multiple checkpoints in a sharded checkpoint setup. If delete_old_dirs is true, attempts to delete recursively the dirname of each path in the input checkpoint_prefixes. This is useful when those paths are non user-facing temporary locations. checkpoint_prefixes: prefixes of V2 checkpoints to merge. destination_prefix: scalar. The desired final prefix. Allowed to be the same as one of the checkpoint_prefixes. delete_old_dirs: see above. filename: string Saves the input tensors to disk. The size of `tensor_names` must match the number of tensors in `data`. `data[i]` is written to `filename` with name `tensor_names[i]`. See also `SaveSlices`. filename: Must have a single element. The name of the file to which we write the tensor. tensor_names: Shape `[N]`. The names of the tensors to be saved. data: `N` tensors to save. shapes_and_slices: string Saves input tensors slices to disk. This is like `Save` except that tensors can be listed in the saved file as being a slice of a larger tensor. `shapes_and_slices` specifies the shape of the larger tensor and the slice that this tensor covers. `shapes_and_slices` must have as many elements as `tensor_names`. Elements of the `shapes_and_slices` input must either be: * The empty string, in which case the corresponding tensor is saved normally. * A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the `dimI` are the dimensions of the larger tensor and `slice-spec` specifies what part is covered by the tensor to save. `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1` where each `sliceI` is either: * The string `-` meaning that the slice covers all indices of this dimension * `start,length` where `start` and `length` are integers. In that case the slice covers `length` indices starting at `start`. See also `Save`. filename: Must have a single element. The name of the file to which we write the tensor. tensor_names: Shape `[N]`. The names of the tensors to be saved. shapes_and_slices: Shape `[N]`. The shapes and slice specifications to use when saving the tensors. data: `N` tensors to save. file_pattern: stringtensor: dtdt: typepreferred_shard: int = -1 Restores a tensor from checkpoint files. Reads a tensor stored in one or several files. If there are several files (for instance because a tensor was saved as slices), `file_pattern` may contain wildcard symbols (`*` and `?`) in the filename portion only, not in the directory portion. If a `file_pattern` matches several files, `preferred_shard` can be used to hint in which file the requested tensor is likely to be found. This op will first open the file at index `preferred_shard` in the list of matching files and try to restore tensors from that file. Only if some tensors or tensor slices are not found in that first file, then the Op opens all the files. Setting `preferred_shard` to match the value passed as the `shard` input of a matching `Save` Op may speed up Restore. This attribute only affects performance, not correctness. The default value -1 means files are processed in order. See also `RestoreSlice`. file_pattern: Must have a single element. The pattern of the files from which we read the tensor. tensor_name: Must have a single element. The name of the tensor to be restored. tensor: The restored tensor. dt: The type of the tensor to be restored. preferred_shard: Index of file to open first if multiple files match `file_pattern`. shape_and_slice: string Restores a tensor from checkpoint files. This is like `Restore` except that restored tensor can be listed as filling only a slice of a larger tensor. `shape_and_slice` specifies the shape of the larger tensor and the slice that the restored tensor covers. The `shape_and_slice` input has the same format as the elements of the `shapes_and_slices` input of the `SaveSlices` op. file_pattern: Must have a single element. The pattern of the files from which we read the tensor. tensor_name: Must have a single element. The name of the tensor to be restored. shape_and_slice: Scalar. The shapes and slice specifications to use when restoring a tensors. tensor: The restored tensor. dt: The type of the tensor to be restored. preferred_shard: Index of file to open first if multiple files match `file_pattern`. See the documentation for `Restore`. basename: stringshard: int32num_shards: int32 Generate a sharded filename. The filename is printf formatted as %s-%05d-of-%05d, basename, shard, num_shards. Generate a glob pattern matching all sharded file names. reader_handle: Ref(string) A Reader that outputs the entire contents of a file as a value. To use, enqueue filenames in a Queue. The output of ReaderRead will be a filename (key) and the contents of that file (value). reader_handle: The handle to reference the Reader. container: If non-empty, this reader is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead. WholeFileReaderV2reader_handle: resourceskip_header_lines: int = 0 A Reader that outputs the lines of a file delimited by '\n'. reader_handle: The handle to reference the Reader. skip_header_lines: Number of lines to skip from the beginning of every file. container: If non-empty, this reader is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead. TextLineReaderV2header_bytes: int = 0record_bytes: intfooter_bytes: int = 0 A Reader that outputs fixed-length records from a file. reader_handle: The handle to reference the Reader. container: If non-empty, this reader is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead. FixedLengthRecordReaderV2compression_type: string = '' A Reader that outputs the records from a TensorFlow Records file. reader_handle: The handle to reference the Reader. container: If non-empty, this reader is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead. TFRecordReaderV2 A Reader that outputs the queued work as both the key and value. To use, enqueue strings in a Queue. ReaderRead will take the front work string and output (work, work). reader_handle: The handle to reference the Reader. container: If non-empty, this reader is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this reader is named in the given bucket with this shared_name. Otherwise, the node name is used instead. IdentityReaderV2ReaderReadqueue_handle: Ref(string)key: stringvalue: string Returns the next record (key, value pair) produced by a Reader. Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). reader_handle: Handle to a Reader. queue_handle: Handle to a Queue, with string work items. key: A scalar. value: A scalar. ReaderReadV2queue_handle: resourceReaderReadUpTonum_records: int64keys: string Returns up to `num_records` (key, value) pairs produced by a Reader. Will dequeue from the input queue if necessary (e.g. when the Reader needs to start reading from a new file since it has finished with the previous file). It may return less than `num_records` even before the last batch. reader_handle: Handle to a `Reader`. queue_handle: Handle to a `Queue`, with string work items. num_records: number of records to read from `Reader`. keys: A 1-D tensor. values: A 1-D tensor. ReaderReadUpToV2ReaderNumRecordsProducedrecords_produced: int64 Returns the number of records this Reader has produced. This is the same as the number of ReaderRead executions that have succeeded. reader_handle: Handle to a Reader. ReaderNumRecordsProducedV2ReaderNumWorkUnitsCompletedunits_completed: int64 Returns the number of work units this Reader has finished processing. reader_handle: Handle to a Reader. ReaderNumWorkUnitsCompletedV2ReaderSerializeStatestate: string Produce a string tensor that encodes the state of a Reader. Not all Readers support being serialized, so this can produce an Unimplemented error. reader_handle: Handle to a Reader. ReaderSerializeStateV2ReaderRestoreState Restore a reader to a previously saved state. Not all Readers support being restored, so this can produce an Unimplemented error. reader_handle: Handle to a Reader. state: Result of a ReaderSerializeState of a Reader with type matching reader_handle. ReaderRestoreStateV2ReaderReset Restore a Reader to its initial clean state. reader_handle: Handle to a Reader. ReaderResetV2ReadFilecontents: string Reads and outputs the entire contents of the input filename. WriteFile Writes contents to the file at input filename. Creates file if not existing. filename: scalar. The name of the file to which we write the contents. contents: scalar. The content to be written to the output file. MatchingFilespattern: stringfilenames: string Returns the set of files matching one or more glob patterns. Note that this routine only supports wildcard characters in the basename portion of the pattern, not in the directory portion. pattern: Shell wildcard pattern(s). Scalar or vector of type string. filenames: A vector of matching filenames. channels must be non-negative, got channelsBad size input type for SetOutputToSizedImage: Expected DT_INT32 but got for input #ResizeAreaimages: Tresized_images: floatT: {uint8, int8, int16, int32, int64, half, float, double}align_corners: bool = false Resize `images` to `size` using area interpolation. Input images can be of different types but output images are always float. images: 4-D with shape `[batch, height, width, channels]`. size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. align_corners: If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Treat similarly the width dimension. resized_images: 4-D with shape `[batch, new_height, new_width, channels]`. ResizeBicubic Resize `images` to `size` using bicubic interpolation. Input images can be of different types but output images are always float. images: 4-D with shape `[batch, height, width, channels]`. size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. align_corners: If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Treat similarly the width dimension. resized_images: 4-D with shape `[batch, new_height, new_width, channels]`. Resize `images` to `size` using bilinear interpolation. Input images can be of different types but output images are always float. images: 4-D with shape `[batch, height, width, channels]`. size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. align_corners: If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Treat similarly the width dimension. resized_images: 4-D with shape `[batch, new_height, new_width, channels]`. grads: floatoriginal_image: TT: {float, half, double} Computes the gradient of bilinear interpolation. grads: 4-D with shape `[batch, height, width, channels]`. original_image: 4-D with shape `[batch, orig_height, orig_width, channels]`, The image tensor that was resized. align_corners: If true, rescale grads by (orig_height - 1) / (height - 1), which exactly aligns the 4 corners of grads and original_image. If false, rescale by orig_height / height. Treat similarly the width dimension. output: 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients with respect to the input image. Input image must have been float or double. resized_images: T Resize `images` to `size` using nearest neighbor interpolation. images: 4-D with shape `[batch, height, width, channels]`. size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The new size for the images. align_corners: If true, rescale input by (new_height - 1) / (height - 1), which exactly aligns the 4 corners of images and resized images. If false, rescale by new_height / height. Treat similarly the width dimension. resized_images: 4-D with shape `[batch, new_height, new_width, channels]`. grads: TT: {uint8, int8, int32, half, float, double} Computes the gradient of nearest neighbor interpolation. grads: 4-D with shape `[batch, height, width, channels]`. size:= A 1-D int32 Tensor of 2 elements: `orig_height, orig_width`. The original input size. align_corners: If true, rescale grads by (orig_height - 1) / (height - 1), which exactly aligns the 4 corners of grads and original_image. If false, rescale by orig_height / height. Treat similarly the width dimension. output: 4-D with shape `[batch, orig_height, orig_width, channels]`. Gradients with respect to the input image. RandomCropimage: Tsize: int64T: {uint8, int8, int16, int32, int64, float, double}Random crop is now pure Python Randomly crop `image`. `size` is a 1-D int64 tensor with 2 elements representing the crop height and width. The values must be non negative. This Op picks a random location in `image` and crops a `height` by `width` rectangle from that location. The random location is picked so the cropped area will fit inside the original image. image: 3-D of shape `[height, width, channels]`. size: 1-D of length 2 containing: `crop_height`, `crop_width`.. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. output: 3-D of shape `[crop_height, crop_width, channels].` DecodeJpegchannels: int = 0ratio: int = 1fancy_upscaling: bool = truetry_recover_truncated: bool = falseacceptable_fraction: float = 1.0dct_method: string = ''image: uint8 Decode a JPEG-encoded image to a uint8 tensor. The attr `channels` indicates the desired number of color channels for the decoded image. Accepted values are: * 0: Use the number of channels in the JPEG-encoded image. * 1: output a grayscale image. * 3: output an RGB image. If needed, the JPEG-encoded image is transformed to match the requested number of color channels. The attr `ratio` allows downscaling the image by an integer factor during decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than downscaling the image later. contents: 0-D. The JPEG-encoded image. channels: Number of color channels for the decoded image. ratio: Downscaling ratio. fancy_upscaling: If true use a slower but nicer upscaling of the chroma planes (yuv420/422 only). try_recover_truncated: If true try to recover an image from truncated input. acceptable_fraction: The minimum required fraction of lines before a truncated input is accepted. dct_method: string specifying a hint about the algorithm used for decompression. Defaults to "" which maps to a system-specific default. Currently valid values are ["INTEGER_FAST", "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg library changes to a version that does not have that specific option.) image: 3-D with shape `[height, width, channels]`.. EncodeJpegformat: {'', 'grayscale', 'rgb'} = ''quality: int = 95progressive: bool = falseoptimize_size: bool = falsechroma_downsampling: bool = truedensity_unit: {'in', 'cm'} = 'in'x_density: int = 300y_density: int = 300xmp_metadata: string = '' JPEG-encode an image. `image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. The attr `format` can be used to override the color format of the encoded output. Values can be: * `''`: Use a default format based on the number of channels in the image. * `grayscale`: Output a grayscale JPEG image. The `channels` dimension of `image` must be 1. * `rgb`: Output an RGB JPEG image. The `channels` dimension of `image` must be 3. If `format` is not specified or is the empty string, a default format is picked in function of the number of channels in `image`: * 1: Output a grayscale image. * 3: Output an RGB image. image: 3-D with shape `[height, width, channels]`. format: Per pixel image format. quality: Quality of the compression from 0 to 100 (higher is better and slower). progressive: If True, create a JPEG that loads progressively (coarse to fine). optimize_size: If True, spend CPU/RAM to reduce size with no quality change. chroma_downsampling: See http://en.wikipedia.org/wiki/Chroma_subsampling. density_unit: Unit used to specify `x_density` and `y_density`: pixels per inch (`'in'`) or centimeter (`'cm'`). x_density: Horizontal pixels per density unit. y_density: Vertical pixels per density unit. xmp_metadata: If not empty, embed this XMP metadata in the image header. contents: 0-D. JPEG-encoded image. AdjustContrastcontrast_factor: floatmin_value: floatoutput: floatUse AdjustContrastv2 instead Deprecated. Disallowed in GraphDef version >= 2. AdjustContrastv2images: float Adjust the contrast of one or more images. `images` is a tensor of at least 3 dimensions. The last 3 dimensions are interpreted as `[height, width, channels]`. The other dimensions only represent a collection of images, such as `[batch, height, width, channels].` Contrast is adjusted independently for each channel of each image. For each channel, the Op first computes the mean of the image pixels in the channel and then adjusts each component of each pixel to `(x - mean) * contrast_factor + mean`. images: Images to adjust. At least 3-D. contrast_factor: A float multiplier for adjusting contrast. output: The contrast-adjusted image or images. AdjustHuedelta: float Adjust the hue of one or more images. `images` is a tensor of at least 3 dimensions. The last dimension is interpretted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A delta is then applied all the hue values, and then remapped back to RGB colorspace. images: Images to adjust. At least 3-D. delta: A float delta to add to the hue. output: The hue-adjusted image or images. AdjustSaturationscale: float Adjust the saturation of one or more images. `images` is a tensor of at least 3 dimensions. The last dimension is interpretted as channels, and must be three. The input image is considered in the RGB colorspace. Conceptually, the RGB colors are first mapped into HSV. A scale is then applied all the saturation values, and then remapped back to RGB colorspace. images: Images to adjust. At least 3-D. scale: A float scale to add to the saturation. output: The hue-adjusted image or images. DecodePngdtype: {uint8, uint16} = DT_UINT8image: dtype Decode a PNG-encoded image to a uint8 or uint16 tensor. The attr `channels` indicates the desired number of color channels for the decoded image. Accepted values are: * 0: Use the number of channels in the PNG-encoded image. * 1: output a grayscale image. * 3: output an RGB image. * 4: output an RGBA image. If needed, the PNG-encoded image is transformed to match the requested number of color channels. contents: 0-D. The PNG-encoded image. channels: Number of color channels for the decoded image. image: 3-D with shape `[height, width, channels]`. EncodePngcompression: int = -1T: {uint8, uint16} = DT_UINT8 PNG-encode an image. `image` is a 3-D uint8 or uint16 Tensor of shape `[height, width, channels]` where `channels` is: * 1: for grayscale. * 2: for grayscale + alpha. * 3: for RGB. * 4: for RGBA. The ZLIB compression level, `compression`, can be -1 for the PNG-encoder default or a value from 0 to 9. 9 is the highest compression level, generating the smallest output, but is slower. image: 3-D with shape `[height, width, channels]`. compression: Compression level. contents: 0-D. PNG-encoded image. DecodeGif Decode the first frame of a GIF-encoded image to a uint8 tensor. GIF with frame or transparency compression are not supported convert animated GIF from compressed to uncompressed by: convert $src.gif -coalesce $dst.gif contents: 0-D. The GIF-encoded image. image: 4-D with shape `[num_frames, height, width, 3]`. RGB order RGBToHSV Converts one or more images from RGB to HSV. Outputs a tensor of the same shape as the `images` tensor, containing the HSV value of the pixels. The output is only well defined if the value in `images` are in `[0,1]`. `output[..., 0]` contains hue, `output[..., 1]` contains saturation, and `output[..., 2]` contains value. All HSV values are in `[0,1]`. A hue of 0 corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. images: 1-D or higher rank. RGB data to convert. Last dimension must be size 3. output: `images` converted to HSV. HSVToRGB Convert one or more images from HSV to RGB. Outputs a tensor of the same shape as the `images` tensor, containing the RGB value of the pixels. The output is only well defined if the value in `images` are in `[0,1]`. See `rgb_to_hsv` for a description of the HSV encoding. images: 1-D or higher rank. HSV data to convert. Last dimension must be size 3. output: `images` converted to RGB. DrawBoundingBoxesboxes: float Draw bounding boxes on a batch of images. Outputs a copy of `images` but draws on top of the pixels zero or more bounding boxes specified by the locations in `boxes`. The coordinates of the each bounding box in `boxes` are encoded as `[y_min, x_min, y_max, x_max]`. The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and height of the underlying image. For example, if an image is 100 x 200 pixels and the bounding box is `[0.1, 0.2, 0.5, 0.9]`, the bottom-left and upper-right coordinates of the bounding box will be `(10, 40)` to `(50, 180)`. Parts of the bounding box may fall outside the image. images: 4-D with shape `[batch, height, width, depth]`. A batch of images. boxes: 3-D with shape `[batch, num_bounding_boxes, 4]` containing bounding boxes. output: 4-D with the same shape as `images`. The batch of input images with bounding boxes drawn on the images. SampleDistortedBoundingBoximage_size: Tbounding_boxes: floatbegin: Tsize: Tbboxes: floatT: {uint8, int8, int16, int32, int64}min_object_covered: float = 0.1aspect_ratio_range: list(float) = [0.75, 1.33]area_range: list(float) = [0.05, 1.0]max_attempts: int = 100use_image_if_no_bounding_boxes: bool = false Generate a single randomly distorted bounding box for an image. Bounding box annotations are often supplied in addition to ground-truth labels in image recognition or object localization tasks. A common technique for training such a system is to randomly distort an image while preserving its content, i.e. *data augmentation*. This Op outputs a randomly distorted localization of an object, i.e. bounding box, given an `image_size`, `bounding_boxes` and a series of constraints. The output of this Op is a single bounding box that may be used to crop the original image. The output is returned as 3 tensors: `begin`, `size` and `bboxes`. The first 2 tensors can be fed directly into `tf.slice` to crop the image. The latter may be supplied to `tf.image.draw_bounding_boxes` to visualize what the bounding box looks like. Bounding boxes are supplied and returned as `[y_min, x_min, y_max, x_max]`. The bounding box coordinates are floats in `[0.0, 1.0]` relative to the width and height of the underlying image. For example, ```python # Generate a single distorted bounding box. begin, size, bbox_for_draw = tf.image.sample_distorted_bounding_box( tf.shape(image), bounding_boxes=bounding_boxes) # Draw the bounding box in an image summary. image_with_box = tf.image.draw_bounding_boxes(tf.expand_dims(image, 0), bbox_for_draw) tf.image_summary('images_with_box', image_with_box) # Employ the bounding box to distort the image. distorted_image = tf.slice(image, begin, size) ``` Note that if no bounding box information is available, setting `use_image_if_no_bounding_boxes = true` will assume there is a single implicit bounding box covering the whole image. If `use_image_if_no_bounding_boxes` is false and no bounding boxes are supplied, an error is raised. image_size: 1-D, containing `[height, width, channels]`. bounding_boxes: 3-D with shape `[batch, N, 4]` describing the N bounding boxes associated with the image. begin: 1-D, containing `[offset_height, offset_width, 0]`. Provide as input to `tf.slice`. size: 1-D, containing `[target_height, target_width, -1]`. Provide as input to `tf.slice`. bboxes: 3-D with shape `[1, 1, 4]` containing the distorted bounding box. Provide as input to `tf.image.draw_bounding_boxes`. seed: If either `seed` or `seed2` are set to non-zero, the random number generator is seeded by the given `seed`. Otherwise, it is seeded by a random seed. seed2: A second seed to avoid seed collision. min_object_covered: The cropped area of the image must contain at least this fraction of any bounding box supplied. The value of this parameter should be non-negative. In the case of 0, the cropped area does not need to overlap any of the bounding boxes supplied. aspect_ratio_range: The cropped area of the image must have an aspect ratio = width / height within this range. area_range: The cropped area of the image must contain a fraction of the supplied image within in this range. max_attempts: Number of attempts at generating a cropped region of the image of the specified constraints. After `max_attempts` failures, return the entire image. use_image_if_no_bounding_boxes: Controls behavior if no bounding boxes supplied. If true, assume an implicit bounding box covering the whole input. If false, raise an error. ExtractGlimpseinput: floatoffsets: floatglimpse: floatcentered: bool = truenormalized: bool = trueuniform_noise: bool = true Extracts a glimpse from the input tensor. Returns a set of windows called glimpses extracted at location `offsets` from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape `[batch_size, glimpse_height, glimpse_width, channels]`. The channels and batch dimensions are the same as that of the input tensor. The height and width of the output windows are specified in the `size` parameter. The argument `normalized` and `centered` controls how the windows are built: * If the coordinates are normalized but not centered, 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. * If the coordinates are both normalized and centered, they range from -1.0 to 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the lower right corner is located at (1.0, 1.0) and the center is at (0, 0). * If the coordinates are not normalized they are interpreted as numbers of pixels. input: A 4-D float tensor of shape `[batch_size, height, width, channels]`. size: A 1-D tensor of 2 elements containing the size of the glimpses to extract. The glimpse height must be specified first, following by the glimpse width. offsets: A 2-D integer tensor of shape `[batch_size, 2]` containing the y, x locations of the center of each window. glimpse: A tensor representing the glimpses `[batch_size, glimpse_height, glimpse_width, channels]`. centered: indicates if the offset coordinates are centered relative to the image, in which case the (0, 0) offset is relative to the center of the input images. If false, the (0,0) offset corresponds to the upper left corner of the input images. normalized: indicates if the offset coordinates are normalized. uniform_noise: indicates if the noise should be generated using a uniform distribution or a Gaussian distribution. box_ind: int32crop_size: int32crops: floatmethod: {'bilinear'} = 'bilinear'extrapolation_value: float = 0 Extracts crops from the input image tensor and bilinearly resizes them (possibly with aspect ratio change) to a common output size specified by `crop_size`. This is more general than the `crop_to_bounding_box` op which extracts a fixed size slice from the input image and does not allow resizing or aspect ratio change. Returns a tensor with `crops` from the input `image` at positions defined at the bounding box locations in `boxes`. The cropped boxes are all resized (with bilinear interpolation) to a fixed `size = [crop_height, crop_width]`. The result is a 4-D tensor `[num_boxes, crop_height, crop_width, depth]`. image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. Both `image_height` and `image_width` need to be positive. boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the `[0, 1]` range are allowed, in which case we use `extrapolation_value` to extrapolate the input image values. box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box refers to. crop_size: A 1-D tensor of 2 elements, `size = [crop_height, crop_width]`. All cropped image patches are resized to this size. The aspect ratio of the image content is not preserved. Both `crop_height` and `crop_width` need to be positive. crops: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. method: A string specifying the interpolation method. Only 'bilinear' is supported for now. extrapolation_value: Value used for extrapolation, when applicable. image_size: int32 Computes the gradient of the crop_and_resize op wrt the input image tensor. grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the `[0, 1]` range are allowed, in which case we use `extrapolation_value` to extrapolate the input image values. box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box refers to. image_size: A 1-D tensor with value `[batch, image_height, image_width, depth]` containing the original image size. Both `image_height` and `image_width` need to be positive. output: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. method: A string specifying the interpolation method. Only 'bilinear' is supported for now. Computes the gradient of the crop_and_resize op wrt the input boxes tensor. grads: A 4-D tensor of shape `[num_boxes, crop_height, crop_width, depth]`. image: A 4-D tensor of shape `[batch, image_height, image_width, depth]`. Both `image_height` and `image_width` need to be positive. boxes: A 2-D tensor of shape `[num_boxes, 4]`. The `i`-th row of the tensor specifies the coordinates of a box in the `box_ind[i]` image and is specified in normalized coordinates `[y1, x1, y2, x2]`. A normalized coordinate value of `y` is mapped to the image coordinate at `y * (image_height - 1)`, so as the `[0, 1]` interval of normalized image height is mapped to `[0, image_height - 1] in image height coordinates. We do allow y1 > y2, in which case the sampled crop is an up-down flipped version of the original image. The width dimension is treated similarly. Normalized coordinates outside the `[0, 1]` range are allowed, in which case we use `extrapolation_value` to extrapolate the input image values. box_ind: A 1-D tensor of shape `[num_boxes]` with int32 values in `[0, batch)`. The value of `box_ind[i]` specifies the image that the `i`-th box refers to. output: A 2-D tensor of shape `[num_boxes, 4]`. method: A string specifying the interpolation method. Only 'bilinear' is supported for now. scores: floatmax_output_size: int32selected_indices: int32iou_threshold: float = 0.5 Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Note that this algorithm is agnostic to where the origin is in the coordinate system. Note that this algorithm is invariant to orthogonal transformations and translations of the coordinate system; thus translating or reflections of the coordinate system result in the same boxes being selected by the algorithm. The output of this operation is a set of integers indexing into the input collection of bounding boxes representing the selected boxes. The bounding box coordinates corresponding to the selected indices can then be obtained using the `tf.gather operation`. For example: selected_indices = tf.image.non_max_suppression( boxes, scores, max_output_size, iou_threshold) selected_boxes = tf.gather(boxes, selected_indices) boxes: A 2-D float tensor of shape `[num_boxes, 4]`. scores: A 1-D float tensor of shape `[num_boxes]` representing a single score corresponding to each box (each row of boxes). max_output_size: A scalar integer tensor representing the maximum number of boxes to be selected by non max suppression. iou_threshold: A float representing the threshold for deciding whether boxes overlap too much with respect to IOU. selected_indices: A 1-D integer tensor of shape `[M]` representing the selected indices from the boxes tensor, where `M <= max_output_size`. len(inputs) < len(outputs)Tin: list(type)Tout: list(type)f: func Computes the gradient function for function f via backpropagation. input: a list of input tensors of size N + M; output: a list of output tensors of size N; Tin: the type list for the input list. Tout: the type list for the input list. f: The function we want to compute the gradient for. The function 'f' must be a numerical function which takes N inputs and produces M outputs. Its gradient function 'g', which is computed by this SymbolicGradient op is a function taking N + M inputs and produces N outputs. I.e. if we have (y1, y2, ..., y_M) = f(x1, x2, ..., x_N), then, g is (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N, dL/dy1, dL/dy2, ..., dL/dy_M), where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the loss function). dL/dx_i is the partial derivative of L with respect to x_i. (Needs some math expert to say the comment above better.) MapAccumulatetheta: K*Tdtheta: K*Tdu: TthetauKdthetaduMapAccumulateGradgindex: int >= 0 A graph node which represents an argument to a function. output: The argument. index: This argument is the index-th argument of the function. A graph node which represents a return value of a function. input: The return value. index: This return value is the index-th return value of the function. output: N * T Converts a list of tensors to an array of tensors. input: N * Toutput: out_typesout_types: list(type) Converts an array of tensors to a list of tensors. partitions: int32outputs: num_partitions * Tnum_partitions: int Partitions `data` into `num_partitions` tensors using indices from `partitions`. For each index tuple `js` of size `partitions.ndim`, the slice `data[js, ...]` becomes part of `outputs[partitions[js]]`. The slices with `partitions[js] = i` are placed in `outputs[i]` in lexicographic order of `js`, and the first dimension of `outputs[i]` is the number of entries in `partitions` equal to `i`. In detail, ```python outputs[i].shape = [sum(partitions == i)] + data.shape[partitions.ndim:] outputs[i] = pack([data[js, ...] for js if partitions[js] == i]) ``` `data.shape` must start with `partitions.shape`. For example: ```python # Scalar partitions. partitions = 1 num_partitions = 2 data = [10, 20] outputs[0] = [] # Empty with shape [0, 2] outputs[1] = [[10, 20]] # Vector partitions. partitions = [0, 0, 1, 1, 0] num_partitions = 2 data = [10, 20, 30, 40, 50] outputs[0] = [10, 20, 50] outputs[1] = [30, 40] ```
partitions: Any shape. Indices in the range `[0, num_partitions)`. num_partitions: The number of partitions to output. indices: N * int32data: N * Tmerged: TT : type Interleave the values from the `data` tensors into a single tensor. Builds a merged tensor such that ```python merged[indices[m][i, ..., j], ...] = data[m][i, ..., j, ...] ``` For example, if each `indices[m]` is scalar or vector, we have ```python # Scalar indices: merged[indices[m], ...] = data[m][...] # Vector indices: merged[indices[m][i], ...] = data[m][i, ...] ``` Each `data[i].shape` must start with the corresponding `indices[i].shape`, and the rest of `data[i].shape` must be constant w.r.t. `i`. That is, we must have `data[i].shape = indices[i].shape + constant`. In terms of this `constant`, the output shape is merged.shape = [max(indices)] + constant Values are merged in order, so if an index appears in both `indices[m][i]` and `indices[n][j]` for `(m,i) < (n,j)` the slice `data[n][j]` will appear in the merged result. For example: ```python indices[0] = 6 indices[1] = [4, 1] indices[2] = [[5, 2], [0, 3]] data[0] = [61, 62] data[1] = [[41, 42], [11, 12]] data[2] = [[[51, 52], [21, 22]], [[1, 2], [31, 32]]] merged = [[1, 2], [11, 12], [21, 22], [31, 32], [41, 42], [51, 52], [61, 62]] ```
handle: Ref(string)component_types: list(type) >= 1shapes: list(shape) >= 0 = []capacity: int = -1min_after_dequeue: int = 0 A queue that randomizes the order of elements. handle: The handle to the queue. component_types: The type of each component in a value. shapes: The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time. capacity: The upper bound on the number of elements in this queue. Negative numbers mean no limit. min_after_dequeue: Dequeue will block unless there would be this many elements after the dequeue or the queue is closed. This ensures a minimum level of mixing of elements. seed: If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used. seed2: A second seed to avoid seed collision. container: If non-empty, this queue is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this queue will be shared under the given name across multiple sessions. RandomShuffleQueueV2handle: resource A queue that produces elements in first-in first-out order. handle: The handle to the queue. component_types: The type of each component in a value. shapes: The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time. capacity: The upper bound on the number of elements in this queue. Negative numbers mean no limit. container: If non-empty, this queue is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this queue will be shared under the given name across multiple sessions. A queue that produces elements in first-in first-out order. Variable-size shapes are allowed by setting the corresponding shape dimensions to 0 in the shape attr. In this case DequeueMany will pad up to the maximum size of any given element in the minibatch. See below for details. handle: The handle to the queue. component_types: The type of each component in a value. shapes: The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. Shapes of fixed rank but variable size are allowed by setting any shape dimension to -1. In this case, the inputs' shape may vary along the given dimension, and DequeueMany will pad the given dimension with zeros up to the maximum shape of all elements in the given batch. If the length of this attr is 0, different queue elements may have different ranks and shapes, but only one element may be dequeued at a time. capacity: The upper bound on the number of elements in this queue. Negative numbers mean no limit. container: If non-empty, this queue is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this queue will be shared under the given name across multiple sessions. component_types: list(type) >= 0 = []shapes: list(shape) >= 0 A queue that produces elements sorted by the first component value. Note that the PriorityQueue requires the first component of any element to be a scalar int64, in addition to the other elements declared by component_types. Therefore calls to Enqueue and EnqueueMany (resp. Dequeue and DequeueMany) on a PriorityQueue will all require (resp. output) one extra entry in their input (resp. output) lists. handle: The handle to the queue. component_types: The type of each component in a value. shapes: The shape of each component in a value. The length of this attr must be either 0 or the same as the length of component_types. If the length of this attr is 0, the shapes of queue elements are not constrained, and only one element may be dequeued at a time. capacity: The upper bound on the number of elements in this queue. Negative numbers mean no limit. container: If non-empty, this queue is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this queue will be shared under the given name across multiple sessions. PriorityQueueV2resource: resourceDeprecated. Do not use.components: TcomponentsTcomponents: list(type) >= 1timeout_ms: int = -1 Enqueues a tuple of one or more tensors in the given queue. The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given element has been enqueued (or 'timeout_ms' elapses, if specified). handle: The handle to a queue. components: One or more tensors from which the enqueued tensors should be taken. timeout_ms: If the queue is full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. Enqueues zero or more tuples of one or more tensors in the given queue. This operation slices each component tensor along the 0th dimension to make multiple queue elements. All of the tuple components must have the same size in the 0th dimension. The components input has k elements, which correspond to the components of tuples stored in the given queue. N.B. If the queue is full, this operation will block until the given elements have been enqueued (or 'timeout_ms' elapses, if specified). handle: The handle to a queue. components: One or more tensors from which the enqueued tensors should be taken. timeout_ms: If the queue is too full, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. components: component_types Dequeues a tuple of one or more tensors from the given queue. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until an element has been dequeued (or 'timeout_ms' elapses, if specified). handle: The handle to a queue. components: One or more tensors that were dequeued as a tuple. component_types: The type of each component in a tuple. timeout_ms: If the queue is empty, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. n: int32 Dequeues n tuples of one or more tensors from the given queue. If the queue is closed and there are fewer than n elements, then an OutOfRange error is returned. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. N.B. If the queue is empty, this operation will block until n elements have been dequeued (or 'timeout_ms' elapses, if specified). handle: The handle to a queue. n: The number of tuples to dequeue. components: One or more tensors that were dequeued as a tuple. component_types: The type of each component in a tuple. timeout_ms: If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. Dequeues n tuples of one or more tensors from the given queue. This operation is not supported by all queues. If a queue does not support DequeueUpTo, then an Unimplemented error is returned. If the queue is closed and there are more than 0 but less than n elements remaining, then instead of returning an OutOfRange error like QueueDequeueMany, less than `n` elements are returned immediately. If the queue is closed and there are 0 elements left in the queue, then an OutOfRange error is returned just like in QueueDequeueMany. Otherwise the behavior is identical to QueueDequeueMany: This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. This operation has k outputs, where k is the number of components in the tuples stored in the given queue, and output i is the ith component of the dequeued tuple. handle: The handle to a queue. n: The number of tuples to dequeue. components: One or more tensors that were dequeued as a tuple. component_types: The type of each component in a tuple. timeout_ms: If the queue has fewer than n elements, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. cancel_pending_enqueues: bool = false Closes the given queue. This operation signals that no more elements will be enqueued in the given queue. Subsequent Enqueue(Many) operations will fail. Subsequent Dequeue(Many) operations will continue to succeed if sufficient elements remain in the queue. Subsequent Dequeue(Many) operations that would block will fail immediately. handle: The handle to a queue. cancel_pending_enqueues: If true, all pending enqueue requests that are blocked on the given queue will be cancelled. Computes the number of elements in the given queue. handle: The handle to a queue. size: The number of elements in the given queue. AccumulatorNumAccumulatednum_accumulated: int32 Returns the number of gradients aggregated in the given accumulators. handle: The handle to an accumulator. num_accumulated: The number of gradients aggregated in the given accumulator. AccumulatorSetGlobalStepnew_global_step: int64 Updates the accumulator with a new value for global_step. Logs warning if the accumulator's value is already higher than new_global_step. handle: The handle to an accumulator. new_global_step: The new global_step value to set. dtype: numbertype A conditional accumulator for aggregating gradients. The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator. handle: The handle to the accumulator. dtype: The type of the value being accumulated. shape: The shape of the values, can be [], in which case shape is unknown. container: If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this accumulator will be shared under the given name across multiple sessions. AccumulatorApplyGradientlocal_step: int64gradient: dtype Applies a gradient to a given accumulator. Does not add if local_step is lesser than the accumulator's global_step. handle: The handle to a accumulator. local_step: The local_step value at which the gradient was computed. gradient: A tensor of the gradient to be accumulated. dtype: The data type of accumulated gradients. Needs to correspond to the type of the accumulator. AccumulatorTakeGradientnum_required: int32average: dtype Extracts the average gradient in the given ConditionalAccumulator, provided that sufficient (i.e., more than num_required) gradients have been accumulated. The op blocks until sufficient gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it returns the average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0. handle: The handle to an accumulator. num_required: Number of gradients required before we return an aggregate. average: The average of the accumulated gradients. dtype: The data type of accumulated gradients. Needs to correspond to the type of the accumulator. A conditional accumulator for aggregating sparse gradients. The accumulator accepts gradients marked with local_step greater or equal to the most recent global_step known to the accumulator. The average can be extracted from the accumulator, provided sufficient gradients have been accumulated. Extracting the average automatically resets the aggregate to 0, and increments the global_step recorded by the accumulator. handle: The handle to the accumulator. dtype: The type of the value being accumulated. shape: The shape of the values. container: If non-empty, this accumulator is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this accumulator will be shared under the given name across multiple sessions. SparseAccumulatorApplyGradientgradient_indices: int64gradient_values: dtypegradient_shape: int64has_known_shape: bool Applies a sparse gradient to a given accumulator. Does not add if local_step is lesser than the accumulator's global_step. handle: The handle to a accumulator. local_step: The local_step value at which the sparse gradient was computed. gradient_indices: Indices of the sparse gradient to be accumulated. Must be a vector. gradient_values: Values are the non-zero slices of the gradient, and must have the same first dimension as indices, i.e., the nnz represented by indices and values must be consistent. gradient_shape: Shape of the sparse gradient to be accumulated. dtype: The data type of accumulated gradients. Needs to correspond to the type of the accumulator. has_known_shape: Boolean indicating whether gradient_shape is unknown, in which case the input is ignored during validation. SparseAccumulatorTakeGradientvalues: dtype Extracts the average sparse gradient in the given SparseConditionalAccumulator, provided that sufficient (i.e., more than num_required) gradients have been accumulated. The op will blocks until sufficient gradients have been accumulated. If the accumulator has already aggregated more than num_required gradients, it will return its average of the accumulated gradients. Also automatically increments the recorded global_step in the accumulator by 1, and resets the aggregate to 0. handle: The handle to a SparseConditionalAccumulator. num_required: Number of gradients required before we return an aggregate. indices: Indices of the average of the accumulated sparse gradients. values: Values of the average of the accumulated sparse gradients. shape: Shape of the average of the accumulated sparse gradients. dtype: The data type of accumulated gradients. Needs to correspond to the type of the accumulator. elem_type: typestack_name: string = '' A stack that produces elements in first-in last-out order. handle: The handle to the stack. elem_type: The type of the elements on the stack. stack_name: Overrides the name used for the temporary stack resource. Default value is the name of the 'Stack' op (which is guaranteed unique). elem: Tswap_memory: bool = false Push an element onto the stack. handle: The handle to a stack. elem: The tensor to be pushed onto the stack. output: The same tensor as the input 'elem'. swap_memory: Swap `elem` to CPU. Default to false. elem: elem_type Pop the element at the top of the stack. handle: The handle to a stack. elem: The tensor that is popped from the top of the stack. elem_type: The type of the elem that is popped. Delete the stack from its resource container. handle: The handle to a stack. element_shape: shape = { unknown_rank: true }dynamic_size: bool = falseclear_after_read: bool = truetensor_array_name: string = ''flow: float An array of Tensors of given size, with data written via Write and read via Read or Pack. handle: The handle to the TensorArray. flow: A scalar used to control gradient flow. size: The size of the array. dtype: The type of the elements on the tensor_array. element_shape: The expected shape of an element, if known. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, gathering zero-size TensorArrays is an error. dynamic_size: A boolean that determines whether writes to the TensorArray are allowed to grow the size. By default, this is not allowed. clear_after_read: If true (default), Tensors in the TensorArray are cleared after being read. This disables multiple read semantics but allows early release of memory. tensor_array_name: Overrides the name used for the temporary tensor_array resource. Default value is the name of the 'TensorArray' op (which is guaranteed unique). flow_in: floatgrad_handle: resourceflow_out: floatsource: string Creates a TensorArray for storing the gradients of values in the given handle. If the given TensorArray gradient already exists, returns a reference to it. Locks the size of the original TensorArray by disabling its dynamic size flag. **A note about the input flow_in:** The handle flow_in forces the execution of the gradient lookup to occur only after certain other operations have occurred. For example, when the forward TensorArray is dynamically sized, writes to this TensorArray may resize the object. The gradient TensorArray is statically sized based on the size of the forward TensorArray when this operation executes. Furthermore, the size of the forward TensorArray is frozen by this call. As a result, the flow is used to ensure that the call to generate the gradient TensorArray only happens after all writes are executed. In the case of dynamically sized TensorArrays, gradient computation should only be performed on read operations that have themselves been chained via flow to occur only after all writes have executed. That way the final size of the forward TensorArray is known when this operation is called. **A note about the source attribute:** TensorArray gradient calls use an accumulator TensorArray object. If multiple gradients are calculated and run in the same session, the multiple gradient nodes may accidentally flow throuth the same accumulator TensorArray. This double counts and generally breaks the TensorArray gradient flow. The solution is to identify which gradient call this particular TensorArray gradient is being called in. This is performed by identifying a unique string (e.g. "gradients", "gradients_1", ...) from the input gradient Tensor's name. This string is used as a suffix when creating the TensorArray gradient object here (the attribute `source`). The attribute `source` is added as a suffix to the forward TensorArray's name when performing the creation / lookup, so that each separate gradient calculation gets its own TensorArray accumulator. handle: The handle to the forward TensorArray. flow_in: A float scalar that enforces proper chaining of operations. source: The gradient source string, used to decide which gradient TensorArray to return. index: int32 Push an element onto the tensor_array. handle: The handle to a TensorArray. index: The position to write to inside the TensorArray. value: The tensor to write to the TensorArray. flow_in: A float scalar that enforces proper chaining of operations. flow_out: A float scalar that enforces proper chaining of operations. value: dtype Read an element from the TensorArray into output `value`. handle: The handle to a TensorArray. dtype: The type of the elem that is returned. flow_in: A float scalar that enforces proper chaining of operations. value: The tensor that is read from the TensorArray. Gather specific elements from the TensorArray into output `value`. All elements selected by `indices` must have the same shape. handle: The handle to a TensorArray. indices: The locations in the TensorArray from which to read tensor elements. dtype: The type of the elem that is returned. element_shape: The expected shape of an element, if known. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, gathering zero-size TensorArrays is an error. flow_in: A float scalar that enforces proper chaining of operations. value: All of the elements in the TensorArray, concatenated along a new axis (the new dimension 0). Scatter the data from the input value into specific TensorArray elements. `indices` must be a vector, its length must match the first dim of `value`. handle: The handle to a TensorArray. indices: The locations at which to write the tensor elements. value: The concatenated tensor to write to the TensorArray. flow_in: A float scalar that enforces proper chaining of operations. flow_out: A float scalar that enforces proper chaining of operations. lengths: int64element_shape_except0: shape = { unknown_rank: true } Concat the elements from the TensorArray into value `value`. Takes `T` elements of shapes ``` (n0 x d0 x d1 x ...), (n1 x d0 x d1 x ...), ..., (n(T-1) x d0 x d1 x ...) ``` and concatenates them into a Tensor of shape: ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)``` All elements must have the same shape (excepting the first dimension). handle: The handle to a TensorArray. dtype: The type of the elem that is returned. flow_in: A float scalar that enforces proper chaining of operations. element_shape_except0: The expected shape of an element, if known, excluding the first dimension. Used to validate the shapes of TensorArray elements. If this shape is not fully specified, concatenating zero-size TensorArrays is an error. value: All of the elements in the TensorArray, concatenated along the first axis. lengths: A vector of the row sizes of the original T elements in the value output. In the example above, this would be the values: `(n1, n2, ..., n(T-1))`. Split the data from the input value into TensorArray elements. Assuming that `lengths` takes on values ```(n0, n1, ..., n(T-1))``` and that `value` has shape ```(n0 + n1 + ... + n(T-1) x d0 x d1 x ...)```, this splits values into a TensorArray with T tensors. TensorArray index t will be the subtensor of values with starting position ```(n0 + n1 + ... + n(t-1), 0, 0, ...)``` and having size ```nt x d0 x d1 x ...``` handle: The handle to a TensorArray. value: The concatenated tensor to write to the TensorArray. lengths: The vector of lengths, how to split the rows of value into the TensorArray. flow_in: A float scalar that enforces proper chaining of operations. flow_out: A float scalar that enforces proper chaining of operations. Get the current size of the TensorArray. handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). flow_in: A float scalar that enforces proper chaining of operations. size: The current size of the TensorArray. Delete the TensorArray from its resource container. This enables the user to close and release the resource in the middle of a step/run. handle: The handle to a TensorArray (output of TensorArray or TensorArrayGrad). Use TensorArrayV3handle: stringDeprecated. Use TensorArrayV3grad_handle: Ref(string)Use TensorArrayGradV3grad_handle: stringDeprecated. Use TensorArrayGradV3Use TensorArrayWriteV3Use TensorArrayReadV3Deprecated. Use TensorArrayReadV3Use TensorArrayGatherV3 with RangeOpUse TensorArrayScatterV3 with RangeOpUse TensorArrayGatherV3Deprecated. Use TensorArrayGatherV3Deprecated. Use TensorArrayScatterV3Deprecated. Use TensorArrayConcatV3Use TensorArraySplitV3Deprecated. Use TensorArraySplitV3Use TensorArraySizeV3Deprecated. Use TensorArraySizeV3Use TensorArrayCloseV3Deprecated. Use TensorArrayCloseV3 Defines a barrier that persists across different graph executions. A barrier represents a key-value map, where each key is a string, and each value is a tuple of tensors. At runtime, the barrier contains 'complete' and 'incomplete' elements. A complete element has defined tensors for all components of its value tuple, and may be accessed using BarrierTakeMany. An incomplete element has some undefined components in its value tuple, and may be updated using BarrierInsertMany. handle: The handle to the barrier. component_types: The type of each component in a value. shapes: The shape of each component in a value. Each shape must be 1 in the first dimension. The length of this attr must be the same as the length of component_types. capacity: The capacity of the barrier. The default capacity is MAX_INT32, which is the largest capacity of the underlying queue. container: If non-empty, this barrier is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this barrier will be shared under the given name across multiple sessions. BarrierInsertManycomponent_index: int For each key, assigns the respective value to the specified component. If a key is not found in the barrier, this operation will create a new incomplete element. If a key is found in the barrier, and the element already has a value at component_index, this operation will fail with INVALID_ARGUMENT, and leave the barrier in an undefined state. handle: The handle to a barrier. component_index: The component of the barrier elements that is being assigned. keys: A one-dimensional tensor of keys, with length n. values: An any-dimensional tensor of values, which are associated with the respective keys. The 0th dimension must have length n. BarrierTakeManynum_elements: int32values: component_typesallow_small_batch: bool = falsewait_for_incomplete: bool = false Takes the given number of completed elements from a barrier. This operation concatenates completed-element component tensors along the 0th dimension to make a single component tensor. Elements come out of the barrier when they are complete, and in the order in which they were placed into the barrier. The indices output provides information about the batch in which each element was originally inserted into the barrier. handle: The handle to a barrier. num_elements: A single-element tensor containing the number of elements to take. indices: A one-dimensional tensor of indices, with length num_elems. These indices refer to the batch in which the values were placed into the barrier (starting with MIN_LONG and increasing with each BarrierInsertMany). keys: A one-dimensional tensor of keys, with length num_elements. values: One any-dimensional tensor per component in a barrier element. All values have length num_elements in the 0th dimension. component_types: The type of each component in a value. allow_small_batch: Allow to return less than num_elements items if barrier is already closed. timeout_ms: If the queue is empty, this operation will block for up to timeout_ms milliseconds. Note: This option is not supported yet. BarrierClose Closes the given barrier. This operation signals that no more new elements will be inserted in the given barrier. Subsequent InsertMany that try to introduce a new key will fail. Subsequent InsertMany operations that just add missing components to already existing elements will continue to succeed. Subsequent TakeMany operations will continue to succeed if sufficient completed elements remain in the barrier. Subsequent TakeMany operations that would block will fail immediately. handle: The handle to a barrier. cancel_pending_enqueues: If true, all pending enqueue requests that are blocked on the barrier's queue will be cancelled. InsertMany will fail, even if no new key is introduced. Computes the number of complete elements in the given barrier. handle: The handle to a barrier. size: The number of complete elements (i.e. those with all of their value components set) in the barrier. Computes the number of incomplete elements in the given barrier. handle: The handle to a barrier. size: The number of incomplete elements (i.e. those with some of their value components not set) in the barrier. LookupTableFindtable_handle: Ref(string)keys: Tindefault_value: Toutvalues: ToutTin: typeTout: type Looks up keys in a table, outputs the corresponding values. The tensor `keys` must of the same type as the keys of the table. The output `values` is of the type of the table values. The scalar `default_value` is the value output for keys not present in the table. It must also be of the same type as the table values. table_handle: Handle to the table. keys: Any shape. Keys to look up. values: Same shape as `keys`. Values found in the table, or `default_values` for missing keys. LookupTableInsert Updates the table to associates keys with values. The tensor `keys` must be of the same type as the keys of the table. The tensor `values` must be of the type of the table values. table_handle: Handle to the table. keys: Any shape. Keys to look up. values: Values to associate with keys. LookupTableSize Computes the number of elements in the given table. table_handle: Handle to the table. size: Scalar that contains number of elements in the table. LookupTableExportkeys: Tkeysvalues: TvaluesTkeys: typeTvalues: type Outputs all keys and values in the table. table_handle: Handle to the table. keys: Vector of all keys present in the table. values: Tensor of all values in the table. Indexed in parallel with `keys`. LookupTableImport Replaces the contents of the table with the specified keys and values. The tensor `keys` must be of the same type as the keys of the table. The tensor `values` must be of the type of the table values. table_handle: Handle to the table. keys: Any shape. Keys to look up. values: Values to associate with keys. use_node_name_sharing: bool = falsekey_dtype: typevalue_dtype: type Creates a non-initialized hash table. This op creates a hash table, specifying the type of its keys and values. Before using the table you will have to initialize it. After initialization the table will be immutable. table_handle: Handle to a table. container: If non-empty, this table is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this table is shared under the given name across multiple sessions. use_node_name_sharing: If true and shared_name is empty, the table is shared using the node name. key_dtype: Type of the table keys. value_dtype: Type of the table values. Creates an empty hash table. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation. table_handle: Handle to a table. container: If non-empty, this table is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this table is shared under the given name across multiple sessions. use_node_name_sharing: If true and shared_name is empty, the table is shared using the node name. key_dtype: Type of the table keys. value_dtype: Type of the table values. value_shape: shape = {} Creates an empty hash table. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a vector. Data can be inserted into the table using the insert operations. It does not support the initialization operation. table_handle: Handle to a table. container: If non-empty, this table is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this table is shared under the given name across multiple sessions. key_dtype: Type of the table keys. value_dtype: Type of the table values. MutableDenseHashTableempty_key: key_dtypeinitial_num_buckets: int = 131072max_load_factor: float = 0.8 Creates an empty hash table that uses tensors as the backing store. It uses "open addressing" with quadratic reprobing to resolve collisions. This op creates a mutable hash table, specifying the type of its keys and values. Each value must be a scalar. Data can be inserted into the table using the insert operations. It does not support the initialization operation. empty_key: The key used to represent empty key buckets internally. Must not be used in insert or lookup operations. table_handle: Handle to a table. container: If non-empty, this table is placed in the given container. Otherwise, a default container is used. shared_name: If non-empty, this table is shared under the given name across multiple sessions. key_dtype: Type of the table keys. value_dtype: Type of the table values. value_shape: The shape of each value. initial_num_buckets: The initial number of hash table buckets. Must be a power to 2. max_load_factor: The maximum ratio between number of entries and number of buckets before growing the table. Must be between 0 and 1. InitializeTablekeys: Tkeyvalues: TvalTkey: typeTval: type Table initializer that takes two tensors for keys and values respectively. table_handle: Handle to a table which will be initialized. keys: Keys of type Tkey. values: Values of type Tval. InitializeTableFromTextFilekey_index: int >= -2value_index: int >= -2vocab_size: int >= -1 = -1delimiter: string = ' ' Initializes a table from a text file. It inserts one key-value pair into the table for each line of the file. The key and value is extracted from the whole line content, elements from the split line based on `delimiter` or the line number (starting from zero). Where to extract the key and value from a line is specified by `key_index` and `value_index`. - A value of -1 means use the line number(starting from zero), expects `int64`. - A value of -2 means use the whole line content, expects `string`. - A value >= 0 means use the index (starting at zero) of the split line based on `delimiter`. table_handle: Handle to a table which will be initialized. filename: Filename of a vocabulary text file. key_index: Column index in a line to get the table `key` values from. value_index: Column index that represents information of a line to get the table `value` values from. vocab_size: Number of elements of the file, use -1 if unknown. delimiter: Delimiter to separate fields in a line. Use GetSessionHandleV2 Store the input tensor in the state of the current session. value: The tensor to be stored. handle: The handle for the tensor stored in the session state, represented as a ResourceHandle object. Get the value of the tensor specified by its handle. handle: The handle for a tensor stored in the session state. value: The tensor for the given handle. dtype: The type of the output value. Delete the tensor specified by its handle in the session. handle: The handle for a tensor stored in the session state. Stagevalues: dtypes Stage values similar to a lightweight Enqueue. The basic functionality of this Op is similar to a queue with many fewer capabilities and options. This Op is optimized for performance. values: a list of tensors container: If non-empty, this queue is placed in the given container. Otherwise, a default container is used. shared_name: It is necessary to match this name to the matching Unstage Op. Unstage Op is similar to a lightweight Dequeue. The basic funtionality is similar to dequeue with many fewer capabilities and options. This Op is optimized for performance. RecordInputfile_random_seed: int = 301file_shuffle_shift_ratio: float = 0file_buffer_size: int = 10000file_parallelism: int = 16batch_size: int = 32 Emits randomized records. records: A tensor of shape [batch_size]. file_pattern: Glob pattern for the data files. file_random_seed: Random seeds used to produce randomized records. file_shuffle_shift_ratio: Shifts the list of files after the list is randomly shuffled. file_buffer_size: The randomization shuffling buffer. file_parallelism: How many sstables are opened and concurrently iterated over. batch_size: The batch size. CTCLossinputs: floatlabels_indices: int64labels_values: int32sequence_length: int32preprocess_collapse_repeated: bool = falsectc_merge_repeated: bool = trueloss: floatgradient: float Calculates the CTC Loss (log probability) for each batch entry. Also calculates the gradient. This class performs the softmax operation for you, so inputs should be e.g. linear projections of outputs by an LSTM. inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. labels_indices: The indices of a `SparseTensor`. `labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for `(batch b, time t)`. labels_values: The values (labels) associated with the given batch and time. sequence_length: A vector containing sequence lengths (batch). preprocess_collapse_repeated: Scalar, if true then repeated labels are collapsed prior to the CTC calculation. ctc_merge_repeated: Scalar. If set to false, *during* CTC calculation repeated non-blank labels will not be merged and are interpreted as individual labels. This is a simplified version of CTC. loss: A vector (batch) containing log-probabilities. gradient: The gradient of `loss`. 3-D, shape: `(max_time x batch_size x num_classes)`. merge_repeated: bool = falsedecoded_indices: int64decoded_values: int64decoded_shape: int64log_probability: float Performs greedy decoding on the logits given in inputs. A note about the attribute merge_repeated: if enabled, when consecutive logits' maximum indices are the same, only the first of these is emitted. Labeling the blank '*', the sequence "A B B * B B" becomes "A B B" if merge_repeated = True and "A B B B B" if merge_repeated = False. Regardless of the value of merge_repeated, if the maximum index of a given time and batch corresponds to the blank, index `(num_classes - 1)`, no new element is emitted. inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. sequence_length: A vector containing sequence lengths, size `(batch_size)`. merge_repeated: If True, merge repeated classes in output. decoded_indices: Indices matrix, size `(total_decoded_outputs x 2)`, of a `SparseTensor`. The rows store: [batch, time]. decoded_values: Values vector, size: `(total_decoded_outputs)`, of a `SparseTensor`. The vector stores the decoded classes. decoded_shape: Shape vector, size `(2)`, of the decoded SparseTensor. Values are: `[batch_size, max_decoded_length]`. log_probability: Matrix, size `(batch_size x 1)`, containing sequence log-probabilities. beam_width: int >= 1top_paths: int >= 1merge_repeated: bool = truedecoded_indices: top_paths * int64decoded_values: top_paths * int64decoded_shape: top_paths * int64 Performs beam search decoding on the logits given in input. A note about the attribute merge_repeated: For the beam search decoder, this means that if consecutive entries in a beam are the same, only the first of these is emitted. That is, when the top path is "A B B B B", "A B" is returned if merge_repeated = True but "A B B B B" is returned if merge_repeated = False. inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits. sequence_length: A vector containing sequence lengths, size `(batch)`. beam_width: A scalar >= 0 (beam search beam width). top_paths: A scalar >= 0, <= beam_width (controls output size). merge_repeated: If true, merge repeated classes in output. decoded_indices: A list (length: top_paths) of indices matrices. Matrix j, size `(total_decoded_outputs[j] x 2)`, has indices of a `SparseTensor`. The rows store: [batch, time]. decoded_values: A list (length: top_paths) of values vectors. Vector j, size `(length total_decoded_outputs[j])`, has the values of a `SparseTensor`. The vector stores the decoded classes for beam j. decoded_shape: A list (length: top_paths) of shape vector. Vector j, size `(2)`, stores the shape of the decoded `SparseTensor[j]`. Its values are: `[batch_size, max_decoded_length[j]]`. log_probability: A matrix, shaped: `(batch_size x top_paths)`. The sequence log-probabilities. pred: booloutput_false: Toutput_true: T Forwards `data` to the output port determined by `pred`. If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise, the data goes to `output_false`. See also `RefSwitch` and `Merge`. data: The tensor to be forwarded to the appropriate output. pred: A scalar that specifies which output port will receive data. output_false: If `pred` is false, data will be forwarded to this output. output_true: If `pred` is true, data will be forwarded to this output. data: Ref(T)output_false: Ref(T)output_true: Ref(T) Forwards the ref tensor `data` to the output port determined by `pred`. If `pred` is true, the `data` input is forwarded to `output_true`. Otherwise, the data goes to `output_false`. See also `Switch` and `Merge`. data: The ref tensor to be forwarded to the appropriate output. pred: A scalar that specifies which output port will receive data. output_false: If `pred` is false, data will be forwarded to this output. output_true: If `pred` is true, data will be forwarded to this output. inputs: Ref(N * T)output: Ref(T) Forwards the `index`th element of `inputs` to `output`. index: A scalar that determines the input that gets selected. inputs: A list of ref tensors, one of which will be forwarded to `output`. output: The forwarded tensor. value_index: int32 Forwards the value of an available tensor from `inputs` to `output`. `Merge` waits for at least one of the tensors in `inputs` to become available. It is usually combined with `Switch` to implement branching. `Merge` forwards the first tensor for become available to `output`, and sets `value_index` to its index in `inputs`. inputs: The input tensors, exactly one of which will become available. output: Will be set to the available input tensor. value_index: The index of the chosen input tensor in `inputs`. frame_name: stringis_constant: bool = falseparallel_iterations: int = 10 Creates or finds a child frame, and makes `data` available to the child frame. This op is used together with `Exit` to create loops in the graph. The unique `frame_name` is used by the `Executor` to identify frames. If `is_constant` is true, `output` is a constant in the child frame; otherwise it may be changed in the child frame. At most `parallel_iterations` iterations are run in parallel in the child frame. data: The tensor to be made available to the child frame. frame_name: The name of the child frame. is_constant: If true, the output is constant within the child frame. parallel_iterations: The number of iterations allowed to run in parallel. output: The same tensor as `data`. Creates or finds a child frame, and makes `data` available to the child frame. The unique `frame_name` is used by the `Executor` to identify frames. If `is_constant` is true, `output` is a constant in the child frame; otherwise it may be changed in the child frame. At most `parallel_iterations` iterations are run in parallel in the child frame. data: The tensor to be made available to the child frame. frame_name: The name of the child frame. is_constant: If true, the output is constant within the child frame. parallel_iterations: The number of iterations allowed to run in parallel. output: The same tensor as `data`. Exits the current frame to its parent frame. Exit makes its input `data` available to the parent frame. data: The tensor to be made available to the parent frame. output: The same tensor as `data`. Makes its input available to the next iteration. data: The tensor to be made available to the next iteration. output: The same tensor as `data`. Forwards the input to the output. This operator represents the loop termination condition used by the "pivot" switches of a loop. input: A boolean scalar, representing the branch predicate of the Switch op. output: The same tensor as `input`. Does nothing. Serves as a control trigger for scheduling. Only useful as a placeholder for control edges. error_msg: string = ''exit_without_error: bool = false Raise a exception to abort the process when called. If exit_without_error is true, the process will exit normally, otherwise it will exit with a SIGABORT signal. Returns nothing but an exception. error_msg: A string which is the message associated with the exception. num_samplednum_trueUniformCandidateSamplertrue_classes: int64sampled_candidates: int64true_expected_count: floatsampled_expected_count: floatnum_true: int >= 1num_sampled: int >= 1unique: boolrange_max: int >= 1 Generates labels for candidate sampling with a uniform distribution. See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels. true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate. true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. sampled_expected_count: A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. num_true: Number of true labels per context. num_sampled: Number of candidates to randomly sample per batch. unique: If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. range_max: The sampler will sample integers from the interval [0, range_max). seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. LogUniformCandidateSampler Generates labels for candidate sampling with a log-uniform distribution. See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels. true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate. true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. sampled_expected_count: A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. num_true: Number of true labels per context. num_sampled: Number of candidates to randomly sample per batch. unique: If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. range_max: The sampler will sample integers from the interval [0, range_max). seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. LearnedUnigramCandidateSampler Generates labels for candidate sampling with a learned unigram distribution. See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels. true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate. true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. sampled_expected_count: A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. num_true: Number of true labels per context. num_sampled: Number of candidates to randomly sample per batch. unique: If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. range_max: The sampler will sample integers from the interval [0, range_max). seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. ThreadUnsafeUnigramCandidateSamplerFixedUnigramCandidateSamplervocab_file: string = ''distortion: float = 1.0num_reserved_ids: int = 0num_shards: int >= 1 = 1shard: int >= 0 = 0unigrams: list(float) = [] Generates labels for candidate sampling with a learned unigram distribution. A unigram sampler could use a fixed unigram distribution read from a file or passed in as an in-memory array instead of building up the distribution from data on the fly. There is also an option to skew the distribution by applying a distortion power to the weights. The vocabulary file should be in CSV-like format, with the last field being the weight associated with the word. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels. true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate. true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. sampled_expected_count: A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. num_true: Number of true labels per context. num_sampled: Number of candidates to randomly sample per batch. unique: If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. range_max: The sampler will sample integers from the interval [0, range_max). vocab_file: Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order, starting from num_reserved_ids. The last entry in each line is expected to be a value corresponding to the count or relative probability. Exactly one of vocab_file and unigrams needs to be passed to this op. distortion: The distortion is used to skew the unigram probability distribution. Each weight is first raised to the distortion's power before adding to the internal unigram distribution. As a result, distortion = 1.0 gives regular unigram sampling (as defined by the vocab file), and distortion = 0.0 gives a uniform distribution. num_reserved_ids: Optionally some reserved IDs can be added in the range [0, ..., num_reserved_ids) by the users. One use case is that a special unknown word token is used as ID 0. These IDs will have a sampling probability of 0. num_shards: A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'shard') indicates the number of partitions that are being used in the overall computation. shard: A sampler can be used to sample from a subset of the original range in order to speed up the whole computation through parallelism. This parameter (together with 'num_shards') indicates the particular partition number of a sampler op, when partitioning is being used. unigrams: A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of vocab_file and unigrams should be passed to this op. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. AllCandidateSampler Generates labels for candidate sampling with a learned unigram distribution. See explanations of candidate sampling and the data formats at go/candidate-sampling. For each batch, this op picks a single set of sampled candidate labels. The advantages of sampling candidates per-batch are simplicity and the possibility of efficient dense matrix multiplication. The disadvantage is that the sampled candidates must be chosen independently of the context and of the true labels. true_classes: A batch_size * num_true matrix, in which each row contains the IDs of the num_true target_classes in the corresponding original label. sampled_candidates: A vector of length num_sampled, in which each element is the ID of a sampled candidate. true_expected_count: A batch_size * num_true matrix, representing the number of times each candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. sampled_expected_count: A vector of length num_sampled, for each sampled candidate representing the number of times the candidate is expected to occur in a batch of sampled candidates. If unique=true, then this is a probability. num_true: Number of true labels per context. num_sampled: Number of candidates to produce per batch. unique: If unique is true, we sample with rejection, so that all sampled candidates in a batch are unique. This requires some approximation to estimate the post-rejection sampling probabilities. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. ComputeAccidentalHitsids: int64weights: floatnum_true: int Computes the ids of the positions in sampled_candidates that match true_labels. When doing log-odds NCE, the result of this op should be passed through a SparseToDense op, then added to the logits of the sampled candidates. This has the effect of 'removing' the sampled labels that match the true labels by making the classifier sure that they are sampled labels. true_classes: The true_classes output of UnpackSparseLabels. sampled_candidates: The sampled_candidates output of CandidateSampler. indices: A vector of indices corresponding to rows of true_candidates. ids: A vector of IDs of positions in sampled_candidates that match a true_label for the row with the corresponding index in indices. weights: A vector of the same length as indices and ids, in which each element is -FLOAT_MAX. num_true: Number of true labels per context. seed: If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An second seed to avoid seed collision. reverse does not work on tensors with more than 8 dimensionsaxis must be >= -1Invalid axis: ; must be in [ must match the outer dimensions of output.shape= must match the inner Indices and updates specified for empty output shapebatch_dim must be < input rank: seq_dim must be < input rank: ExtractImagePatches requires the ksizes attribute to contain 4 values, but got: ExtractImagePatches requires the stride attribute to contain 4 values, but got: ExtractImagePatches requires the rates attribute to contain 4 values, but got: ksizesCannot bitcast types because one of the type sizes is zero.Cannot bitcast due to shape. Input must have even rank <= 6, input rank is indices.shape[-1] must be <= params.rank, but saw indices shape: and params shape: Can not squeeze dim[], expected a dimension of 1, got squeeze_dims[] not in [).Number of ways to split should evenly divide the split dimensionCannot reshape a tensor with elements to shape elements)Fill dimensions must be >= 0perm dim is out of range of input rank Can't split scalarsLength of size_splits should be equal to num_outputsOut of bounds slicing on dimension of length : sizes vector cannot be < -1, but was shape attr must be fully defined.All input shapes must be fully defined.Size of first dimension must be 1.Num elements of hypothesis_shape does not match truth_shape: dim not in the interval ['dim' input must be a tensor with a single valueblock_shape must have rank 1.block_shape must have known size.block_shape must be positivepaddings cannot be negativecrops cannot be negative Concatenates a list of `N` tensors along the first dimension. The input tensors are all required to have size 1 in the first dimension. For example: ```prettyprint # 'x' is [[1, 4]] # 'y' is [[2, 5]] # 'z' is [[3, 6]] parallel_concat([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. ``` The difference between concat and parallel_concat is that concat requires all of the inputs be computed before the operation will begin but doesn't require that the input shapes be known during graph construction. Parallel concat will copy pieces of the input into the output as they become available, in some situations this can provide a performance benefit. values: Tensors to be concatenated. All must have size 1 in the first dimension and same shape. output: The concatenated tensor. shape: the final shape of the result; should be equal to the shapes of any input but with the number of input values in the first dimension. axis: int = 0 Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor. Packs the `N` tensors in `values` into a tensor with rank one higher than each tensor in `values`, by packing them along the `axis` dimension. Given a list of tensors of shape `(A, B, C)`; if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`. if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`. Etc. For example: ```prettyprint # 'x' is [1, 4] # 'y' is [2, 5] # 'z' is [3, 6] pack([x, y, z]) => [[1, 4], [2, 5], [3, 6]] # Pack along first dim. pack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]] ``` This is the opposite of `unpack`. values: Must be of same shape and type. axis: Dimension along which to pack. Negative values wrap around, so the valid range is `[-(R+1), R+1)`. output: The packed tensor. output: num * Tnum: int >= 0 Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors. Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. For example, given a tensor of shape `(A, B, C, D)`; If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`. (Note that the dimension unpacked along is gone, unlike `split`). If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`. Etc. This is the opposite of `pack`. value: 1-D or higher, with `axis` dimension size equal to `num`. axis: Dimension along which to unpack. Negative values wrap around, so the valid range is `[-R, R)`. output: The list of tensors unpacked from `value`. concat_dim: int32 Concatenates tensors along one dimension. concat_dim: 0-D. The dimension along which to concatenate. Must be in the range [0, rank(values)). values: The `N` Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except `concat_dim`. output: A `Tensor` with the concatenation of values stacked along the `concat_dim` dimension. This tensor's shape matches that of `values` except in `concat_dim` where it has the sum of the sizes. Concatenates tensors along one dimension. values: List of `N` Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except `concat_dim`. axis: 0-D. The dimension along which to concatenate. Must be in the range [-rank(values), rank(values)). output: A `Tensor` with the concatenation of values stacked along the `concat_dim` dimension. This tensor's shape matches that of `values` except in `concat_dim` where it has the sum of the sizes. shape: N * int32offset: N * int32 Computes offsets of concat inputs within its output. For example: ```prettyprint # 'x' is [2, 2, 7] # 'y' is [2, 3, 7] # 'z' is [2, 5, 7] concat_offset(2, [x, y, z]) => [0, 0, 0], [0, 2, 0], [0, 5, 0] ``` concat_dim: The dimension along which to concatenate. shape: The `N` int32 vectors representing shape of tensors being concatenated. offset: The `N` int32 vectors representing the starting offset of input tensors within the concatenated output. This is typically used by gradient computations for a concat operation. split_dim: int32output: num_split * T Splits a tensor into `num_split` tensors along one dimension. split_dim: 0-D. The dimension along which to split. Must be in the range `[0, rank(value))`. num_split: The number of ways to split. Must evenly divide `value.shape[split_dim]`. value: The tensor to split. output: They are identically shaped tensors, whose shape matches that of `value` except along `split_dim`, where their sizes are `values.shape[split_dim] / num_split`. size_splits: TlenTlen: {int32, int64} = DT_INT64 Splits a tensor into `num_split` tensors along one dimension. value: The tensor to split. size_splits: list containing the sizes of each output tensor along the split dimension. Must sum to the dimension of value along split_dim. Can contain one -1 indicating that dimension is to be inferred. split_dim: 0-D. The dimension along which to split. Must be in the range `[0, rank(value))`. output: Tensors whose shape matches that of `value` except along `split_dim`, where their sizes are `size_splits[i]`. value: tensor Returns a constant tensor. value: Attr `value` is the tensor to return. memory_region_name: stringtensor: dtype Returns immutable tensor from memory region. The current implementation memmaps the tensor from a file. dtype: Type of the returned tensor. shape: Shape of the returned tensor. memory_region_name: Name of readonly memory region used by the tensor, see NewReadOnlyMemoryRegionFromFile in tensorflow::Env. Returns a tensor of zeros with the same shape and type as x. x: a tensor of type T. y: a tensor of the same shape and type as x but filled with zeros. T: {float, double, int32, int64, complex64, complex128} Returns a tensor of ones with the same shape and type as x. x: a tensor of type T. y: a tensor of the same shape and type as x but filled with ones. Diagdiagonal: T Returns a diagonal tensor with a given diagonal values. Given a `diagonal`, this operation returns a tensor with the `diagonal` and everything else padded with zeros. The diagonal is computed as follows: Assume `diagonal` has dimensions [D1,..., Dk], then the output is a tensor of rank 2k with dimensions [D1,..., Dk, D1,..., Dk] where: `output[i1,..., ik, i1,..., ik] = diagonal[i1, ..., ik]` and 0 everywhere else. For example: ```prettyprint # 'diagonal' is [1, 2, 3, 4] tf.diag(diagonal) ==> [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]] ``` diagonal: Rank k tensor where k is at most 3. DiagPart Returns the diagonal part of the tensor. This operation returns a tensor with the `diagonal` part of the `input`. The `diagonal` part is computed as follows: Assume `input` has dimensions `[D1,..., Dk, D1,..., Dk]`, then the output is a tensor of rank `k` with dimensions `[D1,..., Dk]` where: `diagonal[i1,..., ik] = input[i1, ..., ik, i1,..., ik]`. For example: ```prettyprint # 'input' is [[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]] tf.diag_part(input) ==> [1, 2, 3, 4] ``` input: Rank k tensor where k is 2, 4, or 6. diagonal: The extracted diagonal. MatrixDiag Returns a batched diagonal tensor with a given batched diagonal values. Given a `diagonal`, this operation returns a tensor with the `diagonal` and everything else padded with zeros. The diagonal is computed as follows: Assume `diagonal` has `k` dimensions `[I, J, K, ..., N]`, then the output is a tensor of rank `k+1` with dimensions [I, J, K, ..., N, N]` where: `output[i, j, k, ..., m, n] = 1{m=n} * diagonal[i, j, k, ..., n]`. For example: ```prettyprint # 'diagonal' is [[1, 2, 3, 4], [5, 6, 7, 8]] and diagonal.shape = (2, 4) tf.matrix_diag(diagonal) ==> [[[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]], [[5, 0, 0, 0] [0, 6, 0, 0] [0, 0, 7, 0] [0, 0, 0, 8]]] which has shape (2, 4, 4) ``` diagonal: Rank `k`, where `k >= 1`. output: Rank `k+1`, with `output.shape = diagonal.shape + [diagonal.shape[-1]]`. MatrixSetDiag Returns a batched matrix tensor with new batched diagonal values. Given `input` and `diagonal`, this operation returns a tensor with the same shape and values as `input`, except for the main diagonal of the innermost matrices. These will be overwritten by the values in `diagonal`. The output is computed as follows: Assume `input` has `k+1` dimensions `[I, J, K, ..., M, N]` and `diagonal` has `k` dimensions `[I, J, K, ..., min(M, N)]`. Then the output is a tensor of rank `k+1` with dimensions `[I, J, K, ..., M, N]` where: * `output[i, j, k, ..., m, n] = diagonal[i, j, k, ..., n]` for `m == n`. * `output[i, j, k, ..., m, n] = input[i, j, k, ..., m, n]` for `m != n`. input: Rank `k+1`, where `k >= 1`. diagonal: Rank `k`, where `k >= 1`. output: Rank `k+1`, with `output.shape = input.shape`. MatrixDiagPart Returns the batched diagonal part of a batched tensor. This operation returns a tensor with the `diagonal` part of the batched `input`. The `diagonal` part is computed as follows: Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a tensor of rank `k - 1` with dimensions `[I, J, K, ..., min(M, N)]` where: `diagonal[i, j, k, ..., n] = input[i, j, k, ..., n, n]`. The input must be at least a matrix. For example: ```prettyprint # 'input' is [[[1, 0, 0, 0] [0, 2, 0, 0] [0, 0, 3, 0] [0, 0, 0, 4]], [[5, 0, 0, 0] [0, 6, 0, 0] [0, 0, 7, 0] [0, 0, 0, 8]]] and input.shape = (2, 4, 4) tf.matrix_diag_part(input) ==> [[1, 2, 3, 4], [5, 6, 7, 8]] which has shape (2, 4) ``` input: Rank `k` tensor where `k >= 2`. diagonal: The extracted diagonal(s) having shape `diagonal.shape = input.shape[:-2] + [min(input.shape[-2:])]`. MatrixBandPartnum_lower: int64num_upper: int64band: T Copy a tensor setting everything outside a central band in each innermost matrix to zero. The `band` part is computed as follows: Assume `input` has `k` dimensions `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. The indicator function `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) && (num_upper < 0 || (n-m) <= num_upper)`. For example: ```prettyprint # if 'input' is [[ 0, 1, 2, 3] [-1, 0, 1, 2] [-2, -1, 0, 1] [-3, -2, -1, 0]], tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] [-1, 0, 1, 2] [ 0, -1, 0, 1] [ 0, 0, -1, 0]], tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] [-1, 0, 1, 0] [-2, -1, 0, 1] [ 0, -2, -1, 0]] ``` Useful special cases: ```prettyprint tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. tf.matrix_band_part(input, 0, 0) ==> Diagonal. ``` input: Rank `k` tensor. num_lower: 0-D tensor. Number of subdiagonals to keep. If negative, keep entire lower triangle. num_upper: 0-D tensor. Number of superdiagonals to keep. If negative, keep entire upper triangle. band: Rank `k` tensor of the same shape as input. The extracted banded tensor. dims: boolT: {uint8, int8, int32, int64, bool, half, float, double, complex64, complex128} Reverses specific dimensions of a tensor. Given a `tensor`, and a `bool` tensor `dims` representing the dimensions of `tensor`, this operation reverses each dimension i of `tensor` where `dims[i]` is `True`. `tensor` can have up to 8 dimensions. The number of dimensions of `tensor` must equal the number of elements in `dims`. In other words: `rank(tensor) = size(dims)` For example: ```prettyprint # tensor 't' is [[[[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]], # [[12, 13, 14, 15], # [16, 17, 18, 19], # [20, 21, 22, 23]]]] # tensor 't' shape is [1, 2, 3, 4] # 'dims' is [False, False, False, True] reverse(t, dims) ==> [[[[ 3, 2, 1, 0], [ 7, 6, 5, 4], [ 11, 10, 9, 8]], [[15, 14, 13, 12], [19, 18, 17, 16], [23, 22, 21, 20]]]] # 'dims' is [False, True, False, False] reverse(t, dims) ==> [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23] [[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]]] # 'dims' is [False, False, True, False] reverse(t, dims) ==> [[[[8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] [[20, 21, 22, 23], [16, 17, 18, 19], [12, 13, 14, 15]]]] ``` tensor: Up to 8-D. dims: 1-D. The dimensions to reverse. output: The same shape as `tensor`. Reverses specific dimensions of a tensor. NOTE `tf.reverse` has now changed behavior in preparation for 1.0. `tf.reverse_v2` is currently an alias that will be deprecated before TF 1.0. Given a `tensor`, and a `int32` tensor `axis` representing the set of dimensions of `tensor` to reverse. This operation reverses each dimension `i` for which there exists `j` s.t. `axis[j] == i`. `tensor` can have up to 8 dimensions. The number of dimensions specified in `axis` may be 0 or more entries. If an index is specified more than once, a InvalidArgument error is raised. For example: ```prettyprint # tensor 't' is [[[[ 0, 1, 2, 3], # [ 4, 5, 6, 7], # [ 8, 9, 10, 11]], # [[12, 13, 14, 15], # [16, 17, 18, 19], # [20, 21, 22, 23]]]] # tensor 't' shape is [1, 2, 3, 4] # 'dims' is [3] or 'dims' is -1 reverse(t, dims) ==> [[[[ 3, 2, 1, 0], [ 7, 6, 5, 4], [ 11, 10, 9, 8]], [[15, 14, 13, 12], [19, 18, 17, 16], [23, 22, 21, 20]]]] # 'dims' is '[1]' (or 'dims' is '[-3]') reverse(t, dims) ==> [[[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23] [[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]]]] # 'dims' is '[2]' (or 'dims' is '[-2]') reverse(t, dims) ==> [[[[8, 9, 10, 11], [4, 5, 6, 7], [0, 1, 2, 3]] [[20, 21, 22, 23], [16, 17, 18, 19], [12, 13, 14, 15]]]] ``` tensor: Up to 8-D. axis: 1-D. The indices of the dimensions to reverse. output: The same shape as `tensor`. EditDistancehypothesis_indices: int64hypothesis_values: Thypothesis_shape: int64truth_indices: int64truth_values: Ttruth_shape: int64normalize: bool = true Computes the (possibly normalized) Levenshtein Edit Distance. The inputs are variable-length sequences provided by SparseTensors (hypothesis_indices, hypothesis_values, hypothesis_shape) and (truth_indices, truth_values, truth_shape). The inputs are: hypothesis_indices: The indices of the hypothesis list SparseTensor. This is an N x R int64 matrix. hypothesis_values: The values of the hypothesis list SparseTensor. This is an N-length vector. hypothesis_shape: The shape of the hypothesis list SparseTensor. This is an R-length vector. truth_indices: The indices of the truth list SparseTensor. This is an M x R int64 matrix. truth_values: The values of the truth list SparseTensor. This is an M-length vector. truth_shape: The shape of the truth list SparseTensor. This is an R-length vector. truth_shape: truth indices, vector. normalize: boolean (if true, edit distances are normalized by length of truth). The output is: output: A dense float tensor with rank R - 1. For the example input: // hypothesis represents a 2x1 matrix with variable-length values: // (0,0) = ["a"] // (1,0) = ["b"] hypothesis_indices = [[0, 0, 0], [1, 0, 0]] hypothesis_values = ["a", "b"] hypothesis_shape = [2, 1, 1] // truth represents a 2x2 matrix with variable-length values: // (0,0) = [] // (0,1) = ["a"] // (1,0) = ["b", "c"] // (1,1) = ["a"] truth_indices = [[0, 1, 0], [1, 0, 0], [1, 0, 1], [1, 1, 0]] truth_values = ["a", "b", "c", "a"] truth_shape = [2, 2, 2] normalize = true The output will be: // output is a 2x2 matrix with edit distances normalized by truth lengths. output = [[inf, 1.0], // (0,0): no truth, (0,1): no hypothesis [0.5, 1.0]] // (1,0): addition, (1,1): no hypothesis dims: int32 Creates a tensor filled with a scalar value. This operation creates a tensor of shape `dims` and fills it with `value`. For example: ```prettyprint # Output tensor has shape [2, 3]. fill([2, 3], 9) ==> [[9, 9, 9] [9, 9, 9]] ``` dims: 1-D. Represents the shape of the output tensor. value: 0-D (scalar). Value to fill the returned tensor. @compatibility(numpy) Equivalent to np.full @end_compatibility Creates an empty Tensor with shape `shape` and type `dtype`. The memory can optionally be initialized. This is usually useful in conjunction with inplace operations. shape: 1-D `Tensor` indicating the shape of the output. dtype: The element type of the returned tensor. output: An empty Tensor of the specified type. update: Tloc: int Updates input `value` at `loc` with `update`. If you use this function you will almost certainly want to add a control dependency as done in the implementation of parallel_stack to avoid race conditions. value: A `Tensor` object that will be updated in-place. loc: A scalar indicating the index of the first dimension such that value[loc, :] is updated. update: A `Tensor` of rank one less than `value` if `loc` is a scalar, otherwise of rank equal to `value` that contains the new values for `value`. output: `value` that has been updated accordingly. params: Tparamsoutput: TparamsTparams: type Gather slices from `params` according to `indices`. `indices` must be an integer tensor of any dimension (usually 0-D or 1-D). Produces an output tensor with shape `indices.shape + params.shape[1:]` where: ```python # Scalar indices output[:, ..., :] = params[indices, :, ... :] # Vector indices output[i, :, ..., :] = params[indices[i], :, ... :] # Higher rank indices output[i, ..., j, :, ... :] = params[indices[i, ..., j], :, ..., :] ``` If `indices` is a permutation and `len(indices) == params.shape[0]` then this operation will permute `params` accordingly. `validate_indices`: DEPRECATED. If this operation is assigned to CPU, values in `indices` are always validated to be within range. If assigned to GPU, out-of-bound indices result in unspecified behavior (currently the result is `0`, but this may become an error in the future).
GatherNd Gather values or slices from `params` according to `indices`. `params` is a Tensor of rank `P` and `indices` is a Tensor of rank `Q`. `indices` must be integer tensor, containing indices into `params`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `params`. Produces an output tensor with shape ``` [d_0, ..., d_{Q-2}, params.shape[K], ..., params.shape[P-1]]. ``` Some examples below. Simple indexing into a matrix: ```python indices = [[0, 0], [1, 1]] params = [['a', 'b'], ['c', 'd']] output = ['a', 'd'] ``` Slice indexing into a matrix: ```python indices = [[1], [0]] params = [['a', 'b'], ['c', 'd']] output = [['c', 'd'], ['a', 'b']] ``` Indexing into a 3-tensor: ```python indices = [[1]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = [[['a1', 'b1'], ['c1', 'd1']]] indices = [[0, 1], [1, 0]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = [['c0', 'd0'], ['a1', 'b1']] indices = [[0, 0, 1], [1, 0, 1]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = ['b0', 'b1'] ``` Batched indexing into a matrix: ```python indices = [[[0, 0]], [[0, 1]]] params = [['a', 'b'], ['c', 'd']] output = [['a'], ['b']] ``` Batched slice indexing into a matrix: ```python indices = [[[1]], [[0]]] params = [['a', 'b'], ['c', 'd']] output = [[['c', 'd']], [['a', 'b']]] ``` Batched indexing into a 3-tensor: ```python indices = [[[1]], [[0]]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = [[[['a1', 'b1'], ['c1', 'd1']]], [[['a0', 'b0'], ['c0', 'd0']]]] indices = [[[0, 1], [1, 0]], [[0, 0], [1, 1]]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = [[['c0', 'd0'], ['a1', 'b1']], [['a0', 'b0'], ['c1', 'd1']]] indices = [[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]] params = [[['a0', 'b0'], ['c0', 'd0']], [['a1', 'b1'], ['c1', 'd1']]] output = [['b0', 'b1'], ['d0', 'c1']] ``` params: `P-D`. The tensor from which to gather values. indices: `Q-D`. Index tensor having shape `[d_0, ..., d_{Q-2}, K]`. output: `(P+Q-K-1)-D`. Values from `params` gathered from indices given by `indices`. Return a tensor with the same shape and contents as the input tensor or value. input: Ref(T) Return the same ref tensor as the input ref tensor. Stops gradient computation. When executed in a graph, this op outputs its input tensor as-is. When building ops to compute gradients, this op prevents the contribution of its inputs to be taken into account. Normally, the gradient generator adds ops to a graph to compute the derivatives of a specified 'loss' by recursively finding out inputs that contributed to its computation. If you insert this op in the graph it inputs are masked from the gradient generator. They are not taken into account for computing gradients. This is useful any time you want to compute a value with TensorFlow but need to pretend that the value was a constant. Some examples include: * The *EM* algorithm where the *M-step* should not involve backpropagation through the output of the *E-step*. * Contrastive divergence training of Boltzmann machines where, when differentiating the energy function, the training must not backpropagate through the graph that generated the samples from the model. * Adversarial training, where no backprop should happen through the adversarial example generation process. An identity op that triggers an error if a gradient is requested. When executed in a graph, this op outputs its input tensor as-is. When building ops to compute gradients, the TensorFlow gradient system will return an error when trying to lookup the gradient of this op, because no gradient must ever be registered for this function. This op exists to prevent subtle bugs from silently returning unimplemented gradients in some corner cases. input: any tensor. output: the same input tensor. message: Will be printed in the error when anyone tries to differentiate this operation. message: string Checks a tensor for NaN and Inf values. When run, reports an `InvalidArgument` error if `tensor` has any values that are not a number (NaN) or infinity (Inf). Otherwise, passes `tensor` as-is. message: Prefix of the error message. shape: TshapeTshape: {int32, int64} = DT_INT32 Reshapes a tensor. Given `tensor`, this operation returns a tensor that has the same values as `tensor` with shape `shape`. If one component of `shape` is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a `shape` of `[-1]` flattens into 1-D. At most one component of `shape` can be -1. If `shape` is 1-D or higher, then the operation returns a tensor with shape `shape` filled with the values of `tensor`. In this case, the number of elements implied by `shape` must be the same as the number of elements in `tensor`. For example: ```prettyprint # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9] # tensor 't' has shape [9] reshape(t, [3, 3]) ==> [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # tensor 't' is [[[1, 1], [2, 2]], # [[3, 3], [4, 4]]] # tensor 't' has shape [2, 2, 2] reshape(t, [2, 4]) ==> [[1, 1, 2, 2], [3, 3, 4, 4]] # tensor 't' is [[[1, 1, 1], # [2, 2, 2]], # [[3, 3, 3], # [4, 4, 4]], # [[5, 5, 5], # [6, 6, 6]]] # tensor 't' has shape [3, 2, 3] # pass '[-1]' to flatten 't' reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6] # -1 can also be used to infer the shape # -1 is inferred to be 9: reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], [4, 4, 4, 5, 5, 5, 6, 6, 6]] # -1 is inferred to be 2: reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3], [4, 4, 4, 5, 5, 5, 6, 6, 6]] # -1 is inferred to be 3: reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1], [2, 2, 2], [3, 3, 3]], [[4, 4, 4], [5, 5, 5], [6, 6, 6]]] # tensor 't' is [7] # shape `[]` reshapes to a scalar reshape(t, []) ==> 7 ``` shape: Defines the shape of the output tensor. Computes the inverse permutation of a tensor. This operation computes the inverse of an index permutation. It takes a 1-D integer tensor `x`, which represents the indices of a zero-based array, and swaps each value with its index position. In other words, for an output tensor `y` and an input tensor `x`, this operation computes the following: `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]` The values must include 0. There can be no duplicate values or negative values. For example: ```prettyprint # tensor `x` is [3, 4, 0, 2, 1] invert_permutation(x) ==> [2, 4, 3, 0, 1] ``` x: 1-D. y: 1-D. perm: TpermTperm: {int32, int64} = DT_INT32 Shuffle dimensions of x according to a permutation. The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy: `y.shape[i] == x.shape[perm[i]] for i in [0, 1, ..., rank(x) - 1]` Uniqueidx: out_idxout_idx: {int32, int64} = DT_INT32 Finds unique elements in a 1-D tensor. This operation returns a tensor `y` containing all of the unique elements of `x` sorted in the same order that they occur in `x`. This operation also returns a tensor `idx` the same size as `x` that contains the index of each value of `x` in the unique output `y`. In other words: `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` For example: ```prettyprint # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] y, idx = unique(x) y ==> [1, 2, 4, 7, 8] idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] ``` x: 1-D. y: 1-D. idx: 1-D. UniqueWithCountscount: out_idx Finds unique elements in a 1-D tensor. This operation returns a tensor `y` containing all of the unique elements of `x` sorted in the same order that they occur in `x`. This operation also returns a tensor `idx` the same size as `x` that contains the index of each value of `x` in the unique output `y`. Finally, it returns a third tensor `count` that contains the count of each element of `y` in `x`. In other words: `y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]` For example: ```prettyprint # tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8] y, idx, count = unique_with_counts(x) y ==> [1, 2, 4, 7, 8] idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4] count ==> [2, 1, 3, 1, 2] ``` x: 1-D. y: 1-D. idx: 1-D. count: 1-D. out_type: {int32, int64} = DT_INT32 Returns the shape of a tensor. This operation returns a 1-D integer tensor representing the shape of `input`. For example: ```prettyprint # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] shape(t) ==> [2, 2, 3] ``` output: N * out_type Returns shape of tensors. This operation returns N 1-D integer tensors representing shape of `input[i]s`. seq_lengths: Tlenseq_dim: intbatch_dim: int = 0 Reverses variable length slices. This op first slices `input` along the dimension `batch_dim`, and for each slice `i`, reverses the first `seq_lengths[i]` elements along the dimension `seq_dim`. The elements of `seq_lengths` must obey `seq_lengths[i] <= input.dims[seq_dim]`, and `seq_lengths` must be a vector of length `input.dims[batch_dim]`. The output slice `i` along dimension `batch_dim` is then given by input slice `i`, with the first `seq_lengths[i]` slices along dimension `seq_dim` reversed. For example: ```prettyprint # Given this: batch_dim = 0 seq_dim = 1 input.dims = (4, 8, ...) seq_lengths = [7, 2, 3, 5] # then slices of input are reversed on seq_dim, but only up to seq_lengths: output[0, 0:7, :, ...] = input[0, 7:0:-1, :, ...] output[1, 0:2, :, ...] = input[1, 2:0:-1, :, ...] output[2, 0:3, :, ...] = input[2, 3:0:-1, :, ...] output[3, 0:5, :, ...] = input[3, 5:0:-1, :, ...] # while entries past seq_lens are copied through: output[0, 7:, :, ...] = input[0, 7:, :, ...] output[1, 2:, :, ...] = input[1, 2:, :, ...] output[2, 3:, :, ...] = input[2, 3:, :, ...] output[3, 2:, :, ...] = input[3, 2:, :, ...] ``` In contrast, if: ```prettyprint # Given this: batch_dim = 2 seq_dim = 0 input.dims = (8, ?, 4, ...) seq_lengths = [7, 2, 3, 5] # then slices of input are reversed on seq_dim, but only up to seq_lengths: output[0:7, :, 0, :, ...] = input[7:0:-1, :, 0, :, ...] output[0:2, :, 1, :, ...] = input[2:0:-1, :, 1, :, ...] output[0:3, :, 2, :, ...] = input[3:0:-1, :, 2, :, ...] output[0:5, :, 3, :, ...] = input[5:0:-1, :, 3, :, ...] # while entries past seq_lens are copied through: output[7:, :, 0, :, ...] = input[7:, :, 0, :, ...] output[2:, :, 1, :, ...] = input[2:, :, 1, :, ...] output[3:, :, 2, :, ...] = input[3:, :, 2, :, ...] output[2:, :, 3, :, ...] = input[2:, :, 3, :, ...] ``` input: The input to reverse. seq_lengths: 1-D with length `input.dims(batch_dim)` and `max(seq_lengths) <= input.dims(seq_dim)` seq_dim: The dimension which is partially reversed. batch_dim: The dimension along which reversal is performed. output: The partially reversed input. It has the same shape as `input`. output: int32 Returns the rank of a tensor. This operation returns an integer representing the rank of `input`. For example: ```prettyprint # 't' is [[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]] # shape of tensor 't' is [2, 2, 3] rank(t) ==> 3 ``` **Note**: The rank of a tensor is not the same as the rank of a matrix. The rank of a tensor is the number of indices required to uniquely select each element of the tensor. Rank is also known as "order", "degree", or "ndims." Returns the size of a tensor. This operation returns an integer representing the number of elements in `input`. For example: ```prettyprint # 't' is [[[1, 1,, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]]] size(t) ==> 12 ``` begin: Indexsize: IndexIndex: {int32,int64} Return a slice from 'input'. The output tensor is a tensor with dimensions described by 'size' whose values are extracted from 'input' starting at the offsets in 'begin'. *Requirements*: 0 <= begin[i] <= begin[i] + size[i] <= Di for i in [0, n) begin: begin[i] specifies the offset into the 'i'th dimension of 'input' to slice from. size: size[i] specifies the number of elements of the 'i'th dimension of 'input' to slice. If size[i] is -1, all remaining elements in dimension i are included in the slice (i.e. this is equivalent to setting size[i] = input.dim_size(i) - begin[i]). end: Indexstrides: IndexIndex: {int32, int64}begin_mask: int = 0end_mask: int = 0ellipsis_mask: int = 0new_axis_mask: int = 0shrink_axis_mask: int = 0 Return a strided slice from `input`. Note, most python users will want to use the Python `Tensor.__getitem__` or `Variable.__getitem__` rather than this op directly. The goal of this op is to produce a new tensor with a subset of the elements from the `n` dimensional `input` tensor. The subset is chosen using a sequence of `m` sparse range specifications encoded into the arguments of this function. Note, in some cases `m` could be equal to `n`, but this need not be the case. Each range specification entry can be one of the following: - An ellipsis (...). Ellipses are used to imply zero or more dimensions of full-dimension selection and are produced using `ellipsis_mask`. For example, `foo[...]` is the identity slice. - A new axis. This is used to insert a new shape=1 dimension and is produced using `new_axis_mask`. For example, `foo[:, ...]` where `foo` is shape `(3, 4)` produces a `(1, 3, 4)` tensor. - A range `begin:end:stride`. This is used to specify how much to choose from a given dimension. `stride` can be any integer but 0. `begin` is an integer which represents the index of the first value to select while `end` represents the index of the last value to select. The number of values selected in each dimension is `end - begin` if `stride > 0` and `begin - end` if `stride < 0`. `begin` and `end` can be negative where `-1` is the last element, `-2` is the second to last. `begin_mask` controls whether to replace the explicitly given `begin` with an implicit effective value of `0` if `stride > 0` and `-1` if `stride < 0`. `end_mask` is analogous but produces the number required to create the largest open interval. For example, given a shape `(3,)` tensor `foo[:]`, the effective `begin` and `end` are `0` and `3`. Do not assume this is equivalent to `foo[0:-1]` which has an effective `begin` and `end` of `0` and `2`. Another example is `foo[-2::-1]` which reverses the first dimension of a tensor while dropping the last two (in the original order elements). For example `foo = [1,2,3,4]; foo[-2::-1]` is `[4,3]`. - A single index. This is used to keep only elements that have a given index. For example (`foo[2, :]` on a shape `(5,6)` tensor produces a shape `(6,)` tensor. This is encoded in `begin` and `end` and `shrink_axis_mask`. Each conceptual range specification is encoded in the op's argument. This encoding is best understand by considering a non-trivial example. In particular, `foo[1, 2:4, None, ..., :-3:-1, :]` will be encoded as ```prettyprint begin = [1, 2, x, x, 0, x] # x denotes don't care (usually 0) end = [2, 4, x, x, -3, x] strides = [1, 1, x, x, -1, 1] begin_mask = 1<<4 | 1 << 5 = 48 end_mask = 1<<5 = 32 ellipsis_mask = 1<<3 = 8 new_axis_mask = 1<<2 4 shrink_axis_mask = 1<<0 ``` In this case if `foo.shape` is (5, 5, 5, 5, 5, 5) the final shape of the slice becomes (2, 1, 5, 5, 2, 5). Let us walk step by step through each argument specification. 1. The first argument in the example slice is turned into `begin = 1` and `end = begin + 1 = 2`. To disambiguate from the original spec `2:4` we also set the appropriate bit in `shrink_axis_mask`. 2. `2:4` is contributes 2, 4, 1 to begin, end, and stride. All masks have zero bits contributed. 3. None is a synonym for `tf.newaxis`. This means insert a dimension of size 1 dimension in the final shape. Dummy values are contributed to begin, end and stride, while the new_axis_mask bit is set. 4. `...` grab the full ranges from as many dimensions as needed to fully specify a slice for every dimension of the input shape. 5. `:-3:-1` shows the use of negative indices. A negative index `i` associated with a dimension that has shape `s` is converted to a positive index `s + i`. So `-1` becomes `s-1` (i.e. the last element). This conversion is done internally so begin, end and strides receive x, -3, and -1. The appropriate begin_mask bit is set to indicate the start range is the full range (ignoring the x). 6. `:` indicates that the entire contents of the corresponding dimension is selected. This is equivalent to `::` or `0::1`. begin, end, and strides receive 0, 0, and 1, respectively. The appropriate bits in `begin_mask` and `end_mask` are also set. *Requirements*: `0 != strides[i] for i in [0, m)` `ellipsis_mask must be a power of two (only one ellipsis)` begin: `begin[k]` specifies the offset into the `k`th range specification. The exact dimension this corresponds to will be determined by context. Out-of-bounds values will be silently clamped. If the `k`th bit of `begin_mask` then `begin[k]` is ignored and the full range of the appropriate dimension is used instead. Negative values causes indexing to start from the highest element e.g. If `foo==[1,2,3]` then `foo[-1]==3`. end: `end[i]` is like `begin` with the exception that `end_mask` is used to determine full ranges. strides: `strides[i]` specifies the increment in the `i`th specification after extracting a given element. Negative indices will reverse the original order. Out or range values are clamped to `[0,dim[i]) if slice[i]>0` or `[-1,dim[i]-1] if slice[i] < 0` begin_mask: a bitmask where a bit i being 1 means to ignore the begin value and instead use the largest interval possible. At runtime begin[i] will be replaced with `[0, n-1) if `stride[i] > 0` or `[-1, n-1]` if `stride[i] < 0` end_mask: analogous to `begin_mask` ellipsis_mask: a bitmask where bit `i` being 1 means the `i`th position is actually an ellipsis. One bit at most can be 1. If `ellipsis_mask == 0`, then an implicit ellipsis mask of `1 << (m+1)` is provided. This means that `foo[3:5] == foo[3:5, ...]`. An ellipsis implicitly creates as many range specifications as necessary to fully specify the sliced range for every dimension. For example for a 4-dimensional tensor `foo` the slice `foo[2, ..., 5:8]` implies `foo[2, :, :, 5:8]`. new_axis_mask: a bitmask where bit `i` being 1 means the `i`th specification creates a new shape 1 dimension. For example `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor. shrink_axis_mask: a bitmask where bit `i` implies that the `i`th specification should shrink the dimensionality. begin and end must imply a slice of size 1 in the dimension. For example in python one might do `foo[:, 3, :]` which would result in `shrink_axis_mask` being 2. shape: Index Returns the gradient of `StridedSlice`. Since `StridedSlice` cuts out pieces of its `input` which is size `shape`, its gradient will have the same shape (which is passed here as `shape`). The gradient will be zero in any element that the slice does not select. Arguments are the same as StridedSliceGrad with the exception that `dy` is the input gradient to be propagated and `shape` is the shape of `StridedSlice`'s `input`. Assign `value` to the sliced l-value reference of `ref`. The values of `value` are assigned to the positions in the variable `ref` that are selected by the slice parameters. The slice parameters `begin, `end`, `strides`, etc. work exactly as in `StridedSlice`. NOTE this op currently does not support broadcasting and so `value`'s shape must be exactly the shape produced by the slice of `ref`. multiples: TmultiplesTmultiples: {int32, int64} = DT_INT32 Constructs a tensor by tiling a given tensor. This operation creates a new tensor by replicating `input` `multiples` times. The output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements, and the values of `input` are replicated `multiples[i]` times along the 'i'th dimension. For example, tiling `[a b c d]` by `[2]` produces `[a b c d a b c d]`. input: 1-D or higher. multiples: 1-D. Length must be the same as the number of dimensions in `input` multiples: int32TileGrad has been replaced with reduce_sum Returns the gradient of `Tile`. Since `Tile` takes an input and repeats the input `multiples` times along each dimension, `TileGrad` takes in `multiples` and aggregates each repeated tile of `input` into `output`. index: int64 Returns locations of true values in a boolean tensor. This operation returns the coordinates of true elements in `input`. The coordinates are returned in a 2-D tensor where the first dimension (rows) represents the number of true elements, and the second dimension (columns) represents the coordinates of the true elements. Keep in mind, the shape of the output tensor can vary depending on how many true values there are in `input`. Indices are output in row-major order. For example: ```prettyprint # 'input' tensor is [[True, False] # [True, False]] # 'input' has two true values, so output has two coordinates. # 'input' has rank of 2, so coordinates have two indices. where(input) ==> [[0, 0], [1, 0]] # `input` tensor is [[[True, False] # [True, False]] # [[False, True] # [False, True]] # [[False, False] # [False, True]]] # 'input' has 5 true values, so output has 5 coordinates. # 'input' has rank of 3, so coordinates have three indices. where(input) ==> [[0, 0, 0], [0, 1, 0], [1, 0, 1], [1, 1, 1], [2, 1, 1]] ``` s0: Ts1: Tr0: T Return the shape of s0 op s1 with broadcast. Given `s0` and `s1`, tensors that represent shapes, compute `r0`, the broadcasted shape. `s0`, `s1` and `r0` are all integer vectors. r1: T Return the reduction indices for computing gradients of s0 op s1 with broadcast. This is typically used by gradient computations for a broadcasting operation. paddings: TpaddingsTpaddings: {int32, int64} = DT_INT32 Pads a tensor with zeros. This operation pads a `input` with zeros according to the `paddings` you specify. `paddings` is an integer tensor with shape `[Dn, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many zeros to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many zeros to add after the contents of `input` in that dimension. The padded size of each dimension D of the output is: `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` For example: ```prettyprint # 't' is [[1, 1], [2, 2]] # 'paddings' is [[1, 1], [2, 2]] # rank of 't' is 2 pad(t, paddings) ==> [[0, 0, 0, 0, 0, 0] [0, 0, 1, 1, 0, 0] [0, 0, 2, 2, 0, 0] [0, 0, 0, 0, 0, 0]] ``` Pads a tensor with mirrored values. This operation pads a `input` with mirrored values according to the `paddings` you specify. `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of `input`. For each dimension D of `input`, `paddings[D, 0]` indicates how many values to add before the contents of `input` in that dimension, and `paddings[D, 1]` indicates how many values to add after the contents of `input` in that dimension. Both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than `input.dim_size(D)` (or `input.dim_size(D) - 1`) if `copy_border` is true (if false, respectively). The padded size of each dimension D of the output is: `paddings(D, 0) + input.dim_size(D) + paddings(D, 1)` For example: ```prettyprint # 't' is [[1, 2, 3], [4, 5, 6]]. # 'paddings' is [[1, 1]], [2, 2]]. # 'mode' is SYMMETRIC. # rank of 't' is 2. pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2] [2, 1, 1, 2, 3, 3, 2] [5, 4, 4, 5, 6, 6, 5] [5, 4, 4, 5, 6, 6, 5]] ``` input: The input tensor to be padded. paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of `input`. mode: Either `REFLECT` or `SYMMETRIC`. In reflect mode the padded regions do not include the borders, while in symmetric mode the padded regions do include the borders. For example, if `input` is `[1, 2, 3]` and `paddings` is `[0, 2]`, then the output is `[1, 2, 3, 2, 1]` in reflect mode, and it is `[1, 2, 3, 3, 2]` in symmetric mode. output: The padded tensor. Gradient op for `MirrorPad` op. This op folds a mirror-padded tensor. This operation folds the padded areas of `input` by `MirrorPad` according to the `paddings` you specify. `paddings` must be the same as `paddings` argument given to the corresponding `MirrorPad` op. The folded size of each dimension D of the output is: `input.dim_size(D) - paddings(D, 0) - paddings(D, 1)` For example: ```prettyprint # 't' is [[1, 2, 3], [4, 5, 6], [7, 8, 9]]. # 'paddings' is [[0, 1]], [0, 1]]. # 'mode' is SYMMETRIC. # rank of 't' is 2. pad(t, paddings) ==> [[ 1, 5] [11, 28]] ``` input: The input tensor to be folded. paddings: A two-column matrix specifying the padding sizes. The number of rows must be the same as the rank of `input`. mode: The mode used in the `MirrorPad` op. output: The folded tensor. shape: shape = {} A placeholder op for a value that will be fed into the computation. N.B. This operation will fail with an error if it is executed. It is intended as a way to represent a value that will always be fed, and to provide attrs that enable the fed value to be checked at runtime. output: A placeholder tensor that must be replaced using the feed mechanism. dtype: The type of elements in the tensor. shape: (Optional) The shape of the tensor. If the shape has 0 dimensions, the shape is unconstrained. A placeholder op for a value that will be fed into the computation. N.B. This operation will fail with an error if it is executed. It is intended as a way to represent a value that will always be fed, and to provide attrs that enable the fed value to be checked at runtime. output: A placeholder tensor that must be replaced using the feed mechanism. dtype: The type of elements in the tensor. shape: The shape of the tensor. The shape can be any partially-specified shape. To be unconstrained, pass in a shape with unknown rank. input: dtype A placeholder op that passes through `input` when its output is not fed. input: The default value to produce when `output` is not fed. output: A placeholder tensor that defaults to `input` if it is not fed. dtype: The type of elements in the tensor. shape: The (possibly partial) shape of the tensor. dim: TdimTdim: {int32, int64} = DT_INT32 Inserts a dimension of 1 into a tensor's shape. Given a tensor `input`, this operation inserts a dimension of 1 at the dimension index `dim` of `input`'s shape. The dimension index `dim` starts at zero; if you specify a negative number for `dim` it is counted backward from the end. This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape `[height, width, channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, which will make the shape `[1, height, width, channels]`. Other examples: ```prettyprint # 't' is a tensor of shape [2] shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] shape(expand_dims(t, -1)) ==> [2, 1] # 't2' is a tensor of shape [2, 3, 5] shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] ``` This operation requires that: `-1-input.dims() <= dim <= input.dims()` This operation is related to `squeeze()`, which removes dimensions of size 1. dim: 0-D (scalar). Specifies the dimension index at which to expand the shape of `input`. output: Contains the same data as `input`, but its shape has an additional dimension of size 1 added. squeeze_dims: list(int) >= 0 = [] Removes dimensions of size 1 from the shape of a tensor. Given a tensor `input`, this operation returns a tensor of the same type with all dimensions of size 1 removed. If you don't want to remove all size 1 dimensions, you can remove specific size 1 dimensions by specifying `squeeze_dims`. For example: ```prettyprint # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t)) ==> [2, 3] ``` Or, to remove specific size 1 dimensions: ```prettyprint # 't' is a tensor of shape [1, 2, 1, 3, 1, 1] shape(squeeze(t, [2, 4])) ==> [1, 2, 3, 1] ``` input: The `input` to squeeze. squeeze_dims: If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1. output: Contains the same data as `input`, but has one or more dimensions of size 1 removed. ListDiff Computes the difference between two lists of numbers or strings. Given a list `x` and a list `y`, this operation returns a list `out` that represents all values that are in `x` but not in `y`. The returned list `out` is sorted in the same order that the numbers appear in `x` (duplicates are preserved). This operation also returns a list `idx` that represents the position of each `out` element in `x`. In other words: `out[i] = x[idx[i]] for i in [0, 1, ..., len(out) - 1]` For example, given this input: ```prettyprint x = [1, 2, 3, 4, 5, 6] y = [1, 3, 5] ``` This operation would return: ```prettyprint out ==> [2, 4, 6] idx ==> [1, 3, 5] ``` x: 1-D. Values to keep. y: 1-D. Values to remove. out: 1-D. Values present in `x` but not in `y`. idx: 1-D. Positions of `x` values preserved in `out`. SpaceToBatchNDblock_shape: Tblock_shapeTblock_shape: {int32, int64} = DT_INT32 SpaceToBatch for N-D tensors of type T. This operation divides "spatial" dimensions `[1, ..., M]` of the input into a grid of blocks of shape `block_shape`, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions `[1, ..., M]` correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according to `paddings`. See below for a precise description. input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, where spatial_shape has `M` dimensions. block_shape: 1-D with shape `[M]`, all values must be >= 1. paddings: 2-D with shape `[M, 2]`, all values must be >= 0. `paddings[i] = [pad_start, pad_end]` specifies the padding for input dimension `i + 1`, which corresponds to spatial dimension `i`. It is required that `block_shape[i]` divides `input_shape[i + 1] + pad_start + pad_end`. This operation is equivalent to the following steps: 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input according to `paddings` to produce `padded` of shape `padded_shape`. 2. Reshape `padded` to `reshaped_padded` of shape: [batch] + [padded_shape[1] / block_shape[0], block_shape[0], ..., padded_shape[M] / block_shape[M-1], block_shape[M-1]] + remaining_shape 3. Permute dimensions of `reshaped_padded` to produce `permuted_reshaped_padded` of shape: block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape 4. Reshape `permuted_reshaped_padded` to flatten `block_shape` into the batch dimension, producing an output tensor of shape: [batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape Some examples: (1) For the following input of shape `[1, 2, 2, 1]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ``` The output tensor has shape `[4, 1, 1, 1]` and value: ```prettyprint [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` (2) For the following input of shape `[1, 2, 2, 3]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` The output tensor has shape `[4, 1, 1, 3]` and value: ```prettyprint [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` (3) For the following input of shape `[1, 4, 4, 1]`, `block_shape = [2, 2]`, and `paddings = [[0, 0], [0, 0]]`: ```prettyprint x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ``` The output tensor has shape `[4, 2, 2, 1]` and value: ```prettyprint x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ``` (4) For the following input of shape `[2, 2, 4, 1]`, block_shape = `[2, 2]`, and paddings = `[[0, 0], [2, 0]]`: ```prettyprint x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ``` The output tensor has shape `[8, 1, 3, 1]` and value: ```prettyprint x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], [[[0], [6], [8]]], [[[0], [14], [16]]]] ``` Among others, this operation is useful for reducing atrous convolution into regular convolution. SpaceToBatchblock_size: int >= 2 SpaceToBatch for 4-D tensors of type T. This is a legacy version of the more general SpaceToBatchND. Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the `height` and `width` dimensions are moved to the `batch` dimension. After the zero-padding, both `height` and `width` of the input must be divisible by the block size. input: 4-D with shape `[batch, height, width, depth]`. paddings: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies the padding of the input with zeros across the spatial dimensions as follows: paddings = [[pad_top, pad_bottom], [pad_left, pad_right]] The effective spatial dimensions of the zero-padded input tensor will be: height_pad = pad_top + height + pad_bottom width_pad = pad_left + width + pad_right The attr `block_size` must be greater than one. It indicates the block size. * Non-overlapping blocks of size `block_size x block size` in the height and width dimensions are rearranged into the batch dimension at each location. * The batch of the output tensor is `batch * block_size * block_size`. * Both height_pad and width_pad must be divisible by block_size. The shape of the output will be: [batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth] Some examples: (1) For the following input of shape `[1, 2, 2, 1]` and block_size of 2: ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ``` The output tensor has shape `[4, 1, 1, 1]` and value: ```prettyprint [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` (2) For the following input of shape `[1, 2, 2, 3]` and block_size of 2: ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` The output tensor has shape `[4, 1, 1, 3]` and value: ```prettyprint [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` (3) For the following input of shape `[1, 4, 4, 1]` and block_size of 2: ```prettyprint x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ``` The output tensor has shape `[4, 2, 2, 1]` and value: ```prettyprint x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ``` (4) For the following input of shape `[2, 2, 4, 1]` and block_size of 2: ```prettyprint x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ``` The output tensor has shape `[8, 1, 2, 1]` and value: ```prettyprint x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] ``` Among others, this operation is useful for reducing atrous convolution into regular convolution. BatchToSpaceNDcrops: TcropsTcrops: {int32, int64} = DT_INT32 BatchToSpace for N-D tensors of type T. This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape `block_shape + [batch]`, interleaves these blocks back into the grid defined by the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as the input. The spatial dimensions of this intermediate result are then optionally cropped according to `crops` to produce the output. This is the reverse of SpaceToBatch. See below for a precise description. input: N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, where spatial_shape has M dimensions. block_shape: 1-D with shape `[M]`, all values must be >= 1. crops: 2-D with shape `[M, 2]`, all values must be >= 0. `crops[i] = [crop_start, crop_end]` specifies the amount to crop from input dimension `i + 1`, which corresponds to spatial dimension `i`. It is required that `crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`. This operation is equivalent to the following steps: 1. Reshape `input` to `reshaped` of shape: [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape), input_shape[1], ..., input_shape[N-1]] 2. Permute dimensions of `reshaped` to produce `permuted` of shape [batch / prod(block_shape), input_shape[1], block_shape[0], ..., input_shape[M], block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]] 3. Reshape `permuted` to produce `reshaped_permuted` of shape [batch / prod(block_shape), input_shape[1] * block_shape[0], ..., input_shape[M] * block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]] 4. Crop the start and end of dimensions `[1, ..., M]` of `reshaped_permuted` according to `crops` to produce the output of shape: [batch / prod(block_shape), input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1], input_shape[M+1], ..., input_shape[N-1]] Some examples: (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: ```prettyprint [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` The output tensor has shape `[1, 2, 2, 1]` and value: ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ``` (2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: ```prettyprint [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` The output tensor has shape `[1, 2, 2, 3]` and value: ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` (3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`: ```prettyprint x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ``` The output tensor has shape `[1, 4, 4, 1]` and value: ```prettyprint x = [[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]] ``` (4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [2, 0]]`: ```prettyprint x = [[[[0], [1], [3]]], [[[0], [9], [11]]], [[[0], [2], [4]]], [[[0], [10], [12]]], [[[0], [5], [7]]], [[[0], [13], [15]]], [[[0], [6], [8]]], [[[0], [14], [16]]]] ``` The output tensor has shape `[2, 2, 4, 1]` and value: ```prettyprint x = [[[[1], [2], [3], [4]], [[5], [6], [7], [8]]], [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]] ``` BatchToSpacecrops: Tidx BatchToSpace for 4-D tensors of type T. This is a legacy version of the more general BatchToSpaceND. Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the `batch` dimension are moved in spatial blocks to the `height` and `width` dimensions, followed by cropping along the `height` and `width` dimensions. input: 4-D tensor with shape `[batch*block_size*block_size, height_pad/block_size, width_pad/block_size, depth]`. Note that the batch size of the input tensor must be divisible by `block_size * block_size`. crops: 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows: crops = [[crop_top, crop_bottom], [crop_left, crop_right]] output: 4-D with shape `[batch, height, width, depth]`, where: height = height_pad - crop_top - crop_bottom width = width_pad - crop_left - crop_right The attr `block_size` must be greater than one. It indicates the block size. Some examples: (1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2: ```prettyprint [[[[1]]], [[[2]]], [[[3]]], [[[4]]]] ``` The output tensor has shape `[1, 2, 2, 1]` and value: ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ``` (2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2: ```prettyprint [[[1, 2, 3]], [[4, 5, 6]], [[7, 8, 9]], [[10, 11, 12]]] ``` The output tensor has shape `[1, 2, 2, 3]` and value: ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` (3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2: ```prettyprint x = [[[[1], [3]], [[9], [11]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ``` The output tensor has shape `[1, 4, 4, 1]` and value: ```prettyprint x = [[[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]], [[13], [14], [15], [16]]] ``` (4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2: ```prettyprint x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]], [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]] ``` The output tensor has shape `[2, 2, 4, 1]` and value: ```prettyprint x = [[[[1], [3]], [[5], [7]]], [[[2], [4]], [[10], [12]]], [[[5], [7]], [[13], [15]]], [[[6], [8]], [[14], [16]]]] ``` SpaceToDepth for tensors of type T. Rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the input tensor where values from the `height` and `width` dimensions are moved to the `depth` dimension. The attr `block_size` indicates the input block size and how the data is moved. * Non-overlapping blocks of size `block_size x block size` are rearranged into depth at each location. * The depth of the output tensor is `input_depth * block_size * block_size`. * The input tensor's height and width must be divisible by block_size. That is, assuming the input is in the shape: `[batch, height, width, depth]`, the shape of the output will be: `[batch, height/block_size, width/block_size, depth*block_size*block_size]` This operation requires that the input tensor be of rank 4, and that `block_size` be >=1 and a divisor of both the input `height` and `width`. This operation is useful for resizing the activations between convolutions (but keeping all data), e.g. instead of pooling. It is also useful for training purely convolutional models. For example, given this input of shape `[1, 2, 2, 1]`, and block_size of 2: ```prettyprint x = [[[[1], [2]], [[3], [4]]]] ``` This operation will output a tensor of shape `[1, 1, 1, 4]`: ```prettyprint [[[[1, 2, 3, 4]]]] ``` Here, the input has a batch of 1 and each batch element has shape `[2, 2, 1]`, the corresponding output will have a single element (i.e. width and height are both 1) and will have a depth of 4 channels (1 * block_size * block_size). The output element shape is `[1, 1, 4]`. For an input tensor with larger depth, here of shape `[1, 2, 2, 3]`, e.g. ```prettyprint x = [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` This operation, for block_size of 2, will return the following tensor of shape `[1, 1, 1, 12]` ```prettyprint [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ``` Similarly, for the following input of shape `[1 4 4 1]`, and a block size of 2: ```prettyprint x = [[[[1], [2], [5], [6]], [[3], [4], [7], [8]], [[9], [10], [13], [14]], [[11], [12], [15], [16]]]] ``` the operator will return the following tensor of shape `[1 2 2 4]`: ```prettyprint x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]] ``` block_size: The size of the spatial block. DepthToSpace for tensors of type T. Rearranges data from depth into blocks of spatial data. This is the reverse transformation of SpaceToDepth. More specifically, this op outputs a copy of the input tensor where values from the `depth` dimension are moved in spatial blocks to the `height` and `width` dimensions. The attr `block_size` indicates the input block size and how the data is moved. * Chunks of data of size `block_size * block_size` from depth are rearranged into non-overlapping blocks of size `block_size x block_size` * The width the output tensor is `input_depth * block_size`, whereas the height is `input_height * block_size`. * The depth of the input tensor must be divisible by `block_size * block_size`. That is, assuming the input is in the shape: `[batch, height, width, depth]`, the shape of the output will be: `[batch, height*block_size, width*block_size, depth/(block_size*block_size)]` This operation requires that the input tensor be of rank 4, and that `block_size` be >=1 and that `block_size * block_size` be a divisor of the input depth. This operation is useful for resizing the activations between convolutions (but keeping all data), e.g. instead of pooling. It is also useful for training purely convolutional models. For example, given this input of shape `[1, 1, 1, 4]`, and a block size of 2: ```prettyprint x = [[[[1, 2, 3, 4]]]] ``` This operation will output a tensor of shape `[1, 2, 2, 1]`: ```prettyprint [[[[1], [2]], [[3], [4]]]] ``` Here, the input has a batch of 1 and each batch element has shape `[1, 1, 4]`, the corresponding output will have 2x2 elements and will have a depth of 1 channel (1 = `4 / (block_size * block_size)`). The output element shape is `[2, 2, 1]`. For an input tensor with larger depth, here of shape `[1, 1, 1, 12]`, e.g. ```prettyprint x = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]] ``` This operation, for block size of 2, will return the following tensor of shape `[1, 2, 2, 3]` ```prettyprint [[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]] ``` Similarly, for the following input of shape `[1 2 2 4]`, and a block size of 2: ```prettyprint x = [[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]] ``` the operator will return the following tensor of shape `[1 4 4 1]`: ```prettyprint x = [[ [1], [2], [5], [6]], [ [3], [4], [7], [8]], [ [9], [10], [13], [14]], [ [11], [12], [15], [16]]] ``` block_size: The size of the spatial block, same as in Space2Depth. ExtractImagePatchespatches: Tksizes: list(int) >= 4 Extract `patches` from `images` and put them in the "depth" output dimension. images: 4-D Tensor with shape `[batch, in_rows, in_cols, depth]`. patches: 4-D Tensor with shape `[batch, out_rows, out_cols, ksize_rows * ksize_cols * depth]` containing image patches with size `ksize_rows x ksize_cols x depth` vectorized in the "depth" dimension. ksizes: The size of the sliding window for each dimension of `images`. strides: 1-D of length 4. How far the centers of two consecutive patches are in the images. Must be: `[1, stride_rows, stride_cols, 1]`. rates: 1-D of length 4. Must be: `[1, rate_rows, rate_cols, 1]`. This is the input stride, specifying how far two consecutive patch samples are in the input. Equivalent to extracting patches with `patch_sizes_eff = patch_sizes + (patch_sizes - 1) * (rates - 1)`, followed by subsampling them spatially by a factor of `rates`. padding: The type of padding algorithm to use. We specify the size-related attributes as: ```python ksizes = [1, ksize_rows, ksize_cols, 1] strides = [1, strides_rows, strides_cols, 1] rates = [1, rates_rows, rates_cols, 1] ``` Bitcastoutput: typetype: numbertype Bitcasts a tensor from one type to another without copying data. Given a tensor `input`, this operation returns a tensor that has the same buffer data as `input` with datatype `type`. If the input datatype `T` is larger than the output datatype `type` then the shape changes from [...] to [..., sizeof(`T`)/sizeof(`type`)]. If `T` is smaller than `type`, the operator requires that the rightmost dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from [..., sizeof(`type`)/sizeof(`T`)] to [...]. *NOTE*: Bitcast is implemented as a low-level cast, so machines with different endian orderings will give different results. indices: TIdepth: int32on_value: Toff_value: Taxis: int = -1TI: {uint8, int32, int64} = DT_INT64 Returns a one-hot tensor. The locations represented by indices in `indices` take value `on_value`, while all other locations take value `off_value`. If the input `indices` is rank `N`, the output will have rank `N+1`, The new axis is created at dimension `axis` (default: the new axis is appended at the end). If `indices` is a scalar the output shape will be a vector of length `depth`. If `indices` is a vector of length `features`, the output shape will be: ``` features x depth if axis == -1 depth x features if axis == 0 ``` If `indices` is a matrix (batch) with shape `[batch, features]`, the output shape will be: ``` batch x features x depth if axis == -1 batch x depth x features if axis == 1 depth x batch x features if axis == 0 ``` Examples ========= Suppose that ``` indices = [0, 2, -1, 1] depth = 3 on_value = 5.0 off_value = 0.0 axis = -1 ``` Then output is `[4 x 3]`: ```output = [5.0 0.0 0.0] // one_hot(0) [0.0 0.0 5.0] // one_hot(2) [0.0 0.0 0.0] // one_hot(-1) [0.0 5.0 0.0] // one_hot(1) ``` Suppose that ``` indices = [0, 2, -1, 1] depth = 3 on_value = 0.0 off_value = 3.0 axis = 0 ``` Then output is `[3 x 4]`: ```output = [0.0 3.0 3.0 3.0] [3.0 3.0 3.0 0.0] [3.0 3.0 3.0 3.0] [3.0 0.0 3.0 3.0] // ^ one_hot(0) // ^ one_hot(2) // ^ one_hot(-1) // ^ one_hot(1) ``` Suppose that ``` indices = [[0, 2], [1, -1]] depth = 3 on_value = 1.0 off_value = 0.0 axis = -1 ``` Then output is `[2 x 2 x 3]`: ```output = [ [1.0, 0.0, 0.0] // one_hot(0) [0.0, 0.0, 1.0] // one_hot(2) ][ [0.0, 1.0, 0.0] // one_hot(1) [0.0, 0.0, 0.0] // one_hot(-1) ]``` indices: A tensor of indices. depth: A scalar defining the depth of the one hot dimension. on_value: A scalar defining the value to fill in output when `indices[j] = i`. off_value: A scalar defining the value to fill in output when `indices[j] != i`. axis: The axis to fill (default: -1, a new inner-most axis). output: The one-hot tensor. QuantizeAndDequantizesigned_input: bool = truenum_bits: int = 8range_given: bool = falseinput_min: float = 0input_max: float = 0Replaced by QuantizeAndDequantizeV2 Use QuantizeAndDequantizeV2 instead. input_min: Tinput_max: T Quantizes then dequantizes a tensor. This op simulates the precision loss from the quantized forward pass by: 1. Quantizing the tensor to fixed point numbers, which should match the target quantization method when it is used in inference. 2. Dequantizing it back to floating point numbers for the following ops, most likely matmul. There are different ways to quantize. This version does not use the full range of the output type, choosing to elide the lowest possible value for symmetry (e.g., output range is -127 to 127, not -128 to 127 for signed 8 bit quantization), so that 0.0 maps to 0. To perform this op, we first find the range of values in our tensor. The range we use is always centered on 0, so we find m such that 1. m = max(abs(input_min), abs(input_max)) if range_given is true, 2. m = max(abs(min_elem(input)), abs(max_elem(input))) otherwise. Our input tensor range is then [-m, m]. Next, we choose our fixed-point quantization buckets, [min_fixed, max_fixed]. If signed_input is true, this is [min_fixed, max_fixed ] = [-(1 << (num_bits - 1) - 1), (1 << (num_bits - 1)) - 1]. Otherwise, if signed_input is false, the fixed-point range is [min_fixed, max_fixed] = [0, (1 << num_bits) - 1]. From this we compute our scaling factor, s: s = (max_fixed - min_fixed) / (2 * m). Now we can quantize and dequantize the elements of our tensor. An element e is transformed into e': e' = (e * s).round_to_nearest() / s. Note that we have a different number of buckets in the signed vs. unsigned cases. For example, if num_bits == 8, we get 254 buckets in the signed case vs. 255 in the unsigned case. For example, suppose num_bits = 8 and m = 1. Then [min_fixed, max_fixed] = [-127, 127], and s = (127 + 127) / 2 = 127. Given the vector {-1, -0.5, 0, 0.3}, this is quantized to {-127, -63, 0, 38}, and dequantized to {-1, -63.0/127, 0, 38.0/127}. input: Tensor to quantize and then dequantize. signed_input: If the quantization is signed or unsigned. num_bits: The bitwidth of the quantization. range_given: If the range is given or should be computed from the tensor. input_min: If range_given, this is the min of the range, otherwise this input will be ignored. input_max: If range_given, this is the max of the range, otherwise this input will be ignored. min_range: floatmax_range: floatmode: {'MIN_COMBINED', 'MIN_FIRST'} = 'MIN_COMBINED' Quantize the 'input' tensor of type float to 'output' tensor of type 'T'. [min_range, max_range] are scalar floats that specify the range for the 'input' data. The 'mode' attribute controls exactly which calculations are used to convert the float values to their quantized equivalents. In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: ``` out[i] = (in[i] - min_range) * range(T) / (max_range - min_range) if T == qint8, out[i] -= (range(T) + 1) / 2.0 ``` here `range(T) = numeric_limits::max() - numeric_limits::min()` *MIN_COMBINED Mode Example* Assume the input is type float and has a possible range of [0.0, 6.0] and the output type is quint8 ([0, 255]). The min_range and max_range values should be specified as 0.0 and 6.0. Quantizing from float to quint8 will multiply each value of the input by 255/6 and cast to quint8. If the output type was qint8 ([-128, 127]), the operation will additionally subtract each value by 128 prior to casting, so that the range of values aligns with the range of qint8. If the mode is 'MIN_FIRST', then this approach is used: ``` number_of_steps = 1 << (# of bits in T) range_adjust = number_of_steps / (number_of_steps - 1) range = (range_max - range_min) * range_adjust range_scale = number_of_steps / range quantized = round(input * range_scale) - round(range_min * range_scale) + numeric_limits::min() quantized = max(quantized, numeric_limits::min()) quantized = min(quantized, numeric_limits::max()) ``` The biggest difference between this and MIN_COMBINED is that the minimum range is rounded first, before it's subtracted from the rounded value. With MIN_COMBINED, a small bias is introduced where repeated iterations of quantizing and dequantizing will introduce a larger and larger error. One thing to watch out for is that the operator may choose to adjust the requested minimum and maximum values slightly during the quantization process, so you should always use the output ports as the range for further calculations. For example, if the requested minimum and maximum values are close to equal, they will be separated by a small epsilon value to prevent ill-formed quantized buffers from being created. Otherwise, you can end up with buffers where all the quantized values map to the same float value, which causes problems for operations that have to perform further calculations on them. min_range: The minimum scalar value possibly produced for the input. max_range: The maximum scalar value possibly produced for the input. output: The quantized data produced from the float input. output_min: The actual minimum scalar value used for the output. output_max: The actual maximum scalar value used for the output. Dequantize the 'input' tensor into a float Tensor. [min_range, max_range] are scalar floats that specify the range for the 'input' data. The 'mode' attribute controls exactly which calculations are used to convert the float values to their quantized equivalents. In 'MIN_COMBINED' mode, each value of the tensor will undergo the following: ``` if T == qint8, in[i] += (range(T) + 1)/ 2.0 out[i] = min_range + (in[i]* (max_range - min_range) / range(T)) ``` here `range(T) = numeric_limits::max() - numeric_limits::min()` *MIN_COMBINED Mode Example* If the input comes from a QuantizedRelu6, the output type is quint8 (range of 0-255) but the possible range of QuantizedRelu6 is 0-6. The min_range and max_range values are therefore 0.0 and 6.0. Dequantize on quint8 will take each value, cast to float, and multiply by 6 / 255. Note that if quantizedtype is qint8, the operation will additionally add each value by 128 prior to casting. If the mode is 'MIN_FIRST', then this approach is used: ```c++ number_of_steps = 1 << (# of bits in T) range_adjust = number_of_steps / (number_of_steps - 1) range = (range_max - range_min) * range_adjust range_scale = range / number_of_steps const double offset_input = static_cast(input) - lowest_quantized; result = range_min + ((input - numeric_limits::min()) * range_scale) ``` min_range: The minimum scalar value possibly produced for the input. max_range: The maximum scalar value possibly produced for the input. input_mins: N * float32input_maxes: N * float32 Concatenates quantized tensors along one dimension. concat_dim: 0-D. The dimension along which to concatenate. Must be in the range [0, rank(values)). values: The `N` Tensors to concatenate. Their ranks and types must match, and their sizes must match in all dimensions except `concat_dim`. input_mins: The minimum scalar values for each of the input tensors. input_maxes: The maximum scalar values for each of the input tensors. output_min: The float value that the minimum quantized output value represents. output_max: The float value that the maximum quantized output value represents. output: A `Tensor` with the concatenation of values stacked along the `concat_dim` dimension. This tensor's shape matches that of `values` except in `concat_dim` where it has the sum of the sizes. Reshapes a quantized tensor as per the Reshape op. ``` shape: Defines the shape of the output tensor. input_min: The minimum value of the input. input_max: The maximum value of the input. output_min: This value is copied from input_min. output_max: This value is copied from input_max. x_min: floatx_max: floaty_min: floaty_max: floatoutput_range_given: bool = falsegiven_y_min: float = 0given_y_max: float = 0variance_epsilon: float = 1e-5min_separation: float = 1e-3 Quantized Instance normalization. x: A 4D input Tensor. x_min: The value represented by the lowest quantized input. x_max: The value represented by the highest quantized input. y: A 4D Tensor. y_min: The value represented by the lowest quantized output. y_max: The value represented by the highest quantized output. output_range_given: If True, `given_y_min` and `given_y_min` and `given_y_max` are used as the output range. Otherwise, the implementation computes the output range. given_y_min: Output in `y_min` if `output_range_given` is True. given_y_max: Output in `y_max` if `output_range_given` is True. variance_epsilon: A small float number to avoid dividing by 0. min_separation: Minimum value of `y_max - y_min` shape: TindicesCreates a new tensor by applying sparse `updates` to individual values or slices within a zero tensor of the given `shape` tensor according to indices. This operator is the inverse of the [tf.gather_nd](#gather_nd) operator which extracts values or slices from a given tensor. TODO(simister): Add a link to Variable.__getitem__ documentation on slice syntax. `shape` is a `TensorShape` with rank `P` and `indices` is a `Tensor` of rank `Q`. `indices` must be integer tensor, containing indices into `shape`. It must be shape `[d_0, ..., d_{Q-2}, K]` where `0 < K <= P`. The innermost dimension of `indices` (with length `K`) corresponds to indices into elements (if `K = P`) or slices (if `K < P`) along the `K`th dimension of `shape`. `updates` is Tensor of rank `Q-1+P-K` with shape: ``` [d_0, ..., d_{Q-2}, shape[K], ..., shape[P-1]]. ``` The simplest form of scatter is to insert individual elements in a tensor by index. For example, say we want to insert 4 scattered elements in a rank-1 tensor with 8 elements.
In Python, this scatter operation would look like this: ```python indices = tf.constant([[4], [3], [1], [7]]) updates = tf.constant([9, 10, 11, 12]) shape = tf.constant([8]) scatter = tf.scatter_nd(indices, updates, shape) with tf.Session() as sess: print sess.run(scatter) ``` The resulting tensor would look like this: [0, 11, 0, 10, 9, 0, 0, 12] We can also, insert entire slices of a higher rank tensor all at once. For example, if we wanted to insert two slices in the first dimension of a rank-3 tensor with two matrices of new values.
In Python, this scatter operation would look like this: ```python indices = tf.constant([[0], [2]]) updates = tf.constant([[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]) shape = tf.constant([4, 4, 4]) scatter = tf.scatter_nd(indices, updates, shape) with tf.Session() as sess: print sess.run(scatter) ``` The resulting tensor would look like this: [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]] indices: A Tensor. Must be one of the following types: int32, int64. A tensor of indices into ref. updates: A Tensor. Must have the same type as tensor. A tensor of updated values to store in ref. shape: A vector. The shape of the resulting tensor. output: A new tensor with the given shape and updates applied according to the indices. min: float = -6.0max: float = 6.0outputs: float Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type. Attributes [min; max] define the clamping range for the 'inputs' data. Op divides this range into 255 steps (total of 256 values), then replaces each 'inputs' value with the closest of the quantized step values. Quantization is called fake since the output is still in floating point. gradients: floatbackprops: float Compute gradients for a FakeQuantWithMinMaxArgs operation. gradients: Backpropagated gradients above the FakeQuantWithMinMaxArgs operation. inputs: Values passed as inputs to the FakeQuantWithMinMaxArgs operation. backprops: Backpropagated gradients below the FakeQuantWithMinMaxArgs operation: `gradients * (inputs >= min && inputs <= max)`. min: floatmax: float Fake-quantize the 'inputs' tensor of type float via global float scalars `min` and `max` to 'outputs' tensor of same shape as `inputs`. [min; max] is the clamping range for the 'inputs' data. Op divides this range into 255 steps (total of 256 values), then replaces each 'inputs' value with the closest of the quantized step values. This operation has a gradient and thus allows for training `min` and `max` values. backprops_wrt_input: floatbackprop_wrt_min: floatbackprop_wrt_max: float Compute gradients for a FakeQuantWithMinMaxVars operation. gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation. inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation. min, max: Quantization interval, scalar floats. backprops_wrt_input: Backpropagated gradients w.r.t. inputs: `gradients * (inputs >= min && inputs <= max)`. backprop_wrt_min: Backpropagated gradients w.r.t. min parameter: `sum(gradients * (inputs < min))`. backprop_wrt_max: Backpropagated gradients w.r.t. max parameter: `sum(gradients * (inputs > max))`. Fake-quantize the 'inputs' tensor of type float and one of the shapes: `[d]`, `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]` to 'outputs' tensor of same shape as `inputs`. [min; max] is the clamping range for the 'inputs' data in the corresponding depth channel. Op divides this range into 255 steps (total of 256 values), then replaces each 'inputs' value with the closest of the quantized step values. This operation has a gradient and thus allows for training `min` and `max` values. Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation. gradients: Backpropagated gradients above the FakeQuantWithMinMaxVars operation, shape one of: `[d]`, `[b, d]`, `[b, h, w, d]`. inputs: Values passed as inputs to the FakeQuantWithMinMaxVars operation, shape same as `gradients`. min, max: Quantization interval, floats of shape `[d]`. backprops_wrt_input: Backpropagated gradients w.r.t. inputs, shape same as `inputs`: `gradients * (inputs >= min && inputs <= max)`. backprop_wrt_min: Backpropagated gradients w.r.t. min parameter, shape `[d]`: `sum_per_d(gradients * (inputs < min))`. backprop_wrt_max: Backpropagated gradients w.r.t. max parameter, shape `[d]`: `sum_per_d(gradients * (inputs > max))`. BatchMatrixDiagUse MatrixDiagBatchMatrixSetDiagUse MatrixSetDiagBatchMatrixDiagPartUse MatrixDiagPartBatchMatrixBandPartUse MatrixBandPartx: N*Tdx: N*Taxis: int$N$axisdx:outputdy: num*Tnum: int$numdy: out_typesdy:$out_typesReverseV2Grad for int64 index are not supported.d: int32dd: int32Tidx: {int32, int64}ddd$TidxSliceGrad for int64 index are not supported.begin: int32end: int32stride: int32begin_grad: int32end_grad: int32stride_grad: int32begin_mask: intend_mask: intellipsis_mask: intnew_axis_mask: intshrink_axis_mask: intxsstride$Index$begin_mask$end_mask$ellipsis_mask$new_axis_mask$shrink_axis_maskbegin_gradend_gradstride_gradshape: int32shape_grad: int32dy_grad: Tshape_graddy_gradshapes:output:offset:offset:dx_:output:0shapes:outputd_dimdim: int32d_dim: int32d_dim:y:0size_grad: int32b1xs_bxs_b_sa1size_gradd_dims: int32d_dimsrankrp: int32dp: int32qpdpd: booldd: booldshape: int32dshapedy: num_split*Tnum_split: int$num_splitx: Tindy: N*Tdx: Tinout_types$TinTblock_shapeblock_shapeSpaceToBatchNDOpSpaceToBatchOpSpaceToBatchNDOpSpaceToBatchOpblock_shape rank should be 1 instead of input rank should be >= Maximum number of non-combined block dimensions is but must not exceed padded_shape[]= is not divisible by block_shape[paddings should have shape [, 2] instead of instead of: TcropscropsBatchToSpaceNDOpBatchToSpaceOpBatchToSpaceNDOpBatchToSpaceOpInput batch dimension () is not divisible by product of block sizes (Crops must be non-negativecropped_shape[ must be non-negativecrops should have shape [ 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: : : : : : : : : : : : : : : : : : : : : : : : : : : Z% o 6! #   Z$%?&,'| (*+ - ./0\134@68D9;< >!h?"N #;! ,%Z@&LHA' :C(.D)&E*3F+H,I-wJ.tK/ M0 N1aO203242536c4758\697:8;`9<%:=;>=?= A[PBMQC,ARD7SE/TF<)VGy#WHWIHJNHK$JLJMkKNQ MO M0@ Q2XXRMYSCZT;[U4\V.]W)VG%YpU_ZL`[Da\">c]$8c^2]V.`VeaFOfbGgcAhd=%8DmgM#7@Qhq\1@NWgyxeH\_bpdgc/ccccBcccc8ccccc/Bcccccccccccccccccccccccccccccccccccccc }!1AQa"q2#BR$3br %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz w!1AQaq"2B #3Rbr $4%&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz@XSBK@I2"X!{sbhXE 0~SsAmTbSAA-BKbhTb~XBK!;(@XSBK@I2"I2EA!;I2'7 " 0A-("7 ~ ?aHP1?oM?lb??;i$? {zQ?]rU? * )+ (,5  '-46!&.37<"%/28;=#$019:>?  & %' $(- #),.!"*+/0    !"#      ??SOSLSEBogus message code %dALIGN_TYPE is wrong, please fixMAX_ALLOC_CHUNK is wrong, please fixBogus buffer control modeInvalid component ID %d in SOSInvalid crop requestDCT coefficient out of rangeDCT scaled block size %dx%d not supportedComponent index %d: mismatching sampling ratio %d:%d, %d:%d, %cBogus Huffman table definitionBogus input colorspaceBogus JPEG colorspaceBogus marker lengthWrong JPEG library version: library is %d, caller expects %dSampling factors too large for interleaved scanInvalid memory pool code %dUnsupported JPEG data precision %dInvalid progressive parameters Ss=%d Se=%d Ah=%d Al=%dInvalid progressive parameters at scan script entry %dBogus sampling factorsInvalid scan script at entry %dImproper call to JPEG library in state %dJPEG parameter struct mismatch: library thinks size is %u, caller expects %uBogus virtual array accessBuffer passed to JPEG library is too smallSuspension not allowed hereCCIR601 sampling not implemented yetToo many color components: %d, max %dUnsupported color conversion requestBogus DAC index %dBogus DAC value 0x%xBogus DHT index %dBogus DQT index %dEmpty JPEG image (DNL not supported)Read from EMS failedWrite to EMS failedDidn't expect more than one scanInput file read errorOutput file write error --- out of disk space?Fractional sampling not implemented yetHuffman code size table overflowMissing Huffman code table entryMaximum supported image dimension is %u pixelsEmpty input filePremature end of input fileCannot transcode due to multiple use of quantization table %dScan script does not transmit all dataInvalid color quantization mode changeNot implemented yetRequested feature was omitted at compile timeArithmetic table 0x%02x was not definedBacking store not supportedHuffman table 0x%02x was not definedJPEG datastream contains no imageQuantization table 0x%02x was not definedNot a JPEG file: starts with 0x%02x 0x%02xInsufficient memory (case %d)Cannot quantize more than %d color componentsCannot quantize to fewer than %d colorsCannot quantize to more than %d colorsInvalid JPEG file structure: %s before SOFInvalid JPEG file structure: two SOF markersInvalid JPEG file structure: missing SOS markerUnsupported JPEG process: SOF type 0x%02xInvalid JPEG file structure: two SOI markersFailed to create temporary file %sRead failed on temporary fileSeek failed on temporary fileWrite failed on temporary file --- out of disk space?Application transferred too few scanlinesUnsupported marker type 0x%02xVirtual array controller messed upImage too wide for this implementationRead from XMS failedWrite to XMS failedCopyright (C) 2014, Thomas G. Lane, Guido Vollbeding9a 19-Jan-2014Caution: quantization tables are too coarse for baseline JPEGAdobe APP14 marker: version %d, flags 0x%04x 0x%04x, transform %dUnknown APP0 marker (not JFIF), length %uUnknown APP14 marker (not Adobe), length %uDefine Arithmetic Table 0x%02x: 0x%02xDefine Huffman Table 0x%02xDefine Quantization Table %d precision %dDefine Restart Interval %uFreed EMS handle %uObtained EMS handle %uEnd Of Image %3d %3d %3d %3d %3d %3d %3d %3dJFIF APP0 marker: version %d.%02d, density %dx%d %dWarning: thumbnail image size does not match data length %uJFIF extension marker: type 0x%02x, length %u with %d x %d thumbnail imageMiscellaneous marker 0x%02x, length %uUnexpected marker 0x%02x %4u %4u %4u %4u %4u %4u %4u %4uQuantizing to %d = %d*%d*%d colorsQuantizing to %d colorsSelected %d colors for quantizationAt marker 0x%02x, recovery action %dRST%dSmoothing not supported with nonstandard sampling ratiosStart Of Frame 0x%02x: width=%u, height=%u, components=%d Component %d: %dhx%dv q=%dStart of ImageStart Of Scan: %d components Component %d: dc=%d ac=%d Ss=%d, Se=%d, Ah=%d, Al=%dClosed temporary file %sOpened temporary file %sJFIF extension marker: JPEG-compressed thumbnail image, length %uJFIF extension marker: palette thumbnail image, length %uJFIF extension marker: RGB thumbnail image, length %uUnrecognized component IDs %d %d %d, assuming YCbCrFreed XMS handle %uObtained XMS handle %uUnknown Adobe color transform code %dCorrupt JPEG data: bad arithmetic codeInconsistent progression sequence for component %d coefficient %dCorrupt JPEG data: %u extraneous bytes before marker 0x%02xCorrupt JPEG data: premature end of data segmentCorrupt JPEG data: bad Huffman codeWarning: unknown JFIF revision number %d.%02dPremature end of JPEG fileCorrupt JPEG data: found marker 0x%02x instead of RST%dInvalid SOS parameters for sequential JPEGApplication transferred too many scanlines%s 0 <3?@pL|CsO ,#/ߠ`Pl\cSo_84 ;7HxDtK{Gw($+'רhXdTk[gW2>1 =BrN~AqM}".!-ݢbRn^aQm] :6 95JzFvIyEu*&)%ժjZfViYeU     !(0)" #*1892+$%,3:;4-&'.5<=6/7>?????????????????     !(0)" #*12+$%,34-&.56????????????????     !()" #*+$%,-????????????????     !"#$????????????????   ????????????????  ???????????????? ????????????????@>JPEGMEM%ld%c/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.ccCHECK failed: (options_.initial_block_size) >= (sizeof(Block)): : Initial block size too small for header.CHECK failed: (n) <= (std::numeric_limits::max() - kHeaderSize): ----/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/extension_set.ccCHECK failed: type > 0 && type <= WireFormatLite::MAX_FIELD_TYPE: can't reach here.Multiple extension registrations for type "", field number .CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: !iter->second.is_repeated: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: ((iter->second).is_repeated ? REPEATED : OPTIONAL) == (OPTIONAL): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: ((*extension).is_repeated ? REPEATED : OPTIONAL) == (OPTIONAL): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: iter != extensions_.end(): Index out-of-bounds (field is empty).CHECK failed: ((iter->second).is_repeated ? REPEATED : OPTIONAL) == (REPEATED): CHECK failed: ((*extension).is_repeated ? REPEATED : OPTIONAL) == (REPEATED): CHECK failed: (extension->is_packed) == (packed): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: iter == extensions_.end(): Extension not found.CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: extension->is_repeated: CHECK failed: cpp_type(extension->type) == WireFormatLite::CPPTYPE_MESSAGE: CHECK failed: (extension->type) == (other_extension.type): CHECK failed: (extension->is_packed) == (other_extension.is_packed): CHECK failed: !extension->is_repeated: Non-primitive types can't be packed.CHECK failed: is_repeated: Can't get here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (current_size_) > (0): CHECK failed: (&other) != (this): CHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (n) >= (0): CHECK failed: GetArenaNoVirtual() == NULL: ReleaseLast() called on a RepeatedPtrField that is on an arena, with a type that does not implement MergeFrom. This is unsafe; please implement MergeFrom for your type.N6google8protobuf8internal24GeneratedExtensionFinderEN6google8protobuf8internal15ExtensionFinderE--N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEEN6google8protobuf16RepeatedPtrFieldISsEEN6google8protobuf13RepeatedFieldIbEEN6google8protobuf13RepeatedFieldIdEEN6google8protobuf13RepeatedFieldIfEEN6google8protobuf13RepeatedFieldIyEEN6google8protobuf13RepeatedFieldIjEEN6google8protobuf13RepeatedFieldIxEEN6google8protobuf13RepeatedFieldIiEE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/generated_message_util.ccCHECK failed: false: :--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.ccThe total number of bytes read was CHECK failed: (recursion_budget_) < (recursion_limit_): A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream_inl.hCHECK failed: z.first != NULL: CHECK failed: (first_byte_or_zero) != (0): Caller should provide us with *buffer_ when buffer is non-emptyCHECK failed: (*buffer) == (first_byte): CHECK failed: (first_byte & 0x80) == (0x80): CHECK failed: (first_byte_or_zero) == (buffer_[0]): CHECK failed: (0) == (BufferSize()): Reading dangerously large protocol message. If the message turns out to be larger than bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): CHECK failed: (0) != (value):  --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamEN6google8protobuf2io19ZeroCopyInputStreamE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): 'callback' must not be NULLCHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLN6google8protobuf2io26CopyingOutputStreamAdaptorEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io22LazyStringOutputStreamEN6google8protobuf2io18StringOutputStreamEN6google8protobuf2io17ArrayOutputStreamEN6google8protobuf2io16ArrayInputStreamE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::ResultCallback*>](cannot determine missing fields for lite message)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/message_lite.ccCHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of .This shouldn't be called if all the sizes are equal.Can't message of type "" because it is missing required fields: parseCHECK failed: !coded_out.HadError(): CHECK failed: IsInitialized(): serializeExceeded maximum protobuf size of 2GB.N6google8protobuf11MessageLiteE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): ------/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0length_ >= n/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/bytestream.ccByteSource::CopyTo() overran input.CHECK failed: !(dest_ <= data && data < (dest_ + n)): Append() data[] overlaps with dest_[]CHECK failed: !(outbuf_ <= bytes && bytes < (outbuf_ + capacity_)): Append() bytes[] overlaps with outbuf_[]CHECK failed: !(buf_ <= bytes && bytes < (buf_ + capacity_)): Append() bytes[] overlaps with buf_[]CHECK failed: (n) <= (input_.size()): CHECK failed: (n) <= (limit_): N6google8protobuf7strings15LimitByteSourceEN6google8protobuf7strings15ArrayByteSourceEN6google8protobuf7strings14StringByteSinkEN6google8protobuf7strings20GrowingArrayByteSinkEN6google8protobuf7strings20CheckedArrayByteSinkEN6google8protobuf7strings22UncheckedArrayByteSinkEN6google8protobuf7strings10ByteSourceEN6google8protobuf7strings8ByteSinkE--void google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::set(const char*, google::protobuf::stringpiece_ssize_type)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%d%c%d%u%ld%lu%g%p%lld%llupthread_mutex_lock: pthread_mutex_unlock: vector::_M_insert_auxvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)N6google8protobuf14FatalExceptionEN6google8protobuf8internal16FunctionClosure0EN6google8protobuf7ClosureE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/int128.ccCHECK failed: (0) != (n): Division or mod by zero: dividend.hi=, lo=----size_t to int conversionOKCANCELLEDUNKNOWNINVALID_ARGUMENTDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSS:--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/statusor.ccAttempting to fetch value instead of handling error --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.ccsize too big: details: CHECK failed: (0) <= (pos): CHECK failed: (pos) <= (x.length_): CHECK failed: (len) >= (0): --google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type) /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to arguments. Feel free to add support for more if you need it.--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0--  google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/strutil.ccCHECK failed: errors == NULL: Error reporting not implemented.CHECK failed: dest: \x%02x\%03oCHECK failed: (len) >= (0): CHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not CHECK failed: (digits) < (100): inf-infnan%.*gCHECK failed: snprintf_result > 0 && snprintf_result < kDoubleToBufferSize: CHECK failed: value != NULL: NULL output boolean given.truetyesy1falsefnon0CHECK failed: snprintf_result > 0 && snprintf_result < kFloatToBufferSize: CHECK failed: (out) == (begin + result.size()): CHECK failed: (uintptr_t((a).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (uintptr_t((b).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (out) == (begin + result->size()): CHECK failed: (uintptr_t((c).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (uintptr_t((d).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: s != NULL: len >= input_lenThis can't happen; base64 decoder state = CHECK failed: (len) <= (dest_len): Logic problem? szsrc = CHECK failed: (calc_escaped_size) == (escaped_len): CHECK failed: result != NULL: vmax > 0vmax >= basevmin < 0vmin <= 0 - basevector::_M_insert_auxbasic_string::_S_construct null not valid  00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899>?456789:;<=  !"#$%&'()*+,-./0123>456789:;<= ? !"#$%&'()*+,-./0123ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) const0123456789abcdefbool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = int; std::string = std::basic_string]bool google::protobuf::safe_parse_negative_int(const string&, IntType*) [with IntType = int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = unsigned int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = long long int; std::string = std::basic_string]bool google::protobuf::safe_parse_negative_int(const string&, IntType*) [with IntType = long long int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = long long unsigned int; std::string = std::basic_string]0123456789abcdefint google::protobuf::CalculateBase64EscapedLen(int, bool).==/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/time.cctime.year >= 1 && time.year <= 9999time.month >= 1 && time.month <= 12time.day >= 1 && time.day <= (month == 2 && IsLeapYear(year) ? kDaysInMonth[month] + 1 : kDaysInMonth[month])%03d%06d%09dInvalidTime%04d-%02d-%02dT%02d:%02d:%02d.Z --<QY`w nA:@B;Zx0Ngoogle::protobuf::int64 google::protobuf::internal::{anonymous}::SecondsSinceCommonEra(const google::protobuf::internal::DateTime&)  /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): CHECK failed: (end - target) == (size): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream_inl.hCHECK failed: z.first != NULL: parsingserializing '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): N6google8protobuf8internal29CodedOutputStreamFieldSkipperEN6google8protobuf8internal12FieldSkipperE--/google.protobuf.Anytype.googleapis.com/type.googleprod.com/--google/protobuf/any.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/any.pb.ccCHECK failed: file != NULL: google/protobuf/any.protogoogle.protobuf"& Any type_url (  value ( Bo com.google.protobufBAnyProtoPZ%github.com/golang/protobuf/ptypes/anyGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Any.type_urlCHECK failed: &from != this: N6google8protobuf3AnyE-- /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/api.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/api.pb.ccCHECK failed: file != NULL: google/protobuf/api.protogoogle.protobuf$google/protobuf/source_context.protogoogle/protobuf/type.proto" Api name ( ( methods ( 2.google.protobuf.Method( options ( 2.google.protobuf.Option version ( 6 source_context ( 2.google.protobuf.SourceContext& mixins ( 2.google.protobuf.Mixin' syntax (2.google.protobuf.Syntax" Method name (  request_type_url (  request_streaming ( response_type_url (  response_streaming (( options ( 2.google.protobuf.Option' syntax (2.google.protobuf.Syntax"# Mixin name (  root ( BH com.google.protobufBApiProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Api.namegoogle.protobuf.Api.versionCHECK failed: &from != this: google.protobuf.Method.namegoogle.protobuf.Method.request_type_urlgoogle.protobuf.Method.response_type_urlgoogle.protobuf.Mixin.namegoogle.protobuf.Mixin.root/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf5MixinEN6google8protobuf6MethodEN6google8protobuf3ApiE--8<@(D$ %( void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()File not found./.....//../../Backslashes, consecutive slashes, ".", or ".." are not allowed in the virtual pathRead access is denied for file: vector::_M_insert_auxN6google8protobuf8compiler14DiskSourceTreeEN6google8protobuf8compiler10SourceTreeEN6google8protobuf8compiler23MultiFileErrorCollectorEN6google8protobuf8compiler28SourceTreeDescriptorDatabaseEN6google8protobuf8compiler28SourceTreeDescriptorDatabase24ValidationErrorCollectorE --N6google8protobuf8compiler28SourceTreeDescriptorDatabase24SingleFileErrorCollectorE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)proto3doublefloatuint64fixed64fixed32boolstringgroupbytesuint32sfixed32sfixed64int32int64sint32sint64Expected "".Integer out of range.-infnan}/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/compiler/parser.ccCHECK failed: !location_->has_leading_comments(): CHECK failed: !location_->has_trailing_comments(): ;{allow_aliastrue"" declares 'option allow_alias = false;' which has no effect. Please remove the declaration." declares support for enum aliases but no enum values share field numbers. Please remove the unnecessary 'option allow_alias = true;' declaration.syntaxNo syntax specified for the proto file: . Please use 'syntax = "proto2";' or 'syntax = "proto3";' to specify a syntax version. (Defaulted to proto2 syntax.)proto2Unmatched "}".File must begin with a syntax statement, e.g. 'syntax = "proto2";'.=Expected syntax identifier.Unrecognized syntax identifier "". This parser only recognizes "proto2" and "proto3".messageenumserviceextendimportpackageoptionExpected top-level statement (e.g. "message").Expected message name.message_set_wire_formatReached end of input in message definition (missing '}').extensionsreservedoneofExplicit 'optional' labels are disallowed in the Proto3 syntax. To define 'optional' fields in Proto3, simply remove the 'optional' label, as fields are 'optional' by default.map<Map fields are not allowed in oneofs.Field labels (required/optional/repeated) are not allowed on map fields.Map fields are not allowed to be extensions.,>Expected "required", "optional", or "repeated".Expected field name.Missing field number.Expected field number.Group names must start with a capital letter.Missing group body.keyvalueenforce_utf8[defaultjson_name]Already set option "default".Expected integer for field default value.Unsigned field can't have negative default value.Expected number.falseExpected "true" or "false".Expected string for field default value.Expected string.Expected enum identifier for field default value.Messages can't have default values.Already set option "json_name".Expected string for JSON name.(Expected identifier..)Unexpected end of stream while parsing aggregate value.uninterpreted_optionCHECK failed: uninterpreted_option_field != NULL: No field named "uninterpreted_option" in the Options proto.Trying to read value before any tokens have been read.Unexpected end of stream while parsing option value.Invalid '-' symbol before identifier.Expected integer.Invalid '-' symbol before string.Expected option value.Expected field number range.tomaxExpected field name or number range.Reached end of input in extend definition (missing '}').Expected oneof name.Reached end of input in oneof definition (missing '}').requiredoptionalrepeatedFields in oneofs must not have labels (required / optional / repeated).Expected enum name.Reached end of input in enum definition (missing '}').Expected enum constant name.Missing numeric value for enum constant.Expected service name.Reached end of input in service definition (missing '}').rpcExpected method name.streamreturnsReached end of input in method options (missing '}').Expected message type.Expected type name.Multiple package definitions.publicweakExpected a string naming the file to import./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (&other) != (this): vector::_M_range_insert --Entryvoid google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::UninterpretedOption*; From = google::protobuf::Message]/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUP/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_length_ >= n ERRORdoublefloatint64uint64int32fixed64fixed32boolstringgroupmessagebytesuint32enumsfixed32sfixed64sint32sint64optionalrequiredrepeatedproto2proto3unknown/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.cccan't reach here.8JNgoogle.protobuf.proto2.FileOptionsMessageOptionsFieldOptionsEnumOptionsEnumValueOptionsServiceOptionsMethodOptionsCHECK failed: checkpoints_.empty(): CHECK failed: !checkpoints_.empty(): UNKNOWN_ENUM_VALUE_%s_%d.,CHECK failed: generated_database_->Add(encoded_file_descriptor, size): CHECK failed: has_default_value(): No default valuetruefalse"Messages can't have default values!Can't get here: failed to get default value as stringCannot copy json_name to a proto of a different size.{ }(.) = , $0option $1; $0// $1 syntax = "$0"; import public "$0"; import weak "$0"; import "$0"; package $0; } extend .$0 { $0message $1 { $0 extensions $1 to $2; $0 } $0 extend .$1 { $0 reserved $0, $0 to $1, ; "$0", $0} } map<$0, $1>$0$1$2 $3 = $4 [default = $0[json_name = " [] { ... }; $0 oneof $1 { ... } $0enum $1 { $0$1 = $2 [$0]service $0 { stream $0rpc $1($4.$2) returns ($5.$3) { $0$1} 'out_location' must not be NULLCHECK failed: fallback_database_ == NULL: Cannot call BuildFile on a DescriptorPool that uses a DescriptorDatabase. You must instead find a way to get your file into the underlying database.CHECK failed: mutex_ == NULL: Invalid proto descriptor for file "": : " is not defined." seems to be defined in "", which is not imported by "". To use it here, please add the necessary import." is resolved to "", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., ".") to start from the outermost scope. .placeholder.protoPLACEHOLDER_VALUE.PLACEHOLDER_VALUE" not previously defined in symbols_by_name_, but was defined in symbols_by_parent_; this shouldn't be possible." is already defined." is already defined in ""." is already defined in file "" is already defined (as something other than a package) in file "Missing name." is not a valid identifier..dummyFile recursively imports itself: -> Import "" was listed twice." has not been loaded." was not found or had errors.Missing field: FileDescriptorProto.name.Unrecognized syntax: A file with this name is already in the pool.Invalid public dependency index.Invalid weak dependency index.Reserved range $0 to $1 overlaps with already-defined range $2 to $3.Field name "$0" is reserved multiple times.Extension range $0 to $1 includes field "$2" ($3).Field "$0" uses reserved number $1.Field name "$0" is reserved.Extension range $0 to $1 overlaps with reserved range $2 to $3.Extension range $0 to $1 overlaps with already-defined range $2 to $3.Message extensions cannot have required fields.Repeated fields can't have default values.inf-infnanBoolean default must be true or false.Messages can't have default values.Couldn't parse default value "Field numbers must be positive integers.Field numbers cannot be greater than $0.Field numbers $0 through $1 are reserved for the protocol buffer library implementation.FieldDescriptorProto.extendee not set for extension field.FieldDescriptorProto.oneof_index should not be set for extensions.FieldDescriptorProto.extendee set for non-extension field.FieldDescriptorProto.oneof_index $0 is out of range for type "$1".Extension numbers must be positive integers.Extension range end number must be greater than start number.Reserved numbers must be positive integers.When enum name is stripped and label is PascalCased (), this value label conflicts with Enums must contain at least one value.the global scopeNote that enum values use C++ scoping rules, meaning that enum values are siblings of their type, not children of it. Therefore, "" must be unique within , not just within "Fields in the same oneof must be defined consecutively. "$0" cannot be defined before the completion of the "$1" oneof definition.Oneof must have at least one field." is not a message type."$0" does not declare $1 as an extension number.Fields of oneofs must themselves have label LABEL_OPTIONAL.google.protobuf.Empty" is not a type." is not an enum type.Default value for an enum field must be an identifier.Enum type "" has no value named "Field with primitive type has type_name.Field with message or enum type missing type_name.Extension number $0 has already been used in "$1" by extension "$2".Field number $0 has already been used in "$1" by field "$2".Extension number $0 has already been used in "$1" by extension "$2" defined in $3.Files that do not use optimize_for = LITE_RUNTIME cannot import files which do use this option. This file is not lite, but it imports "" which is.Extension ranges are not allowed in proto3.MessageSet is not supported in proto3.The JSON camel-case name of field "" conflicts with field "". This is not allowed in proto3.Extensions in proto3 are only allowed for defining options.Required fields are not allowed in proto3.Explicit default values are not allowed in proto3." is not a proto3 enum, but is used in "" which is a proto3 message type.Groups are not supported in proto3 syntax.The first enum value must be zero in proto3.Extension numbers cannot be greater than $0.[lazy = true] can only be specified for submessage fields.[packed = true] can only be specified for repeated primitive fields.Extensions of MessageSets must be optional messages.MessageSets cannot have fields, only extensions.Extensions to non-lite types can only be declared in non-lite files. Note that you cannot extend a non-lite type to contain a lite type, but the reverse is allowed.map_entry should not be set explicitly. Use map instead." uses the same enum value as "". If this is intended, set 'option allow_alias = true;' to the enum definition.Files with optimize_for = LITE_RUNTIME cannot define services unless you set both options cc_generic_services and java_generic_sevices to false.EntrykeyvalueKey in map fields cannot be enum types.Key in map fields cannot be float/double, bytes or message types.Enum value in map must define 0 as the first value.Expanded map entry type conflicts with an existing nested message type. conflicts with an existing field. conflicts with an existing enum type. conflicts with an existing oneof type.CHECK failed: builder_: uninterpreted_optionCHECK failed: uninterpreted_options_field != NULL: No field named "uninterpreted_option" in the Options proto.CHECK failed: original_uninterpreted_options_field != NULL: CHECK failed: options->AppendPartialToString(&buf): Protocol message could not be serialized.CHECK failed: options->ParsePartialFromString(buf): Protocol message serialized itself in invalid fashion.Options could not be fully parsed using the proto descriptors compiled into this binary. Missing required fields: Option must have a name.Option must not use reserved name "uninterpreted_option".CHECK failed: options_descriptor: (Option "" is resolved to "()", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., "(." unknown.Option field "" is not a field or extension of message "" is an atomic type, not a message." is a repeated message. Repeated message options must be initialized using an aggregate value.CHECK failed: !out.HadError(): Unexpected failure while serializing option submessage Invalid wire type for CPPTYPE_MESSAGE: CHECK failed: field != NULL: " was already set.Value out of range for int32 option "Value must be integer for int32 option "Value out of range for int64 option "Value must be integer for int64 option "Value out of range for uint32 option "Value must be non-negative integer for uint32 option "Value must be non-negative integer for uint64 option "Value must be number for float option "Value must be number for double option "Value must be identifier for boolean option "Value must be "true" or "false" for boolean option "Value must be identifier for enum-valued option "" for option "". This appears to be a value from a sibling type.Value must be quoted string for string option "; " is a message. To set the entire message, use syntax like " = { }". To set fields within it, use syntax like ".foo = value".CHECK failed: dynamic.get() != NULL: Could not create an instance of Error while parsing option value for "": CHECK failed: (option_field->type()) == (FieldDescriptor::TYPE_GROUP): Invalid wire type for CPPTYPE_INT32: Invalid wire type for CPPTYPE_INT64: Invalid wire type for CPPTYPE_UINT32: Invalid wire type for CPPTYPE_UINT64: google.protobuf.MessageOptionsgoogle.protobuf.FileOptionsgoogle.protobuf.FieldOptionsgoogle.protobuf.EnumOptionsgoogle.protobuf.EnumValueOptionsgoogle.protobuf.ServiceOptionsgoogle.protobuf.MethodOptionsgoogle.protobuf.StreamOptionsImport but not used./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): vector::_M_insert_auxvector::_M_fill_insertN6google8protobuf14DescriptorPool14ErrorCollectorE*N6google8protobuf12_GLOBAL__N_123AggregateErrorCollectorE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DescriptorPool::Tables]void google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constvoid google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::UninterpretedOption*; From = const google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::UnknownFieldSet]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::UnknownFieldSet]const string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constC* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]N6google8protobuf8internal16FunctionClosure1IPSt4pairIPKNS0_20FileDescriptorTablesEPKNS0_14SourceCodeInfoEEEEN6google8protobuf17DescriptorBuilder17OptionInterpreter21AggregateOptionFinderE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)::google::protobuf::FileOptions_OptimizeMode_IsValid(value)::google::protobuf::FieldOptions_CType_IsValid(value)::google::protobuf::FieldOptions_JSType_IsValid(value)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/descriptor.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.ccCHECK failed: file != NULL: google/protobuf/descriptor.protogoogle.protobuf"G FileDescriptorSet2 file ( 2$.google.protobuf.FileDescriptorProto" FileDescriptorProto name (  package (  dependency (  public_dependency ( weak_dependency (6 message_type ( 2 .google.protobuf.DescriptorProto7 enum_type ( 2$.google.protobuf.EnumDescriptorProto8 service ( 2'.google.protobuf.ServiceDescriptorProto8 extension ( 2%.google.protobuf.FieldDescriptorProto- options ( 2.google.protobuf.FileOptions9 source_code_info ( 2.google.protobuf.SourceCodeInfo syntax ( " DescriptorProto name ( 4 field ( 2%.google.protobuf.FieldDescriptorProto8 extension ( 2%.google.protobuf.FieldDescriptorProto5 nested_type ( 2 .google.protobuf.DescriptorProto7 enum_type ( 2$.google.protobuf.EnumDescriptorProtoH extension_range ( 2/.google.protobuf.DescriptorProto.ExtensionRange9 oneof_decl ( 2%.google.protobuf.OneofDescriptorProto0 options ( 2.google.protobuf.MessageOptionsF reserved_range ( 2..google.protobuf.DescriptorProto.ReservedRange reserved_name ( , ExtensionRange start ( end (+ ReservedRange start ( end (" FieldDescriptorProto name (  number (: label (2+.google.protobuf.FieldDescriptorProto.Label8 type (2*.google.protobuf.FieldDescriptorProto.Type type_name (  extendee (  default_value (  oneof_index ( json_name ( . options ( 2.google.protobuf.FieldOptions" Type TYPE_DOUBLE TYPE_FLOAT TYPE_INT64 TYPE_UINT64 TYPE_INT32 TYPE_FIXED64 TYPE_FIXED32 TYPE_BOOL TYPE_STRING  TYPE_GROUP  TYPE_MESSAGE  TYPE_BYTES  TYPE_UINT32  TYPE_ENUM TYPE_SFIXED32 TYPE_SFIXED64 TYPE_SINT32 TYPE_SINT64"C Label LABEL_OPTIONAL LABEL_REQUIRED LABEL_REPEATED"T OneofDescriptorProto name ( . options ( 2.google.protobuf.OneofOptions" EnumDescriptorProto name ( 8 value ( 2).google.protobuf.EnumValueDescriptorProto- options ( 2.google.protobuf.EnumOptions"l EnumValueDescriptorProto name (  number (2 options ( 2!.google.protobuf.EnumValueOptions" ServiceDescriptorProto name ( 6 method ( 2&.google.protobuf.MethodDescriptorProto0 options ( 2.google.protobuf.ServiceOptions" MethodDescriptorProto name (  input_type (  output_type ( / options ( 2.google.protobuf.MethodOptions client_streaming (:false server_streaming (:false" FileOptions java_package (  java_outer_classname ( " java_multiple_files (:false) java_generate_equals_and_hash (B% java_string_check_utf8 (:falseF optimize_for (2).google.protobuf.FileOptions.OptimizeMode:SPEED go_package ( " cc_generic_services (:false$ java_generic_services (:false" py_generic_services (:false deprecated (:false cc_enable_arenas (:false objc_class_prefix$ (  csharp_namespace% ( C uninterpreted_option ( 2$.google.protobuf.UninterpretedOption": OptimizeMode SPEED CODE_SIZE LITE_RUNTIME* J&'" MessageOptions& message_set_wire_format (:false. no_standard_descriptor_accessor (:false deprecated (:false map_entry (C uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* J " FieldOptions: ctype (2#.google.protobuf.FieldOptions.CType:STRING packed (? jstype (2$.google.protobuf.FieldOptions.JSType: JS_NORMAL lazy (:false deprecated (:false weak (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption"/ CType STRING CORD STRING_PIECE"5 JSType JS_NORMAL JS_STRING JS_NUMBER* J"^ OneofOptionsC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* " EnumOptions allow_alias ( deprecated (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "} EnumValueOptions deprecated (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "{ ServiceOptions deprecated! (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "z MethodOptions deprecated! (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* " UninterpretedOption; name ( 2-.google.protobuf.UninterpretedOption.NamePart identifier_value (  positive_int_value ( negative_int_value ( double_value ( string_value (  aggregate_value ( 3 NamePart name_part (  is_extension (" SourceCodeInfo: location ( 2(.google.protobuf.SourceCodeInfo.Location Location path (B span (B leading_comments (  trailing_comments ( ! leading_detached_comments ( " GeneratedCodeInfoA annotation ( 2-.google.protobuf.GeneratedCodeInfo.AnnotationO Annotation path (B source_file (  begin ( end (BX com.google.protobufBDescriptorProtosHZ descriptorGPBGoogle.Protobuf.ReflectionCHECK failed: &from != this:   google.protobuf.FileDescriptorProto.namegoogle.protobuf.FileDescriptorProto.packagegoogle.protobuf.FileDescriptorProto.dependencygoogle.protobuf.FileDescriptorProto.syntax google.protobuf.DescriptorProto.namegoogle.protobuf.DescriptorProto.reserved_name  google.protobuf.FieldDescriptorProto.namegoogle.protobuf.FieldDescriptorProto.extendeegoogle.protobuf.FieldDescriptorProto.type_namegoogle.protobuf.FieldDescriptorProto.default_valuegoogle.protobuf.FieldDescriptorProto.json_namegoogle.protobuf.OneofDescriptorProto.namegoogle.protobuf.EnumDescriptorProto.namegoogle.protobuf.EnumValueDescriptorProto.namegoogle.protobuf.ServiceDescriptorProto.namegoogle.protobuf.MethodDescriptorProto.namegoogle.protobuf.MethodDescriptorProto.input_typegoogle.protobuf.MethodDescriptorProto.output_type  $%google.protobuf.FileOptions.java_packagegoogle.protobuf.FileOptions.java_outer_classnamegoogle.protobuf.FileOptions.go_packagegoogle.protobuf.FileOptions.objc_class_prefixgoogle.protobuf.FileOptions.csharp_namespace !!google.protobuf.UninterpretedOption.NamePart.name_partgoogle.protobuf.UninterpretedOption.identifier_valuegoogle.protobuf.UninterpretedOption.aggregate_valuegoogle.protobuf.SourceCodeInfo.Location.leading_commentsgoogle.protobuf.SourceCodeInfo.Location.trailing_commentsgoogle.protobuf.SourceCodeInfo.Location.leading_detached_commentsgoogle.protobuf.GeneratedCodeInfo.Annotation.source_file/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (current_size_) < (total_size_): CHECK failed: (&other) != (this): N6google8protobuf17GeneratedCodeInfoEN6google8protobuf28GeneratedCodeInfo_AnnotationEN6google8protobuf14SourceCodeInfoEN6google8protobuf23SourceCodeInfo_LocationEN6google8protobuf19UninterpretedOptionEN6google8protobuf28UninterpretedOption_NamePartEN6google8protobuf13MethodOptionsEN6google8protobuf14ServiceOptionsEN6google8protobuf16EnumValueOptionsEN6google8protobuf11EnumOptionsEN6google8protobuf12OneofOptionsEN6google8protobuf12FieldOptionsEN6google8protobuf14MessageOptionsEN6google8protobuf11FileOptionsEN6google8protobuf21MethodDescriptorProtoEN6google8protobuf22ServiceDescriptorProtoEN6google8protobuf24EnumValueDescriptorProtoEN6google8protobuf19EnumDescriptorProtoEN6google8protobuf20OneofDescriptorProtoEN6google8protobuf20FieldDescriptorProtoEN6google8protobuf15DescriptorProtoEN6google8protobuf29DescriptorProto_ReservedRangeEN6google8protobuf30DescriptorProto_ExtensionRangeEN6google8protobuf19FileDescriptorProtoEN6google8protobuf17FileDescriptorSetE--x| ,8HXh 0@P`p(04, $ $ $ !<@PQRXDSTUVWHL,<=>?,<D@EFG,,<=,<,<,<, 08@$( @D0 $(void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()void google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)void google::protobuf::FileOptions::set_optimize_for(google::protobuf::FileOptions_OptimizeMode)void google::protobuf::FieldOptions::set_ctype(google::protobuf::FieldOptions_CType)void google::protobuf::FieldOptions::set_jstype(google::protobuf::FieldOptions_JSType)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor_database.ccInvalid file descriptor data passed to EncodedDescriptorDatabase::Add().File already exists in database: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): vector::_M_insert_auxInvalid symbol name: Symbol name "" conflicts with the existing symbol "".Extension conflicts with extension already in database: extend { = }N6google8protobuf24MergedDescriptorDatabaseEN6google8protobuf22DescriptorPoolDatabaseEN6google8protobuf25EncodedDescriptorDatabaseEN6google8protobuf24SimpleDescriptorDatabaseEN6google8protobuf18DescriptorDatabaseE --google/protobuf/duration.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/duration.pb.ccCHECK failed: file != NULL: google/protobuf/duration.protogoogle.protobuf"* Duration seconds ( nanos (B| com.google.protobufB DurationProtoPZ*github.com/golang/protobuf/ptypes/durationGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf8DurationE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/dynamic_message.ccCan't get here.CHECK failed: is_prototype(): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hi >= 0array_ != NULLptr_ != NULLN6google8protobuf14DynamicMessageEN6google8protobuf21DynamicMessageFactoryE--C& google::protobuf::internal::scoped_array::operator[](std::ptrdiff_t) const [with C = int; std::ptrdiff_t = int]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DynamicMessageFactory::PrototypeMap]google/protobuf/empty.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/empty.pb.ccCHECK failed: file != NULL: google/protobuf/empty.protogoogle.protobuf" EmptyBv com.google.protobufB EmptyProtoPZ'github.com/golang/protobuf/ptypes/emptyGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf5EmptyE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/extension_set_heavy.ccCHECK failed: type > 0 && type <= FieldDescriptor::MAX_TYPE: CHECK failed: ((iter->second).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_OPTIONAL): CHECK failed: (cpp_type((iter->second).type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: (cpp_type(extension->type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: ((*extension).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_OPTIONAL): CHECK failed: (cpp_type((*extension).type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: ((*extension).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_REPEATED): CHECK failed: prototype != NULL: CHECK failed: output->message_prototype != NULL: Extension factory's GetPrototype() returned NULL for extension: Non-primitive types can't be packed.Invalid message set extension./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLvector::_M_insert_auxN6google8protobuf8internal29DescriptorPoolExtensionFinderEN6google8protobuf8internal22MessageSetFieldSkipperE--To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::Message*; From = google::protobuf::MessageLite]google/protobuf/field_mask.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/field_mask.pb.ccCHECK failed: file != NULL: google/protobuf/field_mask.protogoogle.protobuf" FieldMask paths ( BN com.google.protobufBFieldMaskProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.FieldMask.pathsCHECK failed: &from != this: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf9FieldMaskE--keyvalue/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/generated_message_reflection.ccProtocol Buffer reflection usage error: Method : google::protobuf::Reflection:: Message type: Field : Problem : INVALID_CPPTYPECPPTYPE_INT32CPPTYPE_INT64CPPTYPE_UINT32CPPTYPE_UINT64CPPTYPE_DOUBLECPPTYPE_FLOATCPPTYPE_BOOLCPPTYPE_ENUMCPPTYPE_STRINGCPPTYPE_MESSAGE Problem : Field is not the right type for this message: Expected : Field type: Problem : Enum value did not match field type: Expected : Actual : Unimplemented type: CHECK failed: (message1->GetReflection()) == (this): First argument to Swap() (of type "") is not compatible with this reflection object (which is for type ""). Note that the exact same class is required; not just the same descriptor.CHECK failed: (message2->GetReflection()) == (this): Second argument to Swap() (of type "First argument to SwapFields() (of type "Second argument to SwapFields() (of type "HasFieldField does not match message type.Field is repeated; the method requires a singular field.FieldSizeField is singular; the method requires a repeated field.Can't get here.ClearFieldRemoveLastReleaseLastSwapGetInt32SetInt32GetRepeatedInt32SetRepeatedInt32AddInt32GetInt64SetInt64GetRepeatedInt64SetRepeatedInt64AddInt64GetUInt32SetUInt32GetRepeatedUInt32SetRepeatedUInt32AddUInt32GetUInt64SetUInt64GetRepeatedUInt64SetRepeatedUInt64AddUInt64GetFloatSetFloatGetRepeatedFloatSetRepeatedFloatAddFloatGetDoubleSetDoubleGetRepeatedDoubleSetRepeatedDoubleAddDoubleGetBoolSetBoolGetRepeatedBoolSetRepeatedBoolAddBoolGetStringGetStringReferenceSetStringGetRepeatedStringGetRepeatedStringReferenceSetRepeatedStringAddStringGetEnumValueSetEnumSetEnumValueSetEnumValue accepts only valid integer values: value unexpected for field GetRepeatedEnumValueSetRepeatedEnumSetRepeatedEnumValue accepts only valid integer values: value AddEnumAddEnumValue accepts only valid integer values: value GetMessageMutableMessageSetAllocatedMessageReleaseMessageGetRepeatedMessageMutableRepeatedMessageAddMessageAddAllocatedMessage"MutableRawRepeatedField"MutableRawRepeatedFieldCHECK failed: (field->options().ctype()) == (ctype): subtype mismatchCHECK failed: (field->message_type()) == (desc): wrong submessage type"GetRawRepeatedField"GetRawRepeatedField"LookupMapValue"Field is not a map field."InsertOrLookupMapValue""DeleteMapValue""MapBegin""MapEnd""MapSize"CHECK failed: (extensions_offset_) != (-1): Reached impossible case in HasBit().CHECK failed: field->is_repeated(): CHECK failed: field->cpp_type() == cpp_type || (field->cpp_type() == FieldDescriptor::CPPTYPE_ENUM && cpp_type == FieldDescriptor::CPPTYPE_INT32): The type parameter T in RepeatedFieldRef API doesn't match the actual field type (for enums T should be the generated enum type or int32).CHECK failed: (message_type) == (field->message_type()): "GetMapData"/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (n) >= (0): CHECK failed: (current_size_) > (0): vector::reserveCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (&other) != (this): CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: other->GetArenaNoVirtual() != GetArenaNoVirtual(): vector::_M_insert_auxN6google8protobuf8internal26GeneratedMessageReflectionE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/printer.cc Undefined variable in annotation: Variable used for annotation used multiple times: Annotation has negative length from to Unclosed variable name. Undefined variable: Outdent() without matching Indent().--%.1f/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/strtod.ccCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size-1]) == ('5'): CHECK failed: (size) <= (6): --Unexpected end of string.String literals cannot cross line boundaries.Expected hex digits for escape sequence.Expected four hex digits for \u escape sequence.Expected eight hex digits up to 10ffff for \U escape sequenceInvalid escape sequence in string literal."0x" must be followed by hex digits.Numbers starting with leading zero must be in octal."e" must be followed by exponent.Need space between number and identifier.Already saw decimal point or exponent; can't have another one.Hex and octal numbers must be integers."/*" inside block comment. Block comments cannot be nested.End-of-file inside block comment. Comment started here./Invalid control characters encountered in text.Need space between identifier and decimal point.Interpreting non ascii codepoint %d.Proto file starts with 0xEF but not UTF-8 BOM. Only UTF-8 is accepted for proto file.}])/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/tokenizer.cc Tokenizer::ParseFloat() passed text that could not have been tokenized as a float: \U%08xCHECK failed: IsHeadSurrogate(head_surrogate): CHECK failed: IsTrailSurrogate(trail_surrogate): Tokenizer::ParseStringAppend() passed text that could not have been tokenized as a string: vector::_M_insert_auxN6google8protobuf2io14ErrorCollectorE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next().CHECK failed: (final_byte_count) < (target_byte_count): N6google8protobuf2io19LimitingInputStreamEN6google8protobuf2io24ConcatenatingInputStreamEN6google8protobuf2io19OstreamOutputStreamEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io18IstreamInputStreamEN6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/hash.hShould never be called./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map.hProtocol Buffer map usage error: MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetInt64Value type does not match Expected : Actual : MapKey::GetUInt64ValueMapKey::GetInt32ValueMapKey::GetUInt32ValueMapKey::GetBoolValueMapKey::GetStringValueUnsupported: type mismatchUnsupportedCan't get here.MapValueRef::GetInt64ValueMapValueRef::GetUInt64ValueMapValueRef::GetInt32ValueMapValueRef::GetUInt32ValueMapValueRef::GetBoolValueMapValueRef::GetEnumValueMapValueRef::GetStringValueMapValueRef::GetFloatValueMapValueRef::GetDoubleValueMapValueRef::GetMessageValueMapValueRef::type MapValueRef is not initialized./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field.ccCHECK failed: entry_descriptor_ != NULL: CHECK failed: assign_descriptor_callback_ != NULL: valuekeyvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)vector::_M_insert_auxCHECK failed: (iterator_style_) != (kUnknown): CHECK failed: (bucket_index_ & 1) == (0): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): Requested size is too large to fit into size_t.CHECK failed: (it.m_) == (this): CHECK failed: TableEntryIsNonEmptyList(b): CHECK failed: TableEntryIsTree(b): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: table_[b] == table_[b + 1] && (b & 1) == 0: CHECK failed: node_ != NULL && m_ != NULL: CHECK failed: m_->index_of_first_non_null_ == m_->num_buckets_ || m_->table_[m_->index_of_first_non_null_] != NULL: CHECK failed: !tree->empty(): CHECK failed: (table_[b]) == (table_[b ^ 1]): CHECK failed: (bucket_index_ % 2) == (0): CHECK failed: index_of_first_non_null_ == num_buckets_ || table_[index_of_first_non_null_] != NULL: CHECK failed: find(*KeyPtrFromNodePtr(node)) == end(): CHECK failed: (result.bucket_index_) == (b & ~static_cast(1)): CHECK failed: n >= kMinTableSize: CHECK failed: (n & (n - 1)) == (0): CHECK failed: (new_num_buckets) >= (kMinTableSize): CHECK failed: (count) <= (kMaxLength): CHECK failed: !TableEntryIsTree(b) && !TableEntryIsTree(b ^ 1): CHECK failed: (count) == (tree->size()): N6google8protobuf8internal15DynamicMapFieldEN6google8protobuf8internal12MapFieldBaseE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field_inl.hCHECK failed: map_iter->iter_ != NULL: N6google8protobuf3MapINS0_6MapKeyENS0_11MapValueRefEE8InnerMapEN6google8protobuf8hash_mapINS0_6MapKeyEPNS0_7MapPairIS2_NS0_11MapValueRefEEENS0_4hashIS2_EESt8equal_toIS2_ENS0_3MapIS2_S4_E12MapAllocatorISt4pairIKS2_S6_EEEEEN6google8protobuf8internal23TypeDefinedMapFieldBaseINS0_6MapKeyENS0_11MapValueRefEEEN6google8protobuf4hashINS0_6MapKeyEEESt3mapIN6google8protobuf6MapKeyEPNS1_7MapPairIS2_NS1_11MapValueRefEEENS1_4hashIS2_EENS1_3MapIS2_S4_E12MapAllocatorISt4pairIKS2_S6_EEEEkeyvalue/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/reflection_internal.hCHECK failed: this == other_mutator: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/message.ccCHECK failed: (from.GetDescriptor()) == (descriptor): : Tried to merge from a message with a different type. to: , from: : Tried to copy from a message with a different type. to: , CHECK failed: IsInitialized(): Message of type "" is missing required fields: Message class "" implements neither SetCachedSize() nor ByteSize(). Must implement one or the other.Unimplemented EnumValue API.Unimplemented Map Reflection API.File is already registered: CHECK failed: (descriptor->file()->pool()) == (DescriptorPool::generated_pool()): Tried to register a non-generated type with the generated type registry.Type is already registered: File appears to be in generated pool but wasn't registered: Type appears to be in generated pool but wasn't registered: Not implemented.CHECK failed: field->is_repeated(): Should not reach here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (n) >= (0): CHECK failed: (current_size_) > (0): CHECK failed: other->GetArenaNoVirtual() != GetArenaNoVirtual(): CHECK failed: (&other) != (this): N6google8protobuf8internal21RepeatedFieldAccessorEN6google8protobuf14MessageFactoryEN6google8protobuf10ReflectionEN6google8protobuf7MessageE*N6google8protobuf12_GLOBAL__N_123GeneratedMessageFactoryE --CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::Message*; From = const google::protobuf::MessageLite]N6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIbEEN6google8protobuf8internal20RepeatedFieldWrapperIbEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIdEEN6google8protobuf8internal20RepeatedFieldWrapperIdEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIfEEN6google8protobuf8internal20RepeatedFieldWrapperIfEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIyEEN6google8protobuf8internal20RepeatedFieldWrapperIyEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIxEEN6google8protobuf8internal20RepeatedFieldWrapperIxEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIjEEN6google8protobuf8internal20RepeatedFieldWrapperIjEEN6google8protobuf8internal30RepeatedFieldPrimitiveAccessorIiEEN6google8protobuf8internal20RepeatedFieldWrapperIiEEN6google8protobuf8internal31RepeatedPtrFieldMessageAccessorEN6google8protobuf8internal23RepeatedPtrFieldWrapperINS0_7MessageEEEN6google8protobuf8internal30RepeatedPtrFieldStringAccessorEN6google8protobuf8internal23RepeatedPtrFieldWrapperISsEEN6google8protobuf8internal16MapFieldAccessorEN6google8protobuf8internal33RandomAccessRepeatedFieldAccessorE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/reflection_ops.ccCHECK failed: (&from) != (to): CHECK failed: (to->GetDescriptor()) == (descriptor): Tried to merge messages of different types (merge to )([].vector::_M_insert_aux --N6google8protobuf10RpcChannelEN6google8protobuf13RpcControllerEN6google8protobuf7ServiceE--google/protobuf/source_context.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/source_context.pb.ccCHECK failed: file != NULL: $google/protobuf/source_context.protogoogle.protobuf"" SourceContext file_name ( BR com.google.protobufBSourceContextProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.SourceContext.file_nameCHECK failed: &from != this: N6google8protobuf13SourceContextE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/hash.hShould never be called./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map.hProtocol Buffer map usage error: MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetStringValue type does not match Expected : Actual : google/protobuf/struct.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/struct.pb.ccCHECK failed: file != NULL: google/protobuf/struct.protogoogle.protobuf" Struct3 fields ( 2#.google.protobuf.Struct.FieldsEntryE FieldsEntry key ( % value ( 2.google.protobuf.Value:8" Value0 null_value (2.google.protobuf.NullValueH number_value (H string_value ( H bool_value (H/ struct_value ( 2.google.protobuf.StructH0 list_value ( 2.google.protobuf.ListValueHB kind"3 ListValue& values ( 2.google.protobuf.Value* NullValue NULL_VALUEB com.google.protobufB StructProtoPZ1github.com/golang/protobuf/ptypes/struct;structpbGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Struct.FieldsEntry.keyCHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): google.protobuf.Value.string_value/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hi >= 0array_ != NULLptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (iterator_style_) != (kUnknown): CHECK failed: (bucket_index_ & 1) == (0): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hdynamic_cast(&f) != NULLf == NULL || dynamic_cast(f) != NULLCHECK failed: (&other) != (this): CHECK failed: node_ != NULL && m_ != NULL: CHECK failed: m_->index_of_first_non_null_ == m_->num_buckets_ || m_->table_[m_->index_of_first_non_null_] != NULL: CHECK failed: !tree->empty(): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_entry_lite.hCHECK failed: default_instance_ != NULL: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): Requested size is too large to fit into size_t.CHECK failed: (it.m_) == (this): CHECK failed: TableEntryIsNonEmptyList(b): CHECK failed: TableEntryIsTree(b): CHECK failed: table_[b] == table_[b + 1] && (b & 1) == 0: CHECK failed: (table_[b]) == (table_[b ^ 1]): CHECK failed: index_of_first_non_null_ == num_buckets_ || table_[index_of_first_non_null_] != NULL: CHECK failed: find(*KeyPtrFromNodePtr(node)) == end(): CHECK failed: (result.bucket_index_) == (b & ~static_cast(1)): CHECK failed: (bucket_index_ % 2) == (0): CHECK failed: n >= kMinTableSize: CHECK failed: (n & (n - 1)) == (0): CHECK failed: (new_num_buckets) >= (kMinTableSize): CHECK failed: (count) <= (kMaxLength): CHECK failed: !TableEntryIsTree(b) && !TableEntryIsTree(b ^ 1): CHECK failed: (count) == (tree->size()): N6google8protobuf9ListValueEN6google8protobuf5ValueEN6google8protobuf6StructE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_entry.hCHECK failed: (&from) != (this): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field_inl.hCHECK failed: default_entry_ != NULL: CHECK failed: MapFieldBase::repeated_field_ != NULL: CHECK failed: map_iter->iter_ != NULL: CHECK failed: *MapFieldBase::entry_descriptor_ != NULL: To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = const google::protobuf::MessageLite]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::internal::MapField, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = google::protobuf::internal::MapFieldLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]To google::protobuf::internal::down_cast(From&) [with To = const google::protobuf::internal::MapField, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>&; From = const google::protobuf::internal::MapFieldLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::internal::MapEntry, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = const google::protobuf::Message]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::internal::MapEntry, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()C& google::protobuf::internal::scoped_array::operator[](std::ptrdiff_t) const [with C = const google::protobuf::MapPair, google::protobuf::Value>*; std::ptrdiff_t = int]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]N6google8protobuf3MapISsNS0_5ValueEE8InnerMapEN6google8protobuf8hash_mapISsPNS0_7MapPairISsNS0_5ValueEEENS0_4hashISsEESt8equal_toISsENS0_3MapISsS3_E12MapAllocatorISt4pairIKSsS5_EEEEEN6google8protobuf3MapISsNS0_5ValueEEEN6google8protobuf8internal12MapEntryLiteISsNS0_5ValueELNS1_14WireFormatLite9FieldTypeE9ELS5_11ELi0EE15MapEntryWrapperISsS3_LS5_9ELS5_11ELi0EEEN6google8protobuf8internal8MapEntryISsNS0_5ValueELNS1_14WireFormatLite9FieldTypeE9ELS5_11ELi0EEEN6google8protobuf8internal12MapEntryLiteISsNS0_5ValueELNS1_14WireFormatLite9FieldTypeE9ELS5_11ELi0EEEN6google8protobuf8internal8MapFieldISsNS0_5ValueELNS1_14WireFormatLite9FieldTypeE9ELS5_11ELi0EEEN6google8protobuf8internal12MapFieldLiteISsNS0_5ValueELNS1_14WireFormatLite9FieldTypeE9ELS5_11ELi0EEEN6google8protobuf8internal23TypeDefinedMapFieldBaseISsNS0_5ValueEEESt3mapISsPN6google8protobuf7MapPairISsNS1_5ValueEEENS1_4hashISsEENS1_3MapISsS3_E12MapAllocatorISt4pairIKSsS5_EEEE46<=<=--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/substitute.ccstrings::Substitute format string invalid: asked for "$", but only args were given. Full format string was: "".Invalid strings::Substitute() format string: "CHECK failed: (target - output->data()) == (output->size()):  --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUPsize_t to int conversion%s/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/text_format.ccCHECK failed: trees: Index must be in range of repeated field values. Field: Index must be -1 for singular fields.Error parsing text-format : :Warning parsing text-format >}<{[]Non-repeated Any specified multiple times.Extension "" is not defined or is not an extension of "".Message type "" has no field named "CHECK failed: allow_unknown_field_: Non-repeated field "" is specified multiple times.Field "" is specified along with field "", another member of oneof ",;text format contains deprecated field ""trueTruetfalseFalsefInvalid value for boolean field "". Value: "-Expected integer or identifier, got: Unknown enumeration value of "" for field "Reached an unintended state: CPPTYPE_MESSAGEinfinfinitynanInvalid float number: Expected identifier, got: .Expected string, got: Expected integer, got: Integer out of range ()Expect a decimal number, got: Expected double, got: /TextFormat::Parser for Any supports only type.googleapis.com and type.googleprod.com, but found "Could not find type "" stored in google.protobuf.Any.Value of type "" stored in google.protobuf.Any has missing required fieldsExpected "", found " Outdent() without matching Indent()., Message missing required fields: { { } } CHECK failed: output: output specified is NULLProto type not found: failed to parse contentsInvalid key for map field. : [] ] CHECK failed: field->is_repeated() || (index == -1): Index must be -1 for non-repeated fields......CHECK failed: (field->type()) == (FieldDescriptor::TYPE_BYTES): %d: 0x: "" " /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::_M_insert_auxN6google8protobuf10TextFormat6FinderEN6google8protobuf10TextFormat17FieldValuePrinterE*N6google8protobuf12_GLOBAL__N_129FieldValuePrinterUtf8EscapingE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::Message]google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constN6google8protobuf10TextFormat6Parser10ParserImpl20ParserErrorCollectorEgoogle/protobuf/timestamp.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/timestamp.pb.ccCHECK failed: file != NULL: google/protobuf/timestamp.protogoogle.protobuf"+ Timestamp seconds ( nanos (B~ com.google.protobufBTimestampProtoPZ+github.com/golang/protobuf/ptypes/timestampGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf9TimestampE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/type.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/type.pb.ccCHECK failed: file != NULL: google/protobuf/type.protogoogle.protobufgoogle/protobuf/any.proto$google/protobuf/source_context.proto" Type name ( & fields ( 2.google.protobuf.Field oneofs ( ( options ( 2.google.protobuf.Option6 source_context ( 2.google.protobuf.SourceContext' syntax (2.google.protobuf.Syntax" Field) kind (2.google.protobuf.Field.Kind7 cardinality (2".google.protobuf.Field.Cardinality number ( name (  type_url (  oneof_index ( packed (( options ( 2.google.protobuf.Option json_name (  default_value ( " Kind TYPE_UNKNOWN TYPE_DOUBLE TYPE_FLOAT TYPE_INT64 TYPE_UINT64 TYPE_INT32 TYPE_FIXED64 TYPE_FIXED32 TYPE_BOOL TYPE_STRING  TYPE_GROUP  TYPE_MESSAGE  TYPE_BYTES  TYPE_UINT32  TYPE_ENUM TYPE_SFIXED32 TYPE_SFIXED64 TYPE_SINT32 TYPE_SINT64"t Cardinality CARDINALITY_UNKNOWN CARDINALITY_OPTIONAL CARDINALITY_REQUIRED CARDINALITY_REPEATED" Enum name ( - enumvalue ( 2.google.protobuf.EnumValue( options ( 2.google.protobuf.Option6 source_context ( 2.google.protobuf.SourceContext' syntax (2.google.protobuf.Syntax"S EnumValue name (  number (( options ( 2.google.protobuf.Option"; Option name ( # value ( 2.google.protobuf.Any*. Syntax SYNTAX_PROTO2 SYNTAX_PROTO3BL com.google.protobufB TypeProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Type.namegoogle.protobuf.Type.oneofsCHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual():   google.protobuf.Field.namegoogle.protobuf.Field.type_urlgoogle.protobuf.Field.json_namegoogle.protobuf.Field.default_valuegoogle.protobuf.Enum.namegoogle.protobuf.EnumValue.namegoogle.protobuf.Option.name/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf6OptionEN6google8protobuf9EnumValueEN6google8protobuf4EnumEN6google8protobuf5FieldEN6google8protobuf4TypeE--8(<@(,048 $(,0 void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.ccCHECK failed: fields_ != NULL && fields_->size() > 0: CHECK failed: (TYPE_LENGTH_DELIMITED) == (type()): vector::_M_insert_auxvector::_M_fill_insert--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/field_comparator.ccNo comparison code for field of CppType = CHECK failed: FieldDescriptor::CPPTYPE_FLOAT == field->cpp_type() || FieldDescriptor::CPPTYPE_DOUBLE == field->cpp_type(): Field has to be float or double type. Field name is: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/mathutil.hCHECK failed: (T(0) < fraction || T(0) == fraction) && fraction < T(1) && margin >= T(0): N6google8protobuf4util22DefaultFieldComparatorEN6google8protobuf4util15FieldComparatorE--size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_,./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/field_mask_util.ccCHECK failed: !node->children.empty(): Cannot find field "" in message Field " is not a singular message field and cannot have sub-fields.CHECK failed: source.GetDescriptor() == destination->GetDescriptor(): 'destination' must not be NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_):  --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < size_truefalseInfinity-InfinityNaNWrong type. Cannot convert to Bool.Cannot convert to string."nullInvalid data in input.Wrong type. Only String or Bytes can be converted to Bytes.Cannot find enum with given value.=Wrong type. Bool, Enum, String and Cord not supported in GenericConvert.NULL is not a vaild argument.Status::OK is not a valid argument. --char google::protobuf::internal::StringPiecePod::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/default_value_objectwriter.ccCannot resolve type ''.Could not find enum with type ''@typeFailed to resolve type '/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): vector::_M_insert_auxvector::_M_range_insertN6google8protobuf4util9converter24DefaultValueObjectWriterE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::DefaultValueObjectWriter::Node]N6google8protobuf4util9converter24DefaultValueObjectWriter4NodeEN6google8protobuf4util9converter12ObjectWriterEN6google8protobuf4util9converter9DataPieceE--size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_[".Invalid FieldMask ''. Map keys should be represented as ["some_key"].'. Map keys should be at the end of a path segment.'. Cannot find matching '(' for all ')'.'. Cannot find matching ']' for all '['.'. Cannot find matching ')' for all '('. --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < length_\udead\ubee0123456789abcdef\u0000\u0001\u0002\u0003\u0004\u0005\u0006\u0007\b\t\n\u000b\f\r\u000e\u000f\u0010\u0011\u0012\u0013\u0014\u0015\u0016\u0017\u0018\u0019\u001a\u001b\u001c\u001d\u001e\u001f\"\u003c\u003e\\\u007f\u0080\u0081\u0082\u0083\u0084\u0085\u0086\u0087\u0088\u0089\u008a\u008b\u008c\u008d\u008e\u008f\u0090\u0091\u0092\u0093\u0094\u0095\u0096\u0097\u0098\u0099\u009a\u009b\u009c\u009d\u009e\u009f--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::set(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/json_objectwriter.ccJsonObjectWriter was not fully closed.truefalsenull":/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLN6google8protobuf4util9converter16JsonObjectWriterE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::JsonObjectWriter::Element]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::JsonObjectWriter::Element*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]N6google8protobuf4util9converter16JsonObjectWriter15ByteSinkWrapperEN6google8protobuf4util9converter16JsonObjectWriter7ElementEN6google8protobuf4util9converter22StructuredObjectWriterEN6google8protobuf4util9converter22StructuredObjectWriter11BaseElementEsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < length_length_ >= nEncountered non UTF-8 code points.Parsing terminated before end of input.Unknown parse type: Expected a value.Unexpected token./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/json_stream_parser.ccCHECK failed: string_open_ == '\"' || string_open_ == '\'': Closing quote expected in string.Illegal hex string.CHECK failed: ('\\') == (p_.data()[0]): CHECK failed: ('u') == (p_.data()[1]): Invalid escape sequence.Missing low surrogate.Invalid low surrogate.Invalid unicode code point.Unable to parse number.Octal/hex numbers are not valid JSON values.CHECK failed: ('{') == (*p_.data()): Expected , or } after key:value pair.Expected an object key or }.Expected : between key:value pair.CHECK failed: ('[') == (*p_.data()): Expected a value or ] within an array.Expected , or ] after array value. Unexpected end of string. Invalid key or variable name.truefalsenullN6google8protobuf4util9converter16JsonStreamParserE --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)size_t to int conversion--size_t to int conversion.[""][]Root element should not be named.Missing descriptor for field: oneofoneof field '' is already set. Cannot set ''Proto field is not repeating, cannot start list.Root element must be a message.Proto fields must have a name.Cannot find field./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/proto_writer.ccCHECK failed: !done_: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLN6google8protobuf4util9converter11ProtoWriterEN6google8protobuf4util9converter11ProtoWriter12ProtoElementE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::StructuredObjectWriter::BaseElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::io::CodedOutputStream]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::ProtoWriter::ProtoElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::LocationTrackerInterface]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]N6google8protobuf4util9converter21ObjectLocationTrackerEN6google8protobuf4util9converter24LocationTrackerInterfaceEsize_t to int conversionfalse0Invalid map key type./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/protostream_objectsource.ccInput stream is NULL.Invalid map entry.Timestamp seconds exceeds limit for field: Timestamp nanos exceeds limit for field: Duration seconds exceeds limit for field: Duration nanos exceeds limit for field: Duration nanos is non-negative, but seconds is negative for field: -%s%lld%ss@typeInvalid Any, the type_url is missing.valuepathsInvalid FieldMask, unexpected field.,google.protobuf.Timestampgoogle.protobuf.Durationgoogle.protobuf.DoubleValuegoogle.protobuf.FloatValuegoogle.protobuf.Int64Valuegoogle.protobuf.UInt64Valuegoogle.protobuf.Int32Valuegoogle.protobuf.UInt32Valuegoogle.protobuf.BoolValuegoogle.protobuf.StringValuegoogle.protobuf.BytesValuegoogle.protobuf.Anygoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.FieldMaskInvalid configuration. Could not find the type: Nested protocol message not parsed in its entirety.truegoogle.protobuf.MessageOptions.map_entrymap_entryMessage too deep. Max recursion depth reached for type '', field ''%.6f%.3f%.9fNULL is not a vaild argument.Status::OK is not a valid argument./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): N6google8protobuf4util9converter23ProtoStreamObjectSourceE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--N6google8protobuf4util9converter12ObjectSourceEsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/datapiece.hNot a string type.00123456789valueAnyExpect a "value" field for well-known types./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/protostream_objectwriter.ccMismatched EndList found, should not be possible@typeExpect a JSON object.StringMissing @type for any field in fieldsstruct_valueCannot start root message with ListValue.keyRoot element should not be named.list_valuevaluesMapCannot have repeated items ('') within a map.Proto field is not repeating, cannot start list.Cannot bind a list to map for field ''.string_valuenumber_valuebool_valuenull_valueInvalid struct data type. Only number, string, boolean or null values are supported.Invalid data type for timestamp, value is Invalid time format: secondsnanospathsInvalid data type for field mask, value is Invalid data type for duration, value is sIllegal duration format; duration must end with 's'-Invalid duration format, failed to parse secondsInvalid duration format, failed to parse nano secondsDuration value exceeds limitsRoot element must be a message.Field '', Map does not have a value field.type.googleapis.com/google.protobuf.Timestamptype.googleapis.com/google.protobuf.Durationtype.googleapis.com/google.protobuf.FieldMasktype.googleapis.com/google.protobuf.Doubletype.googleapis.com/google.protobuf.Floattype.googleapis.com/google.protobuf.Int64type.googleapis.com/google.protobuf.UInt64type.googleapis.com/google.protobuf.Int32type.googleapis.com/google.protobuf.UInt32type.googleapis.com/google.protobuf.Booltype.googleapis.com/google.protobuf.Stringtype.googleapis.com/google.protobuf.Bytestype.googleapis.com/google.protobuf.DoubleValuetype.googleapis.com/google.protobuf.FloatValuetype.googleapis.com/google.protobuf.Int64Valuetype.googleapis.com/google.protobuf.UInt64Valuetype.googleapis.com/google.protobuf.Int32Valuetype.googleapis.com/google.protobuf.UInt32Valuetype.googleapis.com/google.protobuf.BoolValuetype.googleapis.com/google.protobuf.StringValuetype.googleapis.com/google.protobuf.BytesValuetype.googleapis.com/google.protobuf.ValueRepeated map key: '' is already set.google.protobuf.MessageOptions.map_entrymap_entry/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLvector::_M_insert_auxN6google8protobuf4util9converter23ProtoStreamObjectWriterE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::StructuredObjectWriter::BaseElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoStreamObjectWriter]google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoStreamObjectWriter::Item]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::LocationTrackerInterface]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::ProtoStreamObjectWriter::Item*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]N6google8protobuf8internal26FunctionResultCallback_1_1INS0_4util6StatusEPNS3_9converter23ProtoStreamObjectWriterENS0_11StringPieceEEEN6google8protobuf15ResultCallback1INS0_4util6StatusENS0_11StringPieceEEEN6google8protobuf4util9converter23ProtoStreamObjectWriter4ItemEsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/type_info.ccField '' and '' map to the same camel case name ''.NULL is not a vaild argument.Status::OK is not a valid argument./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): *N6google8protobuf4util9converter12_GLOBAL__N_123TypeInfoForTypeResolverE --N6google8protobuf4util9converter8TypeInfoEsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/type_info_test_helper.ccCHECK failed: pool == descriptors[i]->file()->pool(): Descriptors from different pools are not supported.Can not reach here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::_M_insert_aux type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::TypeInfo]size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/utility.ccCHECK failed: i == str.size() || !isspace(str[i]): /google.protobuf.Timestampgoogle.protobuf.Durationgoogle.protobuf.DoubleValuegoogle.protobuf.FloatValuegoogle.protobuf.Int64Valuegoogle.protobuf.UInt64Valuegoogle.protobuf.Int32Valuegoogle.protobuf.UInt32Valuegoogle.protobuf.BoolValuegoogle.protobuf.StringValuegoogle.protobuf.BytesValuegoogle.protobuf.FieldMasktruefalse10google.protobuf.MessageOptions.map_entrygoogle.protobuf.MessageOptions.message_set_wire_formatInfinity-InfinityNaN/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_):  type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0 : : invalid value for type : missing field type.googleapis.com/JSON transcoder produced invalid protobuf output.N6google8protobuf4util8internal22ZeroCopyStreamByteSinkE*N6google8protobuf4util12_GLOBAL__N_119StatusErrorListenerE --google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)N6google8protobuf4util9converter13ErrorListenerE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUP/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/message_differencer.hCHECK failed: false: IsMatch() is not implemented./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/message_differencer.ccCHECK failed: !key_field_paths_.empty(): CHECK failed: !key_field_paths_[i].empty(): CHECK failed: comparator: Field comparator can't be NULL.CHECK failed: field->is_repeated(): Field must be repeated: CHECK failed: key_comparator == NULL: Cannot treat this repeated field as both Map and Set for comparison. Field name is: CHECK failed: list_fields_.find(field) == list_fields_.end(): Cannot treat the same field as both SET and LIST. Field name is: CHECK failed: set_fields_.find(field) == set_fields_.end(): CHECK failed: (FieldDescriptor::CPPTYPE_MESSAGE) == (field->cpp_type()): Field has to be message type. Field name is: CHECK failed: key->containing_type() == field->message_type(): must be a direct subfield within the repeated field , not Cannot treat this repeated field as both Map and Set for comparison.Cannot treat this repeated field as both Map and List for CHECK failed: child_field->containing_type() == parent_field->message_type(): must be a direct subfield within the field: CHECK failed: (FieldDescriptor::CPPTYPE_MESSAGE) == (parent_field->cpp_type()): has to be of type message.CHECK failed: !parent_field->is_repeated(): cannot be a repeated field.CHECK failed: output: Specified output string was NULLComparison between two messages with different descriptors.descriptors. vs Proto type '' not foundFailed to parse value for .($name$)name[$name$]{ }{ $name$ }CHECK failed: unknown_field != NULL: Cannot print NULL unknown_field.0x"%s"{ ... }added: : deleted: modified: -> moved: : matched: ignored: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::reservevector::_M_insert_auxvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)N6google8protobuf4util18MessageDifferencer14StreamReporterEN6google8protobuf4util18MessageDifferencer14IgnoreCriteriaEN6google8protobuf4util18MessageDifferencer16MapKeyComparatorEN6google8protobuf4util18MessageDifferencer8ReporterE --C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DynamicMessageFactory]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constC* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::ResultCallback2]N6google8protobuf8internal24MethodResultCallback_5_2IbNS0_4util18MessageDifferencerEPKNS0_15FieldDescriptorEPKNS4_16MapKeyComparatorEPKNS0_7MessageESD_RKSt6vectorINS4_13SpecificFieldESaISF_EEiiEEN6google8protobuf15ResultCallback2IbiiEEN6google8protobuf4util18MessageDifferencer30MultipleFieldsMapKeyComparatorE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/time_util.ccCHECK failed: seconds >= TimeUtil::kTimestampMinSeconds && seconds <= TimeUtil::kTimestampMaxSeconds: CHECK failed: seconds >= TimeUtil::kDurationMinSeconds && seconds <= TimeUtil::kDurationMaxSeconds: %03d%06d%09d nA:yIb4-.s0 ʚ;@B@B<%E4Y-%m-%dT%H:%M:%S--size_t to int conversion/Invalid type URL, type URLs must be of the form '/', got: Cannot resolve types from URL: Invalid type URL, unknown type: map_entrytruefalse/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/type_resolver_util.ccMessages can't have default values!*N6google8protobuf4util12_GLOBAL__N_126DescriptorPoolTypeResolverE --N6google8protobuf4util12TypeResolverE/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUPCHECK failed: (TYPE_LENGTH_DELIMITED) == (type()): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wire_format.ccExtensions of MessageSets must be optional messages.CHECK failed: (output->ByteCount()) == (expected_endpoint): : Protocol message serialized to a size different from what was originally expected. Perhaps it was modified by another thread during serialization?/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): N6google8protobuf8internal27UnknownFieldSetFieldSkipperE--google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constgoogle/protobuf/wrappers.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wrappers.pb.ccCHECK failed: file != NULL: google/protobuf/wrappers.protogoogle.protobuf" DoubleValue value (" FloatValue value (" Int64Value value (" UInt64Value value (" Int32Value value (" UInt32Value value ( " BoolValue value (" StringValue value ( " BytesValue value ( B| com.google.protobufB WrappersProtoPZ*github.com/golang/protobuf/ptypes/wrappersGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): google.protobuf.StringValue.valueN6google8protobuf10BytesValueEN6google8protobuf11StringValueEN6google8protobuf9BoolValueEN6google8protobuf11UInt32ValueEN6google8protobuf10Int32ValueEN6google8protobuf11UInt64ValueEN6google8protobuf10Int64ValueEN6google8protobuf10FloatValueEN6google8protobuf11DoubleValueE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.ccCHECK failed: (options_.initial_block_size) >= (sizeof(Block)): : Initial block size too small for header.CHECK failed: (n) <= (std::numeric_limits::max() - kHeaderSize): ----/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/extension_set.ccCHECK failed: type > 0 && type <= WireFormatLite::MAX_FIELD_TYPE: can't reach here.Multiple extension registrations for type "", field number .CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: !iter->second.is_repeated: Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: ((iter->second).is_repeated ? REPEATED : OPTIONAL) == (OPTIONAL): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: ((*extension).is_repeated ? REPEATED : OPTIONAL) == (OPTIONAL): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_INT32): CHECK failed: iter != extensions_.end(): Index out-of-bounds (field is empty).CHECK failed: ((iter->second).is_repeated ? REPEATED : OPTIONAL) == (REPEATED): CHECK failed: ((*extension).is_repeated ? REPEATED : OPTIONAL) == (REPEATED): CHECK failed: (extension->is_packed) == (packed): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_INT64): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_UINT32): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_UINT64): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_FLOAT): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_DOUBLE): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_BOOL): CHECK failed: iter == extensions_.end(): Extension not found.CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_ENUM): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_STRING): CHECK failed: (cpp_type((iter->second).type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: (cpp_type(extension->type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: (cpp_type((*extension).type)) == (WireFormatLite::CPPTYPE_MESSAGE): CHECK failed: extension->is_repeated: CHECK failed: cpp_type(extension->type) == WireFormatLite::CPPTYPE_MESSAGE: CHECK failed: (extension->type) == (other_extension.type): CHECK failed: (extension->is_packed) == (other_extension.is_packed): CHECK failed: !extension->is_repeated: Non-primitive types can't be packed.CHECK failed: is_repeated: Can't get here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (current_size_) > (0): CHECK failed: (&other) != (this): CHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (n) >= (0): CHECK failed: GetArenaNoVirtual() == NULL: ReleaseLast() called on a RepeatedPtrField that is on an arena, with a type that does not implement MergeFrom. This is unsafe; please implement MergeFrom for your type.N6google8protobuf8internal24GeneratedExtensionFinderEN6google8protobuf8internal15ExtensionFinderE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/generated_message_util.ccCHECK failed: false: :--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.ccThe total number of bytes read was CHECK failed: (recursion_budget_) < (recursion_limit_): A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream_inl.hCHECK failed: z.first != NULL: CHECK failed: (first_byte_or_zero) != (0): Caller should provide us with *buffer_ when buffer is non-emptyCHECK failed: (*buffer) == (first_byte): CHECK failed: (first_byte & 0x80) == (0x80): CHECK failed: (first_byte_or_zero) == (buffer_[0]): CHECK failed: (0) == (BufferSize()): Reading dangerously large protocol message. If the message turns out to be larger than bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.CHECK failed: (buffer_size) >= (0): CHECK failed: (0) != (value):  --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream.ccThis ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug.N6google8protobuf2io20ZeroCopyOutputStreamEN6google8protobuf2io19ZeroCopyInputStreamE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream_impl_lite.ccCHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next().CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: Cannot allocate buffer larger than kint32max for StringOutputStream.CHECK failed: (count) <= (target_->size()): 'callback' must not be NULLCHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: BackUp() can only be called after Next().CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). Parameter to BackUp() can't be negative.CHECK failed: (backup_bytes_) == (0): CHECK failed: (buffer_used_) == (buffer_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLN6google8protobuf2io26CopyingOutputStreamAdaptorEN6google8protobuf2io19CopyingOutputStreamEN6google8protobuf2io25CopyingInputStreamAdaptorEN6google8protobuf2io18CopyingInputStreamEN6google8protobuf2io22LazyStringOutputStreamEN6google8protobuf2io18StringOutputStreamEN6google8protobuf2io17ArrayOutputStreamEN6google8protobuf2io16ArrayInputStreamE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::ResultCallback*>](cannot determine missing fields for lite message)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/message_lite.ccCHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization.CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of .This shouldn't be called if all the sizes are equal.Can't message of type "" because it is missing required fields: parseCHECK failed: !coded_out.HadError(): CHECK failed: IsInitialized(): serializeExceeded maximum protobuf size of 2GB.N6google8protobuf11MessageLiteE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.ccCHECK failed: (new_size) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(old_rep->elements[0])): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): ------/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0length_ >= n/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/bytestream.ccByteSource::CopyTo() overran input.CHECK failed: !(dest_ <= data && data < (dest_ + n)): Append() data[] overlaps with dest_[]CHECK failed: !(outbuf_ <= bytes && bytes < (outbuf_ + capacity_)): Append() bytes[] overlaps with outbuf_[]CHECK failed: !(buf_ <= bytes && bytes < (buf_ + capacity_)): Append() bytes[] overlaps with buf_[]CHECK failed: (n) <= (input_.size()): CHECK failed: (n) <= (limit_): N6google8protobuf7strings15LimitByteSourceEN6google8protobuf7strings15ArrayByteSourceEN6google8protobuf7strings14StringByteSinkEN6google8protobuf7strings20GrowingArrayByteSinkEN6google8protobuf7strings20CheckedArrayByteSinkEN6google8protobuf7strings22UncheckedArrayByteSinkEN6google8protobuf7strings10ByteSourceEN6google8protobuf7strings8ByteSinkE--void google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::set(const char*, google::protobuf::stringpiece_ssize_type)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/common.ccThis program requires version of the Protocol Buffer runtime library, but the installed version is . Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "".)This program was compiled against version of the Protocol Buffer runtime library, which is not compatible with the installed version (). Contact the program author for an update. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "%d.%d.%d%c%d%u%ld%lu%g%p%lld%llupthread_mutex_lock: pthread_mutex_unlock: vector::_M_insert_auxvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)N6google8protobuf14FatalExceptionEN6google8protobuf8internal16FunctionClosure0EN6google8protobuf7ClosureE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/int128.ccCHECK failed: (0) != (n): Division or mod by zero: dividend.hi=, lo=----size_t to int conversionOKCANCELLEDUNKNOWNINVALID_ARGUMENTDEADLINE_EXCEEDEDNOT_FOUNDALREADY_EXISTSPERMISSION_DENIEDUNAUTHENTICATEDRESOURCE_EXHAUSTEDFAILED_PRECONDITIONABORTEDOUT_OF_RANGEUNIMPLEMENTEDINTERNALUNAVAILABLEDATA_LOSS:--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/statusor.ccAttempting to fetch value instead of handling error --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.ccsize too big: details: CHECK failed: (0) <= (pos): CHECK failed: (pos) <= (x.length_): CHECK failed: (len) >= (0): --google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type) /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringprintf.ccCHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to arguments. Feel free to add support for more if you need it.--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0--  google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/strutil.ccCHECK failed: errors == NULL: Error reporting not implemented.CHECK failed: dest: \x%02x\%03oCHECK failed: (len) >= (0): CHECK failed: i >= 0: FastHexToBuffer() wants non-negative integers, not CHECK failed: (digits) < (100): inf-infnan%.*gCHECK failed: snprintf_result > 0 && snprintf_result < kDoubleToBufferSize: CHECK failed: value != NULL: NULL output boolean given.truetyesy1falsefnon0CHECK failed: snprintf_result > 0 && snprintf_result < kFloatToBufferSize: CHECK failed: (out) == (begin + result.size()): CHECK failed: (uintptr_t((a).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (uintptr_t((b).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (out) == (begin + result->size()): CHECK failed: (uintptr_t((c).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: (uintptr_t((d).data() - (*result).data())) > (uintptr_t((*result).size())): CHECK failed: s != NULL: len >= input_lenThis can't happen; base64 decoder state = CHECK failed: (len) <= (dest_len): Logic problem? szsrc = CHECK failed: (calc_escaped_size) == (escaped_len): CHECK failed: result != NULL: vmax > 0vmax >= basevmin < 0vmin <= 0 - basevector::_M_insert_auxbasic_string::_S_construct null not valid  00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899>?456789:;<=  !"#$%&'()*+,-./0123>456789:;<= ? !"#$%&'()*+,-./0123ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) const0123456789abcdefbool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = int; std::string = std::basic_string]bool google::protobuf::safe_parse_negative_int(const string&, IntType*) [with IntType = int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = unsigned int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = long long int; std::string = std::basic_string]bool google::protobuf::safe_parse_negative_int(const string&, IntType*) [with IntType = long long int; std::string = std::basic_string]bool google::protobuf::safe_parse_positive_int(std::string, IntType*) [with IntType = long long unsigned int; std::string = std::basic_string]0123456789abcdefint google::protobuf::CalculateBase64EscapedLen(int, bool).==/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/time.cctime.year >= 1 && time.year <= 9999time.month >= 1 && time.month <= 12time.day >= 1 && time.day <= (month == 2 && IsLeapYear(year) ? kDaysInMonth[month] + 1 : kDaysInMonth[month])%03d%06d%09dInvalidTime%04d-%02d-%02dT%02d:%02d:%02d.Z --<QY`w nA:@B;Zx0Ngoogle::protobuf::int64 google::protobuf::internal::{anonymous}::SecondsSinceCommonEra(const google::protobuf::internal::DateTime&)  /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wire_format_lite.ccCHECK failed: (value.size()) <= (kint32max): CHECK failed: (end - target) == (size): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream_inl.hCHECK failed: z.first != NULL: parsingserializing '%s'String field contains invalid UTF-8 data when a protocol buffer. Use the 'bytes' type if you intend to send raw bytes. /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): N6google8protobuf8internal29CodedOutputStreamFieldSkipperEN6google8protobuf8internal12FieldSkipperE--/google.protobuf.Anytype.googleapis.com/type.googleprod.com/--google/protobuf/any.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/any.pb.ccCHECK failed: file != NULL: google/protobuf/any.protogoogle.protobuf"& Any type_url (  value ( Bo com.google.protobufBAnyProtoPZ%github.com/golang/protobuf/ptypes/anyGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Any.type_urlCHECK failed: &from != this: N6google8protobuf3AnyE-- /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/api.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/api.pb.ccCHECK failed: file != NULL: google/protobuf/api.protogoogle.protobuf$google/protobuf/source_context.protogoogle/protobuf/type.proto" Api name ( ( methods ( 2.google.protobuf.Method( options ( 2.google.protobuf.Option version ( 6 source_context ( 2.google.protobuf.SourceContext& mixins ( 2.google.protobuf.Mixin' syntax (2.google.protobuf.Syntax" Method name (  request_type_url (  request_streaming ( response_type_url (  response_streaming (( options ( 2.google.protobuf.Option' syntax (2.google.protobuf.Syntax"# Mixin name (  root ( BH com.google.protobufBApiProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Api.namegoogle.protobuf.Api.versionCHECK failed: &from != this: google.protobuf.Method.namegoogle.protobuf.Method.request_type_urlgoogle.protobuf.Method.response_type_urlgoogle.protobuf.Mixin.namegoogle.protobuf.Mixin.root/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf5MixinEN6google8protobuf6MethodEN6google8protobuf3ApiE--8<@(D$ %( void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()File not found./.....//../../Backslashes, consecutive slashes, ".", or ".." are not allowed in the virtual pathRead access is denied for file: vector::_M_insert_auxN6google8protobuf8compiler14DiskSourceTreeEN6google8protobuf8compiler10SourceTreeEN6google8protobuf8compiler23MultiFileErrorCollectorEN6google8protobuf8compiler28SourceTreeDescriptorDatabaseEN6google8protobuf8compiler28SourceTreeDescriptorDatabase24ValidationErrorCollectorE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)proto3doublefloatuint64fixed64fixed32boolstringgroupbytesuint32sfixed32sfixed64int32int64sint32sint64Expected "".Integer out of range.-infnan}/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/compiler/parser.ccCHECK failed: !location_->has_leading_comments(): CHECK failed: !location_->has_trailing_comments(): ;{allow_aliastrue"" declares 'option allow_alias = false;' which has no effect. Please remove the declaration." declares support for enum aliases but no enum values share field numbers. Please remove the unnecessary 'option allow_alias = true;' declaration.syntaxNo syntax specified for the proto file: . Please use 'syntax = "proto2";' or 'syntax = "proto3";' to specify a syntax version. (Defaulted to proto2 syntax.)proto2Unmatched "}".File must begin with a syntax statement, e.g. 'syntax = "proto2";'.=Expected syntax identifier.Unrecognized syntax identifier "". This parser only recognizes "proto2" and "proto3".messageenumserviceextendimportpackageoptionExpected top-level statement (e.g. "message").Expected message name.message_set_wire_formatReached end of input in message definition (missing '}').extensionsreservedoneofExplicit 'optional' labels are disallowed in the Proto3 syntax. To define 'optional' fields in Proto3, simply remove the 'optional' label, as fields are 'optional' by default.map<Map fields are not allowed in oneofs.Field labels (required/optional/repeated) are not allowed on map fields.Map fields are not allowed to be extensions.,>Expected "required", "optional", or "repeated".Expected field name.Missing field number.Expected field number.Group names must start with a capital letter.Missing group body.keyvalueenforce_utf8[defaultjson_name]Already set option "default".Expected integer for field default value.Unsigned field can't have negative default value.Expected number.falseExpected "true" or "false".Expected string for field default value.Expected string.Expected enum identifier for field default value.Messages can't have default values.Already set option "json_name".Expected string for JSON name.(Expected identifier..)Unexpected end of stream while parsing aggregate value.uninterpreted_optionCHECK failed: uninterpreted_option_field != NULL: No field named "uninterpreted_option" in the Options proto.Trying to read value before any tokens have been read.Unexpected end of stream while parsing option value.Invalid '-' symbol before identifier.Expected integer.Invalid '-' symbol before string.Expected option value.Expected field number range.tomaxExpected field name or number range.Reached end of input in extend definition (missing '}').Expected oneof name.Reached end of input in oneof definition (missing '}').requiredoptionalrepeatedFields in oneofs must not have labels (required / optional / repeated).Expected enum name.Reached end of input in enum definition (missing '}').Expected enum constant name.Missing numeric value for enum constant.Expected service name.Reached end of input in service definition (missing '}').rpcExpected method name.streamreturnsReached end of input in method options (missing '}').Expected message type.Expected type name.Multiple package definitions.publicweakExpected a string naming the file to import./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (&other) != (this): vector::_M_range_insert --Entryvoid google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::UninterpretedOption*; From = google::protobuf::Message]/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUP/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_length_ >= n ERRORdoublefloatint64uint64int32fixed64fixed32boolstringgroupmessagebytesuint32enumsfixed32sfixed64sint32sint64optionalrequiredrepeatedproto2proto3unknown/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.cccan't reach here.8JNgoogle.protobuf.proto2.FileOptionsMessageOptionsFieldOptionsEnumOptionsEnumValueOptionsServiceOptionsMethodOptionsCHECK failed: checkpoints_.empty(): CHECK failed: !checkpoints_.empty(): UNKNOWN_ENUM_VALUE_%s_%d.,CHECK failed: generated_database_->Add(encoded_file_descriptor, size): CHECK failed: has_default_value(): No default valuetruefalse"Messages can't have default values!Can't get here: failed to get default value as stringCannot copy json_name to a proto of a different size.{ }(.) = , $0option $1; $0// $1 syntax = "$0"; import public "$0"; import weak "$0"; import "$0"; package $0; } extend .$0 { $0message $1 { $0 extensions $1 to $2; $0 } $0 extend .$1 { $0 reserved $0, $0 to $1, ; "$0", $0} } map<$0, $1>$0$1$2 $3 = $4 [default = $0[json_name = " [] { ... }; $0 oneof $1 { ... } $0enum $1 { $0$1 = $2 [$0]service $0 { stream $0rpc $1($4.$2) returns ($5.$3) { $0$1} 'out_location' must not be NULLCHECK failed: fallback_database_ == NULL: Cannot call BuildFile on a DescriptorPool that uses a DescriptorDatabase. You must instead find a way to get your file into the underlying database.CHECK failed: mutex_ == NULL: Invalid proto descriptor for file "": : " is not defined." seems to be defined in "", which is not imported by "". To use it here, please add the necessary import." is resolved to "", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., ".") to start from the outermost scope. .placeholder.protoPLACEHOLDER_VALUE.PLACEHOLDER_VALUE" not previously defined in symbols_by_name_, but was defined in symbols_by_parent_; this shouldn't be possible." is already defined." is already defined in ""." is already defined in file "" is already defined (as something other than a package) in file "Missing name." is not a valid identifier..dummyFile recursively imports itself: -> Import "" was listed twice." has not been loaded." was not found or had errors.Missing field: FileDescriptorProto.name.Unrecognized syntax: A file with this name is already in the pool.Invalid public dependency index.Invalid weak dependency index.Reserved range $0 to $1 overlaps with already-defined range $2 to $3.Field name "$0" is reserved multiple times.Extension range $0 to $1 includes field "$2" ($3).Field "$0" uses reserved number $1.Field name "$0" is reserved.Extension range $0 to $1 overlaps with reserved range $2 to $3.Extension range $0 to $1 overlaps with already-defined range $2 to $3.Message extensions cannot have required fields.Repeated fields can't have default values.inf-infnanBoolean default must be true or false.Messages can't have default values.Couldn't parse default value "Field numbers must be positive integers.Field numbers cannot be greater than $0.Field numbers $0 through $1 are reserved for the protocol buffer library implementation.FieldDescriptorProto.extendee not set for extension field.FieldDescriptorProto.oneof_index should not be set for extensions.FieldDescriptorProto.extendee set for non-extension field.FieldDescriptorProto.oneof_index $0 is out of range for type "$1".Extension numbers must be positive integers.Extension range end number must be greater than start number.Reserved numbers must be positive integers.When enum name is stripped and label is PascalCased (), this value label conflicts with Enums must contain at least one value.the global scopeNote that enum values use C++ scoping rules, meaning that enum values are siblings of their type, not children of it. Therefore, "" must be unique within , not just within "Fields in the same oneof must be defined consecutively. "$0" cannot be defined before the completion of the "$1" oneof definition.Oneof must have at least one field." is not a message type."$0" does not declare $1 as an extension number.Fields of oneofs must themselves have label LABEL_OPTIONAL.google.protobuf.Empty" is not a type." is not an enum type.Default value for an enum field must be an identifier.Enum type "" has no value named "Field with primitive type has type_name.Field with message or enum type missing type_name.Extension number $0 has already been used in "$1" by extension "$2".Field number $0 has already been used in "$1" by field "$2".Extension number $0 has already been used in "$1" by extension "$2" defined in $3.Files that do not use optimize_for = LITE_RUNTIME cannot import files which do use this option. This file is not lite, but it imports "" which is.Extension ranges are not allowed in proto3.MessageSet is not supported in proto3.The JSON camel-case name of field "" conflicts with field "". This is not allowed in proto3.Extensions in proto3 are only allowed for defining options.Required fields are not allowed in proto3.Explicit default values are not allowed in proto3." is not a proto3 enum, but is used in "" which is a proto3 message type.Groups are not supported in proto3 syntax.The first enum value must be zero in proto3.Extension numbers cannot be greater than $0.[lazy = true] can only be specified for submessage fields.[packed = true] can only be specified for repeated primitive fields.Extensions of MessageSets must be optional messages.MessageSets cannot have fields, only extensions.Extensions to non-lite types can only be declared in non-lite files. Note that you cannot extend a non-lite type to contain a lite type, but the reverse is allowed.map_entry should not be set explicitly. Use map instead." uses the same enum value as "". If this is intended, set 'option allow_alias = true;' to the enum definition.Files with optimize_for = LITE_RUNTIME cannot define services unless you set both options cc_generic_services and java_generic_sevices to false.EntrykeyvalueKey in map fields cannot be enum types.Key in map fields cannot be float/double, bytes or message types.Enum value in map must define 0 as the first value.Expanded map entry type conflicts with an existing nested message type. conflicts with an existing field. conflicts with an existing enum type. conflicts with an existing oneof type.CHECK failed: builder_: uninterpreted_optionCHECK failed: uninterpreted_options_field != NULL: No field named "uninterpreted_option" in the Options proto.CHECK failed: original_uninterpreted_options_field != NULL: CHECK failed: options->AppendPartialToString(&buf): Protocol message could not be serialized.CHECK failed: options->ParsePartialFromString(buf): Protocol message serialized itself in invalid fashion.Options could not be fully parsed using the proto descriptors compiled into this binary. Missing required fields: Option must have a name.Option must not use reserved name "uninterpreted_option".CHECK failed: options_descriptor: (Option "" is resolved to "()", which is not defined. The innermost scope is searched first in name resolution. Consider using a leading '.'(i.e., "(." unknown.Option field "" is not a field or extension of message "" is an atomic type, not a message." is a repeated message. Repeated message options must be initialized using an aggregate value.CHECK failed: !out.HadError(): Unexpected failure while serializing option submessage Invalid wire type for CPPTYPE_MESSAGE: CHECK failed: field != NULL: " was already set.Value out of range for int32 option "Value must be integer for int32 option "Value out of range for int64 option "Value must be integer for int64 option "Value out of range for uint32 option "Value must be non-negative integer for uint32 option "Value must be non-negative integer for uint64 option "Value must be number for float option "Value must be number for double option "Value must be identifier for boolean option "Value must be "true" or "false" for boolean option "Value must be identifier for enum-valued option "" for option "". This appears to be a value from a sibling type.Value must be quoted string for string option "; " is a message. To set the entire message, use syntax like " = { }". To set fields within it, use syntax like ".foo = value".CHECK failed: dynamic.get() != NULL: Could not create an instance of Error while parsing option value for "": CHECK failed: (option_field->type()) == (FieldDescriptor::TYPE_GROUP): Invalid wire type for CPPTYPE_INT32: Invalid wire type for CPPTYPE_INT64: Invalid wire type for CPPTYPE_UINT32: Invalid wire type for CPPTYPE_UINT64: google.protobuf.MessageOptionsgoogle.protobuf.FileOptionsgoogle.protobuf.FieldOptionsgoogle.protobuf.EnumOptionsgoogle.protobuf.EnumValueOptionsgoogle.protobuf.ServiceOptionsgoogle.protobuf.MethodOptionsgoogle.protobuf.StreamOptionsImport but not used./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): vector::_M_insert_auxvector::_M_fill_insertN6google8protobuf14DescriptorPool14ErrorCollectorE*N6google8protobuf12_GLOBAL__N_123AggregateErrorCollectorE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DescriptorPool::Tables]void google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constvoid google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::UninterpretedOption*; From = const google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::UnknownFieldSet]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::UnknownFieldSet]const string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constC* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.h::google::protobuf::FieldDescriptorProto_Label_IsValid(value)::google::protobuf::FieldDescriptorProto_Type_IsValid(value)::google::protobuf::FileOptions_OptimizeMode_IsValid(value)::google::protobuf::FieldOptions_CType_IsValid(value)::google::protobuf::FieldOptions_JSType_IsValid(value)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/descriptor.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor.pb.ccCHECK failed: file != NULL: google/protobuf/descriptor.protogoogle.protobuf"G FileDescriptorSet2 file ( 2$.google.protobuf.FileDescriptorProto" FileDescriptorProto name (  package (  dependency (  public_dependency ( weak_dependency (6 message_type ( 2 .google.protobuf.DescriptorProto7 enum_type ( 2$.google.protobuf.EnumDescriptorProto8 service ( 2'.google.protobuf.ServiceDescriptorProto8 extension ( 2%.google.protobuf.FieldDescriptorProto- options ( 2.google.protobuf.FileOptions9 source_code_info ( 2.google.protobuf.SourceCodeInfo syntax ( " DescriptorProto name ( 4 field ( 2%.google.protobuf.FieldDescriptorProto8 extension ( 2%.google.protobuf.FieldDescriptorProto5 nested_type ( 2 .google.protobuf.DescriptorProto7 enum_type ( 2$.google.protobuf.EnumDescriptorProtoH extension_range ( 2/.google.protobuf.DescriptorProto.ExtensionRange9 oneof_decl ( 2%.google.protobuf.OneofDescriptorProto0 options ( 2.google.protobuf.MessageOptionsF reserved_range ( 2..google.protobuf.DescriptorProto.ReservedRange reserved_name ( , ExtensionRange start ( end (+ ReservedRange start ( end (" FieldDescriptorProto name (  number (: label (2+.google.protobuf.FieldDescriptorProto.Label8 type (2*.google.protobuf.FieldDescriptorProto.Type type_name (  extendee (  default_value (  oneof_index ( json_name ( . options ( 2.google.protobuf.FieldOptions" Type TYPE_DOUBLE TYPE_FLOAT TYPE_INT64 TYPE_UINT64 TYPE_INT32 TYPE_FIXED64 TYPE_FIXED32 TYPE_BOOL TYPE_STRING  TYPE_GROUP  TYPE_MESSAGE  TYPE_BYTES  TYPE_UINT32  TYPE_ENUM TYPE_SFIXED32 TYPE_SFIXED64 TYPE_SINT32 TYPE_SINT64"C Label LABEL_OPTIONAL LABEL_REQUIRED LABEL_REPEATED"T OneofDescriptorProto name ( . options ( 2.google.protobuf.OneofOptions" EnumDescriptorProto name ( 8 value ( 2).google.protobuf.EnumValueDescriptorProto- options ( 2.google.protobuf.EnumOptions"l EnumValueDescriptorProto name (  number (2 options ( 2!.google.protobuf.EnumValueOptions" ServiceDescriptorProto name ( 6 method ( 2&.google.protobuf.MethodDescriptorProto0 options ( 2.google.protobuf.ServiceOptions" MethodDescriptorProto name (  input_type (  output_type ( / options ( 2.google.protobuf.MethodOptions client_streaming (:false server_streaming (:false" FileOptions java_package (  java_outer_classname ( " java_multiple_files (:false) java_generate_equals_and_hash (B% java_string_check_utf8 (:falseF optimize_for (2).google.protobuf.FileOptions.OptimizeMode:SPEED go_package ( " cc_generic_services (:false$ java_generic_services (:false" py_generic_services (:false deprecated (:false cc_enable_arenas (:false objc_class_prefix$ (  csharp_namespace% ( C uninterpreted_option ( 2$.google.protobuf.UninterpretedOption": OptimizeMode SPEED CODE_SIZE LITE_RUNTIME* J&'" MessageOptions& message_set_wire_format (:false. no_standard_descriptor_accessor (:false deprecated (:false map_entry (C uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* J " FieldOptions: ctype (2#.google.protobuf.FieldOptions.CType:STRING packed (? jstype (2$.google.protobuf.FieldOptions.JSType: JS_NORMAL lazy (:false deprecated (:false weak (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption"/ CType STRING CORD STRING_PIECE"5 JSType JS_NORMAL JS_STRING JS_NUMBER* J"^ OneofOptionsC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* " EnumOptions allow_alias ( deprecated (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "} EnumValueOptions deprecated (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "{ ServiceOptions deprecated! (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* "z MethodOptions deprecated! (:falseC uninterpreted_option ( 2$.google.protobuf.UninterpretedOption* " UninterpretedOption; name ( 2-.google.protobuf.UninterpretedOption.NamePart identifier_value (  positive_int_value ( negative_int_value ( double_value ( string_value (  aggregate_value ( 3 NamePart name_part (  is_extension (" SourceCodeInfo: location ( 2(.google.protobuf.SourceCodeInfo.Location Location path (B span (B leading_comments (  trailing_comments ( ! leading_detached_comments ( " GeneratedCodeInfoA annotation ( 2-.google.protobuf.GeneratedCodeInfo.AnnotationO Annotation path (B source_file (  begin ( end (BX com.google.protobufBDescriptorProtosHZ descriptorGPBGoogle.Protobuf.ReflectionCHECK failed: &from != this:   google.protobuf.FileDescriptorProto.namegoogle.protobuf.FileDescriptorProto.packagegoogle.protobuf.FileDescriptorProto.dependencygoogle.protobuf.FileDescriptorProto.syntax google.protobuf.DescriptorProto.namegoogle.protobuf.DescriptorProto.reserved_name  google.protobuf.FieldDescriptorProto.namegoogle.protobuf.FieldDescriptorProto.extendeegoogle.protobuf.FieldDescriptorProto.type_namegoogle.protobuf.FieldDescriptorProto.default_valuegoogle.protobuf.FieldDescriptorProto.json_namegoogle.protobuf.OneofDescriptorProto.namegoogle.protobuf.EnumDescriptorProto.namegoogle.protobuf.EnumValueDescriptorProto.namegoogle.protobuf.ServiceDescriptorProto.namegoogle.protobuf.MethodDescriptorProto.namegoogle.protobuf.MethodDescriptorProto.input_typegoogle.protobuf.MethodDescriptorProto.output_type  $%google.protobuf.FileOptions.java_packagegoogle.protobuf.FileOptions.java_outer_classnamegoogle.protobuf.FileOptions.go_packagegoogle.protobuf.FileOptions.objc_class_prefixgoogle.protobuf.FileOptions.csharp_namespace !!google.protobuf.UninterpretedOption.NamePart.name_partgoogle.protobuf.UninterpretedOption.identifier_valuegoogle.protobuf.UninterpretedOption.aggregate_valuegoogle.protobuf.SourceCodeInfo.Location.leading_commentsgoogle.protobuf.SourceCodeInfo.Location.trailing_commentsgoogle.protobuf.SourceCodeInfo.Location.leading_detached_commentsgoogle.protobuf.GeneratedCodeInfo.Annotation.source_file/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (current_size_) < (total_size_): CHECK failed: (&other) != (this): N6google8protobuf17GeneratedCodeInfoEN6google8protobuf28GeneratedCodeInfo_AnnotationEN6google8protobuf14SourceCodeInfoEN6google8protobuf23SourceCodeInfo_LocationEN6google8protobuf19UninterpretedOptionEN6google8protobuf28UninterpretedOption_NamePartEN6google8protobuf13MethodOptionsEN6google8protobuf14ServiceOptionsEN6google8protobuf16EnumValueOptionsEN6google8protobuf11EnumOptionsEN6google8protobuf12OneofOptionsEN6google8protobuf12FieldOptionsEN6google8protobuf14MessageOptionsEN6google8protobuf11FileOptionsEN6google8protobuf21MethodDescriptorProtoEN6google8protobuf22ServiceDescriptorProtoEN6google8protobuf24EnumValueDescriptorProtoEN6google8protobuf19EnumDescriptorProtoEN6google8protobuf20OneofDescriptorProtoEN6google8protobuf20FieldDescriptorProtoEN6google8protobuf15DescriptorProtoEN6google8protobuf29DescriptorProto_ReservedRangeEN6google8protobuf30DescriptorProto_ExtensionRangeEN6google8protobuf19FileDescriptorProtoEN6google8protobuf17FileDescriptorSetE--x| ,8HXh 0@P`p(04, $ $ $ !<@PQRXDSTUVWHL,<=>?,<D@EFG,,<=,<,<,<, 08@$( @D0 $(void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()void google::protobuf::FieldDescriptorProto::set_label(google::protobuf::FieldDescriptorProto_Label)void google::protobuf::FieldDescriptorProto::set_type(google::protobuf::FieldDescriptorProto_Type)void google::protobuf::FileOptions::set_optimize_for(google::protobuf::FileOptions_OptimizeMode)void google::protobuf::FieldOptions::set_ctype(google::protobuf::FieldOptions_CType)void google::protobuf::FieldOptions::set_jstype(google::protobuf::FieldOptions_JSType)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/descriptor_database.ccInvalid file descriptor data passed to EncodedDescriptorDatabase::Add().File already exists in database: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): vector::_M_insert_auxInvalid symbol name: Symbol name "" conflicts with the existing symbol "".Extension conflicts with extension already in database: extend { = }N6google8protobuf24MergedDescriptorDatabaseEN6google8protobuf22DescriptorPoolDatabaseEN6google8protobuf25EncodedDescriptorDatabaseEN6google8protobuf24SimpleDescriptorDatabaseEN6google8protobuf18DescriptorDatabaseE --google/protobuf/duration.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/duration.pb.ccCHECK failed: file != NULL: google/protobuf/duration.protogoogle.protobuf"* Duration seconds ( nanos (B| com.google.protobufB DurationProtoPZ*github.com/golang/protobuf/ptypes/durationGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf8DurationE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/dynamic_message.ccCan't get here.CHECK failed: is_prototype(): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hi >= 0array_ != NULLptr_ != NULLN6google8protobuf14DynamicMessageEN6google8protobuf21DynamicMessageFactoryE--C& google::protobuf::internal::scoped_array::operator[](std::ptrdiff_t) const [with C = int; std::ptrdiff_t = int]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DynamicMessageFactory::PrototypeMap]google/protobuf/empty.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/empty.pb.ccCHECK failed: file != NULL: google/protobuf/empty.protogoogle.protobuf" EmptyBv com.google.protobufB EmptyProtoPZ'github.com/golang/protobuf/ptypes/emptyGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf5EmptyE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/extension_set_heavy.ccCHECK failed: type > 0 && type <= FieldDescriptor::MAX_TYPE: CHECK failed: ((iter->second).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_OPTIONAL): CHECK failed: (cpp_type((iter->second).type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: (cpp_type(extension->type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: ((*extension).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_OPTIONAL): CHECK failed: (cpp_type((*extension).type)) == (FieldDescriptor::CPPTYPE_MESSAGE): CHECK failed: ((*extension).is_repeated ? FieldDescriptor::LABEL_REPEATED : FieldDescriptor::LABEL_OPTIONAL) == (FieldDescriptor::LABEL_REPEATED): CHECK failed: prototype != NULL: CHECK failed: output->message_prototype != NULL: Extension factory's GetPrototype() returned NULL for extension: Non-primitive types can't be packed.Invalid message set extension./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLvector::_M_insert_auxN6google8protobuf8internal29DescriptorPoolExtensionFinderEN6google8protobuf8internal22MessageSetFieldSkipperE--To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::Message*; From = google::protobuf::MessageLite]google/protobuf/field_mask.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/field_mask.pb.ccCHECK failed: file != NULL: google/protobuf/field_mask.protogoogle.protobuf" FieldMask paths ( BN com.google.protobufBFieldMaskProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.FieldMask.pathsCHECK failed: &from != this: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf9FieldMaskE--keyvalue/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/generated_message_reflection.ccProtocol Buffer reflection usage error: Method : google::protobuf::Reflection:: Message type: Field : Problem : INVALID_CPPTYPECPPTYPE_INT32CPPTYPE_INT64CPPTYPE_UINT32CPPTYPE_UINT64CPPTYPE_DOUBLECPPTYPE_FLOATCPPTYPE_BOOLCPPTYPE_ENUMCPPTYPE_STRINGCPPTYPE_MESSAGE Problem : Field is not the right type for this message: Expected : Field type: Problem : Enum value did not match field type: Expected : Actual : Unimplemented type: CHECK failed: (message1->GetReflection()) == (this): First argument to Swap() (of type "") is not compatible with this reflection object (which is for type ""). Note that the exact same class is required; not just the same descriptor.CHECK failed: (message2->GetReflection()) == (this): Second argument to Swap() (of type "First argument to SwapFields() (of type "Second argument to SwapFields() (of type "HasFieldField does not match message type.Field is repeated; the method requires a singular field.FieldSizeField is singular; the method requires a repeated field.Can't get here.ClearFieldRemoveLastReleaseLastSwapGetInt32SetInt32GetRepeatedInt32SetRepeatedInt32AddInt32GetInt64SetInt64GetRepeatedInt64SetRepeatedInt64AddInt64GetUInt32SetUInt32GetRepeatedUInt32SetRepeatedUInt32AddUInt32GetUInt64SetUInt64GetRepeatedUInt64SetRepeatedUInt64AddUInt64GetFloatSetFloatGetRepeatedFloatSetRepeatedFloatAddFloatGetDoubleSetDoubleGetRepeatedDoubleSetRepeatedDoubleAddDoubleGetBoolSetBoolGetRepeatedBoolSetRepeatedBoolAddBoolGetStringGetStringReferenceSetStringGetRepeatedStringGetRepeatedStringReferenceSetRepeatedStringAddStringGetEnumValueSetEnumSetEnumValueSetEnumValue accepts only valid integer values: value unexpected for field GetRepeatedEnumValueSetRepeatedEnumSetRepeatedEnumValue accepts only valid integer values: value AddEnumAddEnumValue accepts only valid integer values: value GetMessageMutableMessageSetAllocatedMessageReleaseMessageGetRepeatedMessageMutableRepeatedMessageAddMessageAddAllocatedMessage"MutableRawRepeatedField"MutableRawRepeatedFieldCHECK failed: (field->options().ctype()) == (ctype): subtype mismatchCHECK failed: (field->message_type()) == (desc): wrong submessage type"GetRawRepeatedField"GetRawRepeatedField"LookupMapValue"Field is not a map field."InsertOrLookupMapValue""DeleteMapValue""MapBegin""MapEnd""MapSize"CHECK failed: (extensions_offset_) != (-1): Reached impossible case in HasBit().CHECK failed: field->is_repeated(): CHECK failed: field->cpp_type() == cpp_type || (field->cpp_type() == FieldDescriptor::CPPTYPE_ENUM && cpp_type == FieldDescriptor::CPPTYPE_INT32): The type parameter T in RepeatedFieldRef API doesn't match the actual field type (for enums T should be the generated enum type or int32).CHECK failed: (message_type) == (field->message_type()): "GetMapData"/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (n) >= (0): CHECK failed: (current_size_) > (0): vector::reserveCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (&other) != (this): CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): CHECK failed: other->GetArenaNoVirtual() != GetArenaNoVirtual(): vector::_M_insert_auxN6google8protobuf8internal26GeneratedMessageReflectionE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/printer.cc Undefined variable in annotation: Variable used for annotation used multiple times: Annotation has negative length from to Unclosed variable name. Undefined variable: Outdent() without matching Indent().--%.1f/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/strtod.ccCHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size-1]) == ('5'): CHECK failed: (size) <= (6): --Unexpected end of string.String literals cannot cross line boundaries.Expected hex digits for escape sequence.Expected four hex digits for \u escape sequence.Expected eight hex digits up to 10ffff for \U escape sequenceInvalid escape sequence in string literal."0x" must be followed by hex digits.Numbers starting with leading zero must be in octal."e" must be followed by exponent.Need space between number and identifier.Already saw decimal point or exponent; can't have another one.Hex and octal numbers must be integers."/*" inside block comment. Block comments cannot be nested.End-of-file inside block comment. Comment started here./Invalid control characters encountered in text.Need space between identifier and decimal point.Interpreting non ascii codepoint %d.Proto file starts with 0xEF but not UTF-8 BOM. Only UTF-8 is accepted for proto file.}])/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/tokenizer.cc Tokenizer::ParseFloat() passed text that could not have been tokenized as a float: \U%08xCHECK failed: IsHeadSurrogate(head_surrogate): CHECK failed: IsTrailSurrogate(trail_surrogate): Tokenizer::ParseStringAppend() passed text that could not have been tokenized as a string: vector::_M_insert_auxN6google8protobuf2io14ErrorCollectorE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/zero_copy_stream_impl.ccclose() failed: CHECK failed: !is_closed_: Can't BackUp() after failed Next().CHECK failed: (final_byte_count) < (target_byte_count): N6google8protobuf2io19LimitingInputStreamEN6google8protobuf2io24ConcatenatingInputStreamEN6google8protobuf2io19OstreamOutputStreamEN6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamEN6google8protobuf2io18IstreamInputStreamEN6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamEN6google8protobuf2io16FileOutputStreamEN6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamEN6google8protobuf2io15FileInputStreamEN6google8protobuf2io15FileInputStream22CopyingFileInputStreamE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/hash.hShould never be called./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map.hProtocol Buffer map usage error: MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetInt64Value type does not match Expected : Actual : MapKey::GetUInt64ValueMapKey::GetInt32ValueMapKey::GetUInt32ValueMapKey::GetBoolValueMapKey::GetStringValueUnsupported: type mismatchUnsupportedCan't get here.MapValueRef::GetInt64ValueMapValueRef::GetUInt64ValueMapValueRef::GetInt32ValueMapValueRef::GetUInt32ValueMapValueRef::GetBoolValueMapValueRef::GetEnumValueMapValueRef::GetStringValueMapValueRef::GetFloatValueMapValueRef::GetDoubleValueMapValueRef::GetMessageValueMapValueRef::type MapValueRef is not initialized./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field.ccCHECK failed: entry_descriptor_ != NULL: CHECK failed: assign_descriptor_callback_ != NULL: valuekeyvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)vector::_M_insert_auxCHECK failed: (iterator_style_) != (kUnknown): CHECK failed: (bucket_index_ & 1) == (0): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): Requested size is too large to fit into size_t.CHECK failed: (it.m_) == (this): CHECK failed: TableEntryIsNonEmptyList(b): CHECK failed: TableEntryIsTree(b): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: table_[b] == table_[b + 1] && (b & 1) == 0: CHECK failed: node_ != NULL && m_ != NULL: CHECK failed: m_->index_of_first_non_null_ == m_->num_buckets_ || m_->table_[m_->index_of_first_non_null_] != NULL: CHECK failed: !tree->empty(): CHECK failed: (table_[b]) == (table_[b ^ 1]): CHECK failed: (bucket_index_ % 2) == (0): CHECK failed: index_of_first_non_null_ == num_buckets_ || table_[index_of_first_non_null_] != NULL: CHECK failed: find(*KeyPtrFromNodePtr(node)) == end(): CHECK failed: (result.bucket_index_) == (b & ~static_cast(1)): CHECK failed: n >= kMinTableSize: CHECK failed: (n & (n - 1)) == (0): CHECK failed: (new_num_buckets) >= (kMinTableSize): CHECK failed: (count) <= (kMaxLength): CHECK failed: !TableEntryIsTree(b) && !TableEntryIsTree(b ^ 1): CHECK failed: (count) == (tree->size()): N6google8protobuf8internal15DynamicMapFieldEN6google8protobuf8internal12MapFieldBaseE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field_inl.hCHECK failed: map_iter->iter_ != NULL: keyvalue/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/reflection_internal.hCHECK failed: this == other_mutator: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/message.ccCHECK failed: (from.GetDescriptor()) == (descriptor): : Tried to merge from a message with a different type. to: , from: : Tried to copy from a message with a different type. to: , CHECK failed: IsInitialized(): Message of type "" is missing required fields: Message class "" implements neither SetCachedSize() nor ByteSize(). Must implement one or the other.Unimplemented EnumValue API.Unimplemented Map Reflection API.File is already registered: CHECK failed: (descriptor->file()->pool()) == (DescriptorPool::generated_pool()): Tried to register a non-generated type with the generated type registry.Type is already registered: File appears to be in generated pool but wasn't registered: Type appears to be in generated pool but wasn't registered: Not implemented.CHECK failed: field->is_repeated(): Should not reach here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): CHECK failed: (n) >= (0): CHECK failed: (current_size_) > (0): CHECK failed: other->GetArenaNoVirtual() != GetArenaNoVirtual(): CHECK failed: (&other) != (this): N6google8protobuf8internal21RepeatedFieldAccessorEN6google8protobuf14MessageFactoryEN6google8protobuf10ReflectionEN6google8protobuf7MessageE*N6google8protobuf12_GLOBAL__N_123GeneratedMessageFactoryE --CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::Message*; From = const google::protobuf::MessageLite]/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/reflection_ops.ccCHECK failed: (&from) != (to): CHECK failed: (to->GetDescriptor()) == (descriptor): Tried to merge messages of different types (merge to )([].vector::_M_insert_aux --N6google8protobuf10RpcChannelEN6google8protobuf13RpcControllerEN6google8protobuf7ServiceE--google/protobuf/source_context.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/source_context.pb.ccCHECK failed: file != NULL: $google/protobuf/source_context.protogoogle.protobuf"" SourceContext file_name ( BR com.google.protobufBSourceContextProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.SourceContext.file_nameCHECK failed: &from != this: N6google8protobuf13SourceContextE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/hash.hShould never be called./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map.hProtocol Buffer map usage error: MapKey::type MapKey is not initialized. Call set methods to initialize MapKey.MapKey::GetStringValue type does not match Expected : Actual : google/protobuf/struct.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/struct.pb.ccCHECK failed: file != NULL: google/protobuf/struct.protogoogle.protobuf" Struct3 fields ( 2#.google.protobuf.Struct.FieldsEntryE FieldsEntry key ( % value ( 2.google.protobuf.Value:8" Value0 null_value (2.google.protobuf.NullValueH number_value (H string_value ( H bool_value (H/ struct_value ( 2.google.protobuf.StructH0 list_value ( 2.google.protobuf.ListValueHB kind"3 ListValue& values ( 2.google.protobuf.Value* NullValue NULL_VALUEB com.google.protobufB StructProtoPZ1github.com/golang/protobuf/ptypes/struct;structpbGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Struct.FieldsEntry.keyCHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): google.protobuf.Value.string_value/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hi >= 0array_ != NULLptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (iterator_style_) != (kUnknown): CHECK failed: (bucket_index_ & 1) == (0): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hdynamic_cast(&f) != NULLf == NULL || dynamic_cast(f) != NULLCHECK failed: (&other) != (this): CHECK failed: node_ != NULL && m_ != NULL: CHECK failed: m_->index_of_first_non_null_ == m_->num_buckets_ || m_->table_[m_->index_of_first_non_null_] != NULL: CHECK failed: !tree->empty(): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_entry_lite.hCHECK failed: default_instance_ != NULL: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): Requested size is too large to fit into size_t.CHECK failed: (it.m_) == (this): CHECK failed: TableEntryIsNonEmptyList(b): CHECK failed: TableEntryIsTree(b): CHECK failed: table_[b] == table_[b + 1] && (b & 1) == 0: CHECK failed: (table_[b]) == (table_[b ^ 1]): CHECK failed: index_of_first_non_null_ == num_buckets_ || table_[index_of_first_non_null_] != NULL: CHECK failed: find(*KeyPtrFromNodePtr(node)) == end(): CHECK failed: (result.bucket_index_) == (b & ~static_cast(1)): CHECK failed: (bucket_index_ % 2) == (0): CHECK failed: n >= kMinTableSize: CHECK failed: (n & (n - 1)) == (0): CHECK failed: (new_num_buckets) >= (kMinTableSize): CHECK failed: (count) <= (kMaxLength): CHECK failed: !TableEntryIsTree(b) && !TableEntryIsTree(b ^ 1): CHECK failed: (count) == (tree->size()): N6google8protobuf9ListValueEN6google8protobuf5ValueEN6google8protobuf6StructE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_entry.hCHECK failed: (&from) != (this): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/map_field_inl.hCHECK failed: default_entry_ != NULL: CHECK failed: MapFieldBase::repeated_field_ != NULL: CHECK failed: map_iter->iter_ != NULL: CHECK failed: *MapFieldBase::entry_descriptor_ != NULL: To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = const google::protobuf::MessageLite]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::internal::MapField, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = google::protobuf::internal::MapFieldLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]To google::protobuf::internal::down_cast(From&) [with To = const google::protobuf::internal::MapField, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>&; From = const google::protobuf::internal::MapFieldLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]To google::protobuf::internal::down_cast(From*) [with To = const google::protobuf::internal::MapEntry, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = const google::protobuf::Message]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::internal::MapEntry, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>*; From = google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()C& google::protobuf::internal::scoped_array::operator[](std::ptrdiff_t) const [with C = const google::protobuf::MapPair, google::protobuf::Value>*; std::ptrdiff_t = int]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::internal::MapEntryLite, google::protobuf::Value, (google::protobuf::internal::WireFormatLite::FieldType)9u, (google::protobuf::internal::WireFormatLite::FieldType)11u, 0>]46<=<=--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/substitute.ccstrings::Substitute format string invalid: asked for "$", but only args were given. Full format string was: "".Invalid strings::Substitute() format string: "CHECK failed: (target - output->data()) == (output->size()):  --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUPsize_t to int conversion%s/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/text_format.ccCHECK failed: trees: Index must be in range of repeated field values. Field: Index must be -1 for singular fields.Error parsing text-format : :Warning parsing text-format >}<{[]Non-repeated Any specified multiple times.Extension "" is not defined or is not an extension of "".Message type "" has no field named "CHECK failed: allow_unknown_field_: Non-repeated field "" is specified multiple times.Field "" is specified along with field "", another member of oneof ",;text format contains deprecated field ""trueTruetfalseFalsefInvalid value for boolean field "". Value: "-Expected integer or identifier, got: Unknown enumeration value of "" for field "Reached an unintended state: CPPTYPE_MESSAGEinfinfinitynanInvalid float number: Expected identifier, got: .Expected string, got: Expected integer, got: Integer out of range ()Expect a decimal number, got: Expected double, got: /TextFormat::Parser for Any supports only type.googleapis.com and type.googleprod.com, but found "Could not find type "" stored in google.protobuf.Any.Value of type "" stored in google.protobuf.Any has missing required fieldsExpected "", found " Outdent() without matching Indent()., Message missing required fields: { { } } CHECK failed: output: output specified is NULLProto type not found: failed to parse contentsInvalid key for map field. : [] ] CHECK failed: field->is_repeated() || (index == -1): Index must be -1 for non-repeated fields......CHECK failed: (field->type()) == (FieldDescriptor::TYPE_BYTES): %d: 0x: "" " /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::_M_insert_auxN6google8protobuf10TextFormat6FinderEN6google8protobuf10TextFormat17FieldValuePrinterE*N6google8protobuf12_GLOBAL__N_129FieldValuePrinterUtf8EscapingE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::Message]google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constgoogle/protobuf/timestamp.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/timestamp.pb.ccCHECK failed: file != NULL: google/protobuf/timestamp.protogoogle.protobuf"+ Timestamp seconds ( nanos (B~ com.google.protobufBTimestampProtoPZ+github.com/golang/protobuf/ptypes/timestampGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): N6google8protobuf9TimestampE--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/io/coded_stream.hrecursion_budget_ < recursion_limit_google/protobuf/type.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/type.pb.ccCHECK failed: file != NULL: google/protobuf/type.protogoogle.protobufgoogle/protobuf/any.proto$google/protobuf/source_context.proto" Type name ( & fields ( 2.google.protobuf.Field oneofs ( ( options ( 2.google.protobuf.Option6 source_context ( 2.google.protobuf.SourceContext' syntax (2.google.protobuf.Syntax" Field) kind (2.google.protobuf.Field.Kind7 cardinality (2".google.protobuf.Field.Cardinality number ( name (  type_url (  oneof_index ( packed (( options ( 2.google.protobuf.Option json_name (  default_value ( " Kind TYPE_UNKNOWN TYPE_DOUBLE TYPE_FLOAT TYPE_INT64 TYPE_UINT64 TYPE_INT32 TYPE_FIXED64 TYPE_FIXED32 TYPE_BOOL TYPE_STRING  TYPE_GROUP  TYPE_MESSAGE  TYPE_BYTES  TYPE_UINT32  TYPE_ENUM TYPE_SFIXED32 TYPE_SFIXED64 TYPE_SINT32 TYPE_SINT64"t Cardinality CARDINALITY_UNKNOWN CARDINALITY_OPTIONAL CARDINALITY_REQUIRED CARDINALITY_REPEATED" Enum name ( - enumvalue ( 2.google.protobuf.EnumValue( options ( 2.google.protobuf.Option6 source_context ( 2.google.protobuf.SourceContext' syntax (2.google.protobuf.Syntax"S EnumValue name (  number (( options ( 2.google.protobuf.Option"; Option name ( # value ( 2.google.protobuf.Any*. Syntax SYNTAX_PROTO2 SYNTAX_PROTO3BL com.google.protobufB TypeProtoPGPBGoogle.Protobuf.WellKnownTypesbproto3google.protobuf.Type.namegoogle.protobuf.Type.oneofsCHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual():   google.protobuf.Field.namegoogle.protobuf.Field.type_urlgoogle.protobuf.Field.json_namegoogle.protobuf.Field.default_valuegoogle.protobuf.Enum.namegoogle.protobuf.EnumValue.namegoogle.protobuf.Option.name/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (n) >= (0): CHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): CHECK failed: (&other) != (this): N6google8protobuf6OptionEN6google8protobuf9EnumValueEN6google8protobuf4EnumEN6google8protobuf5FieldEN6google8protobuf4TypeE--8(<@(,048 $(,0 void google::protobuf::io::CodedInputStream::UnsafeDecrementRecursionDepth()/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.ccCHECK failed: fields_ != NULL && fields_->size() > 0: CHECK failed: (TYPE_LENGTH_DELIMITED) == (type()): vector::_M_insert_auxvector::_M_fill_insert--/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/field_comparator.ccNo comparison code for field of CppType = CHECK failed: FieldDescriptor::CPPTYPE_FLOAT == field->cpp_type() || FieldDescriptor::CPPTYPE_DOUBLE == field->cpp_type(): Field has to be float or double type. Field name is: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/mathutil.hCHECK failed: (T(0) < fraction || T(0) == fraction) && fraction < T(1) && margin >= T(0): N6google8protobuf4util22DefaultFieldComparatorEN6google8protobuf4util15FieldComparatorE--size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_,./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/field_mask_util.ccCHECK failed: !node->children.empty(): Cannot find field "" in message Field " is not a singular message field and cannot have sub-fields.CHECK failed: source.GetDescriptor() == destination->GetDescriptor(): 'destination' must not be NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/logging.h/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_):  --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < size_truefalseInfinity-InfinityNaNWrong type. Cannot convert to Bool.Cannot convert to string."nullInvalid data in input.Wrong type. Only String or Bytes can be converted to Bytes.Cannot find enum with given value.=Wrong type. Bool, Enum, String and Cord not supported in GenericConvert.NULL is not a vaild argument.Status::OK is not a valid argument. --char google::protobuf::internal::StringPiecePod::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/default_value_objectwriter.ccCannot resolve type ''.Could not find enum with type ''@typeFailed to resolve type '/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): vector::_M_insert_auxvector::_M_range_insertN6google8protobuf4util9converter24DefaultValueObjectWriterE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::DefaultValueObjectWriter::Node]--size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_[".Invalid FieldMask ''. Map keys should be represented as ["some_key"].'. Map keys should be at the end of a path segment.'. Cannot find matching '(' for all ')'.'. Cannot find matching ']' for all '['.'. Cannot find matching ')' for all '('. --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < length_\udead\ubee0123456789abcdef\u0000\u0001\u0002\u0003\u0004\u0005\u0006\u0007\b\t\n\u000b\f\r\u000e\u000f\u0010\u0011\u0012\u0013\u0014\u0015\u0016\u0017\u0018\u0019\u001a\u001b\u001c\u001d\u001e\u001f\"\u003c\u003e\\\u007f\u0080\u0081\u0082\u0083\u0084\u0085\u0086\u0087\u0088\u0089\u008a\u008b\u008c\u008d\u008e\u008f\u0090\u0091\u0092\u0093\u0094\u0095\u0096\u0097\u0098\u0099\u009a\u009b\u009c\u009d\u009e\u009f--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::set(const char*, google::protobuf::stringpiece_ssize_type)size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/json_objectwriter.ccJsonObjectWriter was not fully closed.truefalsenull":/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLN6google8protobuf4util9converter16JsonObjectWriterE --C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::JsonObjectWriter::Element]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::JsonObjectWriter::Element*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 00 <= ii < length_length_ >= nEncountered non UTF-8 code points.Parsing terminated before end of input.Unknown parse type: Expected a value.Unexpected token./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/json_stream_parser.ccCHECK failed: string_open_ == '\"' || string_open_ == '\'': Closing quote expected in string.Illegal hex string.CHECK failed: ('\\') == (p_.data()[0]): CHECK failed: ('u') == (p_.data()[1]): Invalid escape sequence.Missing low surrogate.Invalid low surrogate.Invalid unicode code point.Unable to parse number.Octal/hex numbers are not valid JSON values.CHECK failed: ('{') == (*p_.data()): Expected , or } after key:value pair.Expected an object key or }.Expected : between key:value pair.CHECK failed: ('[') == (*p_.data()): Expected a value or ] within an array.Expected , or ] after array value. Unexpected end of string. Invalid key or variable name.truefalsenullN6google8protobuf4util9converter16JsonStreamParserE --char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constgoogle::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)void google::protobuf::StringPiece::remove_prefix(google::protobuf::stringpiece_ssize_type)size_t to int conversion--size_t to int conversion.[""][]Root element should not be named.Missing descriptor for field: oneofoneof field '' is already set. Cannot set ''Proto field is not repeating, cannot start list.Root element must be a message.Proto fields must have a name.Cannot find field./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/proto_writer.ccCHECK failed: !done_: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLN6google8protobuf4util9converter11ProtoWriterEN6google8protobuf4util9converter11ProtoWriter12ProtoElementE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::StructuredObjectWriter::BaseElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::io::CodedOutputStream]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::ProtoWriter::ProtoElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::LocationTrackerInterface]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]size_t to int conversionfalse0Invalid map key type./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/protostream_objectsource.ccInput stream is NULL.Invalid map entry.Timestamp seconds exceeds limit for field: Timestamp nanos exceeds limit for field: Duration seconds exceeds limit for field: Duration nanos exceeds limit for field: Duration nanos is non-negative, but seconds is negative for field: -%s%lld%ss@typeInvalid Any, the type_url is missing.valuepathsInvalid FieldMask, unexpected field.,google.protobuf.Timestampgoogle.protobuf.Durationgoogle.protobuf.DoubleValuegoogle.protobuf.FloatValuegoogle.protobuf.Int64Valuegoogle.protobuf.UInt64Valuegoogle.protobuf.Int32Valuegoogle.protobuf.UInt32Valuegoogle.protobuf.BoolValuegoogle.protobuf.StringValuegoogle.protobuf.BytesValuegoogle.protobuf.Anygoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.FieldMaskInvalid configuration. Could not find the type: Nested protocol message not parsed in its entirety.truegoogle.protobuf.MessageOptions.map_entrymap_entryMessage too deep. Max recursion depth reached for type '', field ''%.6f%.3f%.9fNULL is not a vaild argument.Status::OK is not a valid argument./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): N6google8protobuf4util9converter23ProtoStreamObjectSourceE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/datapiece.hNot a string type.00123456789valueAnyExpect a "value" field for well-known types./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/protostream_objectwriter.ccMismatched EndList found, should not be possible@typeExpect a JSON object.StringMissing @type for any field in fieldsstruct_valueCannot start root message with ListValue.keyRoot element should not be named.list_valuevaluesMapCannot have repeated items ('') within a map.Proto field is not repeating, cannot start list.Cannot bind a list to map for field ''.string_valuenumber_valuebool_valuenull_valueInvalid struct data type. Only number, string, boolean or null values are supported.Invalid data type for timestamp, value is Invalid time format: secondsnanospathsInvalid data type for field mask, value is Invalid data type for duration, value is sIllegal duration format; duration must end with 's'-Invalid duration format, failed to parse secondsInvalid duration format, failed to parse nano secondsDuration value exceeds limitsRoot element must be a message.Field '', Map does not have a value field.type.googleapis.com/google.protobuf.Timestamptype.googleapis.com/google.protobuf.Durationtype.googleapis.com/google.protobuf.FieldMasktype.googleapis.com/google.protobuf.Doubletype.googleapis.com/google.protobuf.Floattype.googleapis.com/google.protobuf.Int64type.googleapis.com/google.protobuf.UInt64type.googleapis.com/google.protobuf.Int32type.googleapis.com/google.protobuf.UInt32type.googleapis.com/google.protobuf.Booltype.googleapis.com/google.protobuf.Stringtype.googleapis.com/google.protobuf.Bytestype.googleapis.com/google.protobuf.DoubleValuetype.googleapis.com/google.protobuf.FloatValuetype.googleapis.com/google.protobuf.Int64Valuetype.googleapis.com/google.protobuf.UInt64Valuetype.googleapis.com/google.protobuf.Int32Valuetype.googleapis.com/google.protobuf.UInt32Valuetype.googleapis.com/google.protobuf.BoolValuetype.googleapis.com/google.protobuf.StringValuetype.googleapis.com/google.protobuf.BytesValuetype.googleapis.com/google.protobuf.ValueRepeated map key: '' is already set.google.protobuf.MessageOptions.map_entrymap_entry/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULL/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/casts.hf == NULL || dynamic_cast(f) != NULLvector::_M_insert_auxN6google8protobuf4util9converter23ProtoStreamObjectWriterE type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::StructuredObjectWriter::BaseElement*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoStreamObjectWriter]google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::ProtoStreamObjectWriter::Item]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::ProtoWriter::ProtoElement]C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::util::converter::LocationTrackerInterface]To google::protobuf::internal::down_cast(From*) [with To = google::protobuf::util::converter::ProtoStreamObjectWriter::Item*; From = google::protobuf::util::converter::StructuredObjectWriter::BaseElement]size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/type_info.ccField '' and '' map to the same camel case name ''.NULL is not a vaild argument.Status::OK is not a valid argument./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_): *N6google8protobuf4util9converter12_GLOBAL__N_123TypeInfoForTypeResolverE --size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/type_info_test_helper.ccCHECK failed: pool == descriptors[i]->file()->pool(): Descriptors from different pools are not supported.Can not reach here./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::_M_insert_aux type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::util::converter::TypeInfo]size_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.h0 <= ii < length_/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/internal/utility.ccCHECK failed: i == str.size() || !isspace(str[i]): /google.protobuf.Timestampgoogle.protobuf.Durationgoogle.protobuf.DoubleValuegoogle.protobuf.FloatValuegoogle.protobuf.Int64Valuegoogle.protobuf.UInt64Valuegoogle.protobuf.Int32Valuegoogle.protobuf.UInt32Valuegoogle.protobuf.BoolValuegoogle.protobuf.StringValuegoogle.protobuf.BytesValuegoogle.protobuf.FieldMasktruefalse10google.protobuf.MessageOptions.map_entrygoogle.protobuf.MessageOptions.message_set_wire_formatInfinity-InfinityNaN/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (index) >= (0): CHECK failed: (index) < (current_size_):  type.googleapis.com%E4Y-%m-%dT%H:%M:%S%Y-%m-%dT%H:%M:%S nA:b4yIʚ;type.googleapis.com/google.protobuf.NullValuegoogle.protobuf.Structgoogle.protobuf.Valuegoogle.protobuf.ListValuegoogle.protobuf.Timestampgoogle.protobuf.Durationtype.googleapis.com/google.protobuf.Valuetype.googleapis.com/google.protobuf.Structgoogle.protobuf.Anytype.googleapis.com/google.protobuf.FieldMask--char google::protobuf::StringPiece::operator[](google::protobuf::stringpiece_ssize_type) constsize_t to int conversion/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/stringpiece.hlen >= 0 : : invalid value for type : missing field type.googleapis.com/JSON transcoder produced invalid protobuf output.N6google8protobuf4util8internal22ZeroCopyStreamByteSinkE*N6google8protobuf4util12_GLOBAL__N_119StatusErrorListenerE --google::protobuf::StringPiece::StringPiece(const char*, google::protobuf::stringpiece_ssize_type)/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUP/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/message_differencer.hCHECK failed: false: IsMatch() is not implemented./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/message_differencer.ccCHECK failed: !key_field_paths_.empty(): CHECK failed: !key_field_paths_[i].empty(): CHECK failed: comparator: Field comparator can't be NULL.CHECK failed: field->is_repeated(): Field must be repeated: CHECK failed: key_comparator == NULL: Cannot treat this repeated field as both Map and Set for comparison. Field name is: CHECK failed: list_fields_.find(field) == list_fields_.end(): Cannot treat the same field as both SET and LIST. Field name is: CHECK failed: set_fields_.find(field) == set_fields_.end(): CHECK failed: (FieldDescriptor::CPPTYPE_MESSAGE) == (field->cpp_type()): Field has to be message type. Field name is: CHECK failed: key->containing_type() == field->message_type(): must be a direct subfield within the repeated field , not Cannot treat this repeated field as both Map and Set for comparison.Cannot treat this repeated field as both Map and List for CHECK failed: child_field->containing_type() == parent_field->message_type(): must be a direct subfield within the field: CHECK failed: (FieldDescriptor::CPPTYPE_MESSAGE) == (parent_field->cpp_type()): has to be of type message.CHECK failed: !parent_field->is_repeated(): cannot be a repeated field.CHECK failed: output: Specified output string was NULLComparison between two messages with different descriptors.descriptors. vs Proto type '' not foundFailed to parse value for .($name$)name[$name$]{ }{ $name$ }CHECK failed: unknown_field != NULL: Cannot print NULL unknown_field.0x"%s"{ ... }added: : deleted: modified: -> moved: : matched: ignored: /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/stubs/scoped_ptr.hptr_ != NULLvector::reservevector::_M_insert_auxvector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu)N6google8protobuf4util18MessageDifferencer14StreamReporterEN6google8protobuf4util18MessageDifferencer14IgnoreCriteriaEN6google8protobuf4util18MessageDifferencer16MapKeyComparatorEN6google8protobuf4util18MessageDifferencer8ReporterE --C& google::protobuf::internal::scoped_ptr::operator*() const [with C = google::protobuf::Message]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::DynamicMessageFactory]C* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::Message]google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constC* google::protobuf::internal::scoped_ptr::operator->() const [with C = google::protobuf::ResultCallback2]/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/time_util.ccCHECK failed: seconds >= TimeUtil::kTimestampMinSeconds && seconds <= TimeUtil::kTimestampMaxSeconds: CHECK failed: seconds >= TimeUtil::kDurationMinSeconds && seconds <= TimeUtil::kDurationMaxSeconds: %03d%06d%09d nA:yIb4-.s0 ʚ;@B@B<%E4Y-%m-%dT%H:%M:%S--size_t to int conversion/Invalid type URL, type URLs must be of the form '/', got: Cannot resolve types from URL: Invalid type URL, unknown type: map_entrytruefalse/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/util/type_resolver_util.ccMessages can't have default values!*N6google8protobuf4util12_GLOBAL__N_126DescriptorPoolTypeResolverE --/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/unknown_field_set.hCHECK failed: fields_ != NULL: type() == TYPE_VARINTtype() == TYPE_FIXED32type() == TYPE_FIXED64type() == TYPE_LENGTH_DELIMITEDtype() == TYPE_GROUPCHECK failed: (TYPE_LENGTH_DELIMITED) == (type()): /home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wire_format.ccExtensions of MessageSets must be optional messages.CHECK failed: (output->ByteCount()) == (expected_endpoint): : Protocol message serialized to a size different from what was originally expected. Perhaps it was modified by another thread during serialization?/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/repeated_field.hCHECK failed: (static_cast(new_size)) <= ((std::numeric_limits::max() - kRepHeaderSize) / sizeof(Element)): Requested size is too large to fit into size_t./home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/arena.hCHECK failed: (num_elements) <= (std::numeric_limits::max() / sizeof(T)): N6google8protobuf8internal27UnknownFieldSetFieldSkipperE--google::protobuf::uint64 google::protobuf::UnknownField::varint() constgoogle::protobuf::uint32 google::protobuf::UnknownField::fixed32() constgoogle::protobuf::uint64 google::protobuf::UnknownField::fixed64() constconst string& google::protobuf::UnknownField::length_delimited() constconst google::protobuf::UnknownFieldSet& google::protobuf::UnknownField::group() constgoogle/protobuf/wrappers.proto/home/fursin/CK/local/env/cee3fdac8c24b118/protobuf-3.1.0/src/google/protobuf/wrappers.pb.ccCHECK failed: file != NULL: google/protobuf/wrappers.protogoogle.protobuf" DoubleValue value (" FloatValue value (" Int64Value value (" UInt64Value value (" Int32Value value (" UInt32Value value ( " BoolValue value (" StringValue value ( " BytesValue value ( B| com.google.protobufB WrappersProtoPZ*github.com/golang/protobuf/ptypes/wrappersGPBGoogle.Protobuf.WellKnownTypesbproto3CHECK failed: &from != this: CHECK failed: GetArenaNoVirtual() == other->GetArenaNoVirtual(): google.protobuf.StringValue.valueN6google8protobuf10BytesValueEN6google8protobuf11StringValueEN6google8protobuf9BoolValueEN6google8protobuf11UInt32ValueEN6google8protobuf10Int32ValueEN6google8protobuf11UInt64ValueEN6google8protobuf10Int64ValueEN6google8protobuf10FloatValueEN6google8protobuf11DoubleValueE--N10__cxxabiv117__array_type_infoESt9bad_allocSt16bad_array_lengthSt20bad_array_new_lengthSt8bad_castSt10bad_typeidN10__cxxabiv117__class_type_infoETLLLLTTTTLT""""""""LT"PLTL""PTTLLLLLLLLTTTTTLL*>>>L**$$>>>>>>>>>>>>>>>*>>>>>>>>>>00>>*>>>>>>>>>*$>>>>>>>>>>>>>**>,,,,*>>>>><****@<66***************<**************<*********<6*************<****<*<**<<bbbbbbb bTbp|bbbbbb">bbbbbbbbbbbbbb06666666666666r666666666666 666F 6666666r6|xtp>VBFFFFFFFFFFF|FdPFFFFFFFFFFF@FFFFFFFFFFFFFFFFF,++*4*111~)0)40%%%x%d%,(((''''&F)F)F)0)0)0)0)0)0)0)((0)0)B(<)%$#p.#--X/*--4,,,43///t3R%0%2*23&j#4"1"!B! DDDDEPTTTTTTTTTTTTTTTTTTTTTPPPQPPPQQRtRRRRR8STTTTST,PN9__gnu_cxx24__concurrence_lock_errorEN9__gnu_cxx26__concurrence_unlock_errorEN10__cxxabiv115__forced_unwindEN10__cxxabiv119__foreign_exceptionESt9exceptionSt13bad_exceptionN10__cxxabiv116__enum_type_infoEN10__cxxabiv120__function_type_infoEvPvPKvbPbPKbwPwPKwDsPDsPKDsDiPDiPKDicPcPKcaPaPKahPhPKhsPsPKstPtPKtiPiPKijPjPKjlPlPKlmPmPKmxPxPKxyPyPKyfPfPKfdPdPKdePePKeDfPDfPKDfDdPDdPKDdDePDePKDeDnPDnPKDnN10__cxxabiv123__fundamental_type_infoEN9__gnu_cxx20recursive_init_errorESt16nested_exceptionN10__cxxabiv117__pbase_type_infoEN10__cxxabiv129__pointer_to_member_type_infoEN10__cxxabiv119__pointer_type_infoEN10__cxxabiv120__si_class_type_infoESt9type_infoN10__cxxabiv121__vmi_class_type_infoESt7codecvtIwc9mbstate_tESt7codecvtIcc9mbstate_tESt10ctype_baseSt5ctypeIcEDSt5ctypeIwESt12ctype_bynameIwE St12ctype_bynameIcE "7Yyb= Um*/Eo %1Bs( ݊=!5W̌yp)pT~m 8 ݥ)?C_sm$ "7Yyb= Um*/Eo %1Bs( ݊=!5W̌yp)pT~m 8 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__gnu_debug::_Error_formatter::_M_print_string(const char*) constvoid __gnu_debug::_Error_formatter::_M_error() constvoid __gnu_debug::_Error_formatter::_Parameter::_M_print_field(const __gnu_debug::_Error_formatter*, const char*) constSt13basic_fstreamIwSt11char_traitsIwEESt13basic_filebufIwSt11char_traitsIwEESt14basic_ofstreamIwSt11char_traitsIwEESt14basic_ifstreamIcSt11char_traitsIcEESt13basic_fstreamIcSt11char_traitsIcEESt13basic_filebufIcSt11char_traitsIcEESt14basic_ofstreamIcSt11char_traitsIcEESt14basic_ifstreamIwSt11char_traitsIwEESt17bad_function_call*N12_GLOBAL__N_121future_error_categoryENSt13__future_base12_Result_baseESt12future_error %)+/5;=CGIOSYagmq%9QgAk7wU eMW5 _A%]O"%!(i+.26E;@UEJQW^fnxǁ]ٗ;1q3L+hy?QAp[Wa ;U[DGqV1J A S C%piq@Ѻ h#=O&Yr)7,'0#|4W8n={vBGM)T [ǂbjMs|;wM slo%Qwہ(@[ zwm9a}zYjc,Zl5z8ox|gu*j*-+] ))VݿQPI K~ EZ CH 1I ]P`[_4M(O  ɝ1u+"]$'E+.%2K6խH;!}#@idEKu8QDWd_^fFoCLcx?)錽s+qQ Kx 5    5&   @4 4  ? qelV, ߰p 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F ݞ}$y$ 8intjMXCD Ǟ m ` Y  F ݞ}$y$m 8D(intjMXCD Ǟ m ` Y dV F ݞ}oD(ToTPlowTQUٿ aٿ[ٿOٿ  , y, 8intjMXCD Ǟ m ` Y  F ݞ}$y$ 8l8gintjMXCD?3@:nC Ǟ m ` Y  F ݞ}gMl8naMbMcOPxPySyPy zPy , y,^ 8HTintjMXCDAAOBH ǞE m ` Y  F ݞ}vMHnaMHbM[cOPxPySyPynzPy , y, 8(E intjMXCD Ǟ m `U%V,͟W Y  F ݞ}M(laMbMQcOP xPyPQ zP , y, 8X1!intjMXCD Ǟ m ` Y cOdVe F ݞ}MXxaM bM#cOPQxPTUyP;zPS , y, 8"intjMXCD Ǟ m ` Y  F ݞ}$y$W8lH"intjMXCDAAOBH Ǟ mG ` Y  F ݞ}MlHnaMbMcOPxPySyPyzPy , y,8(s#intjMXCD Ǟ m `U%V, YY  F ݞ}M(laMbMQcOP xPyPQ zP% , y,8X_$intjMXCD Ǟ m ` Y cOdV Fg ݞ}MXxaMRbMjcOPQxPTUyPzP , y,8M%intjMXCD Ǟ m ` Y  F ݞ}$y$P848%intjMXCD?3@:nC Ǟ m ` Y  F ݞ}Щ48nabcPxySyyzy , y,8lH&intjMXCDAAOBH ǞE m ` Y  F ݞ}ߩlHna:bMcPxySyy`zys , y,r8('intjMXCD Ǟ m `U%V,͟W Y  F ݞ}(labQcP xyQ z , y,8Xz(intjMXCD Ǟ m ` Y cOdVe F ݞ}XxabcPQxTUy-zE , y,8h)intjMXCD Ǟ m ` Y  F ݞ}$y$84H)intjMXCDAAOBH Ǟ mG ` Y  F ݞ} 4HnabcPxySyyzy , y,k8|(*intjMXCD Ǟ m `U%V, YY  F ݞ}|(labQcP xyQ z , y, 8X+intjMXCD Ǟ m ` Y cOdV Fg ݞ}*XxaDb\cPQxTUytz , y,8,intjMXCD Ǟ m ` Y  F ݞ}$y$w8 ,intjMXCD?3@:nC Ǟ m ` Y  F ݞ}94 da4c6Px7y y7yz7yP , y,ws8(-intjMXCDAAOBH ǞE m ` Y  F ݞ}H4(da4c6Px7y y7y z7yP , y,w8D.intjMXCD Ǟ m `U%V,͟W Y  F ݞ}W4Dda4& c6x7 y79  z7Z  , y,~8X /intjMXCD Ǟ m ` Y cOdVe F ݞ}f4X ka4m c6RSx7 y7m  z7  , y,Q80intjMXCD Ǟ m ` Y  F ݞ}$y$w8x(0intjMXCDAAOBH Ǟ mG ` Y  F ݞ}u4x(da4 c6Px7y y7y z7yP , y,w(81intjMXCD Ǟ m `U%V, YY  F ݞ}4da4 c6x7 y7  z7 ! , y,~8 2intjMXCD Ǟ m ` Y cOdV Fg ݞ}4 ka4!c6RSx7 y7! z76! , y,f83intjMXCD Ǟ m ` Y  F ݞ}$y$t84intjMXCDnC Ǟ m ` Y  F ݞ}M%yEay^!byq![R1 , y, Wy y y tD84intjMXCD ǞE m ` Y  F ݞ}M%Ea!b![R1 , y,  t85intjMXCD Ǟ m `͟W Y  F ݞ}M%Ea!b![R1 , y,  u86intjMXCD Ǟ m ` Y e F ݞ}M%ϪFa!b!\}1 , y,  687intjMXCD Ǟ m ` Y  F ݞ}$y$tm88intjMXCD Ǟ mG ` Y  F ݞ}M%ުEa"b" [R1 , y,  t8 8intjMXCD Ǟ m ` YY  F ݞ}M% Ea&"b9"[R1 , y,  u89intjMXCD Ǟ m ` Y  Fg ݞ}M%FaL"bd"$\}1 , y,  _8:intjMXCD Ǟ m ` Y  F ݞ}$y$t8,;intjMXCDnC Ǟ m ` Y  F ݞ}M% y,Eay|"by"4[R1 , y, y y y t=84;intjMXCD ǞE m ` Y  F ݞ}M%4Ea"b"<[R1 , y,  t8<<intjMXCD Ǟ m `͟W Y  F ݞ}M%)<Ea"b"D[R1 , y,  u 8D=intjMXCD Ǟ m ` Y e F ݞ}M%8DFa"b#T\}1 , y, ޥ /!8>intjMXCD Ǟ m ` Y  F ݞ}$y$tf!8\?intjMXCD Ǟ mG ` Y  F ݞ}M%G\Ea#b1#d[R1 , y,  t "8d?intjMXCD Ǟ m ` YY  F ݞ}M%VdEaD#bW#l[R1 , y,  u"8l@intjMXCD Ǟ m ` Y  Fg ݞ}M%elFaj#b#|\}1 , y,  X#8AintjMXCD Ǟ m ` Y  F ݞ}$y$t#8BintjMXCDnC Ǟ m ` Y  F ݞ}M%tyEay#by#[R0 , y, y y y t6$8BintjMXCD ǞE m ` Y  F ݞ}M%Ea#b#[R0 , y,  t$8CintjMXCD Ǟ m `͟W Y  F ݞ}M%Ea#b#[R0 , y,  u%8DintjMXCD Ǟ m ` Y e F ݞ}M%Fa $b$$\}0 , y, ޥ (&8EintjMXCD Ǟ m ` Y  F ݞ}$y$t_&8FintjMXCD Ǟ mG ` Y  F ݞ}M%Ea<$bO$[R0 , y,  t'8FintjMXCD Ǟ m ` YY  F ݞ}M%Eab$bu$[R0 , y,  u'8GintjMXCD Ǟ m ` Y  Fg ݞ}M%«Fa$b$\}0 , y,  Q(8HintjMXCD Ǟ m ` Y  F ݞ}$y${(8IintjMXCDnC Ǟ m ` Y  F ݞ}M%ϫyLay$b$bR1QQ , y, y y {/)8IintjMXCD ǞE m ` Y  F ݞ}M%߫La$b$bR1QQ , y,  {)8JintjMXCD Ǟ m `͟W Y  F ݞ}M%La %b3%bR1QQ , y,  {}*8KintjMXCD Ǟ m ` Y e F ݞ}M%LaT%bl%bS1RR , y, ' $+8LintjMXCD Ǟ m ` Y  F ݞ}$y${[+8MintjMXCD Ǟ mG ` Y  F ݞ}M%La%b%bR1QQ , y, H {,8MintjMXCD Ǟ m ` YY  F ݞ}M%La%b% bR1QQ , y, i {,8 NintjMXCD Ǟ m ` Y  Fg ݞ}M%/ La%b &bS1RR , y, | P-8OintjMXCD Ǟ m ` Y  F ݞ}$y$x-8PintjMXCD?3nC Ǟ m ` Y  F ݞ}M%?ea.&bQc xy.& zyA& , y,x%.8$PintjMXCDAA ǞE m ` Y  F ݞ}M%M$ea]&bQc xy]& zyp& , y,x.84QintjMXCD Ǟ m `U%͟W Y  F ݞ}M%[4ea&bQc x& z& , y,za/8< RXintjMCD Ǟ m ` Y cVe F ݞ}M3i< ga&b&cx&z& , y,/8SintjMXCD Ǟ m ` Y  F ݞ}$y$x'08HTintjMXCDAA Ǟ mG ` Y  F ݞ}M%wHea'bQc xy' zy+' , y,x08XTintjMXCD Ǟ m `U% YY  F ݞ}M%XeaG'bQc xG' zZ' , y,zc18` UXintjMCD Ǟ m ` Y cV Fg ݞ}M3` ga}'b'cx}'z' , y,18VintjMXCD Ǟ m ` Y  F ݞ}$y$t)28l$WintjMXCD@: 1DǞ m ` Y  F ݞ}hl$aah'bh'cjxky' ykyQzky ( , y,t284XintjMXCDOBH Ǟ GFm ` Y  F ݞ}h4aah(bh2(cjxkyE( ykyQzkyX( , y,ve38$XintjMXCD Ǟ m `V, eXY  F ݞ}h$cahk(bh~(cjxkk(yk~(zk( , y,38HYintjMXCD Ǟ m ` Y dV fF ݞ}ѬhHsah(bhRScjPQxkVWykTU zk( , y,48ZintjMXCD Ǟ m ` Y  F ݞ}$y$t4804;[intjMXCDOBH Ǟ m `H Y  F ݞ}h04aah(bh)cjxky) ykyQzky-) , y,vi58d$*\intjMXCD Ǟ m `V, Y Z F ݞ}hd$cah@)bhS)cjxk@)ykS)zkf) , y,58H]intjMXCD Ǟ m ` Y dV F ݞh}hHsah)bhRScjPQxkVWykTU zk) , y,68^intjMXCD Ǟ m ` Y  F ݞ}$y$v68s^intjMXCD@: 1DǞ m ` Y  F ݞ}ca)b)cPxy)yyQzy* , y,v`78([_intjMXCDOBH Ǟ GFm ` Y  F ݞ}!(ca%*b8*cPxyK*yyQzy^* , y,t78E`intjMXCD Ǟ m `V, eXY  F ݞ}1aa*bQcP x*yQ z* , y,88$$+aXintjMCD Ǟ m ` Y d% fF ݞ}A$$pa*bRScPQ x*yRS z* 3 y3198bintjMXCD Ǟ m ` Y  F ݞ}$y$vh98H(tbintjMXCDOBH Ǟ m `H Y  F ݞ}QH(ca+b.+cPxyA+yyQzyT+ , y,t98p^cintjMXCD Ǟ m `V, Y Z F ݞ}apaaw+bQcP xw+yQ z+ , y,:8$DdXintjMCD Ǟ m ` Y d% F ݞh}q$pa+bRScPQ x+yRS z+ 3 y39;8*eintjMXCD Ǟ m ` Y  F ݞ}$y$Pp;8eintjMXCD@: 1DǞ m ` Y  F ݞ}K=aK,cMzNy , y,P;8pfintjMXCDOBH Ǟ GFm ` Y  F ݞ}K=aK$,cMzNy , y,P<8SgintjMXCD Ǟ m `V, eXY  F ݞ}K=aK7,cMzN , y,P=8 6hintjMXCD Ǟ m ` Y dV fF ݞ}K =aKJ,cMzN , y,=8iintjMXCD Ǟ m ` Y  F ݞ}$y$P=8|iintjMXCDOBH Ǟ m `H Y  F ݞ}K=aKb,cMzNy , y,Pr>8_jintjMXCD Ǟ m `V, Y Z F ݞ}ѭK=aKu,cMzN , y,P?8 BkintjMXCD Ǟ m ` Y dV F ݞh}K =aK,cMzN , y,?8%lintjMXCD Ǟ m ` Y  F ݞ}$y$t?8lintjMXCD 1DǞ m ` Y  F ݞ}M%Ea,b,[R1 , y, > tn@8kmintjMXCD Ǟ GFm ` Y  F ݞ}M%Ea,b,[R1 , y, _ tA8NnintjMXCD Ǟ m ` eXY  F ݞ}M%Ea,b,[R1 , y, r uA81ointjMXCD Ǟ m ` Y  fF ݞ}M%!Fa-b*- \}1 , y,  `B8pintjMXCD Ǟ m ` Y  F ݞ}$y$tB8zpintjMXCD Ǟ m `H Y  F ݞ}M%1EaB-bU-[R1 , y, £ t>C8]qintjMXCD Ǟ m ` Y Z F ݞ}M%AEah-b{-$[R1 , y,  uC8$@rintjMXCD Ǟ m ` Y  F ݞh}M%Q$Fa-b-4\}1 , y,  D8&sintjMXCD Ǟ m ` Y  F ݞ}$y$tD8<sintjMXCD 1DǞ m ` Y  F ݞ}M%a<Ea-b-D[R1 , y, 1 tgE8DltintjMXCD Ǟ GFm ` Y  F ݞ}M%qDEa-b-L[R1 , y, D tF8LOuintjMXCD Ǟ m ` eXY  F ݞ}M%LEa .b.T[R1 , y, W uF8T2vintjMXCD Ǟ m ` Y  fF ݞ}M%TFa0.bH.d\}1 , y, j YG8wintjMXCD Ǟ m ` Y  F ݞ}$y$tG8l{wintjMXCD Ǟ m `H Y  F ݞ}M%lEa`.bs.t[R1 , y, } t7H8t^xintjMXCD Ǟ m ` Y Z F ݞ}M%tEa.b.|[R1 , y,  uH8|AyintjMXCD Ǟ m ` Y  F ݞh}M%|Fa.b.\}1 , y,  I8'zintjMXCD Ǟ m ` Y  F ݞ}$y$tI8zintjMXCD 1DǞ m ` Y  F ݞ}M%Ѯ Ea .b .[R0 , y, 1 t`J8m{intjMXCD Ǟ GFm ` Y  F ݞ}M% Ea /b /[R0 , y, D tK8P|intjMXCD Ǟ m ` eXY  F ݞ}M% Ea (/b ;/[R0 , y, W uK83}intjMXCD Ǟ m ` Y  fF ݞ}M% Fa N/b f/\}0 , y, j RL8~intjMXCD Ǟ m ` Y  F ݞ}$y$tL8|~intjMXCD Ǟ m `H Y  F ݞ}M%  Ea ~/b /[R0 , y, } t0M8_intjMXCD Ǟ m ` Y Z F ݞ}M% Ea /b /[R0 , y,  uM8BintjMXCD Ǟ m ` Y  F ݞh}M%+ Fa /b /\}0 , y,  {N8(intjMXCD Ǟ m ` Y  F ݞ}$y${N8intjMXCD 1DǞ m ` Y  F ݞ}M%:La/b 0bR1QQ , y,  {YO8nintjMXCD Ǟ GFm ` Y  F ݞ}M%KLa.0bA0bR1QQ , y,  {P8QintjMXCD Ǟ m ` eXY  F ݞ}M%\Lab0bu0bR1QQ , y,  {P84intjMXCD Ǟ m ` Y  fF ݞ}M%mLa0b0 bS1RR , y,  NQ8intjMXCD Ǟ m ` Y  F ݞ}$y${Q8 zintjMXCD Ǟ m `H Y  F ݞ}M%~ La0b0bR1QQ , y, 8 {,R8]intjMXCD Ǟ m ` Y Z F ݞ}M%La1b1bR1QQ , y, L {R8@intjMXCD Ǟ m ` Y  F ݞh}M%La71bO1$bS1RR , y, o zS8#intjMXCD Ǟ m ` Y  F ݞ}$y$xS8$ intjMXCD@: 1DǞ m ` Y  F ݞ}M%$ eap1bQc xyp1 zy1 , y,xOT80jintjMXCDOBH Ǟ GFm ` Y  F ݞ}M%0ea1bQc xy1 zy1 , y,xT8@NintjMXCD Ǟ m `V, eXY  F ݞ}M%ϯ@ea1bQc x1 z1 , y,zU8H 3XintjMCD Ǟ m ` Y d% fF ݞ}M3ޯH ga2b2cx2z<2 , y,V8intjMXCD Ǟ m ` Y  F ݞ}$y$xQV8TzintjMXCDOBH Ǟ m `H Y  F ݞ}M%TeaY2bQc xyY2 zyl2 , y,xV8d^intjMXCD Ǟ m `V, Y Z F ݞ}M%dea2bQc x2 z2 , y,zW8l CXintjMCD Ǟ m ` Y d% F ݞh}M3 l ga2b2cx2z2 , y,X8'intjMXCD Ǟ m ` Y  F ݞ}$y$SX8x intjMXCD?3AAnC ǞE m ` Y  F ݞ}x qa3cxy3z'3 +% , y,X8rintjMXCD?3nC Ǟ m `U%͟W Y  F ݞ}8qaB3cxyB3zU3 +% , y,Y8WXjintMCD?,nC Ǟ m ` Y cVe F ݞ}Iqap3cxyp3z3 +38 : y:-Z8=intjMXCD Ǟ m ` Y  F ݞ}$y$dZ8 intjMXCD?3AAnC Ǟ mG ` Y  F ݞ}Z qa3cxy3z3 +% , y,[8 intjMXCD?3nC Ǟ m `U% YY  F ݞ}j qa3cxy3z3 +% , y,[8nXjintMCD?,nC Ǟ m ` Y cV Fg ݞ}zqa3cxy3z4 +3 : y:>\8VintjMXCD Ǟ m ` Y  F ݞ}$y$u\8 intjMXCD?3@:nC 1DǞ m ` Y  F ݞ} qa+4cxy+4z>4 +% , y,]8intjMXCD?3OBHnC Ǟ GFm ` Y  F ݞ}qaU4cxyU4zh4 +% , y,]8intjMXCD?3nC Ǟ m `V, eXY  F ݞ}qa4cxy4z4 +% , y,O^8mXjintMCD?,nC Ǟ m ` Y d% fF ݞ}qa4cxy4z4 +39 : y:^8SintjMXCD Ǟ m ` Y  F ݞ}$y$$_8 intjMXCD?3OBHnC Ǟ m `H Y  F ݞ}ΰ qa4cxy4z4 +% , y,_8 intjMXCD?3nC Ǟ m `V, Y Z F ݞ}߰ qa5cxy5z#5 +% , y,``8(XjintMCD?,nC Ǟ m ` Y d% F ݞh}(qa>5cxy>5zQ5 +3 : y:`8kintjMXCD Ǟ m ` Y  F ݞ}$y$a5a8D,ΜintjMXCD?3nC Ǟ m ` Y  F ݞ}hyD,Nahn5xjy5zkyily5 , y,la8p,intjMXCD?3AAnC Ǟ m ` Y  F ݞ}hp,Yah5xjy5zkily5 , y,lSb8,intjMXCD?3nC Ǟ m `U% Y  F ݞ}!h,Yah6xjy6zkily-6 , y,lb80intjMXCD?3nC Ǟ m ` Y cO F ݞ}1h0YahL6xjy_6zkilyw6 , y,qc8intjMXCD Ǟ m ` Y  F ݞ}$y$bc8intjMXCD?:nC Ǟ m ` Y  F ݞ}%AnOan6xp6zq63y3b*d8 СintjMXCD?:nC Ǟ m ` Y  F ݞ}%%Qn Oan6xp6zq63y3d84 intjMXCD?3AAnC ǞE m ` Y  F ݞ}a4 qa6cx7zy&7 +% , y,Je8@intjMXCDAA ǞE m `U%͟W Y  F ݞ}r@qaA7cxyA7zT7 +% , y,e8HXintjMCDA, ǞE m ` Y cVe F ݞ}Hqao7cxyo7z7 +30 : y:f8jintjMXCD Ǟ m ` Y  F ݞ}$y$yf8\ ͥintjMXCDAA ǞE mG ` Y  F ݞ}\ fa7cxy7zy7 +% , y,[g8h intjMXCDAA ǞE m `U% YY  F ݞ}h qa7cxy7z7 +% , y,g8tXintjMCDA, ǞE m ` Y cV Fg ݞ}tqa7cxy7z 8 +3 : y:h8intjMXCD Ǟ m ` Y  F ݞ}$y$h8 intjMXCD@:AA 1DǞE m ` Y  F ݞ}ı qa&8cx98zyP8 +% , y,li8 ЩintjMXCDAAOBH ǞE GFm ` Y  F ݞ}ձ qak8cxyk8z~8 +% , y, j8intjMXCDAA ǞE m `V, eXY  F ݞ}qa8cxy8z8 +% , y,j8XintjMCDA, ǞE m ` Y d% fF ݞ}qa8cxy8z8 +31 : y:Fk8intjMXCD Ǟ m ` Y  F ݞ}$y$}k8 intjMXCDAAOBH ǞE m `H Y  F ݞ} qa8cxy9z9 +% , y,l8 έintjMXCDAA ǞE m `V, Y Z F ݞ} qa9cxy9z19 +% , y,l8XintjMCDA, ǞE m ` Y d% F ݞh}*qaL9cxyL9z_9 +3 : y:Wm8intjMXCD Ǟ m ` Y  F ݞ}$y$lm8$intjMXCD?3AA ǞE m ` Y  F ݞ};hy$Yah|9xj9zkyil9 , y,an8$intjMXCDAA ǞE m ` Y  F ݞ}Khy$Nah9xjy9zkyily9 , y,ln8@$ױintjMXCDAA ǞE m `U% Y  F ݞ}[h@$Yah:xjy%:zkily>: , y,l;o8d(òintjMXCDAA ǞE m ` Y cO F ݞ}khd(Yah]:xjyp:zkily: , y,o8intjMXCD Ǟ m ` Y  F ݞ}$y$bp8intjMXCDAH ǞE m ` Y  F ݞ}%{nOan:xp:zq:3y3bp8 intjMXCDAH ǞE m ` Y  F ݞ}%%n Oan:xp:zq:3y3q8intjMXCD?3nC Ǟ m `U%͟W Y  F ݞ}qa;cx!;zy!; +% , y,q8ƶintjMXCDAA ǞE m `U%͟W Y  F ݞ}qa8;cxK;zyK; +% , y,Ar8XintjMCD Ǟ m `U,͟W Y cVe F ݞ}qab;cxb;zu; +, 3 y3r8intjMXCD Ǟ m ` Y  F ݞ}$y$s8intjMXCDAA Ǟ mG `U%͟W Y  F ݞ}βqa;cx;zy; +% , y,ys8ٹintjMXCD Ǟ m `U%͟W YY  F ݞ}޲fa;cx;z; +% , y,zRt8intjMXCD Ǟ m `U%͟W Y cO Fg ݞ}gaPcxz+% , y,t8intjMXCD Ǟ m ` Y  F ݞ}$y$u8intjMXCD@: 1DǞ m `U%͟W Y  F ݞ}qa<cx'<zy'< +% , y,u8 intjMXCDOBH Ǟ GFm `U%͟W Y  F ݞ} qa><cxQ<zyQ< +% , y,Tv8нintjMXCD Ǟ m `U%V,͟W eXY  F ݞ} qah<cxh<z{< +% , y,v8XintjMCD Ǟ m `U,͟W Y d% fF ݞ}1qa<cx<z< +,! 3 y3w8intjMXCD Ǟ m ` Y  F ݞ}$y$w80intjMXCDOBH Ǟ m `HU%͟W Y  F ݞ}B0qa<cx<zy= +% , y,ex88intjMXCD Ǟ m `U%V,͟W Y Z F ݞ}S8qa=cx-=z-= +% , y,|y8@intjMXCD Ǟ m `U%͟W Y dV F ݞh}d@iaO=cxz+% , y,y8intjMXCD Ǟ m ` Y  F ݞ}$y$py8P intjMXCD?3 Ǟ m `U%͟W Y  F ݞ}uhyP ]ahb=xju=zky=il=,y,pKz8p intjMXCDAA Ǟ m `U%͟W Y  F ݞ}hyp ]ah=xj=zky!>il;>,y,ez8 intjMXCD Ǟ m `U%͟W Y  F ݞ}h RahZ>xjm>zk>il>,y,pO{8$XintjMCD Ǟ m `U,͟W Y cV F ݞ}h$]ah>xj>zk$?ilB?3y3{8intjMXCD Ǟ m ` Y  F ݞ}$y$b|8intjMXCD Ǟ m `U,͟W Y  F ݞ}%nOana?xpa?zqt?3y3b|8intjMXCD Ǟ m `U,͟W Y  F ݞ}%%ųnOan?xp?zq?3y3| }8intjMXCD?3nC Ǟ m ` Y cOe F ݞ}ճia?cxzy+%8 , y,|}8intjMXCDAA ǞE m ` Y cOe F ݞ}ia?cxzy+%0 , y,*~8intjMXCD Ǟ m `U%͟W Y cOe F ݞ}ma?cQxzQ +% , y,~8intjMXCD Ǟ m ` Y  F ݞ}$y$|~8intjMXCDAA Ǟ mG ` Y cOe F ݞ}ia@cxzy+%8 , y,|8 intjMXCD Ǟ m `U% YY cOe F ݞ} ia'@cxz+%0 , y,q8(intjMXCD Ǟ m ` Y cOe Fg ݞ}((^a?@cxz+% , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$|88intjMXCD@: 1DǞ m ` Y cOe F ݞ}88iaW@cxzy+%7 , y,|r8@intjMXCDOBH Ǟ GFm ` Y cOe F ݞ}I@iao@cxzy+%/ , y,8H XintjMCD Ǟ m `V, eXY cVe F ݞ}ZH qa@cx@z@ +3 , y,|8T intjMXCD Ǟ m ` Y cOdVe fF ݞ}kT ia@cxz+% , y,.8intjMXCD Ǟ m ` Y  F ݞ}$y$|e8`intjMXCDOBH Ǟ m `H Y cOe F ݞ}|`ia@cxzy+%7 , y,|8hintjMXCD Ǟ m `V, Y ZcOe F ݞ}hiaAcxz+%/ , y,8pXintjMCD Ǟ m ` Y cVd%e F ݞh}pqaAcx5Az5A +, 3 y3!8intjMXCD Ǟ m ` Y  F ݞ}$y$lX88intjMXCD?3 Ǟ m ` Y cOe F ݞ}hy8YahOAxjgAzkyilA , y,l88intjMXCDAA Ǟ m ` Y cOe F ݞ}hy8YahAxjAzkyilA , y,lv80intjMXCD Ǟ m `U% Y cOe F ݞ}ϴh0YahAxjAzkilB , y,a8(@intjMXCD Ǟ m ` Y cOe F ݞ}ߴh(@Nah3BxjKBzkilcB , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$bˇ8h/intjMXCD Ǟ m ` Y cVe F ݞ}%nhOanBxpBzqB3y3bM8|intjMXCD Ǟ m ` Y cVe F ݞ}%%n|OanBxpBzqB3y3ψ8intjMXCD Ǟ m ` Y  F ݞ}$y$8\intjMXCD Ǟ m ` Y  F ݞ}$y$=8intjMXCD Ǟ m ` Y  F ݞ}$y$t8"intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8KintjMXCD Ǟ m ` Y  F ݞ}$y$P8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8tintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$,8:intjMXCD Ǟ m ` Y  F ݞ}$y$c8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$ы8cintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$?8)intjMXCD Ǟ m ` Y  F ݞ}$y$v8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8RintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$R8intjMXCD Ǟ m ` Y  F ݞ}$y$8{intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8AintjMXCD?3AAnC Ǟ mG ` Y  F ݞ}maPcPxPzyP+% , y,y8 &intjMXCDAA ǞE mG ` Y  F ݞ} faBcxyBzyC +% , y,&8intjMXCDAA Ǟ mG `U%͟W Y  F ݞ}/qa)Ccxy)CzintjMXCD Ǟ m ` Y  F ݞ}$y$|P8intjMXCD@: 1DǞ m ` Y cO Fg ݞ}ia3Lcxzy+% , y,|ߪ8intjMXCDOBH Ǟ GFm ` Y cO Fg ݞ}iaKLcxzy+% , y,n8kintjMXCD Ǟ m `V, eXY cO Fg ݞ}macLcxz{L +% , y,| 8PintjMXCD Ǟ m ` Y cOdV fFg ݞ}iaLcxz+%! , y,86intjMXCD Ǟ m ` Y  F ݞ}$y$Ҭ8 XintjMCDOB, Ǟ m `H Y cV Fg ݞ}' qaLcxLzyL +3 : y:p8 XintjMCD Ǟ m `V, Y ZcV Fg ݞ}8 qaMcxMz5M +3 , y,|8 iintjMXCD Ǟ m ` Y cOdV Fg ݞh}I iaQMcxz+% , y,8NintjMXCD Ǟ m ` Y  F ݞ}$y$lԮ8 4intjMXCD?3 Ǟ m ` Y cO Fg ݞ}Zhy 4YahiMxjMzkyilM , y,lc8T4intjMXCDAA Ǟ m ` Y cO Fg ݞ}jhyT4YahMxjMzkyilM , y,p8,XintjMCD Ǟ m `U3 Y cV Fg ݞ}zh,]ahNxjNzk1NilPN,y,et8<qXintjMCD Ǟ m ` Y cV Fg ݞ}h<RahlNxjNzkNilN3y38dintjMXCD Ǟ m ` Y  F ݞ}$y$b-8intjMXCD Ǟ m ` Y cV Fg ݞ}%nOanNxpNzqO3y3b8intjMXCD Ǟ m ` Y cV Fg ݞ}%%nOan2Oxp2OzqJO3y318intjMXCD Ǟ m ` Y  F ݞ}$y$h8intjMXCD Ǟ m ` Y  F ݞ}$y$8WintjMXCD Ǟ m ` Y  F ݞ}$y$ֲ8intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 intjMXCD Ǟ m ` Y  F ݞ}$y$D8 intjMXCD Ǟ m ` Y  F ݞ}$y${8 intjMXCD Ǟ m ` Y  F ݞ}$y$8F!intjMXCD Ǟ m ` Y  F ݞ}$y$8!intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 "intjMXCD Ǟ m ` Y  F ݞ}$y$W8o"intjMXCD Ǟ m ` Y  F ݞ}$y$8"intjMXCD Ǟ m ` Y  F ݞ}$y$Ŵ85#intjMXCD Ǟ m ` Y  F ݞ}$y$8#intjMXCD Ǟ m ` Y  F ݞ}$y$38#intjMXCD Ǟ m ` Y  F ݞ}$y$j8^$intjMXCD Ǟ m ` Y  F ݞ}$y$8$intjMXCD Ǟ m ` Y  F ݞ}$y$ص8$%intjMXCD Ǟ m ` Y  F ݞ}$y$8%intjMXCD Ǟ m ` Y  F ݞ}$y$F8%intjMXCD Ǟ m ` Y  F ݞ}$y$}8M&intjMXCD Ǟ m ` Y  F ݞ}$y$8&intjMXCD Ǟ m ` Y  F ݞ}$y$8'intjMXCD Ǟ m ` Y  F ݞ}$y$"8v'intjMXCD Ǟ m ` Y  F ݞ}$y$Y8 'intjMXCD?3@:nC 1DǞ m ` Y  F ݞ} qalOcxOzyO +% , y,8( (intjMXCD@:AA 1DǞE m ` Y  F ݞ}˸( qaOcxyOzO +% , y,84 )intjMXCD@: 1DǞ m `U%͟W Y  F ݞ}ܸ4 qaOcxyOzO +% , y,|38@*intjMXCD@: 1DǞ m ` Y cOe F ݞ}@iaOcxyz+%7 , y,¹8v+intjMXCD Ǟ m ` Y  F ݞ}$y$8P +intjMXCD@:AA 1DǞ mG ` Y  F ݞ}P qaPcxyPz-P +% , y,8\ ,intjMXCD@: 1DǞ m `U% YY  F ݞ}\ qaGPcxyGPzZP +% , y,58h-XintjMCD@, 1DǞ m ` Y cV Fg ݞ} hqatPcxytPzP +3 : y:ӻ8.intjMXCD Ǟ m ` Y  F ݞ}$y$ 8x .intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}1x qaPcxyPzP +% , y,8/intjMXCD@: 1DǞ m `V, eXY  F ݞ}DqaPcxyPzP +% , y,|F80intjMXCD@: 1DǞ m ` Y dV fF ݞ}WiaPcxyz+%8 , y,ս81intjMXCD Ǟ m ` Y  F ݞ}$y$ 82intjMXCD@:OBH 1DǞ m `H Y  F ݞ}jqaQcxyQz$Q +% , y,8 2intjMXCD@: 1DǞ m `V, Y Z F ݞ}| qaintjMXCD Ǟ m ` Y  F ݞ}$y$8X >intjMXCDAAOBH Ǟ GFmG ` Y  F ݞ}JX qaScx,Szy,S +% , y,8d ?intjMXCDOBH Ǟ GFm `U% YY  F ݞ}[d qaCScxyVSzmS +% , y,K8p@XintjMCDOB, Ǟ GFm ` Y cV Fg ݞ}lpqaScxySzS +3 : y:8AintjMXCD Ǟ m ` Y  F ݞ}$y$ 8 AintjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}} qaScxSzyS +% , y,8BintjMXCDOBH Ǟ GFm `V, eXY  F ݞ}qaScxySzT +% , y,\8CXintjMCDOB, Ǟ GFm ` Y d% fF ݞ}qa+Tcxy+Tz>T +30 : y:8DintjMXCD Ǟ m ` Y  F ݞ}$y$y18 EintjMXCDOBH Ǟ GFm `H Y  F ݞ} fa\TcxyoTzyoT +% , y,8 EintjMXCDOBH Ǟ GFm `V, Y Z F ݞ}Ⱥ qaTcxyTzT +% , y,m8FXintjMCDOB, Ǟ GFm ` Y d% F ݞh}ںqaTcxyTzT +3 : y: 8GintjMXCD Ǟ m ` Y  F ݞ}$y$gB8(HintjMXCD?3OBH Ǟ GFm ` Y  F ݞ}hyTahTxjzkyil,y,g8 IintjMXCDAAOBH Ǟ GFm ` Y  F ݞ}hyTahTxjzkyil,y,gF8IintjMXCDOBH Ǟ GFm `U% Y  F ݞ}hTahUxjyzkily,y,g8 JintjMXCDOBH Ǟ GFm ` Y cO F ݞ}h TahUxjyzkily,y,J8KintjMXCD Ǟ m ` Y  F ݞ}$y$b8LintjMXCDOBO Ǟ GFm ` Y  F ݞ}%0nOan.Uxp.UzqAU3y3b8$MintjMXCDOBO Ǟ GFm ` Y  F ݞ}%%An$Oan_Uxp_UzqrU3y388MintjMXCD?3nC Ǟ m `V, eXY  F ݞ}R8qaUcxUzyU +% , y,#8@NintjMXCDAA ǞE m `V, eXY  F ݞ}c@qaUcxUzyV +% , y,8HOintjMXCD Ǟ m `U%V,͟W eXY  F ݞ}tHqa,Vcx?Vz?V +% , y,_8PPXintjMCD Ǟ m `V, eXY cVe F ݞ}PqaaVcxaVztV +3 , y,8QintjMXCD Ǟ m ` Y  F ݞ}$y$48dQintjMXCDAA Ǟ mG `V, eXY  F ݞ}dqaVcxVzyV +% , y,8lRintjMXCD Ǟ m `U%V, eXYY  F ݞ}lqaVcxVzV +% , y,p8t SXintjMCD Ǟ m `V, eXY cV Fg ݞ}t qaWcx%WzGW +3 , y,8TintjMXCD Ǟ m ` Y  F ݞ}$y$E8TintjMXCD@: 1DǞ m `V, eXY  F ݞ}ɻqabWcxuWzyW +% , y,8UintjMXCDOBH Ǟ GFm `V, eXY  F ݞ}ܻqaWcxWzyW +% , y,8VXintjMCD Ǟ m `V, eXY d% fF ݞ}qaWcxWz X +3 , y,8WintjMXCD Ǟ m ` Y  F ݞ}$y$V8 XintjMXCDOBH Ǟ m `HV, eXY  F ݞ}qa+Xcx>Xzy`X +% , y,y8XintjMXCD Ǟ m `V, eXY Z F ݞ}fawXcxXzX +% , y,z8YintjMXCD Ǟ m `V, eXY dV F ݞh}&gaPcxz+% , y,!8ZintjMXCD Ǟ m ` Y  F ݞ}$y$gX8[intjMXCD?3 Ǟ m `V, eXY  F ݞ}8hyTahXxjzkyil,y,g8\intjMXCDAA Ǟ m `V, eXY  F ݞ}IhyTahXxjzkyil,y,g\8\intjMXCD Ǟ m `U%V, eXY  F ݞ}ZhTahXxjzkil,y,g8 ]intjMXCD Ǟ m `V, eXY cO F ݞ}kh TahXxjzkil,y,`8^intjMXCD Ǟ m ` Y  F ݞ}$y$b8_intjMXCD Ǟ m `V3 eXY  F ݞ}%|nOanXxpXzq Y3y3b8_intjMXCD Ǟ m `V3 eXY  F ݞ}%%nOan)Yxp)Yzq]cxy>]zQ] +38 : y:n8ƇintjMXCD Ǟ m ` Y  F ݞ}$y$8l )intjMXCDOBH Ǟ m `HV, Y Z F ݞ}l qao]cxyo]z] +% , y,C8xXintjMCDOB, Ǟ m `H Y d% F ݞh}xqa]cxy]z] +3 : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$m8 YintjMXCD?3OBH Ǟ m `H Y  F ݞ}Ͼhy Zah]xj]zky]il , y,m8 ?intjMXCDAAOBH Ǟ m `H Y  F ݞ}hy Zah]xj ^zky ^il , y,m:8 %intjMXCDOBH Ǟ m `HU% Y  F ݞ}h Zah!^xjy4^zkK^ily , y,m8 XintjMCDOB, Ǟ m `H Y cV F ݞ}hZahf^xjyy^zk^ily : y:\8intjMXCD Ǟ m ` Y  F ݞ}$y$b8TintjMXCDOBO Ǟ m `H Y  F ݞ}%nOan^xp^zq^3y3b8$;intjMXCDOBO Ǟ m `H Y  F ݞ}%%$n$Oan^xp^zq^3y38"intjMXCD?3nC Ǟ m `V, Y Z F ݞ}5qa_cx$_zyF_ +% , y,58intjMXCDAA ǞE m `V, Y Z F ݞ}Fqa]_cxp_zy_ +% , y,8 intjMXCD Ǟ m `U%V,͟W Y Z F ݞ}W qa_cx_z_ +% , y,q8ђXintjMCD Ǟ m `V, Y ZcVe F ݞ}hqa_cx_z_ +3/ , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$F8(intjMXCDAA Ǟ mG `V, Y Z F ݞ}y(qa`cx"`zyD` +% , y,80intjMXCD Ǟ m `U%V, YY Z F ݞ}0qa[`cxn`zn` +% , y,88XintjMCD Ǟ m `V, Y ZcV Fg ݞ}8qa`cx`z` +3 , y, 8ʖintjMXCD Ǟ m ` Y  F ݞ}$y$W8L-intjMXCD@: 1DǞ m `V, Y Z F ݞ}Lqa`cx`zy` +% , y,8TintjMXCDOBH Ǟ GFm `V, Y Z F ݞ}TmaPcPxPzyP+% , y,y8XintjMXCD Ǟ m `V, eXY Z F ݞ}пXfa acx aza +% , y,$8`ܙXintjMCD Ǟ m `V, Y Zd% fF ݞ}`qaAacxAazTa +30 , y,8šintjMXCD Ǟ m ` Y  F ݞ}$y$8t%intjMXCDOBH Ǟ m `HV, Y Z F ݞ}tqaracxazya +% , y,8| XintjMCD Ǟ m `V, Y Zd% F ݞh}|qaacxaza +3 , y,58intjMXCD Ǟ m ` Y  F ݞ}$y$ml8 SintjMXCD?3 Ǟ m `V, Y Z F ݞ}hy Zahaxjbzkybil , y,m89intjMXCDAA Ǟ m `V, Y Z F ݞ}+hyZahbxj+bzky+bil , y,m8intjMXCD Ǟ m `U%V, Y Z F ݞ}<hZahBbxjUbzkUbil , y,m8 XintjMCD Ǟ m `V, Y ZcV F ݞ}Mh Zahwbxjbzkbil , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$b8LintjMXCD Ǟ m `V3 Y Z F ݞ}%^nOanbxpbzqb3y3bi81intjMXCD Ǟ m `V3 Y Z F ݞ}%%onOanbxpbzq c3y38 XintjMCD?AnC Ǟ m ` Y d% F ݞh} qa-ccxEczy_c +3 : y:8 XintjMCDAO ǞE m ` Y d% F ݞh} qa{ccxczyc +3 : y:'8 XintjMCD Ǟ m `U3͟W Y d% F ݞh} qaccxcz d +3 , y,8 ΥXintjMCD Ǟ m ` Y cVd%e F ݞh} qa'dcx'dz?d +, 3 y3c8intjMXCD Ǟ m ` Y  F ݞ}$y$8$ XintjMCDAO Ǟ mG ` Y d% F ݞh}$ qaYdcxqdzyd +3 : y:880 XintjMCD Ǟ m `U3 YY d% F ݞh}0 qadcxdzd +3 , y,8< XintjMCD Ǟ m ` Y cVd% Fg ݞh}< qadcx ez e +, 3 y3t 8ϩintjMXCD Ǟ m ` Y  F ݞ}$y$| 8H2intjMXCD@: 1DǞ m ` Y dV F ݞh}Hia7ecxzy+% , y,|: 8PintjMXCDOBH Ǟ GFm ` Y dV F ݞh} PiaOecxzy+% , y, 8XintjMXCD Ǟ m `V, eXY dV F ݞh}XpaPQcPxzP +% , y,qg 8\intjMXCD Ǟ m ` Y dV fF ݞh}-\^agecxz+% , y, 8ǭintjMXCD Ǟ m ` Y  F ݞ}$y$- 8p *XintjMCDOB, Ǟ m `H Y d% F ݞh}?p qaecxezye +3 : y: 8| XintjMCD Ǟ m `V, Y Zd% F ݞh}R| qaecxeze +3 , y,i 8intjMXCD Ǟ m ` Y  F ݞ}$y$e 8 ]intjMXCD?3 Ǟ m ` Y dV F ݞh}ehy Rahfxjzkyil , y,e/8 CintjMXCDAA Ǟ m ` Y dV F ݞh}vhy Rah3fxjzkyil , y,g8)intjMXCD Ǟ m `U% Y dV F ݞh}hTahKfxjzkQ il , y,e\8 intjMXCD Ǟ m ` Y cOdV F ݞh}h Rahcfxjzkil , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$b"8VintjMXCD Ǟ m ` Y d] F ݞh}%nOan{fxp{fzqf3y3b8;intjMXCD Ǟ m ` Y d] F ݞh}%%nOanfxpfzqf3y3&8 intjMXCD Ǟ m ` Y  F ݞ}$y$]8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8IintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$98intjMXCD Ǟ m ` Y  F ݞ}$y$p8rintjMXCD Ǟ m ` Y  F ݞ}$y$8ոintjMXCD Ǟ m ` Y  F ݞ}$y$88intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$L8intjMXCD Ǟ m ` Y  F ݞ}$y$8aintjMXCD Ǟ m ` Y  F ݞ}$y$8ĺintjMXCD Ǟ m ` Y  F ݞ}$y$8'intjMXCD Ǟ m ` Y  F ݞ}$y$(8intjMXCD Ǟ m ` Y  F ݞ}$y$_8intjMXCD Ǟ m ` Y  F ݞ}$y$8PintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$;8yintjMXCD Ǟ m ` Y  F ݞ}$y$r8ܽintjMXCD Ǟ m ` Y  F ݞ}$y$8?intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$SN8hintjMXCD?3nC Ǟ m ` Y  F ݞ}@ayfczy g , y,^8MintjMXCD?3AA ǞE m ` Y  F ݞ}Kay3gczTg , y,^l82intjMXCD?3 Ǟ m `U%͟W Y  F ݞ}Kaygczg , y,^8XjintMCD?, Ǟ m ` Y cVe F ݞ}Kaygczg : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$^8`intjMXCD?3AA Ǟ mG ` Y  F ݞ} Kay)hczJh , y,^P8EintjMXCD?3 Ǟ m `U% YY  F ݞ}Kayyhczh , y,^8 *XjintMCD?, Ǟ m ` Y cV Fg ݞ}+ Kayhczh : y:n8intjMXCD Ǟ m ` Y  F ݞ}$y$[8<sintjMXCD?3@: 1DǞ m ` Y  F ݞ};<Hayicz , y,[48DXintjMXCD?3OBH Ǟ GFm ` 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, y, P8` intjMXCD?3nC Ǟ m `U%V,͟W Y  F ݞ}w`acPzPxy olow  ; o f ^x DD 5x + % , y,!QQ8 XjintMCD?,nC Ǟ m ` Y cVd%e F ݞ}a]czPQ xy] op low ; o f ^D%8%% 5 + 38:y::R8 intjMXCD Ǟ m ` Y  F ݞ}$y$qR8 ? intjMXCD?3AAOBHnC Ǟ mG ` Y  F ݞ} aՇczxyՇ olow ; o f ^~ 5~ , y, -S8`( intjMXCD?3nC Ǟ m `U%V, YY  F ݞ}`acPzPxy olow> ; o f ^x Db 5x + % , y,S8H XjintMCD?,nC Ǟ m ` Y cVd% Fg ݞ}Ha{czxy{ olow ; o f ^ D+ 5 + 3 : y:T88! intjMXCD Ǟ m ` Y  F ݞ}$y$T8,D! intjMXCD?3@:nC 1DǞ m ` Y  F ݞ},DaYcPzPxyl oylowy ;y oy D + % , y,U8pT" intjMXCD?3AAOBHnC Ǟ GFm ` Y  F ݞ}pTa6cPzPxyI o\low ; o D + % , y,xV8D# intjMXCD?3nC Ǟ m `U%V, eXY  F ݞ}DåcPzPxyߊ olow ; o D: + % , y,6W8h$ XjintMCD?3nC Ǟ m ` Y cVd% fF ݞ}haczSR xy o lowً ; o D + :9 A yAX8% intjMXCD Ǟ m ` Y  F ݞ}$y$8X8pP & intjMXCD?3AAOBHnC Ǟ m `H Y  F ݞ}(pPacPzPxy/ oBlowf ; o D + % , y,X8D ' intjMXCD?3nC Ǟ m `U%V, Y Z F ݞ}<DaЌcPzPxy olow ; o D> + % , y,Y8t( XjintMCD?3nC Ǟ m ` Y cVd% F ݞh }PtaczSR xy o lowݍ ; o D + : A yAZ8+) intjMXCD Ǟ m ` Y  F ݞ}$y$Z8xd) intjMXCD?3AAOBHnC ǞE m ` Y  F ݞ}dxda!cPzyPx4 oKlowf ; o f ^ 5 + % , y, t[8`* intjMXCDAA ǞE m `U%V,͟W Y  F ݞ}x`acPzPxy oΎlow ; o f ^x D  5x + % , y,!A\8<~+ XintjMCDA, ǞE m ` Y cVd%e F ݞ}<a9czPQ xyL oe low ; o f ^D%@%% 5 + 30:y:*]8, intjMXCD Ǟ m ` Y  F ݞ}$y$a]8X, intjMXCDAAOBH ǞE mG ` Y  F ݞ}XaΏcPzyPxy oy lowy9 ;y oy fy ^y~ 5y~ + % , y,.^8` - intjMXCDAA ǞE m `U%V, YY  F ݞ}` aczxy olow ; o f ^x 5x , y,^8l. XintjMCDA, ǞE m ` Y cVd% Fg ݞ}laczŐxy olowB ; o f ^ D 5 + 3 : y:_8/ intjMXCD Ǟ m ` Y  F ݞ}$y$_8P(Y0 intjMXCD@:AAOBH 1DǞE m ` Y  F ݞ}P(acPzyPx olowّ ; o + % , y,`8xLN1 intjMXCDAAOBH ǞE GFm ` Y  F ݞ}xLacPzPxy oyGlowyk ;y oy D + % , y,Ja8@K2 intjMXCDAA ǞE m `U%V, eXY  F ݞ}@acPzPxy olowC ; o Di + % , y,b8dN3 XintjMCDA, ǞE m ` Y cVd% fF ݞ}daczSR xy˓ o low  ; o D7 + :1 A yAb8Z4 intjMXCD Ǟ m ` Y  F ݞ}$y$ c8h4 intjMXCDAAOBH ǞE m `H Y  F ݞ})haQcPzPxyd oylowy} ;y oy + % , y,c8@5 intjMXCDAA ǞE m `U%V, Y Z F ݞ}=@acPzPxy o͔low ; o D + % , y,wd8p6 XintjMCDA, ǞE m ` Y cVd% F ݞh }QpafczSR xyy o low ; o D + : A yABe87 intjMXCD Ǟ m ` Y  F ݞ}$y$ye80H+8 intjMXCD?3nC Ǟ m `U%V,͟W Y  F ݞ}e0HacSzySx olowC ; o f ^ 5 + % , y,7f8xX9 intjMXCDAA ǞE m `U%V,͟W Y  F ݞ}yxXalcSzySxl olow ; o f ^~ 5~ + % , y,g8: XintjMCD Ǟ m `U,͟W Y cVd%e F ݞ} aؖcz\R xؖ o low' ; o f ^ Df 5 + , 3y3g8; intjMXCD Ǟ m ` Y  F ݞ}$y$h8X; intjMXCDAA Ǟ mG `U%V,͟W Y  F ݞ}XacSzySx olowח ; o f ^~ 5~ + % , y,h8Dq< intjMXCD Ǟ m `U%V,͟W YY  F ݞ}DacPzPx o&lowa ; o f ^x 5x + % , y,i8,e= intjMXCD Ǟ m `U%͟W Y cOdV Fg ݞ},aPczxolow; o f ^ 5 , y,\j8K> intjMXCD Ǟ m ` Y  F ݞ}$y$j84 > intjMXCD@: 1DǞ m `U%V,͟W Y  F ݞ}4 acPzyPx olow ; o + % , y,3k8T(? intjMXCDOBH Ǟ GFm `U%V,͟W Y  F ݞ}T(acPzyPx olow ; o + % , y,k8|0@ intjMXCD Ǟ m `U%V,͟W eXY  F ݞ}|0aYcPzPxY olow ; o DS + % , y,l8\A XintjMCD Ǟ m `U,͟W Y cVd% fF ݞ}\aczSR x  o* lowT ; o D + ,! : y:zm8B intjMXCD Ǟ m ` Y  F ݞ}$y$m8 (B intjMXCDOBH Ǟ m `HU%V,͟W Y  F ݞ}* (acPzyPx olowޚ ; o + % , y,`n80 C intjMXCD Ǟ m `U%V,͟W Y Z F ݞ}>0 acPzPx- olow@ ; o + % , y,o8@ `D XintjMCD Ǟ m `U,͟W Y cVd% F ݞh }R@ `abcPQzPQxb olow ; o D + , : y:o8E intjMXCD Ǟ m ` Y  F ݞ}$y$p8 |?F intjMXCD?3nC Ǟ m ` Y cOdVe F ݞ}f |a^cPzyPx ovlow ; o f ^ 5 + %8 , y,p8 =G intjMXCDAA ǞE m ` Y cOdVe F ݞ}z acPzyPx olow ; o f ^~ 5~ + %0 , y,q8 =H intjMXCD Ǟ m `U%͟W Y cOdVe F ݞ} aޜcPzPx olow ; o f ^x 5x + % , y,Vr8xy+ olow> ; o 5~ + % , y,8 intjMXCD@: 1DǞ m `U%V, YY  F ݞ} aXczkxyX olowk ; o D 5x + % , y,D8 XintjMCD@, 1DǞ m ` Y cVd% Fg ݞ}aczxy olow ; o D 5 + 3 : y:8ӓ intjMXCD Ǟ m ` Y  F ݞ}$y$78 6 intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ} aczȹxy olowȹ ; o D + % , y,8 intjMXCD@: 1DǞ m `V, eXY  F ݞ} aczxy olow ; o D + % , y,8 intjMXCD@: 1DǞ m ` Y dV fF ݞ}aczxyolow; oD+ %8 , y,/8 intjMXCD Ǟ m ` Y  F ݞ}$y$f8O intjMXCD@:OBH 1DǞ m `H Y  F ݞ}5a"cz5xy" olow5 ; o , y,8  8 intjMXCD@: 1DǞ m `V, Y Z F ݞ}J  aMcz`xyM olow` ; o D + % , y,8,! XintjMCD@, 1DǞ m ` Y d% F ݞh}_,azczxyz olow ; o D + 3 : y:m8 intjMXCD Ǟ m ` Y  F ݞ}$y$8@ k intjMXCD?3AAOBHnC Ǟ GFm ` Y  F ݞ}t@ aczyx olowغ ; o 5 + % , y,Q8L S intjMXCDAAOBH ǞE GFm ` Y  F ݞ}L aczyx olow ; o 5y~ + % , y, 8X ; intjMXCDOBH Ǟ GFm `U%V,͟W Y  F ݞ}X acz,xy olow, ; o D 5x + % , y,ɮ8d$ XintjMCDOB, Ǟ GFm ` Y cVd%e F ݞ}daGczZxyG olowZ ; o D 5 + 3/ : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$8t q intjMXCDAAOBH Ǟ GFmG ` Y  F ݞ}t axczyxx olow ; o 5y~ + % , y,x8 Y intjMXCDOBH Ǟ GFm `U%V, YY  F ݞ} aczxy olow ; o 5x + % , y,48B XintjMCDOB, Ǟ GFm ` Y cVd% Fg ݞ}aλczxyλ olow ; o D 5 + 3 : y:8, intjMXCD Ǟ m ` Y  F ݞ}$y$'8 intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ} aczyx olow, ; o + % , y,Ų8w intjMXCDOBH Ǟ GFm `V, eXY  F ݞ}aCczVxyC olowV ; o D + % , y,r8] XintjMCDOB, Ǟ GFm ` Y d% fF ݞ},aqczxyq olow ; o D + 30 : y:8D intjMXCD Ǟ m ` Y  F ݞ}$y$V8 intjMXCDOBH Ǟ GFm `H Y  F ݞ}B aczyxy oylowy ;y oy + % , y,8 intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}W a̼cz߼xy̼ olow߼ ; o , y,8x XintjMCDOB, Ǟ GFm ` Y d% F ݞh}lacz xy olow  ; o D + 3 : y:]8_ intjMXCD Ǟ m ` Y  F ݞ}$y$8§ intjMXCD?3nC Ǟ m `U%V, eXY  F ݞ}a(czy;x( olowR ; o 5 + % , y,A8 intjMXCDAA ǞE m `U%V, eXY  F ݞ}atczyxt olow ; o 5~ + % , y,8 intjMXCD Ǟ m `U%V,͟W eXY  F ݞ}aczӽx olowӽ ; o 5x + % , y,8t XintjMCD Ǟ m `V, eXY cVd%e F ݞ}aczx olow ; o D 5 + 3 , y,u8[ intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCDAA Ǟ mG `U%V, eXY  F ݞ}a%czy8x% olowO ; o 5~ + % , y,h8$ intjMXCD Ǟ m `U%V, eXYY  F ݞ}$aqczxq olow ; o 5x + % , y,$8, XintjMCD Ǟ m `V3 eXY cVd% Fg ݞ},aczx olow ; o 5 + , 3 y38s intjMXCD Ǟ m ` Y  F ݞ}$y$8<֮ intjMXCD@: 1DǞ m `V, eXY  F ݞ} <aczyx olow  ; o + % , y,8D intjMXCDOBH Ǟ GFm `V, eXY  F ݞ}#Da/czyBx/ olowY ; o + % , y,b8L XintjMCD Ǟ m `V, eXY d% fF ݞ}9La{czx{ olow ; o D + 3 , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$F8` intjMXCDOBH Ǟ m `HV, eXY  F ݞ}O`aczyx olowֿ ; o + % , y,8hҲ intjMXCD Ǟ m `V, eXY Z F ݞ}dhacz x olow  ; o + % , y,8p intjMXCD Ǟ m `V, eXY dV F ݞh}ypaPczxolow; o , y,>8 intjMXCD Ǟ m ` Y  F ݞ}$y$u8x intjMXCD?3nC Ǟ m ` Y cOdV fF ݞ}xa-czyxolow;o 5+ %9 , y,8 intjMXCDAA ǞE m ` Y cOdV fF ݞ}aEczyxolow; o 5~+ %1 , y,8Ͷ intjMXCD Ǟ m `U%͟W Y cOdV fF ݞ}a]czxolow; o 5x+ %! , y,m8 XintjMCD Ǟ m ` Y cVd%e fF ݞ} auczxu olow ; o 5 + , 3 y3)8 intjMXCD Ǟ m ` Y  F ݞ}$y$`8 intjMXCDAA Ǟ mG ` Y cOdV fF ݞ}aczyxolow; o 5~+ %9 , y, 8 intjMXCD Ǟ m `U% YY cOdV fF ݞ}aczxolow; o 5x+ %1 , y,8Ⱥ intjMXCD Ǟ m ` Y cOdV fFg ݞ}aczxolow; o 5+ %! , y,g8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD@: 1DǞ m ` Y dV fF ݞ}aczyxolow;o+ %8 , y,-8 intjMXCDOBH Ǟ GFm ` Y dV fF ݞ}0aczyxolow; o+ %0 , y,8޽ intjMXCD Ǟ m `V, eXY dV fF ݞ}Fa/cQzQx olow ; o + % , y,x8ľ intjMXCD Ǟ m ` Y  F ݞ}$y$8' intjMXCDOBH Ǟ m `H Y dV fF ݞ}\aGczyxolow; o+ %8 , y,M8 intjMXCD Ǟ m `V, Y ZdV fF ݞ}qa_czxolow; o+ %0 , y,8 intjMXCD Ǟ m ` Y dV fF ݞh}awczxolow; o+ % , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$8= intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$.8 intjMXCD Ǟ m ` Y  F ݞ}$y$e8f intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8, intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 intjMXCD Ǟ m ` Y  F ݞ}$y$A8 intjMXCD Ǟ m ` Y  F ݞ}$y$x8U intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8~ intjMXCD Ǟ m ` Y  F ݞ}$y$T8 intjMXCD Ǟ m ` Y  F ݞ}$y$8D intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$08m intjMXCD Ǟ m ` Y  F ݞ}$y$g8 intjMXCD?3AAOBHnC Ǟ m `H Y  F ݞ}acPzyPx olow ; o 5 + % , y,8 4 intjMXCDAAOBH ǞE m `H Y  F ݞ} 4acPzyPx olow ; o DA 5y~ + % , y,8@ ( intjMXCDOBH Ǟ m `HU%V,͟W Y  F ݞ}@ (arcPzPxyr olow ; o D 5x + % , y,8h D XintjMCDOBO Ǟ m `H Y cVd%e F ݞ}h DacPQzPQxy o |,%9%%low ; o D |,%9%% 5 + 37 , y,}8 intjMXCD Ǟ m ` Y  F ݞ}$y$8  intjMXCDAAOBH Ǟ mG `H Y  F ݞ} a(czy;x( olow; ; o 5y~ + % , y,p8 intjMXCDOBH Ǟ m `HU%V, YY  F ݞ} aRczexyR olowe ; o D 5x + % , y,,8  XintjMCDOB, Ǟ m `H Y cVd% Fg ݞ} aczxy olow ; o D 5 + 3 : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 $ intjMXCD@:OBH 1DǞ m `H Y  F ݞ}' aczyx olow ; o , y,8 intjMXCDOBH Ǟ GFm `H Y  F ݞ}< acPzyPxy oylowy4 ;y oy Dya + % , y,{8! intjMXCDOBH Ǟ m `HV, eXY  F ݞ}Q!acPzPxy olow ; o D + % , y,98 !4 XintjMCDOBO Ǟ m `H Y d% fF ݞ}f !4aczRS xy o low ; o D + 38 , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$;8T! P intjMXCDOBH Ǟ m `HV, Y Z F ݞ}{T! a\czoxy\ olowo ; o D + % , y,8`!9 XintjMCDOB, Ǟ m `H Y d% F ݞh}`!aczxy olow ; o D + 3 : y:8# intjMXCD Ǟ m ` Y  F ݞ}$y$8p! intjMXCD?3nC Ǟ m `U%V, Y Z F ݞ}p!aczyx olow ; o 5 + % , y,y8!n intjMXCDAA ǞE m `U%V, Y Z F ݞ}!a(czy;x( olowd ; o 5~ + % , y,58! V intjMXCD Ǟ m `U%V,͟W Y Z F ݞ}! acPzPx olow ; o D 5x + % , y,8!DE XintjMCD Ǟ m `V, Y ZcVd%e F ݞ}!DacRSzRSx o+lowH ; o D+ 5 + 3/ , y,8= intjMXCD Ǟ m ` Y  F ݞ}$y$8! intjMXCDAA Ǟ mG `U%V, Y Z F ݞ}!avczyxv olow ; o 5~ + % , y,8" intjMXCD Ǟ m `U%V, YY Z F ݞ} "aczx olow ; o 5x + % , y,~8"n XintjMCD Ǟ m `V, Y ZcVd% Fg ݞ}"acz.x olow. ; o D 5 + 3 , y,:8U intjMXCD Ǟ m ` Y  F ݞ}$y$q80" intjMXCD@: 1DǞ m `V, Y Z F ݞ}30"aKczy^xK olow ; o + % , y,8@" intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}H@"aczyx olow ; o , y,8T" intjMXCD Ǟ m `V, eXY Z F ݞ}]T"acPzPx olow ; o D + % , y,z8d",u XintjMCD Ǟ m `V, Y Zd% fF ݞ}rd",aAczRS xA oT lowq ; o DT + 30 , y,E8h intjMXCD Ǟ m ` Y  F ݞ}$y$|8" intjMXCDOBH Ǟ m `HV, Y Z F ݞ}"aczyx olow ; o + % , y,)8" XintjMCD Ǟ m `V, Y Zd% F ݞh}"acz"x olow" ; o D + 3 , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 8" intjMXCD?3nC Ǟ m ` Y cOdV F ݞh }" a?czyx? olow? ; o 5 + % , y,8", intjMXCDAA ǞE m ` Y cOdV F ݞh }",aWczyxW olowW ; o 5~ + % , y,v8# XintjMCD Ǟ m `U3͟W Y cVd% F ݞh }# aoczxo olow ; o 5x + 3 , y,28(#D intjMXCD Ǟ m ` Y cOdVe F ݞh}(#DacPQzPQx olowPQ ; o D 5 + % , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$28l#, intjMXCDAA Ǟ mG ` Y cOdV F ݞh }l#,aczyx olow ; o 5~ + % , y,8#( intjMXCD Ǟ m `U% YY cOdV F ݞh }#(a czx  olow  ; o 5x + % , y,8# XintjMCD Ǟ m ` Y cVd% Fg ݞh}+# a%cz=x% olow= ; o 5 + , 3 y3f8 intjMXCD Ǟ m ` Y  F ݞ}$y$8# 2 intjMXCD@: 1DǞ m ` Y dV F ݞh}?# agczyxg olowg ; o + % , y,;8#, intjMXCDOBH Ǟ GFm ` Y dV F ݞh}T#,aczyx olow ; o + % , y,8$ intjMXCD Ǟ m `V, eXY dV F ݞh}i$aczxolow; o , y,80$, intjMXCD Ǟ m ` Y dV fF ݞh}~0$,aczRS x o low ; o D + % , y,O8 intjMXCD Ǟ m ` Y  F ݞ}$y$8\$,A intjMXCDOBH Ǟ m `H Y dV F ݞh}\$,a'czyx' olow' ; o + % , y,38$() intjMXCD Ǟ m `V, Y ZdV F ݞh}$(a?czx? olow? ; o + % , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$8t intjMXCD Ǟ m ` Y  F ݞ}$y$N8 intjMXCD Ǟ m ` Y  F ݞ}$y$8: intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$*8c intjMXCD Ǟ m ` Y  F ݞ}$y$a8 intjMXCD Ǟ m ` Y  F ݞ}$y$8) intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$=8R intjMXCD Ǟ m ` Y  F ݞ}$y$t8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8{ intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$P8A intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8$t intjMXCD?3@:nC Ǟ m ` Y  F ݞ}$tayWcPzyPxyW oyxlowy ;y oy fy ^y 5y D +% , y,|8$%| intjMXCD?3AAOBH ǞE m ` Y  F ݞ}$%|aycPzPxy o8low ; o f ^~ 5~ D +% , y,I8%\ intjMXCD?3 Ǟ m `U%V,͟W Y  F ݞ}%\aycSzSxy olowB ; o f ^x 5x D +% , y,8% XjintMCD?, Ǟ m ` Y cVd%e F ݞ}%aycz^Q xy o low ; o f ^ 5 DW +3?:y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$'8&|b intjMXCD?3AAOBH Ǟ mG ` Y  F ݞ} &|aycPzPxy olow ; o f ^~ 5~ D +% , y,88'`` intjMXCD?3 Ǟ m `U%V, YY  F ݞ}8'`aycSzSxy o6low ; o f ^x 5x D +% , y,8'V XjintMCD?, Ǟ m ` Y cVd% Fg ݞ}1'ayczxy o=lowy ; o f ^ 5 D +3 : y:8b intjMXCD Ǟ m ` Y  F ݞ}$y$8t( intjMXCD?3@: 1DǞ m ` Y  F ݞ}Dt(ay cz,xy  oy lowy, ;y oy , y,8( intjMXCD?3AAOBH Ǟ GFm ` Y  F ݞ}X(ayYczzxyY olowz ; o , y,;8( intjMXCD?3 Ǟ m `U%V, eXY  F ݞ}l(ayczxy o,low ; o , y,8(w XjintMCD?3 Ǟ m ` Y cVd% fF ݞ}(ayWczxxyW olowx ; o A yA8_ intjMXCD Ǟ m ` Y  F ݞ}$y$8(H intjMXCD?3AAOBH Ǟ m `H Y  F ݞ}(HaycPzPxy o+lowO ; o D +% , y,8)8 intjMXCD?3 Ǟ m `U%V, Y Z F ݞ})8ay cPzPxy  o\low ; o D +% , y,f8<)T XjintMCD?, Ǟ m ` Y cVd% F ݞh}<)TayTczuxyT olow ; o D +: A yA"8 intjMXCD Ǟ m ` Y  F ݞ}$y$Y8)& intjMXCD?3AAOBHnC Ǟ m ` Y  F ݞ})a1cPzyPx1 oRlow ; o f ^ 5 D +% , y,8*| intjMXCDAAOBH ǞE m ` Y  F ݞ}*|aycPzyPxy oy=lowy ;y oy fy ^y~ 5y~ D +% , y,8*\ intjMXCDAA Ǟ m `U%V,͟W Y  F ݞ}*\aycSzSxy olow= ; o f ^x 5x D +% , y,8* XintjMCDA, Ǟ m ` Y cVd%e F ݞ} *aycz^Q xy o low ; o f ^ 5 DR +3?:y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$8+|~ intjMXCDAAOBH Ǟ mG ` Y  F ݞ}+|ay|cPzyPxy| oylowy ;y oy fy ^y~ 5y~ D +% , y,8,,`| intjMXCDAA Ǟ m `U%V, YY  F ݞ}/,,`aycSzSxy o!loww ; o f ^x 5x D +% , y,\8,r XintjMCDA, Ǟ m ` Y cVd% Fg ݞ}B,ayczxy o(lowd ; o f ^ 5 D +3 : y:'8~ intjMXCD Ǟ m ` Y  F ݞ}$y$^8h-P intjMXCD@:AAOBH 1DǞ m ` Y  F ݞ}Uh-PacPzyPx oIlowm ; o DqH% +% , y,8- intjMXCDAAOBH Ǟ GFm ` Y  F ݞ}i-ayczxy oylowy ;y oy , y,8- intjMXCDAA Ǟ m `U%V, eXY  F ݞ}}-ay,czMxy, olowM ; o , y,8- XintjMCDA3 Ǟ m ` Y cVd% fF ݞ}-ayczxy olow ; o A yAP8 intjMXCD Ǟ m ` Y  F ݞ}$y$8-L intjMXCDAAOBH Ǟ m `H Y  F ݞ}-Lay,cPzPxy, oylowy ;y oy DqH% +% , y,T8<.4 intjMXCDAA Ǟ m `U%V, Y Z F ݞ}<.4aycPzPxy o/low^ ; o D +% , y,8p.T XintjMCDA, Ǟ m ` Y cVd% F ݞh}p.Tayczxy olow= ; o D +: A yA8 intjMXCD Ǟ m ` Y  F ݞ}$y$8.lX intjMXCD?3nC Ǟ m `U%V, Y  F ݞ}.lacPzyPx olow  ; o f ^ 5 DR +% , y,80/|U intjMXCDAA ǞE m `U%V, Y  F ݞ}0/|acPzyPx olow ; o f ^~ 5~ D> +% , y,8/\R intjMXCD Ǟ m `U%V,͟W Y  F ݞ}/\acSzSx olow ; o f ^x 5x DD +% , y,] 80D XintjMCD Ǟ m `U, Y cVd%e F ݞ}0akcz^Q xk o low ; o f ^ 5 D +,?3y37 8Q intjMXCD Ǟ m ` Y  F ݞ}$y$n 80| intjMXCDAA Ǟ mG `U%V, Y  F ݞ}-0|aHcPzyPxH oilow ; o f ^~ 5~ D +% , y,; 8D1` intjMXCD Ǟ m `U%V, YY  F ݞ}@D1`a4cSzSx4 oUlow ; o f ^x 5x D  +% , y, 81 XintjMCD Ǟ m `U, Y cVd% Fg ݞ}S1aKczxK olow ; o f ^ 5 D +, 3 y3 8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 82@ intjMXCD@: 1DǞ m `U%V, Y  F ݞ}f2@acPzyPx olow' ; o D +% , y, 82H intjMXCDOBH Ǟ GFm `U%V, Y  F ݞ}z2HacPzyPx oolow ; o Df +% , y,w83 intjMXCD Ǟ m `U%V, eXY  F ݞ}3aczx olow ; o , y,383 XintjMCD Ǟ m `U3 Y cVd% fF ݞ}3a5czVx5 olowV ; o : y:8! intjMXCD Ǟ m ` Y  F ݞ}$y$&803HA" intjMXCDOBH Ǟ m `HU%V, Y  F ݞ}03HacPzyPx olow ; o D +% , y,8x38># intjMXCD Ǟ m `U%V, Y Z F ݞ}x38a{cPzPx{ olow ; o DX +% , y,83T:$ XintjMCD Ǟ m `U, Y cVd% F ݞh}3Taczx olowG ; o D +, : y:^8C% intjMXCD Ǟ m ` Y  F ݞ}$y$84% XintjMCD?:nC Ǟ m ` Y cVd% F ݞ}4ac\zy\x olowM ; o f ^ 5 D +, 3 y3`84& XintjMCDAH ǞE m ` Y cVd% F ݞ}4aec\zy\x olow ; o f ^~ 5~ D +,3y3:85' XintjMCD Ǟ m `U,͟W Y cVd% F ݞ}5acPzPxA oi low ; o f ^x 5xD +,3y3!8p6( XintjMCD Ǟ m ` Y cVd%e F ݞ}+p6acz\Q x o low ; o f ^ 5 DU +,?3y38) intjMXCD Ǟ m ` Y  F ݞ}$y$2847A* XintjMCDAH Ǟ mG ` Y cVd% F ݞ}>47ac\zy\x olow ; o f ^~ 5~ D +,3y3 8 8M+ XintjMCD Ǟ m `U, YY cVd% F ݞ}Q 8a3cPzPx[ o low ; o f ^x 5xD +,3y388\, XintjMCD Ǟ m ` Y cVd% Fg ݞ}d8acz\Q x o low' ; o f ^ 5D +,3y38^- intjMXCD Ǟ m ` Y  F ݞ}$y$89d- XintjMCD@, 1DǞ m ` Y cVd% F ݞ}w9daOcRzyRxw olow ; o D +3 : y:8 :p. XintjMCDOBO Ǟ GFm ` Y cVd% F ݞ} :pacRzyRx  o!low[ ; o D +3 : y:8|:X/ XintjMCD Ǟ m `V: eXY cVd% F ݞ}|:XascRzRx olow ; o D +3 : y:c8:0 XMintjCD Ǟ m ` Y c,d% fF ݞ}:acz-x olow- ; o A yA81 intjMXCD Ǟ m ` Y  F ݞ}$y$V8:p2 XintjMCDOBO Ǟ m `H Y cVd% F ݞ}:pagcRzyRx olow ; o D +3 : y:!8`;l%3 XintjMCD Ǟ m `V: Y ZcVd% F ݞ}`;lacRzRx! o9lows ; o D +3 : y:8;P34 XintjMCD Ǟ m ` Y cVd% F ݞh};Paczx olow ; o D +3 : y:895 intjMXCD Ǟ m ` Y  F ݞ}$y$85 intjMXCD Ǟ m ` Y  F ݞ}$y$#85 intjMXCD Ǟ m ` Y  F ݞ}$y$Z8b6 intjMXCD Ǟ m ` Y  F ݞ}$y$86 intjMXCD Ǟ m ` Y  F ݞ}$y$8(7 intjMXCD Ǟ m ` Y  F ݞ}$y$87 intjMXCD Ǟ m ` Y  F ݞ}$y$6 87 intjMXCD Ǟ m ` Y  F ݞ}$y$m 8Q8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 88 intjMXCD Ǟ m ` Y  F ݞ}$y$ 89 intjMXCD Ǟ m ` Y  F ݞ}$y$!8z9 intjMXCD Ǟ m ` Y  F ݞ}$y$I!89 intjMXCD Ǟ m ` Y  F ݞ}$y$!8@: intjMXCD Ǟ m ` Y  F ݞ}$y$!8: intjMXCD Ǟ m ` Y  F ݞ}$y$!8; intjMXCD Ǟ m ` Y  F ݞ}$y$%"8i; intjMXCD Ǟ m ` Y  F ݞ}$y$\"8; intjMXCD Ǟ m ` Y  F ݞ}$y$"8/< intjMXCD Ǟ m ` Y  F ݞ}$y$p"8<\< intjMXCD?:nC Ǟ m ` Y  F ݞ}%<\]aUHsz cP 3 y3pj#8x<d= intjMXCDAH ǞE m ` Y  F ݞ}%x<d]aHz cP 3 y3p $8<dx> intjMXCD Ǟ m `U,͟W Y  F ݞ}%)<d]a H'zE cP 3 y3u$8@=\j? 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ǞE m ` Y cV Fg ݞ}qacxyz +3 : y: 8 intjMXCD Ǟ m ` Y  F ݞ}$y$A8 intjMXCD@:AA 1DǞE m ` Y  F ݞ}ı qacxzy  +% , y,߼8 ˧ intjMXCDAAOBH ǞE GFm ` Y  F ݞ}ձ qa%cxy%z8 +% , y,}8 intjMXCDAA ǞE m `V, eXY  F ݞ}qaOcxyOzb +% , y,8 XintjMCDA, ǞE m ` Y d% fF ݞ}qa}cxy}z +31 : y:8~ intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCDAAOBH ǞE m `H Y  F ݞ} qacxyz +% , y,8 ɫ intjMXCDAA ǞE m `V, Y Z F ݞ} qacxyz +% , y,,8 XintjMCDA, ǞE m ` Y d% F ݞh}*qacxyz +3 : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$l8 $ intjMXCD?3AA ǞE m ` Y  F ݞ};hy $Yah6xjIzkyilb , y,a80$ intjMXCDAA ǞE m ` Y  F ݞ}Khy0$Nahxjyzkyily , y,l8T$ү intjMXCDAA ǞE m `U% Y  F ݞ}[hT$Yahxjyzkily , y,l8x( intjMXCDAA ǞE m ` Y cO F ݞ}khx(Yahxjy*zkilyC , y,=8 intjMXCD Ǟ m ` Y  F ݞ}$y$bt8 intjMXCDAH ǞE m ` Y  F ݞ}%{nOanbxpbzqu3y3b8 intjMXCDAH ǞE m ` Y  F ݞ}%%n Oanxpzq3y3x8ܳ intjMXCD?3nC Ǟ m `U%͟W Y  F ݞ}qacxzy +% , y,8 intjMXCDAA ǞE m `U%͟W Y  F ݞ}qacxzy +% , y,8 XintjMCD Ǟ m `U,͟W Y cVe F ݞ}qacxz/ +, 3 y3R8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCDAA Ǟ mG `U%͟W Y  F ݞ}βqaMcx`zy +% , y,y'8Է 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ݞ}$y$|r8, intjMXCDAA Ǟ mG ` Y cOe F ݞ},iacxzy+%8 , y,|84 intjMXCD Ǟ m `U% YY cOe F ݞ}4iacxz+%0 , y,q8< intjMXCD Ǟ m ` Y cOe Fg ݞ}(<^acxz+% , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$|V8L intjMXCD@: 1DǞ m ` Y cOe F ݞ}8Liacxzy+%7 , y,|8T intjMXCDOBH Ǟ GFm ` Y cOe F ݞ}ITia)cxzy+%/ , y,t8\ XintjMCD Ǟ m `V, eXY cVe F ݞ}Z\ qaAcxYzs +3 , y,|8h intjMXCD Ǟ m ` Y cOdVe fF ݞ}kh iacxz+% , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$|8t intjMXCDOBH Ǟ m `H Y cOe F ݞ}|tiacxzy+%7 , y,|g8| intjMXCD Ǟ m `V, Y ZcOe F ݞ}|iacxz+%/ , y,8 XintjMCD Ǟ m ` Y cVd%e F ݞh}qacxz +, 3 y38 intjMXCD Ǟ m ` Y  F ݞ}$y$l88 intjMXCD?3 Ǟ m ` Y cOe F ݞ}hy8Yah xj!zkyil9 , y,lZ88 intjMXCDAA Ǟ m ` Y cOe F ݞ}hy8YahUxjmzkyil , y,l8 0 intjMXCD Ǟ m `U% Y cOe F ݞ}ϴh 0Yahxjzkil , y,ax8<@ intjMXCD Ǟ m ` Y cOe F ݞ}ߴh<@Nahxjzkil , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$b>8|* intjMXCD Ǟ m ` Y cVe F ݞ}%n|OanIxpIzqa3y3b8 intjMXCD Ǟ m ` Y cVe F ݞ}%%nOanxpzq3y3B8 intjMXCD Ǟ m ` Y  F ݞ}$y$y8W intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$U8 intjMXCD Ǟ m ` Y  F ݞ}$y$8F intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$18o intjMXCD Ǟ m ` Y  F ݞ}$y$h8 intjMXCD Ǟ m ` Y  F ݞ}$y$85 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 intjMXCD Ǟ m ` Y  F ݞ}$y$D8^ intjMXCD Ǟ m ` Y  F ݞ}$y${8 intjMXCD Ǟ m ` Y  F ݞ}$y$8$ intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 intjMXCD Ǟ m ` Y  F ݞ}$y$W8M intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8v intjMXCD Ǟ m ` Y  F ݞ}$y$38 intjMXCD Ǟ m ` Y  F ݞ}$y$j8< intjMXCD?3AAnC Ǟ mG ` Y  F ݞ}maPcPxPzyP+% , y,y8 ! intjMXCDAA ǞE mG ` Y  F ݞ} facxyzy +% , y,8 intjMXCDAA Ǟ mG `U%͟W Y  F ݞ}/qacxyz +% , y,78 XintjMCDA, Ǟ mG ` Y cVe F ݞ}?qacxyz$ +38 : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$ 8 7 intjMXCDAA Ǟ mG `U% YY  F ݞ}O qaBcxyBzU +% , y,8 XintjMCDA, Ǟ mG ` Y cV Fg ݞ}`qapcxypz +3 : y:H8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 h intjMXCD@:AA 1DǞ mG ` Y  F ݞ}q qacxzy +% , y,8 P intjMXCDAAOBH Ǟ GFmG ` Y  F ݞ} qacxyz +% , y,88 intjMXCDAA Ǟ mG `V, eXY  F ݞ}qacxyz +% , y,Y8 XintjMCDA, Ǟ mG ` Y d% fF ݞ}qa"cxy"z5 +39 : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$.80 f intjMXCDAAOBH Ǟ mG `H Y  F ݞ}0 qaScxySzf +% , y,8< N intjMXCDAA Ǟ mG `V, Y Z F ݞ}Ƶ< qa}cxy}z +% , y,j8H4 XintjMCDA, Ǟ mG ` Y d% F ݞh}׵Hqacxyz +3 : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$l?8d, intjMXCD?3AA Ǟ mG ` Y  F ݞ}hyd,Yahxjzkyil , y,a8$m intjMXCDAA Ǟ mG ` Y  F ݞ}hy$Nah&xjy9zkyilyR , y,l]8$Y intjMXCDAA Ǟ mG `U% Y  F ݞ}h$Yahqxjyzkily , y,l8(E intjMXCDAA Ǟ mG ` Y cO F ݞ}h(Yahxjyzkily , y,{82 intjMXCD Ǟ m ` Y  F ݞ}$y$b8 intjMXCDAH Ǟ mG ` Y  F ݞ}%(nOanxpzq3y3b48 | intjMXCDAH Ǟ mG ` Y  F ݞ}%%8n Oan8xp8zqK3y38<c intjMXCD?3nC Ǟ m `U% YY  F ݞ}H<qamcxzy +% , y,T8DH intjMXCDAA ǞE m `U% YY  F ݞ}XDmaPcPxPzyP+% , y,y8H- intjMXCD Ǟ m `U%͟W YY  F ݞ}hHfacxz +% , y,8P XintjMCD Ǟ m `U, YY cVe F ݞ}xPqacxz +,0 3 y3!8 intjMXCD Ǟ m ` Y  F ݞ}$y$X8d[ intjMXCDAA Ǟ mG `U% YY  F ݞ}dqacxzy +% , y,8l@ XintjMCD Ǟ m `U, YY cV Fg ݞ}lqa'cx'z: +, 3 y38( intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD@: 1DǞ m `U% YY  F ݞ}qaWcxjzyj +% , y,i8 p intjMXCDOBH Ǟ GFm `U% YY  F ݞ} qacxzy +% , y,8X intjMXCD Ǟ m `U%V, eXYY  F ݞ}̶qacxz +% , y,8= XintjMCD Ǟ m `U, YY d% fF ݞ}ݶqacxz +,1 3 y3C8# intjMXCD Ǟ m ` Y  F ݞ}$y$z8 intjMXCDOBH Ǟ m `HU% YY  F ݞ}qacx(zy( +% , y,8k intjMXCD Ǟ m `U%V, YY Z F ݞ}qa?cx?zR +% , y,8P XintjMCD Ǟ m `U, YY d% F ݞh}qatcxtz +, 3 y3T88 intjMXCD Ǟ m ` Y  F ݞ}$y$p8( intjMXCD?3 Ǟ m `U% YY  F ݞ}!hy(]ahxjzkyil,y,p 8 ( intjMXCDAA Ǟ m `U% YY  F ݞ}1hy (]ah xj3zkycil},y,e84 q intjMXCD Ǟ m `U% YY  F ݞ}Ah4 Rahxjzkil,y,p8T$Z XintjMCD Ǟ m `U, YY cV F ݞ}QhT$]ah#xj6zkfil3y38D intjMXCD Ǟ m ` Y  F ݞ}$y$b8x intjMXCD Ǟ m `U, YY  F ݞ}%anxOanxpzq3y3bL8 intjMXCD Ǟ m `U, YY  F ݞ}%%qnOanxpzq3y3|8q intjMXCD?3nC Ǟ m ` Y cO Fg ݞ}ia cxzy+% , y,|]8V intjMXCDAA ǞE m ` Y cO Fg ݞ}ia!cxzy+% , y,8; intjMXCD Ǟ m `U%͟W Y cO Fg ݞ}paPQcPxzP +% , y,q8 intjMXCD Ǟ m ` Y cOe Fg ݞ}^a9cxz+% , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$P8 i XintjMCDAO Ǟ mG ` Y cV Fg ݞ} qaQcxizy +3 : y:8 Q XintjMCD Ǟ m `U3 YY cV Fg ݞ}ҷ qacxz +3 , y,89 intjMXCD Ǟ m ` Y  F ݞ}$y$|8 intjMXCD@: 1DǞ m ` Y cO Fg ݞ}iacxzy+% , y,|R8 intjMXCDOBH Ǟ GFm ` Y cO Fg ݞ}iacxzy+% , y,8f intjMXCD Ǟ m `V, eXY cO Fg ݞ}macxz5 +% , y,|8K intjMXCD Ǟ m ` Y cOdV fFg ݞ}iaWcxz+%! , y,81 intjMXCD Ǟ m ` Y  F ݞ}$y$E8  XintjMCDOB, Ǟ m `H Y cV Fg ݞ}' qaocxzy +3 : y:8 | XintjMCD Ǟ m `V, Y ZcV Fg ݞ}8 qacxz +3 , y,|8( d intjMXCD Ǟ m ` Y cOdV Fg ݞh}I( ia cxz+% , y,8I intjMXCD Ǟ m ` Y  F ݞ}$y$lG844 intjMXCD?3 Ǟ m ` Y cO Fg ݞ}Zhy44Yah#xj;zkyilS , y,l8h4 intjMXCDAA Ǟ m ` Y cO Fg ݞ}jhyh4Yahoxjzkyil , y,pe8, XintjMCD Ǟ m `U3 Y cV Fg ݞ}zh,]ahxjzkil ,y,e8<l XintjMCD Ǟ m ` Y cV Fg ݞ}h<Rah&xj>zklil3y3i8_ intjMXCD Ǟ m ` Y  F ݞ}$y$b8 intjMXCD Ǟ m ` Y cV Fg ݞ}%nOanxpzq3y3b"8 intjMXCD Ǟ m ` Y cV Fg ݞ}%%nOanxpzq3y38 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8R intjMXCD Ǟ m ` Y  F ݞ}$y$I8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8{ intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$%8A intjMXCD Ǟ m ` Y  F ݞ}$y$\8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8j intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$880! intjMXCD Ǟ m ` Y  F ݞ}$y$o8! intjMXCD Ǟ m ` Y  F ݞ}$y$8! intjMXCD Ǟ m ` Y  F ݞ}$y$8Y" intjMXCD Ǟ m ` Y  F ݞ}$y$ 8" intjMXCD Ǟ m ` Y  F ݞ}$y$K 8# intjMXCD Ǟ m ` Y  F ݞ}$y$ 8# intjMXCD Ǟ m ` Y  F ݞ}$y$ 8# intjMXCD Ǟ m ` Y  F ݞ}$y$ 8H$ intjMXCD Ǟ m ` Y  F ݞ}$y$' 8$ intjMXCD Ǟ m ` Y  F ݞ}$y$^ 8% intjMXCD Ǟ m ` Y  F ݞ}$y$ 8q% intjMXCD Ǟ m ` Y  F ݞ}$y$ 80 % intjMXCD?3@:nC 1DǞ m ` Y  F ݞ}0 qa&cx9zy9 +% , y,j 8< & intjMXCD@:AA 1DǞE m ` Y  F ݞ}˸< qaPcxyPzc +% , y, 8H ' intjMXCD@: 1DǞ m `U%͟W Y  F ݞ}ܸH qa}cxy}z +% , y,| 8T( intjMXCD@: 1DǞ m ` Y cOe F ݞ}Tiacxyz+%7 , y,5 8q) intjMXCD Ǟ m ` Y  F ݞ}$y$l 8d ) intjMXCD@:AA 1DǞ mG ` Y  F ݞ}d qacxyz +% , y, 8p * intjMXCD@: 1DǞ m `U% YY  F ݞ}p qa cxy z  +% , y,8|+ XintjMCD@, 1DǞ m ` Y cV Fg ݞ} |qa. cxy. zA  +3 : y:F8, intjMXCD Ǟ m ` Y  F ݞ}$y$}8 , intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}1 qa^ cxy^ zq  +% , y,8- intjMXCD@: 1DǞ m `V, eXY  F ݞ}Dqa cxy z  +% , y,|8. intjMXCD@: 1DǞ m ` Y dV fF ݞ}Wia cxyz+%8 , y,H8/ intjMXCD Ǟ m ` Y  F ݞ}$y$80 intjMXCD@:OBH 1DǞ m `H Y  F ݞ}jqa cxy z  +% , y,8 0 intjMXCD@: 1DǞ m `V, Y Z F ݞ}| qa cxy z  +% , y,81 XintjMCD@, 1DǞ m ` Y d% F ݞh}qa# cxy# z6  +3 : y:Y82 intjMXCD Ǟ m ` Y  F ݞ}$y$g83 intjMXCD?3@: 1DǞ m ` Y  F ݞ}hyTahS xjzkyil,y,g83 intjMXCD@:AA 1DǞ m ` Y  F ݞ}hTahf xjyzkily,y,g84 intjMXCD@: 1DǞ m `U% Y  F ݞ}¹hTahy xjyzkily,y,g8 5 intjMXCD@: 1DǞ m ` Y cO F ݞ}ӹh Tah xjyzkily,y,86 intjMXCD Ǟ m ` Y  F ݞ}$y$b87 intjMXCD@A 1DǞ m ` Y  F ݞ}%nOan xp zq 3y3bQ8 7 intjMXCD@A 1DǞ m ` Y  F ݞ}%%n Oan xp zq 3y388 8 intjMXCD?3OBHnC Ǟ GFm ` Y  F ݞ}8 qa cx zy/  +% , y,q8D 9 intjMXCDAAOBH ǞE GFm ` Y  F ݞ}D qaJ cx] zy]  +% , y,8P : intjMXCDOBH Ǟ GFm `U%͟W Y  F ݞ}(P qat cxyt z  +% , y,8\; XintjMCDOB, Ǟ GFm ` Y cVe F ݞ}9\qa cxy z  +3/ : y:K8w< intjMXCD Ǟ m ` Y  F ݞ}$y$8l < intjMXCDAAOBH Ǟ GFmG ` Y  F ݞ}Jl qa cx zy  +% , y, 8x = intjMXCDOBH Ǟ GFm `U% YY  F ݞ}[x qa cxy z'  +% , y,8> XintjMCDOB, Ǟ GFm ` Y cV Fg ݞ}lqaB cxyB zU  +3 : y:\8? intjMXCD Ǟ m ` Y  F ݞ}$y$8 ? intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}} qar cx zy  +% , y,18@ intjMXCDOBH Ǟ GFm `V, eXY  F ݞ}qa cxy z  +% , y,8A XintjMCDOB, Ǟ GFm ` Y d% fF ݞ}qa cxy z  +30 : y:m8B intjMXCD Ǟ m ` Y  F ݞ}$y$y8 C intjMXCDOBH Ǟ GFm `H Y  F ݞ} fa cxy) zy)  +% , y,B8 C intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}Ⱥ qa@ cxy@ zS  +% , y,8D XintjMCDOB, Ǟ GFm ` Y d% F ݞh}ںqal cxyl z  +3 : y:~8E intjMXCD Ǟ m ` Y  F ݞ}$y$g8#F intjMXCD?3OBH Ǟ GFm ` Y  F ݞ}hyTah xjzkyil,y,g7 8G intjMXCDAAOBH Ǟ GFm ` Y  F ݞ}hyTah xjzkyil,y,g 8G intjMXCDOBH Ǟ GFm `U% Y  F ݞ}hTah xjyzkily,y,g;!8 H intjMXCDOBH Ǟ GFm ` Y cO F ݞ}h Tah xjyzkily,y,!8I intjMXCD Ǟ m ` Y  F ݞ}$y$b!8 J intjMXCDOBO Ǟ GFm ` Y  F ݞ}%0n Oan xp zq 3y3bv"8($K intjMXCDOBO Ǟ GFm ` Y  F ݞ}%%An($Oanxpzq,3y3"8LK intjMXCD?3nC Ǟ m `V, eXY  F ݞ}RLqaNcxazy +% , y,#8TL intjMXCDAA ǞE m `V, eXY  F ݞ}cTqacxzy +% , y,4$8\M intjMXCD Ǟ m `U%V,͟W eXY  F ݞ}t\qacxz +% , y,$8dN XintjMCD Ǟ m `V, eXY cVe F ݞ}dqacxz. +3 , y,p%8}O intjMXCD Ǟ m ` Y  F ݞ}$y$%8xO intjMXCDAA Ǟ mG `V, eXY  F ݞ}xqaKcx^zy +% , y,E&8P intjMXCD Ǟ m `U%V, eXYY  F ݞ}qacxz +% , y,&8 Q XintjMCD Ǟ m `V, eXY cV Fg ݞ} qacxz +3 , y,'8R intjMXCD Ǟ m ` Y  F ݞ}$y$'8R intjMXCD@: 1DǞ m `V, eXY  F ݞ}ɻqacx/zyQ +% , y,V(8S intjMXCDOBH Ǟ GFm `V, eXY  F ݞ}ܻqahcx{zy +% , y,(8T XintjMCD Ǟ m `V, eXY d% fF ݞ}qacxz +3 , y,)8U intjMXCD Ǟ m ` Y  F ݞ}$y$)8V intjMXCDOBH Ǟ m `HV, eXY  F ݞ}qacxzy +% , y,yg*8V intjMXCD Ǟ m `V, eXY Z F ݞ}fa1cxDzD +% , y,z+8W intjMXCD Ǟ m `V, eXY dV F ݞh}&gaPcxz+% , y,+8X intjMXCD Ǟ m ` Y  F ݞ}$y$g+8Y intjMXCD?3 Ǟ m `V, eXY  F ݞ}8hyTahfxjzkyil,y,gM,8Y intjMXCDAA Ǟ m `V, eXY  F ݞ}IhyTahyxjzkyil,y,g,8Z intjMXCD Ǟ m `U%V, eXY  F ݞ}ZhTahxjzkil,y,gQ-8 [ intjMXCD Ǟ m `V, eXY cO F ݞ}kh Tahxjzkil,y,-8\ intjMXCD Ǟ m ` Y  F ݞ}$y$b .8] intjMXCD Ǟ m `V3 eXY  F ݞ}%|nOanxpzq3y3b.8] intjMXCD Ǟ m `V3 eXY  F ݞ}%%nOanxpzq3y3|/8$^ intjMXCD?3nC Ǟ m ` Y dV fF ݞ}$iacxzy+%9 , y,|/8,_ intjMXCDAA ǞE m ` Y dV fF ݞ},ia0cxzy+%1 , y,|,084` intjMXCD Ǟ m `U%͟W Y dV fF ݞ}4iaHcxz+%! , y,08< a XintjMCD Ǟ m ` Y cVd%e fF ݞ}Ѽ< qa`cxxzx +, 3 y3Y18ob intjMXCD Ǟ m ` Y  F ݞ}$y$|18Hb intjMXCDAA Ǟ mG ` Y dV fF ݞ}Hiacxzy+%9 , y,|28Pc intjMXCD Ǟ m `U% YY dV fF ݞ}Piacxz+%1 , y,|28Xd intjMXCD Ǟ m ` Y cOdV fFg ݞ}Xiacxz+%! , y,=38e intjMXCD Ǟ m ` Y  F ݞ}$y$|t38le intjMXCD@: 1DǞ m ` Y dV fF ݞ}liacxzy+%8 , y,|48tf intjMXCDOBH Ǟ GFm ` Y dV fF ݞ}(tiacxzy+%0 , y,48|g intjMXCD Ǟ m `V, eXY dV fF ݞ};|macQxzQ +% , y,058h intjMXCD Ǟ m ` Y  F ݞ}$y$|g58h intjMXCDOBH Ǟ m `H Y dV fF ݞ}Nia2cxzy+%8 , y,|58i intjMXCD Ǟ m `V, Y ZdV fF ݞ}`iaJcxz+%0 , y,q68j intjMXCD Ǟ m ` Y dV fF ݞh}r^abcxz+% , y,78k intjMXCD Ǟ m ` Y  F ݞ}$y$gK78 l intjMXCD?3 Ǟ m ` Y dV fF ݞ}hyTahzxjzkyil,y,g78l intjMXCDAA Ǟ m ` Y dV fF ݞ}hyTahxjzkyil,y,gO88m intjMXCD Ǟ m `U% Y dV fF ݞ}hTahxjzkil,y,g88 n intjMXCD Ǟ m ` Y cOdV fF ݞ}h Tahxjzkil,y,S98o intjMXCD Ǟ m ` Y  F ݞ}$y$b98p intjMXCD Ǟ m ` Y d] fF ݞ}%ȽnOanxpzq3y3b :8p intjMXCD Ǟ m ` Y d] fF ݞ}%%ٽnOanxpzq(3y3:8q intjMXCD Ǟ m ` Y  F ݞ}$y$:8.r intjMXCD Ǟ m ` Y  F ݞ}$y$:8r intjMXCD Ǟ m ` Y  F ݞ}$y$3;8r intjMXCD Ǟ m ` Y  F ݞ}$y$j;8Ws intjMXCD Ǟ m ` Y  F ݞ}$y$;8s intjMXCD Ǟ m ` Y  F ݞ}$y$;8t intjMXCD Ǟ m ` Y  F ݞ}$y$<8t intjMXCD Ǟ m ` Y  F ݞ}$y$F<8t intjMXCD Ǟ m ` Y  F ݞ}$y$}<8Fu intjMXCD Ǟ m ` Y  F ݞ}$y$<8u intjMXCD Ǟ m ` Y  F ݞ}$y$<8 v intjMXCD Ǟ m ` Y  F ݞ}$y$"=8ov intjMXCD Ǟ m ` Y  F ݞ}$y$Y=8v intjMXCD Ǟ m ` Y  F ݞ}$y$=85w intjMXCD Ǟ m ` Y  F ݞ}$y$=8w intjMXCD Ǟ m ` Y  F ݞ}$y$=8w intjMXCD Ǟ m ` Y  F ݞ}$y$5>8^x intjMXCD Ǟ m ` Y  F ݞ}$y$l>8x intjMXCD Ǟ m ` Y  F ݞ}$y$>8$y intjMXCD Ǟ m ` Y  F ݞ}$y$>8y intjMXCD Ǟ m ` Y  F ݞ}$y$?8y intjMXCD Ǟ m ` Y  F ݞ}$y$H?8Mz intjMXCD Ǟ m ` Y  F ݞ}$y$?8z intjMXCD Ǟ m ` Y  F ݞ}$y$?8 { intjMXCD?3OBHnC Ǟ m `H Y  F ݞ} qaJcx]zyt +% , y,T@8 { intjMXCDAAOBH ǞE m `H Y  F ݞ} qacxzy +% , y,@8| intjMXCDOBH Ǟ m `HU%͟W Y  F ݞ} qacxyz +% , y,A8} XintjMCDOB, Ǟ m `H Y cVe F ݞ}qacxyz +37 : y:.B8~ intjMXCD Ǟ m ` Y  F ݞ}$y$eB8,  intjMXCDAAOBH Ǟ mG `H Y  F ݞ}., qacx+zy+ +% , y,C88  intjMXCDOBH Ǟ m `HU% YY  F ݞ}?8 qaBcxyBzU +% , y,C8D߀ XintjMCDOB, Ǟ m `H Y cV Fg ݞ}PDqapcxypz +3 : y:?D8Ɓ intjMXCD Ǟ m ` Y  F ݞ}$y$vD8T) intjMXCD@:OBH 1DǞ m `H Y  F ݞ}aTmaPcPxPzyP+% , y,yE8X  intjMXCDOBH Ǟ GFm `H Y  F ݞ}sX facxyzy +% , y,E8d intjMXCDOBH Ǟ m `HV, eXY  F ݞ}dqacxyz +% , y,CF8lۄ XintjMCDOB, Ǟ m `H Y d% fF ݞ}lqacxyz  +38 : y:F8 intjMXCD Ǟ m ` Y  F ݞ}$y$G8 $ intjMXCDOBH Ǟ m `HV, Y Z F ݞ} qa)cxy)z< +% , y,G8 XintjMCDOB, Ǟ m `H Y d% F ݞh}qaWcxyWzj +3 : y:TH8 intjMXCD Ǟ m ` Y  F ݞ}$y$mH8 T intjMXCD?3OBH Ǟ m `H Y  F ݞ}Ͼhy Zahxjzkyil , y,mI8 : intjMXCDAAOBH Ǟ m `H Y  F ݞ}hy Zahxjzkyil , y,mI8 intjMXCDOBH Ǟ m `HU% Y  F ݞ}h Zahxjyzkily , y,m>J8 XintjMCDOB, Ǟ m `H Y cV F ݞ}hZah xjy3zkJily : y:J8 intjMXCD Ǟ m ` Y  F ݞ}$y$bK8O intjMXCDOBO Ǟ m `H Y  F ݞ}%nOanexpezqx3y3bK8$6 intjMXCDOBO Ǟ m `H Y  F ݞ}%%$n$Oanxpzq3y3 L8 intjMXCD?3nC Ǟ m `V, Y Z F ݞ}5qacxzy +% , y,L8 intjMXCDAA ǞE m `V, Y Z F ݞ}Fqacx*zyL +% , y,FM8  intjMXCD Ǟ m `U%V,͟W Y Z F ݞ}W qaccxczv +% , y,M8(̐ XintjMCD Ǟ m `V, Y ZcVe F ݞ}h(qacxz +3/ , y,N8 intjMXCD Ǟ m ` Y  F ݞ}$y$N8< intjMXCDAA Ǟ mG `V, Y Z F ݞ}y<qacxzy +% , y,WO8D intjMXCD Ǟ m `U%V, YY Z F ݞ}Dqacx(z( +% , y,O8Lߓ XintjMCD Ǟ m `V, Y ZcV Fg ݞ}LqaJcxJz] +3 , y,P8Ŕ intjMXCD Ǟ m ` Y  F ݞ}$y$P8`( intjMXCD@: 1DǞ m `V, Y Z F ݞ}`qazcxzy +% , y,hQ8h intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}hmaPcPxPzyP+% , y,yQ8l intjMXCD Ǟ m `V, eXY Z F ݞ}пlfacxz +% , y,R8tח XintjMCD Ǟ m `V, Y Zd% fF ݞ}tqacxz +30 , y,5S8 intjMXCD Ǟ m ` Y  F ݞ}$y$lS8 intjMXCDOBH Ǟ m `HV, Y Z F ݞ}qa,cx?zya +% , y, T8 XintjMCD Ǟ m `V, Y Zd% F ݞh}qaxcxxz +3 , y,T8 intjMXCD Ǟ m ` Y  F ݞ}$y$mT8 N intjMXCD?3 Ǟ m `V, Y Z F ݞ}hy Zahxjzkyil , y,mpU84 intjMXCDAA Ǟ m `V, Y Z F ݞ}+hyZahxjzkyil , y,mV8 intjMXCD Ǟ m `U%V, Y Z F ݞ}<hZahxjzkil , y,mV8 XintjMCD Ǟ m `V, Y ZcV F ݞ}Mh Zah1xjDzkfil , y,#W8 intjMXCD Ǟ m ` Y  F ݞ}$y$bZW8G intjMXCD Ǟ m `V3 Y Z F ݞ}%^nOanxpzq3y3bW8, intjMXCD Ǟ m `V3 Y Z F ݞ}%%onOanxpzq3y3^X8  XintjMCD?AnC Ǟ m ` Y d% F ݞh} qacxzy +3 : y:X8 XintjMCDAO ǞE m ` Y d% F ݞh} qa5cxMzyg +3 : y:Y8 XintjMCD Ǟ m `U3͟W Y d% F ݞh} qacxz +3 , y,8Z8 ɣ XintjMCD Ǟ m ` Y cVd%e F ݞh} qacxz +, 3 y3Z8 intjMXCD Ǟ m ` Y  F ݞ}$y$ [88  XintjMCDAO Ǟ mG ` Y d% F ݞh}8 qacx+zyE +3 : y:[8D XintjMCD Ǟ m `U3 YY d% F ݞh}D qaacxyz +3 , y,I\8P XintjMCD Ǟ m ` Y cVd% Fg ݞh}P qacxz +, 3 y3\8ʧ intjMXCD Ǟ m ` Y  F ݞ}$y$|]8\- intjMXCD@: 1DǞ m ` Y dV F ݞh}\iacxzy+% , y,|]8d intjMXCDOBH Ǟ GFm ` Y dV F ݞh} dia cxzy+% , y,<^8l intjMXCD Ǟ m `V, eXY dV F ݞh}lpaPQcPxzP +% , y,q^8pܪ intjMXCD Ǟ m ` Y dV fF ݞh}-p^a!cxz+% , y,i_8« intjMXCD Ǟ m ` Y  F ݞ}$y$_8 % XintjMCDOB, Ǟ m `H Y d% F ݞh}? qa9cxQzyk +3 : y:>`8 XintjMCD Ǟ m `V, Y Zd% F ݞh}R qacxz +3 , y,`8 intjMXCD Ǟ m ` Y  F ݞ}$y$ea8 X intjMXCD?3 Ǟ m ` Y dV F ݞh}ehy Rahxjzkyil , y,ea8 > intjMXCDAA Ǟ m ` Y dV F ݞh}vhy Rahxjzkyil , y,g1b8$ intjMXCD Ǟ m `U% Y dV F ݞh}hTahxjzkQ il , y,eb8 intjMXCD Ǟ m ` Y cOdV F ݞh}h Rahxjzkil , y,^c8 intjMXCD Ǟ m ` Y  F ݞ}$y$bc8Q intjMXCD Ǟ m ` Y d] F ݞh}%nOan5xp5zqM3y3bd86 intjMXCD Ǟ m ` Y d] F ݞh}%%nOankxpkzq3y3d8 intjMXCD Ǟ m ` Y  F ݞ}$y$d8~ intjMXCD Ǟ m ` Y  F ݞ}$y$e8 intjMXCD Ǟ m ` Y  F ݞ}$y$>e8D intjMXCD Ǟ m ` Y  F ݞ}$y$ue8 intjMXCD Ǟ m ` Y  F ݞ}$y$e8 intjMXCD Ǟ m ` Y  F ݞ}$y$e8m intjMXCD Ǟ m ` Y  F ݞ}$y$f8ж intjMXCD Ǟ m ` Y  F ݞ}$y$Qf83 intjMXCD Ǟ m ` Y  F ݞ}$y$f8 intjMXCD Ǟ m ` Y  F ݞ}$y$f8 intjMXCD Ǟ m ` Y  F ݞ}$y$f8\ intjMXCD Ǟ m ` Y  F ݞ}$y$-g8 intjMXCD Ǟ m ` Y  F ݞ}$y$dg8" intjMXCD Ǟ m ` Y  F ݞ}$y$g8 intjMXCD Ǟ m ` Y  F ݞ}$y$g8 intjMXCD Ǟ m ` Y  F ݞ}$y$ h8K intjMXCD Ǟ m ` Y  F ݞ}$y$@h8 intjMXCD Ǟ m ` Y  F ݞ}$y$wh8 intjMXCD Ǟ m ` Y  F ݞ}$y$h8t intjMXCD Ǟ m ` Y  F ݞ}$y$h8׻ intjMXCD Ǟ m ` Y  F ݞ}$y$i8: intjMXCD Ǟ m ` Y  F ݞ}$y$Si8 intjMXCD Ǟ m ` Y  F ݞ}$y$i8 intjMXCD Ǟ m ` Y  F ݞ}$y$Si8c intjMXCD?3nC Ǟ m ` Y  F ݞ}@ayczy , y,^Pj8H intjMXCD?3AA ǞE m ` Y  F ݞ}Kaycz  , y,^j8- intjMXCD?3 Ǟ m `U%͟W Y  F ݞ}Kay= cz^  , y,^nk8 XjintMCD?, Ǟ m ` Y cVe F ݞ}Kay cz  : y:k8 intjMXCD Ǟ m ` Y  F ݞ}$y$^4l8$[ intjMXCD?3AA Ǟ mG ` Y  F ݞ} $Kay cz! , y,^l8,@ intjMXCD?3 Ǟ m `U% YY  F ݞ},Kay3!czT! , y,^Rm84% XjintMCD?, Ǟ m ` Y cV Fg ݞ}+4Kay!cz! : y:m8 intjMXCD Ǟ m ` Y  F ݞ}$y$[n8Pn intjMXCD?3@: 1DǞ m ` Y  F ݞ};PHay!cz , y,[n8XS intjMXCD?3OBH Ǟ GFm ` Y  F ݞ}LXHay!cz , y,[6o8`8 intjMXCD?3 Ǟ m `V, eXY  F ݞ}]`Hay"cz , y,[o8h  intjMXCD?3 Ǟ m ` Y dV fF ݞ}nh Hay:"cz , y,Tp8 intjMXCD Ǟ m ` Y  F ݞ}$y$^p8te intjMXCD?3OBH Ǟ m `H Y  F ݞ}tKay["cz|" , y,^q8|J intjMXCD?3 Ǟ m `V, Y Z F ݞ}|Kay"cz" , y,^q8/ XjintMCD?, Ǟ m ` Y d% F ݞh}Kay"cz# : y:8r8 intjMXCD Ǟ m ` Y  F ݞ}$y$^or8 x intjMXCD?3AAnC Ǟ m ` Y  F ݞ} KaQ#czyr# , y,Sr8\ intjMXCDAA ǞE m ` Y  F ݞ}@ay#czy# , y,^s8A intjMXCDAA Ǟ m `U%͟W Y  F ݞ}Kay#cz$ , y,^t8& XintjMCDA, Ǟ m ` Y cVe F ݞ}Kay1$czR$ : y:t8 intjMXCD Ǟ m ` Y  F ݞ}$y$St8o intjMXCDAA Ǟ mG ` Y  F ݞ}@ay$czy$ , y,^qu8T intjMXCDAA Ǟ m `U% YY  F ݞ}Kay$cz$ , y,^v89 XintjMCDA, Ǟ m ` Y cV Fg ݞ}Kay%cz@% : y:v8 intjMXCD Ǟ m ` Y  F ݞ}$y$[v8 intjMXCD@:AA 1DǞ m ` Y  F ݞ}"Has%czy , y,[Uw8g intjMXCDAAOBH Ǟ GFm ` Y  F ݞ}3Hay%cz , y,[w8L intjMXCDAA Ǟ m `V, eXY  F ݞ}DHay%cz , y,[sx8  1 intjMXCDAA Ǟ m ` Y dV fF ݞ}U  Hay%cz , y,y8 intjMXCD Ǟ m ` Y  F ݞ}$y$^9y8y intjMXCDAAOBH Ǟ m `H Y  F ݞ}fKay%cz& , y,^y8 ^ intjMXCDAA Ǟ m `V, Y Z F ݞ}w Kay?&cz`& , y,^Wz8(C XintjMCDA, Ǟ m ` Y d% F ݞh}(Kay&cz& : y:z8) intjMXCD Ǟ m ` Y  F ݞ}$y$^{8< intjMXCD?3nC Ǟ m `U% Y  F ݞ}< Ka&czy' , y,^{8H p intjMXCDAA ǞE m `U% Y  F ݞ}H Ka-'czyN' , y,S;|8TT intjMXCD Ǟ m `U%͟W Y  F ݞ}T@au'cz' , y,^|8\9 XintjMCD Ǟ m `U, Y cVe F ݞ}\Ka'cz' 3 y3Y}8 intjMXCD Ǟ m ` Y  F ݞ}$y$^}8p intjMXCDAA Ǟ mG `U% Y  F ݞ}p Ka(czy/( , y,S~8|f intjMXCD Ǟ m `U% YY  F ݞ}|@aV(czw( , y,^~8K XintjMCD Ǟ m `U, Y cV Fg ݞ}Ka(cz( 3 y3=81 intjMXCD Ǟ m ` Y  F ݞ}$y$[t8 intjMXCD@: 1DǞ m `U% Y  F ݞ} Ha(czy , y,[8y intjMXCDOBH Ǟ GFm `U% Y  F ݞ}Ha)czy , y,[8^ intjMXCD Ǟ m `U%V, eXY  F ݞ}+Ha/)cz , y,[!8 C intjMXCD Ǟ m `U% Y dV fF ݞ}< HaP)cz , y,8( intjMXCD Ǟ m ` Y  F ݞ}$y$^8 intjMXCDOBH Ǟ m `HU% Y  F ݞ}MKaq)czy) , y,^v8p intjMXCD Ǟ m `U%V, Y Z F ݞ}^Ka)cz) , y,^8U XintjMCD Ǟ m `U, Y d% F ݞh}oKa)cz* 3 y38; intjMXCD Ǟ m ` Y  F ݞ}$y$^˃8 XintjMCD?:nC Ǟ m ` Y cV F ݞ} KaN*czyv* 3 y3^Z8 XintjMCDAH ǞE m ` Y cV F ݞ} Ka*czy* 3 y3^8f XintjMCD Ǟ m `U,͟W Y cV F ݞ}Ka*cz* 3 y3Ox8K XintjMCD Ǟ m ` Y cVe F ݞ}<a+cz3y381 intjMXCD Ǟ m ` Y  F ݞ}$y$^/8 XintjMCDAH Ǟ mG ` Y cV F ݞ} Ka>+czyf+ 3 y3^8(x XintjMCD Ǟ m `U, YY cV F ݞ}(Ka}+cz+ 3 y3OM80] XintjMCD Ǟ m ` Y cV Fg ݞ}0<a+cz3y3͇8B intjMXCD Ǟ m ` Y  F ݞ}$y$[8H XintjMCD@A 1DǞ m ` Y cV F ݞ}HHa+czy 3 y3[8P XintjMCDOBO Ǟ GFm ` Y cV F ݞ}PHa,czy 3 y3["8Xo XintjMCD Ǟ m `V3 eXY cV F ݞ}XHa?,cz 3 y3[8` T XintjMCD Ǟ m ` Y cVd% fF ݞ}#` Hag,cz 3 y3@89 intjMXCD Ǟ m ` Y  F ݞ}$y$^w8l XintjMCDOBO Ǟ m `H Y cV F ݞ}4lKa,czy, 3 y3^8t XintjMCD Ǟ m `V3 Y ZcV F ݞ}EtKa,cz, 3 y3Z8|f XintjMCD Ǟ m ` Y cVd% F ݞh}V|Ga-cz3y38L intjMXCD Ǟ m ` Y  F ݞ}$y$L8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8u intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$(8; intjMXCD Ǟ m ` Y  F ݞ}$y$_8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$͍8d intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$;8* intjMXCD Ǟ m ` Y  F ݞ}$y$r8 intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$8S intjMXCD Ǟ m ` Y  F ݞ}$y$8 intjMXCD Ǟ m ` Y  F ݞ}$y$N8 intjMXCD Ǟ m ` Y  F ݞ}$y$8| intjMXCD Ǟ m ` Y  F ݞ}$y$8 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ݞ}%%n| [a|6H~6z(7 c 3 y3n8 f XintjMCD Ǟ m ` Y c]e F ݞ}%%~| [a|n7H~7z7 c : y:"8KintjMXCD Ǟ m ` Y  F ݞ}$y$nY84 intjMXCDA, Ǟ mG ` Y  F ݞ}%%|4 [a|8H~>8z|8 c : y:n8P intjMXCD Ǟ m `U, YY  F ݞ}%%|P [a|8H~8z(9 c 3 y3n8l xXintjMCD Ǟ m ` Y c] Fg ݞ}%%|l [a|n9H~9z9 c : y:38]intjMXCD Ǟ m ` Y  F ݞ}$y$nj8 intjMXCD@, 1DǞ m ` Y  F ݞ}%%| [a|:H~>:z|: c : y:n8 intjMXCDOB, Ǟ GFm ` Y  F ݞ}%%| [a|:H~:z(; c : y:n8 intjMXCD Ǟ m `V, eXY  F ݞ}%%| [a|n;H~;z; c , y,nD8 oXintjMCD Ǟ m ` Y d, fF ݞ}%%| [a|<H~><z|< c : y:8TintjMXCD Ǟ m ` Y  F ݞ}$y$n8 intjMXCDOB, Ǟ m `H Y  F ݞ}%%| [a|<H~<z$= c : y:n8 intjMXCD Ǟ m `V, Y Z F ݞ}%%| [a|j=H~=z= c , y,nU80 XintjMCD Ǟ m ` Y d, F ݞh}%%$|0 [a|>H~:>zx> c : y:8fintjMXCD Ǟ m ` Y  F ݞ}$y$*8L xintjMXCD?3AAOBHnC ǞE m ` Y  F ݞ}JL xa>cPzPxy> o>low-? ; o f ^~ Dd? 5~ + % , y, 8 `intjMXCD?3nC Ǟ m `U%V,͟W Y  F ݞ}w `a?cPzPxy? o?low? ; o f ^x D? 5x + % , y,!Ĥ8$ XjintMCD?,nC Ǟ m ` Y cVd%e F ݞ}$ a@czPQ xy@ o*@ lowf@ ; o f ^D%8%% 5 + 38:y:8intjMXCD Ǟ m ` Y  F ݞ}$y$8 :intjMXCD?3AAOBHnC Ǟ mG ` Y  F ݞ} a@cz@xy@ olow@ ; o f ^~ 5~ , y, 8 `#intjMXCD?3nC Ǟ m `U%V, YY  F ݞ} `a@cPzPxy@ o@low@ ; o f ^x DA 5x + % , y,m8\ XjintMCD?,nC Ǟ m ` Y cVd% Fg ݞ}\ a5AczHAxy5A opAlowA ; o f ^ DA 5 + 3 : y:883intjMXCD Ǟ m ` Y  F ݞ}$y$o8@DintjMXCD?3@:nC 1DǞ m ` Y  F ݞ}@DaBcPzPxy&B oyOBlowypB ;y oy DB + % , y,-8T intjMXCD?3AAOBHnC Ǟ GFm ` Y  F ݞ}TaBcPzPxyC oClow:C ; o D^C + % , y,8D!intjMXCD?3nC Ǟ m `U%V, eXY  F ݞ}DaCcPzPxyC oClowC ; o DC + % , y,8h"XjintMCD?3nC Ǟ m ` Y cVd% fF ݞ}haCDczSR xyVD oiD lowD ; o DD + :9 A yAt8#intjMXCD Ǟ m ` Y  F ݞ}$y$8P$intjMXCD?3AAOBHnC Ǟ m `H Y  F ݞ}(PaDcPzPxyD oDlow E ; o DDE + % , y,i8D%intjMXCD?3nC Ǟ m `U%V, Y Z F ݞ}<DaEcPzPxyE oElowE ; o DE + % , y,'8t &XjintMCD?3nC Ǟ m ` Y cVd% F ݞh }PtaGFczSR xyZF omF lowF ; o DF + : A yA8&'intjMXCD Ǟ m ` Y  F ݞ}$y$)8d'intjMXCD?3AAOBHnC ǞE m ` Y  F ݞ}ddaFcPzyPxF oGlow G ; o f ^ 5 + % , y, 8`(intjMXCDAA ǞE m `U%V,͟W Y  F ݞ}x`a\GcPzPxyoG oGlowG ; o f ^x DG 5x + % , y,!8Py)XintjMCDA, ǞE m ` Y cVd%e F ݞ}PaGczPQ xyH oH low[H ; o f ^D%@%% 5 + 30:y:8*intjMXCD Ǟ m ` Y  F ݞ}$y$԰8X*intjMXCDAAOBH ǞE mG ` Y  F ݞ}XaHcPzyPxyH oyHlowyH ;y oy fy ^y~ 5y~ + % , y,8t +intjMXCDAA ǞE m `U%V, YY  F ݞ}t a@IczSIxy@I olowSI ; o f ^x 5x , y,]8,XintjMCDA, ǞE m ` Y cVd% Fg ݞ}alIczIxyI oIlowI ; o f ^ D9J 5 + 3 : y:(8-intjMXCD Ǟ m ` Y  F ݞ}$y$_8d(T.intjMXCD@:AAOBH 1DǞE m ` Y  F ݞ}d(agJcPzyPxzJ olowJ ; o + % , y,8LI/intjMXCDAAOBH ǞE GFm ` Y  F ݞ}LaJcPzPxyJ oyKlowy%K ;y oy DzK + % , y,8@F0intjMXCDAA ǞE m `U%V, eXY  F ݞ}@aKcPzPxyK oKlowK ; o D#L + % , y,{8dI1XintjMCDA, ǞE m ` Y cVd% fF ݞ}darLczSR xyL oL lowL ; o DL + :1 A yAF8U2intjMXCD Ǟ m ` Y  F ݞ}$y$}8|2intjMXCDAAOBH ǞE m `H Y  F ݞ})|a McPzPxyM oylowy7M ;y oy + % , y,,8@3intjMXCDAA ǞE m `U%V, Y Z F ݞ}=@a]McPzPxypM oMlowM ; o DM + % , y,8p4XintjMCDA, ǞE m ` Y cVd% F ݞh }Qpa NczSR xy3N oJN lowtN ; o DN + : A yA85intjMXCD Ǟ m ` Y  F ݞ}$y$8DH&6intjMXCD?3nC Ǟ m `U%V,͟W Y  F ݞ}eDHaNcSzySxN oNlowN ; o f ^ 5 + % , y,8X7intjMXCDAA ǞE m `U%V,͟W Y  F ݞ}yXa&OcSzySx&O o9OlowiO ; o f ^~ 5~ + % , y,w8 8XintjMCD Ǟ m `U,͟W Y cVd%e F ݞ} aOcz\R xO oO lowO ; o f ^ D P 5 + , 3y3Q89intjMXCD Ǟ m ` Y  F ݞ}$y$8X{9intjMXCDAA Ǟ mG `U%V,͟W Y  F ݞ}XaNPcSzySxNP oaPlowP ; o f ^~ 5~ + % , y,U8Dl:intjMXCD Ǟ m `U%V,͟W YY  F ݞ}DaPcPzPxP oPlowQ ; o f ^x 5x + % , y,"8@`;intjMXCD Ǟ m `U%͟W Y cOdV Fg ݞ}@aPczxolow; o f ^ 5 , y,Ͻ8F<intjMXCD Ǟ m ` Y  F ݞ}$y$8H <intjMXCD@: 1DǞ m `U%V,͟W Y  F ݞ}H a9QcPzyPxLQ olowjQ ; o + % , y,8h(=intjMXCDOBH Ǟ GFm `U%V,͟W Y  F ݞ}h(aQcPzyPxQ olowQ ; o + % , y,U80>intjMXCD Ǟ m `U%V,͟W eXY  F ݞ}0aRcPzPxR oDaScPzPxS olowS ; o + % , y,8T`BXintjMCD Ǟ m `U,͟W Y cVd% F ݞh }RT`aTcPQzPQxT oETlowoT ; o DT + , : y:@8CintjMXCD Ǟ m ` Y  F ݞ}$y$w8|:DintjMXCD?3nC Ǟ m ` Y cOdVe F ݞ}f|aUcPzyPx o0Ulow ; o f ^ 5 + %8 , y,3808EintjMXCDAA ǞE m ` Y cOdVe F ݞ}z0aXUcPzyPx opUlow ; o f ^~ 5~ + %0 , y,88FintjMXCD Ǟ m `U%͟W Y cOdVe F ݞ}aUcPzPx oUlow ; o f ^x 5x + % , y,87GintjMXCD Ǟ m ` Y  F ݞ}$y$8DGintjMXCDAA Ǟ mG ` Y cOdVe F ݞ}DaUcPzyPx oUlow ; o f ^~ 5~ + %8 , y,8HintjMXCD Ǟ m `U% YY cOdVe F ݞ}aVcPzPx o0Vlow ; o f ^x 5x + %0 , y,8XIintjMXCD Ǟ m ` Y cOdVe Fg ݞ}XaXVczpVx oVlowV ; o f ^ 5 + % , y,a8JintjMXCD Ǟ m ` Y  F ݞ}$y$8,JXintjMCD@A 1DǞ m ` Y cVd%e F ݞ},aWcRzyRxW o WlowXintjMXCD Ǟ m ` Y  F ݞ}$y$8XintjMXCD Ǟ m ` Y  F ݞ}$y$8h<YintjMXCD?3AAOBHnC Ǟ mG ` Y  F ݞ}gh<aYcPzyPxY olowY ; o f ^ 5 , y,8|YintjMXCDAAOBH ǞE mG ` Y  F ݞ}z|aZcPzyPxy#Z oyIZlowyZ ;y oy fy ^y~ DZ 5y~ + % , y, W8 hZintjMXCDAA Ǟ mG `U%V,͟W Y  F ݞ} ha[cPzPxy$[ o=[low\[ ; o f ^x D[ 5x + % , y,!$8[XintjMCDA, Ǟ mG ` Y cVd%e F ݞ}a[czPQ xy[ o[ low\ ; o f ^D%8%% 5 + 38:y: 8]intjMXCD Ǟ m ` Y  F ݞ}$y$ D8T`k]intjMXCDAA Ǟ mG `U%V, YY  F ݞ}T`aJ\cPzPxy]\ ov\low\ ; o f ^x D\ 5x + % , y,8c^XintjMCDA, Ǟ mG ` Y cVd% Fg ݞ}a\cz\xy] o5]lowq] ; o f ^ D] 5 + 3 : y:8{_intjMXCD Ǟ m ` Y  F ݞ}$y$8 P_intjMXCD@:AAOBH 1DǞ mG ` Y  F ݞ} Pa]cPzyPx] o$^lowH^ ; o D^ + % , y,8 L`intjMXCDAAOBH Ǟ GFmG ` Y  F ݞ} La^cPzPxy^ oy_lowy>_ ;y oy D_ + % , y,84!@aintjMXCDAA Ǟ mG `U%V, eXY  F ݞ}4!@a_cPzPxy_ o_low` ; o D<` + % , y,>8t!dbXintjMCDA, Ǟ mG ` Y cVd% fF ݞ}t!da`czSR xy` o` low` ; o D a + :9 A yA 8cintjMXCD Ǟ m ` Y  F ݞ}$y$@8!LKdintjMXCDAAOBH Ǟ mG `H Y  F ݞ}+!La$acPzPxy7a oylalowya ;y oy Da + % , y,8$"@HeintjMXCDAA Ǟ mG `U%V, Y Z F ݞ}?$"@abcPzPxy-b oDblowhb ; o Db + % , y,8d"pKfXintjMCDA, Ǟ mG ` Y cVd% F ݞh }Sd"pabczSR xyb oc low1c ; o D\c + : A yA8dgintjMXCD Ǟ m ` Y  F ݞ}$y$8"HgintjMXCD?3nC Ǟ m `U%V, YY  F ݞ}g"HawccSzySxwc oclowc ; o f ^ 5 + % , y,|8#8hintjMXCDAA ǞE m `U%V, YY  F ݞ}z#8accPzyPxc olowd ; o f ^~ 5~ , y,:8T#`iintjMXCD Ǟ m `U%V,͟W YY  F ݞ}T#`adcSzSxd o2dlowd ; o f ^x Dd 5x + % , y,!8#jXintjMCD Ǟ m `U, YY cVd%e F ݞ}#aecz\Q xe o1e lowme ; o f ^D%@%% 5 + ,03y38kintjMXCD Ǟ m ` Y  F ݞ}$y$'8|$XlintjMXCDAA Ǟ mG `U%V, YY  F ݞ}|$XaecSzySxe oelowf ; o f ^~ 5~ + % , y,8$mXintjMCD Ǟ m `U, YY cVd% Fg ݞ}$a.fczAfxif oflowf ; o f ^ DFg 5 + , 3 y38nintjMXCD Ǟ m ` Y  F ݞ}$y$8% vnintjMXCD@: 1DǞ m `U%V, YY  F ݞ}% atgcPzyPxg olowg ; o + % , y,8%@fointjMXCDOBH Ǟ GFm `U%V, YY  F ݞ}%@agcPzyPxg o hlow+h ; o Dh + % , y,T8&8`pintjMXCD Ǟ m `U%V, eXYY  F ݞ}&8ahcPzPxh ohlowi ; o D^i + % , y,8H&\\qXintjMCD Ǟ m `U, YY cVd% fF ݞ}H&\aiczSR xi oi lowj ; o DGj + ,1 : y:8drintjMXCD Ǟ m ` Y  F ݞ}$y$8&(rintjMXCDOBH Ǟ m `HU%V, YY  F ݞ}+&(aajcPzyPxtj olowj ; o + % , y,8&0sintjMXCD Ǟ m `U%V, YY Z F ݞ}?&0ajcPzPxj ojlowk ; o DS + % , y,8&htXintjMCD Ǟ m `U, YY cVd% F ݞh }S&hankczSR xk ok lowk ; o Dl + , : y:[8uintjMXCD Ǟ m ` Y  F ݞ}$y$8d'"vintjMXCD?3nC Ǟ m ` Y cOdV Fg ݞ}gd'a2lcPzyPxJl orllowJl ; o f ^ 5 + % , y,P8'*wintjMXCDAA ǞE m ` Y cOdV Fg ݞ}z'alcPzyPxl ollowl ; o f ^~ 5~ + % , y,8(L*xintjMXCD Ǟ m `U%͟W Y cOdV Fg ݞ}(LalcPzPxl olowl ; o f ^x 5x , y, 8(yintjMXCD Ǟ m ` Y cOdVe Fg ݞ}(amcz\R xm o2m lownm ; o f ^D 5 + % ,y,8#zintjMXCD Ǟ m ` Y  F ݞ}$y$8)zintjMXCDAA Ǟ mG ` Y cOdV Fg ݞ})amcPzyPxm omlowm ; o f ^~ 5~ + % , y,8*{intjMXCD Ǟ m `U% YY cOdV Fg ݞ}*amcPzPxm omlowm ; o f ^x 5x + % , y,8|intjMXCD Ǟ m ` Y  F ݞ}$y$8*0|XintjMCD@A 1DǞ m ` Y cVd% Fg ݞ}*0ancRzyRxn o.nlowJn ; o + , 3 y3y8*<}XintjMCDOBO Ǟ GFm ` Y cVd% Fg ݞ}*<adncRzyRxdn o|nlown ; o + , 3 y378 +l~XintjMCD Ǟ m `V: eXY cVd% Fg ݞ} +lancPzPxn onlowo ; o D + 3 : y:8x+\XintjMCD Ǟ m ` Y cVd% fFg ݞ}x+\aPoczSR xho oo lowo ; o D + 3!:y:8рintjMXCD Ǟ m ` Y  F ݞ}$y$8+<4XintjMCDOBO Ǟ m `H Y cVd% Fg ݞ}++<aocRzyRxo o plow&p ; o + , 3 y38,8&XintjMCD Ǟ m `V3 Y ZcVd% Fg ݞ}?,8a@pcRzRx@p oXplowtp ; o + , 3 y38H,`XintjMCD Ǟ m ` Y cVd% Fg ݞh}SH,`apcPQzPQxp oplowp ; o D + 3 : y:X8intjMXCD Ǟ m ` Y  F ݞ}$y$8yintjMXCD Ǟ m ` Y  F ݞ}$y$8܄intjMXCD Ǟ m ` Y  F ݞ}$y$8?intjMXCD Ǟ m ` Y  F ݞ}$y$48intjMXCD Ǟ m ` Y  F ݞ}$y$k8intjMXCD Ǟ m ` Y  F ݞ}$y$8hintjMXCD Ǟ m ` Y  F ݞ}$y$8ˆintjMXCD Ǟ m ` Y  F ݞ}$y$8.intjMXCD Ǟ m ` Y  F ݞ}$y$G8intjMXCD Ǟ m ` Y  F ݞ}$y$~8intjMXCD Ǟ m ` Y  F ݞ}$y$8WintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$#8intjMXCD Ǟ m ` Y  F ݞ}$y$Z8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8FintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$68, intjMXCD?3@:nC 1DǞ m ` Y  F ݞ}g, aNqczyaqxNq olowaq ; o 5y + % , y,8, intjMXCD@:AAOBH 1DǞE m ` Y  F ݞ}{, axqczqxyxq olowq ; o D 5~ + % , y,8, ݌intjMXCD@: 1DǞ m `U%V,͟W Y  F ݞ}, aqczqxyq olowq ; o D 5x + % , y,[8,ƍintjMXCD@: 1DǞ m ` Y cOdVe F ݞ},aqczxyolow; oD 5+ %7 , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$?8, intjMXCD@:AAOBH 1DǞ mG ` Y  F ݞ}, aqczqxyq olowq ; o 5~ + % , y,8, intjMXCD@: 1DǞ m `U%V, YY  F ݞ}, arcz%rxyr olow%r ; o D 5x + % , y,8,XintjMCD@, 1DǞ m ` Y cVd% Fg ݞ},a?rczRrxy?r olowRr ; o D 5 + 3 : y:s8ΑintjMXCD Ǟ m ` Y  F ݞ}$y$8- 1intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}- aorczrxyor olowr ; o D + % , y,W8-intjMXCD@: 1DǞ m `V, eXY  F ݞ} -arczrxyr olowr ; o D + % , y,8-intjMXCD@: 1DǞ m ` Y dV fF ݞ}-arczxyolow; oD+ %8 , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$8,-JintjMXCD@:OBH 1DǞ m `H Y  F ݞ}5,-arczrxyr olowr ; o , y,84- 3intjMXCD@: 1DǞ m `V, Y Z F ݞ}J4- asczsxys olows ; o D + % , y,38@-XintjMCD@, 1DǞ m ` Y d% F ݞh}_@-a4sczGsxy4s olowGs ; o D + 3 : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$8T- fintjMXCD?3AAOBHnC Ǟ GFm ` Y  F ݞ}tT- adsczywsxds olows ; o 5 + % , y,8`- NintjMXCDAAOBH ǞE GFm ` Y  F ݞ}`- asczysxs olows ; o 5y~ + % , y,8l- 6intjMXCDOBH Ǟ GFm `U%V,͟W Y  F ݞ}l- asczsxys olows ; o D 5x + % , y,<8x-XintjMCDOB, Ǟ GFm ` Y cVd%e F ݞ}x-atcztxyt olowt ; o D 5 + 3/ : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$/8- lintjMXCDAAOBH Ǟ GFmG ` Y  F ݞ}- a2tczyEtx2t olowEt ; o 5y~ + % , y,8- TintjMXCDOBH Ǟ GFm `U%V, YY  F ݞ}- a\tczotxy\t olowot ; o 5x + % , y,8-=XintjMCDOB, Ǟ GFm ` Y cVd% Fg ݞ}-atcztxyt olowt ; o D 5 + 3 : y:c8'intjMXCD Ǟ m ` Y  F ݞ}$y$8- intjMXCD@:OBH 1DǞ GFm ` Y  F ݞ}- atczytxt olowt ; o + % , y,88-rintjMXCDOBH Ǟ GFm `V, eXY  F ݞ}-atczuxyt olowu ; o D + % , y,8-XXintjMCDOB, Ǟ GFm ` Y d% fF ݞ},-a+ucz>uxy+u olow>u ; o D + 30 : y:8?intjMXCD Ǟ m ` Y  F ݞ}$y$8- intjMXCDOBH Ǟ GFm `H Y  F ݞ}B- a\uczyouxy\u oylowyou ;y oy + % , y,v8- intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}W- auczuxyu olowu ; o , y,# 8-sXintjMCDOB, Ǟ GFm ` Y d% F ݞh}l-auczuxyu olowu ; o D + 3 : y: 8ZintjMXCD Ǟ m ` Y  F ݞ}$y$ 8.intjMXCD?3nC Ǟ m `U%V, eXY  F ݞ}.auczyuxu olow v ; o 5 + % , y, 8 .intjMXCDAA ǞE m `U%V, eXY  F ݞ} .a.vczyAvx.v olowXv ; o 5~ + % , y,p 8.intjMXCD Ǟ m `U%V,͟W eXY  F ݞ}.azvczvxzv olowv ; o 5x + % , y,, 8.oXintjMCD Ǟ m `V, eXY cVd%e F ݞ}.avczvxv olowv ; o D 5 + 3 , y, 8VintjMXCD Ǟ m ` Y  F ݞ}$y$ 80.intjMXCDAA Ǟ mG `U%V, eXY  F ݞ}0.avczyvxv olow w ; o 5~ + % , y, 88.intjMXCD Ǟ m `U%V, eXYY  F ݞ}8.a+wcz>wx+w olow>w ; o 5x + % , y,8@.XintjMCD Ǟ m `V3 eXY cVd% Fg ݞ}@.a`wczswx olowsw ; o 5 + , 3 y3S8nintjMXCD Ǟ m ` Y  F ݞ}$y$8P.ѬintjMXCD@: 1DǞ m `V, eXY  F ݞ} P.awczywxw oloww ; o + % , y,(8X.intjMXCDOBH Ǟ GFm `V, eXY  F ݞ}#X.awczywxw olowx ; o + % , y,8`.XintjMCD Ǟ m `V, eXY d% fF ݞ}9`.a5xczHxx5x olowHx ; o D + 3 , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$8t.intjMXCDOBH Ǟ m `HV, eXY  F ݞ}Ot.afxczyyxxfx olowx ; o + % , y,f8|.ͰintjMXCD Ǟ m `V, eXY Z F ݞ}d|.axczxxx olowx ; o + % , y,8.intjMXCD Ǟ m `V, eXY dV F ݞh}y.aPczxolow; o , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$8.intjMXCD?3nC Ǟ m ` Y cOdV fF ݞ}.axczyxolow;o 5+ %9 , y,8.intjMXCDAA ǞE m ` Y cOdV fF ݞ}.axczyxolow; o 5~+ %1 , y,38.ȴintjMXCD Ǟ m `U%͟W Y cOdV fF ݞ}.ayczxolow; o 5x+ %! , y,8. XintjMCD Ǟ m ` Y cVd%e fF ݞ}. a/yczGyx/y olowGy ; o 5 + , 3 y38intjMXCD Ǟ m ` Y  F ݞ}$y$8.intjMXCDAA Ǟ mG ` Y cOdV fF ݞ}.aqyczyxolow; o 5~+ %9 , y,8.ݷintjMXCD Ǟ m `U% YY cOdV fF ݞ}.ayczxolow; o 5x+ %1 , y,-8.øintjMXCD Ǟ m ` Y cOdV fFg ݞ}.ayczxolow; o 5+ %! , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$8. intjMXCD@: 1DǞ m ` Y dV fF ݞ}.ayczyxolow;o+ %8 , y,8.intjMXCDOBH Ǟ GFm ` Y dV fF ݞ}0.ayczyxolow; o+ %0 , y,>8.ٻintjMXCD Ǟ m `V, eXY dV fF ݞ}F.aycQzQx olow ; o + % , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$"8."intjMXCDOBH Ǟ m `H Y dV fF ݞ}\.azczyxolow; o+ %8 , y,8.intjMXCD Ǟ m `V, Y ZdV fF ݞ}q.azczxolow; o+ %0 , y,^8.intjMXCD Ǟ m ` Y dV fF ݞh}.a1zczxolow; o+ % , y,8տintjMXCD Ǟ m ` Y  F ݞ}$y$388intjMXCD Ǟ m ` Y  F ݞ}$y$j8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8aintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$F8'intjMXCD Ǟ m ` Y  F ݞ}$y$}8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8PintjMXCD Ǟ m ` Y  F ݞ}$y$"8intjMXCD Ǟ m ` Y  F ݞ}$y$Y8intjMXCD Ǟ m ` Y  F ݞ}$y$8yintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8?intjMXCD Ǟ m ` Y  F ݞ}$y$5 8intjMXCD Ǟ m ` Y  F ݞ}$y$l 8intjMXCD Ǟ m ` Y  F ݞ}$y$ 8hintjMXCD Ǟ m ` Y  F ݞ}$y$ 8 /intjMXCD?3AAOBHnC Ǟ m `H Y  F ݞ} /aIzcPzyPxIz olow\z ; o 5 + % , y,!8 /4intjMXCDAAOBH ǞE m `H Y  F ݞ} /4azzcPzyPxzz ozlowz ; o Dz 5y~ + % , y,V"8T/(intjMXCDOBH Ǟ m `HU%V,͟W Y  F ݞ}T/(a,{cPzPxy,{ o?{lowe{ ; o D?{ 5x + % , y,##8|/DXintjMCDOBO Ǟ m `H Y cVd%e F ݞ}|/Da{cPQzPQxy{ o |,%9%%low{ ; o D |,%9%% 5 + 37 , y,#8intjMXCD Ǟ m ` Y  F ݞ}$y$'$8/ intjMXCDAAOBH Ǟ mG `H Y  F ݞ}/ a{czy{x{ olow{ ; o 5y~ + % , y,$8/ intjMXCDOBH Ǟ m `HU%V, YY  F ݞ}/ a |cz|xy | olow| ; o D 5x + % , y,%8/XintjMCDOB, Ǟ m `H Y cVd% Fg ݞ}/a:|czM|xy:| olowM| ; o D 5 + 3 : y:[&8intjMXCD Ǟ m ` Y  F ݞ}$y$&8/intjMXCD@:OBH 1DǞ m `H Y  F ݞ}'/aj|czy}|x| olow}| ; o , y,0'8/ intjMXCDOBH Ǟ GFm `H Y  F ݞ}</ a|cPzyPxy| oy|lowy| ;y oy Dy} + % , y,'80intjMXCDOBH Ǟ m `HV, eXY  F ݞ}Q0a=}cPzPxy=} oP}lowg} ; o DP} + % , y,(8404XintjMCDOBO Ǟ m `H Y d% fF ݞ}f404a}czRS xy} o} low} ; o D} + 38 , y,w)8intjMXCD Ǟ m ` Y  F ݞ}$y$)8h0 KintjMXCDOBH Ǟ m `HV, Y Z F ݞ}{h0 a~cz)~xy~ olow)~ ; o D + % , y,[*8t04XintjMCDOB, Ǟ m `H Y d% F ݞh}t0aD~czW~xyD~ olowW~ ; o D + 3 : y:+8intjMXCD Ǟ m ` Y  F ݞ}$y$?+80intjMXCD?3nC Ǟ m `U%V, Y Z F ݞ}0at~czy~xt~ olow~ ; o 5 + % , y,+80iintjMXCDAA ǞE m `U%V, Y Z F ݞ}0a~czy~x~ olow ; o 5~ + % , y,,80 QintjMXCD Ǟ m `U%V,͟W Y Z F ݞ}0 aRcPzPxR oplow ; o Dp 5x + % , y,u-80D@XintjMCD Ǟ m `V, Y ZcVd%e F ݞ}0DacRSzRSx olow ; o D 5 + 3/ , y,B.88intjMXCD Ǟ m ` Y  F ݞ}$y$y.81intjMXCDAA Ǟ mG `U%V, Y Z F ݞ}1a0czyCx0 olowl ; o 5~ + % , y,5/8(1intjMXCD Ǟ m `U%V, YY Z F ݞ} (1aczx olow ; o 5x + % , y,/801iXintjMCD Ǟ m `V, Y ZcVd% Fg ݞ}01aՀczxՀ olow ; o D 5 + 3 , y,08PintjMXCD Ǟ m ` Y  F ݞ}$y$08D1intjMXCD@: 1DǞ m `V, Y Z F ݞ}3D1aczyx olow@ ; o + % , y,18T1intjMXCDOBH Ǟ GFm `V, Y Z F ݞ}HT1asczyxs olow ; o , y,/28h1intjMXCD Ǟ m `V, eXY Z F ݞ}]h1acPzPx olowف ; o D + % , y,28x1,pXintjMCD Ǟ m `V, Y Zd% fF ݞ}rx1,aczRS x o low+ ; o D + 30 , y,38cintjMXCD Ǟ m ` Y  F ݞ}$y$381intjMXCDOBH Ǟ m `HV, Y Z F ݞ}1aYczylxY olow ; o + % , y,481XintjMCD Ǟ m `V, Y Zd% F ݞh}1aɂcz܂xɂ olow܂ ; o D + 3 , y,I58intjMXCD Ǟ m ` Y  F ݞ}$y$581 intjMXCD?3nC Ǟ m ` Y cOdV F ݞh }1 aczyx olow ; o 5 + % , y,-681,intjMXCDAA ǞE m ` Y cOdV F ݞh }1,aczyx olow ; o 5~ + % , y,682 XintjMCD Ǟ m `U3͟W Y cVd% F ݞh }2 a)czAx) olowm ; o 5x + 3 , y,78<2DintjMXCD Ǟ m ` Y cOdVe F ݞh}<2DacPQzPQx olowPQ ; o D 5 + % , y,n88intjMXCD Ǟ m ` Y  F ݞ}$y$882,intjMXCDAA Ǟ mG ` Y cOdV F ݞh }2,aczyx olow ; o 5~ + % , y,a982(intjMXCD Ǟ m `U% YY cOdV F ݞh }2(aǃczxǃ olowǃ ; o 5x + % , y,:82 XintjMCD Ǟ m ` Y cVd% Fg ݞh}+2 a߃czx߃ olow ; o 5 + , 3 y3:8intjMXCD Ǟ m ` Y  F ݞ}$y$;82 -intjMXCD@: 1DǞ m ` Y dV F ݞh}?2 a!czyx! olow! ; o + % , y,;83,intjMXCDOBH Ǟ GFm ` Y dV F ݞh}T3,a9czyx9 olow9 ; o + % , y,[<8,3intjMXCD Ǟ m `V, eXY dV F ݞh}i,3aQczxolow; o , y,<8D3,intjMXCD Ǟ m ` Y dV fF ݞh}~D3,aiczRS x o lowɄ ; o D + % , y,=8intjMXCD Ǟ m ` Y  F ݞ}$y$=8p3,<intjMXCDOBH Ǟ m `H Y dV F ݞh}p3,aczyx olow ; o + % , y,>83($intjMXCD Ǟ m `V, Y ZdV F ݞh}3(aczx olow ; o + % , y,S?8 intjMXCD Ǟ m ` Y  F ݞ}$y$?8ointjMXCD Ǟ m ` Y  F ݞ}$y$?8intjMXCD Ǟ m ` Y  F ݞ}$y$?85intjMXCD Ǟ m ` Y  F ݞ}$y$/@8intjMXCD Ǟ m ` Y  F ݞ}$y$f@8intjMXCD Ǟ m ` Y  F ݞ}$y$@8^intjMXCD Ǟ m ` Y  F ݞ}$y$@8intjMXCD Ǟ m ` Y  F ݞ}$y$ A8$intjMXCD Ǟ m ` Y  F ݞ}$y$BA8intjMXCD Ǟ m ` Y  F ݞ}$y$yA8intjMXCD Ǟ m ` Y  F ݞ}$y$A8MintjMXCD Ǟ m ` Y  F ݞ}$y$A8intjMXCD Ǟ m ` Y  F ݞ}$y$B8intjMXCD Ǟ m ` Y  F ݞ}$y$UB8vintjMXCD Ǟ m ` Y  F ݞ}$y$B8intjMXCD Ǟ m ` Y  F ݞ}$y$B8<intjMXCD Ǟ m ` Y  F ݞ}$y$B8intjMXCD Ǟ m ` Y  F ݞ}$y$1C83tintjMXCD?3@:nC Ǟ m ` Y  F ݞ}3taycPzyPxy oy2lowym ;y oy fy ^y 5y D +% , y,C884|intjMXCD?3AAOBH ǞE m ` Y  F ݞ}84|ayхcPzPxyх olowP ; o f ^~ 5~ D +% , y,D84\intjMXCD?3 Ǟ m `U%V,͟W Y  F ݞ}4\aycSzSxy o҆low ; o f ^x 5x DG +% , y,E85XjintMCD?, Ǟ m ` Y cVd%e F ݞ}5ay^cz^Q xy^ o low ; o f ^ 5 D +3?:y:cF8intjMXCD Ǟ m ` Y  F ݞ}$y$F85|]intjMXCD?3AAOBH Ǟ mG ` Y  F ݞ} 5|ay;cPzPxy; o\lowp ; o f ^~ 5~ D +% , y,gG8L6`[intjMXCD?3 Ǟ m `U%V, YY  F ݞ}L6`ayψcSzSxyψ olowF ; o f ^x 5x D} +% , y,4H86QXjintMCD?, Ǟ m ` Y cVd% Fg ݞ}16ayczωxy olow3 ; o f ^ 5 D +3 : y:H8]intjMXCD Ǟ m ` Y  F ݞ}$y$6I87intjMXCD?3@: 1DǞ m ` Y  F ݞ}D7ayŊczxyŊ oyŊlowy ;y oy , y,I87intjMXCD?3AAOBH Ǟ GFm ` Y  F ݞ}X7aycz4xy oglow4 ; o , y,J87intjMXCD?3 Ǟ m `U%V, eXY  F ݞ}l7ayczxy olow ; o , y,jK87rXjintMCD?3 Ǟ m ` Y cVd% fF ݞ}7aycz2xy oglow2 ; o A yA&L8ZintjMXCD Ǟ m ` Y  F ݞ}$y$]L87HintjMXCD?3AAOBH Ǟ m `H Y  F ݞ}7HaycPzPxy olow  ; o D +% , y,M888intjMXCD?3 Ǟ m `U%V, Y Z F ݞ}88ayÍcPzPxyÍ olow: ; o D +% , y,M8P8TXjintMCD?, Ǟ m ` Y cVd% F ݞh}P8Taycz/xy oWlow ; o DЏ +: A yAN8intjMXCD Ǟ m ` Y  F ݞ}$y$N88!intjMXCD?3AAOBHnC Ǟ m ` Y  F ݞ}8acPzyPx o lowf ; o f ^ 5 D +% , y,O8,9|intjMXCDAAOBH ǞE m ` Y  F ݞ},9|ay֐cPzyPxy֐ oylowyU ;y oy fy ^y~ 5y~ D +% , y,WP89\intjMXCDAA Ǟ m `U%V,͟W Y  F ݞ}9\aycSzSxy o͑low ; o f ^x 5x DB +% , y,$Q8: XintjMCDA, Ǟ m ` Y cVd%e F ݞ} :ayYcz^Q xyY oz low ; o f ^ 5 D  +3?:y:Q8 intjMXCD Ǟ m ` Y  F ݞ}$y$5R8:|y intjMXCDAAOBH Ǟ mG ` Y  F ݞ}:|ay6cPzyPxy6 oyWlowyk ;y oy fy ^y~ 5y~ D +% , y,S8@;`w intjMXCDAA Ǟ m `U%V, YY  F ݞ}/@;`aycSzSxy oۓlow1 ; o f ^x 5x Dh +% , y,S8;m XintjMCDA, Ǟ m ` Y cVd% Fg ݞ}B;ayczxy olow ; o f ^ 5 D +3 : y:T8y intjMXCD Ǟ m ` Y  F ݞ}$y$T8|<P intjMXCD@:AAOBH 1DǞ m ` Y  F ݞ}U|<PacPzyPx olow' ; o DqH% +% , y,U8< intjMXCDAAOBH Ǟ GFm ` Y  F ݞ}i<ayczxy oylowy ;y oy , y,KV8<intjMXCDAA Ǟ m `U%V, eXY  F ݞ}}<ayczxy o:low ; o , y,W8<XintjMCDA3 Ǟ m ` Y cVd% fF ݞ}<ayeczxye olow ; o A yAW8intjMXCD Ǟ m ` Y  F ݞ}$y$W8=LintjMXCDAAOBH Ǟ m `H Y  F ݞ}=LaycPzPxy oy9lowy] ;y oy DqH% +% , y,X8P=4intjMXCDAA Ǟ m `U%V, Y Z F ݞ}P=4ayȘcPzPxyȘ olow ; o Db +% , y,Y8=TXintjMCDA, Ǟ m ` Y cVd% F ݞh}=Tayczxy o͙low ; o DF +: A yAAZ8intjMXCD Ǟ m ` Y  F ݞ}$y$xZ8=lSintjMXCD?3nC Ǟ m `U%V, Y  F ݞ}=laacPzyPxa olowĚ ; o f ^ 5 D  +% , y,6[8D>|PintjMXCDAA ǞE m `U%V, Y  F ݞ}D>|aMcPzyPxM onlow ; o f ^~ 5~ D +% , y,\8>\MintjMXCD Ǟ m `U%V,͟W Y  F ݞ}>\a9cSzSx9 oZlow ; o f ^x 5x D +% , y,\8??XintjMCD Ǟ m `U, Y cVd%e F ݞ}?a%cz^Q x% oF low ; o f ^ 5 D؝ +,?3y3]8LintjMXCD Ǟ m ` Y  F ݞ}$y$]8?|intjMXCDAA Ǟ mG `U%V, Y  F ݞ}-?|acPzyPx o#lowe ; o f ^~ 5~ D +% , y,^8X@`intjMXCD Ǟ m `U%V, YY  F ݞ}@X@`acSzSx olow} ; o f ^x 5x Dğ +% , y,{_8@XintjMCD Ǟ m `U, Y cVd% Fg ݞ}S@aczJx orlow ; o f ^ 5 D< +, 3 y3F`8intjMXCD Ǟ m ` Y  F ݞ}$y$}`8A@intjMXCD@: 1DǞ m `U%V, Y  F ݞ}fA@ajcPzyPxj olow ; o DW +% , y,,a8AHintjMXCDOBH Ǟ GFm `U%V, Y  F ݞ}zAHa֢cPzyPx֢ o)low ; o D  +% , y,a8B intjMXCD Ǟ m `U%V, eXY  F ݞ}Baczx olow ; o , y,b8,BXintjMCD Ǟ m `U3 Y cVd% fF ݞ},Baczx oAlow ; o : y:bc8intjMXCD Ǟ m ` Y  F ݞ}$y$c8DBH< intjMXCDOBH Ǟ m `HU%V, Y  F ݞ}DBHalcPzyPxl olow@ ; o D +% , y,Wd8B89!intjMXCD Ǟ m `U%V, Y Z F ݞ}B8a5cPzPx5 olow ; o D +% , y,e8BT5"XintjMCD Ǟ m `U, Y cVd% F ݞh}BTaczx oרlow ; o DP +, : y:e8>#intjMXCD Ǟ m ` Y  F ݞ}$y$f8C#XintjMCD?:nC Ǟ m ` Y cVd% F ݞ}Cakc\zy\x o˩low ; o f ^ 5 D +, 3 y3f8C$XintjMCDAH ǞE m ` Y cVd% F ݞ}Cac\zy\xg olow ; o f ^~ 5~ D +,3y3g8D%XintjMCD Ǟ m `U,͟W Y cVd% F ݞ}DaӪcPzPx o# low ; o f ^x 5xD +,3y3h8E&XintjMCD Ǟ m ` Y cVd%e F ݞ}+EaKcz\Q xs o low׫ ; o f ^ 5 D +,?3y3ni8'intjMXCD Ǟ m ` Y  F ݞ}$y$i8HF<(XintjMCDAH Ǟ mG ` Y cVd% F ݞ}>HFa9c\zy\x olowլ ; o f ^~ 5~ D +,3y3j8 GH)XintjMCD Ǟ m `U, YY cVd% F ݞ}Q GacPzPx o= low ; o f ^x 5xD +,3y3fk8GW*XintjMCD Ǟ m ` Y cVd% Fg ݞ}dGaecz\Q x o low ; o f ^ 5D +,3y3Ml8Y+intjMXCD Ǟ m ` Y  F ݞ}$y$l8Hd+XintjMCD@, 1DǞ m ` Y cVd% F ݞ}wHda cRzyRx1 oIlow ; o D +3 : y:@m8 Ip,XintjMCDOBO Ǟ GFm ` Y cVd% F ݞ} IpacRzyRxî oۮlow ; o D +3 : y: n8IX-XintjMCD Ǟ m `V: eXY cVd% F ݞ}IXa-cRzRxU omlow ; o D +3 : y:n8I.XMintjCD Ǟ m ` Y c,d% fF ݞ}Iaczx olow ; o A yAo8/intjMXCD Ǟ m ` Y  F ݞ}$y$o8Jp0XintjMCDOBO Ǟ m `H Y cVd% F ݞ}Jpa!cRzyRxI oalow ; o D +3 : y:p8tJl 1XintjMCD Ǟ m `V: Y ZcVd% F ݞ}tJlacRzRx۰ olow- ; o D +3 : y:_q8JP.2XintjMCD Ǟ m ` Y cVd% F ݞh}JPaEczmx olowױ ; o D +3 : y:(r843intjMXCD Ǟ m ` Y  F ݞ}$y$_r83intjMXCD Ǟ m ` Y  F ݞ}$y$r83intjMXCD Ǟ m ` Y  F ݞ}$y$r8]4intjMXCD Ǟ m ` Y  F ݞ}$y$s84intjMXCD Ǟ m ` Y  F ݞ}$y$;s8#5intjMXCD Ǟ m ` Y  F ݞ}$y$rs85intjMXCD Ǟ m ` Y  F ݞ}$y$s85intjMXCD Ǟ m ` Y  F ݞ}$y$s8L6intjMXCD Ǟ m ` Y  F ݞ}$y$t86intjMXCD Ǟ m ` Y  F ݞ}$y$Nt87intjMXCD Ǟ m ` Y  F ݞ}$y$t8u7intjMXCD Ǟ m ` Y  F ݞ}$y$t87intjMXCD Ǟ m ` Y  F ݞ}$y$t8;8intjMXCD Ǟ m ` Y  F ݞ}$y$*u88intjMXCD Ǟ m ` Y  F ݞ}$y$au89intjMXCD Ǟ m ` Y  F ݞ}$y$u8d9intjMXCD Ǟ m ` Y  F ݞ}$y$u89intjMXCD Ǟ m ` Y  F ݞ}$y$v8*:intjMXCD Ǟ m ` Y  F ݞ}$y$p=v80K\:intjMXCD?:nC Ǟ m ` Y  F ݞ}%0K\]aH-zK cP 3 y3pv8Kd;intjMXCDAH ǞE m ` Y  F ݞ}%Kd]aiHz cP 3 y3p}w8Kds<intjMXCD Ǟ m `U,͟W Y  F ݞ}%)Kd]aòHz cP 3 y3ux8TL\e=intjMXCD Ǟ m ` Y cVe F ݞ}%<TL\baH;zY cPQ 3 y3x8X>intjMXCD Ǟ m ` Y  F ݞ}$y$px8Ld>intjMXCDAH Ǟ mG ` Y  F ݞ}%OLd]aHz cP 3 y3py8Md?intjMXCD Ǟ m `U, YY  F ݞ}%bMd]a۳Hz cP 3 y3u4z8xM`@intjMXCD Ǟ m ` Y cV Fg ݞ}%uxM`ba5HSzq cPQ 3 y3z8AintjMXCD Ǟ m ` Y  F ݞ}$y$p {8M\AintjMXCD@A 1DǞ m ` Y  F ݞ}%M\]aHzմ cT 3 y3p{84NdBintjMXCDOBO Ǟ GFm ` Y  F ݞ}%4Nd]aHz/ cT 3 y3pK|8NdCintjMXCD Ǟ m `V3 eXY  F ݞ}%Nd]aMHkz cT 3 y3u|8N\DintjMXCD Ǟ m ` Y d] fF ݞ}%N\baHŵz cPQ 3 y3}8EintjMXCD Ǟ m ` Y  F ݞ}$y$p}8XOd$FintjMXCDOBO Ǟ m `H Y  F ݞ}%XOd]a H)zG cT 3 y3pb~8OdGintjMXCD Ǟ m `V3 Y Z F ݞ}%Od]aeHz cT 3 y3u8 P\ HintjMXCD Ǟ m ` Y d] F ݞh}% P\baHݶz cPQ 3 y38HintjMXCD Ǟ m ` Y  F ݞ}$y$p8|Px_IintjMXCD?:nC Ǟ m ` Y  F ݞ}%%|Px]a#HKzm cP 3 y3py8PRJintjMXCDAH ǞE m ` Y  F ݞ}%%'P]aHzշ cP 3 y3p8tQxEKintjMXCD Ǟ m `U,͟W Y  F ݞ}%%:tQx]aHz= cP 3 y3u8Q|8LintjMXCD Ǟ m ` Y cVe F ݞ}%%MQ|ba[Hz cPQ 3 y3Y8+MintjMXCD Ǟ m ` Y  F ݞ}$y$p8hRMintjMXCDAH Ǟ mG ` Y  F ݞ}%%`hR]a͸Hz cP 3 y3p08RxNintjMXCD Ǟ m `U, YY  F ݞ}%%sRx]a5H]z cP 3 y3uЃ8`S|tOintjMXCD Ǟ m ` Y cV Fg ݞ}%%`S|baHŹz cPQ 3 y3p8gPintjMXCD Ǟ m ` Y  F ݞ}$y$p8StPintjMXCD@A 1DǞ m ` Y  F ݞ}%%St]aH7zY cT 3 y3pG8PT|QintjMXCDOBO Ǟ GFm ` Y  F ݞ}%%PT|]awHz cT 3 y3p8TtRintjMXCD Ǟ m `V3 eXY  F ݞ}%%Tt]aߺHz) cT 3 y3u8@UxSintjMXCD Ǟ m ` Y d] fF ݞ}%%@UxbaGHoz cPQ 3 y3'8TintjMXCD Ǟ m ` Y  F ݞ}$y$p^8U|TintjMXCDOBO Ǟ m `H Y  F ݞ}%%U|]aHz cT 3 y3p84VtUintjMXCD Ǟ m `V3 Y Z F ݞ}%%4Vt]a!HIzk cT 3 y3u8VtVintjMXCD Ǟ m ` Y d] F ݞh}%%VtbaHzӼ cPQ 3 y3>8WintjMXCD Ǟ m ` Y  F ݞ}$y$lu8W(5XintjMXCD?3@:nC Ǟ m ` Y  F ݞ}%W(Yaxyziy& , y,l8DW0"YintjMXCD?3OBHnC Ǟ m ` Y  F ݞ}8DW0YaExyXziyp , y,l8tW,ZintjMXCD?3nC Ǟ m `V, Y  F ݞ}KtW,Yaxyziy , y,l"8W0ZintjMXCD?3nC Ǟ m ` Y dV F ݞ}^W0Yaٽxyziy , y,8[intjMXCD Ǟ m ` Y  F ݞ}$y$l8W(P\intjMXCD@:AA ǞE m ` Y  F ݞ}qyW(Ya#x6zyiO , y,lw8W,=]intjMXCDAAOBH ǞE m ` Y  F ݞ}W,Yanxyziy , y,l8$X$+^intjMXCDAA ǞE m `V, Y  F ݞ}$X$Yaxy̾ziy , y,l8HX(_intjMXCDAA ǞE m ` Y dV F ݞ}HX(Yaxyziy0 , y,$8`intjMXCD Ǟ m ` Y  F ݞ}$y$p[8pX$g`intjMXCD@: Ǟ m `U%͟W Y  F ݞ}ypX$]aOxbzyi,y,pݎ8X(QaintjMXCDOBH Ǟ m `U%͟W Y  F ݞ}yX(]a˿x޿zyi(,y,p_8X 80Z$SlintjMXCDAA Ǟ mG `V, Y  F ݞ}{0Z$Ya$xy7ziyP , y,l͕8TZ(?mintjMXCDAA Ǟ mG ` Y dV F ݞ}TZ(Yaoxyziy , y,\8,nintjMXCD Ǟ m ` Y  F ݞ}$y$p8|Z$nintjMXCD@: Ǟ m `U% YY  F ݞ}y|Z$]axzyi ,y,p8Z(yointjMXCDOBH Ǟ m `U% YY  F ݞ}yZ(]a+x>zyni,y,p8Z dpintjMXCD Ǟ m `U%V, YY  F ݞ}Z ]axzi,y,p8Z$MqXintjMCD Ǟ m `U, YY d% F ݞ}Z$]a.xAzqi3y387rintjMXCD Ǟ m ` Y  F ݞ}$y$lҘ8 [0rintjMXCD@: Ǟ m ` Y cO Fg ݞ}y [0Yaxzyi , y,la8<[4sintjMXCDOBH Ǟ m ` Y cO Fg ݞ}y<[4Yaxzyi* , y,p8p[,ptXintjMCD Ǟ m `V, Y cV Fg ݞ}p[,]aFx^zvi,y,pr8[<ZuXintjMCD Ǟ m ` Y cVd% Fg ݞ}&[<]axzi3y38MvintjMXCD Ǟ m ` Y  F ݞ}$y$+8vintjMXCD Ǟ m ` Y  F ݞ}$y$b8wintjMXCD Ǟ m ` Y  F ݞ}$y$8vwintjMXCD Ǟ m ` Y  F ݞ}$y$Л8wintjMXCD Ǟ m ` Y  F ݞ}$y$88[xintjMXCD@: 1DǞ m ` Y  F ݞ}9y[IaAxyzyiy,y,g8[yintjMXCD@:OBH 1DǞ m ` Y  F ݞ}M[TaTxyziy,y,gB8[izintjMXCD@: 1DǞ m `V, Y  F ݞ}a[Tagxyziy,y,gĝ8[ N{intjMXCD@: 1DǞ m ` Y dV F ݞ}u[ Tazxyziy,y,F83|intjMXCD Ǟ m ` Y  F ݞ}$y$g}8[|intjMXCD@:OBH Ǟ GFm ` Y  F ݞ}y[Taxzyi,y,\8\{}intjMXCDOBH Ǟ GFm ` Y  F ݞ}y\Iaxyzyiy,y,g8 \`~intjMXCDOBH Ǟ GFm `V, Y  F ݞ} \Taxyziy,y,g8\ EintjMXCDOBH Ǟ GFm ` Y dV F ݞ}\ Taxyziy,y,8*intjMXCD Ǟ m ` Y  F ݞ}$y$g8 \intjMXCD@: Ǟ m `V, eXY  F ݞ}y \Taxzyi,y,g>8(\rintjMXCDOBH Ǟ m `V, eXY  F ݞ}y(\Taxzyi,y,\80\WintjMXCD Ǟ m `V, eXY  F ݞ}0\Iaxzi,y,gB88\ <intjMXCD Ǟ m `V, eXY dV F ݞ}8\ Taxzi,y,Ģ8!intjMXCD Ǟ m ` Y  F ݞ}$y$g8D\intjMXCD@: Ǟ m ` Y dV fF ݞ})yD\Ta%xzyi,y,g}8L\iintjMXCDOBH Ǟ m ` Y dV fF ݞ}=yL\Ta=xzyi,y,g8T\NintjMXCD Ǟ m `V, Y dV fF ݞ}QT\TaUxzi,y,\8\\ 3intjMXCD Ǟ m ` Y dV fF ݞ}e\\ Iamxzi,y,8intjMXCD Ǟ m ` Y  F ݞ}$y$:8{intjMXCD Ǟ m ` Y  F ݞ}$y$q8ވintjMXCD Ǟ m ` Y  F ݞ}$y$8AintjMXCD Ǟ m ` Y  F ݞ}$y$ߥ8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$mM8h\ jintjMXCD@:OBH Ǟ m `H Y  F ݞ}yyh\ Zaxzyi , y,bަ8t\ PintjMXCDOBH Ǟ m `H Y  F ݞ}yt\ Oaxyzyiy , y,mo8\ 6intjMXCDOBH Ǟ m `HV, Y  F ݞ}\ Zaxyziy , y,m8\XintjMCDOB, Ǟ m `H Y d% F ݞ}\Zaxy1zHiy : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$mȨ8\ eintjMXCD@: Ǟ m `V, Y Z F ݞ}y\ Zacxvzyi , y,mY8\KintjMXCDOBH Ǟ m `V, Y Z F ݞ}y\Zaxzyi , y,b8\0intjMXCD Ǟ m `V, Y Z F ݞ}\Oaxzi , y,m{8\ XintjMCD Ǟ m `V, Y Zd% F ݞ}\ Za0xCzei , y, 8intjMXCD Ǟ m ` Y  F ݞ}$y$eC8\^intjMXCD@: Ǟ m ` Y dV F ݞh}y\Raxzyi , y,eҫ8\ CintjMXCDOBH Ǟ m ` Y dV F ݞh}-y\ Raxzyi , y,ga8\)intjMXCD Ǟ m `V, Y dV F ݞh}A\TaxzQ i , y,Z8\ intjMXCD Ǟ m ` Y dV F ݞh}U\ Gaxzi , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$ŭ8VintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$38intjMXCD Ǟ m ` Y  F ݞ}$y$j8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$Sخ8\EintjMXCD?3nC Ǟ m ` Y  F ݞ}i\@ayczy , y,^g8\*intjMXCD?3AA ǞE m ` Y  F ݞ}|\Kay(czI , y,^8\intjMXCD?3 Ǟ m `U%͟W Y  F ݞ}\Kayxcz , y,^8]XjintMCD?, Ǟ m ` Y cVe F ݞ}]Kaycz : y:8ڛintjMXCD Ǟ m ` Y  F ݞ}$y$^K8]=intjMXCD?3AA Ǟ mG ` Y  F ݞ}]Kaycz? , y,^ڱ8 ]"intjMXCD?3 Ǟ m `U% YY  F ݞ} ]Kayncz , y,^i8(]XjintMCD?, Ǟ m ` Y cV Fg ݞ}(]Kaycz : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$[/8D]PintjMXCD?3@: 1DǞ m ` Y  F ݞ}D]Haycz , y,[8L]5intjMXCD?3OBH Ǟ GFm ` Y  F ݞ}L]Hay3cz , y,[M8T]intjMXCD?3 Ǟ m `V, eXY  F ݞ}T]HayTcz , y,[ܴ8\] intjMXCD?3 Ǟ m ` Y dV fF ݞ}*\] Hayucz , y,k8intjMXCD Ǟ m ` Y  F ݞ}$y$^8h]GintjMXCD?3OBH Ǟ m `H Y  F ݞ}>h]Kaycz , y,^18p],intjMXCD?3 Ǟ m `V, Y Z F ݞ}Rp]Kaycz , y,^8x]XjintMCD?, Ǟ m ` Y d% F ݞh}fx]Kay6czW : y:O8intjMXCD Ǟ m ` Y  F ݞ}$y$^8] ZintjMXCD?3OBHnC Ǟ m ` Y  F ݞ}z] Kaczy , y,^8]>intjMXCDAAOBH ǞE m ` Y  F ݞ}]Kaczy , y,^8]#intjMXCDOBH Ǟ m `U%͟W Y  F ݞ}]Kaycz= , y,^38]XintjMCDOB, Ǟ m ` Y cVe F ݞ}]Kaylcz : y:¹8intjMXCD Ǟ m ` Y  F ݞ}$y$^8]QintjMXCDAAOBH Ǟ mG ` Y  F ݞ}]Kaczy , y,^8]6intjMXCDOBH Ǟ m `U% YY  F ݞ}]Kay cz+ , y,^8]XintjMCDOB, Ǟ m ` Y cV Fg ݞ}]KayZcz{ : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$[ݻ8]dintjMXCD@:OBH 1DǞ m ` Y  F ݞ}]Haczy , y,Pl8]IintjMXCDOBH Ǟ GFm ` Y  F ݞ}]=ayczy , y,[8].intjMXCDOBH Ǟ m `V, eXY  F ݞ}']Haycz , y,[8] intjMXCDOBH Ǟ m ` Y dV fF ݞ};] Haycz , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$SP8^[intjMXCDOBH Ǟ m `H Y  F ݞ}O^@ay2czyS , y,^߾8 ^@intjMXCDOBH Ǟ m `V, Y Z F ݞ}c ^Kayzcz , y,^n8^%XintjMCDOB, Ǟ m ` Y d% F ݞh}w^Kaycz : y:8 intjMXCD Ǟ m ` Y  F ݞ}$y$^48(^ nintjMXCD?3nC Ǟ m `V, Y  F ݞ}(^ Ka czyA , y,^84^ RintjMXCDAA ǞE m `V, Y  F ݞ}4^ Kahczy , y,^R8@^6intjMXCD Ǟ m `U%V,͟W Y  F ݞ}@^Kacz , y,^8H^XintjMCD Ǟ m `V, Y cVe F ݞ}H^Kacz , y,p8intjMXCD Ǟ m ` Y  F ݞ}$y$^8\^ dintjMXCDAA Ǟ mG `V, Y  F ݞ}\^ KaIczyj , y,^68h^HintjMXCD Ǟ m `U%V, YY  F ݞ}h^Kacz , y,^8p^-XintjMCD Ǟ m `V, Y cV Fg ݞ}p^Kacz , y,T8intjMXCD Ǟ m ` Y  F ݞ}$y$[8^vintjMXCD@: 1DǞ m `V, Y  F ݞ}^Ha(czy , y,[8^[intjMXCDOBH Ǟ GFm `V, Y  F ݞ}$^HaIczy , y,P8^@intjMXCD Ǟ m `V, eXY  F ݞ}8^=ajcz , y,[88^ %intjMXCD Ǟ m `V, Y dV fF ݞ}L^ Hacz , y,8 intjMXCD Ǟ m ` Y  F ݞ}$y$^8^mintjMXCDOBH Ǟ m `HV, Y  F ݞ}`^Kaczy , y,S8^RintjMXCD Ǟ m `V, Y Z F ݞ}t^@acz , y,^8^7XintjMCD Ǟ m `V, Y d% F ݞh}^Ka7czU , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$^8^ XintjMCD?:nC Ǟ m ` Y d% F ݞ}^ Kaczy 3 y3^q8^ dXintjMCDAH ǞE m ` Y d% F ݞ}^ Kaczy 3 y3^8^HXintjMCD Ǟ m `U,͟W Y d% F ݞ}^Kacz/ 3 y3Z8^-XintjMCD Ǟ m ` Y cVd%e F ݞ}^GaQcz3y38intjMXCD Ǟ m ` Y  F ݞ}$y$^F8_ vXintjMCDAH Ǟ mG ` Y d% F ݞ}_ Kayczy 3 y3^8 _ZXintjMCD Ǟ m `U, YY d% F ݞ} _Kacz 3 y3Zd8_?XintjMCD Ǟ m ` Y cVd% Fg ݞ}_Gacz3y38$intjMXCD Ǟ m ` Y  F ݞ}$y$[8,_XintjMCD@A 1DǞ m ` Y d% F ݞ}!,_Ha*czy 3 y3[84_lXintjMCDOBO Ǟ GFm ` Y d% F ݞ}54_HaRczy 3 y3[98<_QXintjMCD Ǟ m `V3 eXY d% F ݞ}I<_Hazcz 3 y3P8D_ 6XintjMCD Ǟ m ` Y d% fF ݞ}]D_ =acz 3 y3W8intjMXCD Ǟ m ` Y  F ݞ}$y$^8P_~XintjMCDOBO Ǟ m `H Y d% F ݞ}qP_Kaczy 3 y3^8X_cXintjMCD Ǟ m `V3 Y Zd% F ݞ}X_Ka cz1 3 y3O8`_HXintjMCD Ǟ m ` Y d% F ݞh}`_<aScz3y3,8.intjMXCD Ǟ m ` Y  F ݞ}$y$c8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8WintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$?8intjMXCD Ǟ m ` Y  F ݞ}$y$v8intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$8FintjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$R8 intjMXCD Ǟ m ` Y  F ݞ}$y$8ointjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$85intjMXCD Ǟ m ` Y  F ݞ}$y$.8intjMXCD Ǟ m ` Y  F ݞ}$y$e8intjMXCD Ǟ m ` Y  F ݞ}$y$8^intjMXCD Ǟ m ` Y  F ݞ}$y$8intjMXCD Ǟ m ` Y  F ݞ}$y$ 8$intjMXCD Ǟ m ` Y  F ݞ}$y$A8t_ intjMXCD?3@:nC Ǟ m ` Y  F ݞ}+t_ a+y{c-Pz.yPx/y{ o0low0 ;1 o1 D3  +4% , y,8_,sintjMXCD?3AAOBH ǞE m ` Y  F ݞ}+_,a+y;c-Pz.Px/y; o0\low0 ;1 o1 D3 +4% , y,8__intjMXCD?3 Ǟ m `U%V,͟W Y  F ݞ}+_a+y+c-Pz.Px/y+ o0elow0 ;1 o1 D3e +4% , y,l8_LHXintjMCD?: Ǟ m ` Y cVd%e F ݞ}+_La+yc-RSz.RSx/y o0low0 ;1 o1 D3 +4,? 3 y3*8CintjMXCD Ǟ m ` Y  F ݞ}$y$a8(`4intjMXCD?3AAOBH Ǟ mG ` Y  F ݞ}+(`4a+y2c-Pz.Px/y2 o0Slow0 ;1 o1 D3 +4% , y,8\`intjMXCD?3 Ǟ m `U%V, YY  F ݞ}+\`a+y"c-Pz.Px/y" o0Clow0Z ;1 o1 D3C +4% , y,8l`8XintjMCD?: Ǟ m ` Y cVd% Fg ݞ}1+l`8a+y|c-z.SR x/y| o0 %!%%low0SR ;1 o1 D3 %!%% +4, 3 y38}intjMXCD Ǟ m ` Y  F ݞ}$y$8` intjMXCD?3@: 1DǞ m ` Y  F ݞ}G+` a+yc-z.x/y o0low0 ;1 o1 , y,8` intjMXCD?3OBH Ǟ GFm ` Y  F ݞ}^+` a+yc-z. x/y o0?low0  ;1 o1 , y,W8` intjMXCD?3 Ǟ m `V, eXY  F ݞ}u+` a+yjc-z.x/yj o0low0 ;1 o1 , y,8`XjintMCD?3 Ǟ m ` Y d% fF ݞ}+`a+yc-z. x/y o0?low0  ;1 o1 A yA8wintjMXCD Ǟ m ` Y  F ݞ}$y$8` intjMXCD?3OBH Ǟ m `H Y  F ݞ}+` a+yjc-Pz.Px/yj o0low0 ;1 o1 D3 +4% , y,8`intjMXCD?3 Ǟ m `V, Y Z F ݞ}+`a+y-c-Pz.Px/y- o0Nlow0e ;1 o1 D3N +4% , y,8 a4intjMXCD?3 Ǟ m ` Y dV F ݞh}+ a4a+yc-z.RS x/y o0low0RS ;1 o1 D3 +4% , y,K8intjMXCD Ǟ m ` Y  F ݞ}$y$8@a$intjMXCD?3AAOBHnC Ǟ m ` Y  F ݞ}+@a$a+c-Pz.yPx/ o0low0 ;1 o1 D3 +4% , y,18da$intjMXCDAAOBH ǞE m ` Y  F ݞ}+da$a+9c-Pz.yPx/9 o0slow0 ;1 o1 D3s +4% , y,8aintjMXCDOBH Ǟ m `U%V,͟W Y  F ݞ}+aa+yc-Pz.Px/y o0low0* ;1 o1 D3 +4% , y,8aLXintjMCDOBO Ǟ m ` Y cVd%e F ݞ}*+aLa+y[c-RSz.RSx/y[ o0|low0 ;1 o1 D3| +4,? 3 y3k8intjMXCD Ǟ m ` Y  F ݞ}$y$8a,8intjMXCDAAOBH Ǟ mG ` Y  F ݞ}@+a,a+c-Pz.yPx/ o0 low01 ;1 o1 D3  +4% , y,`8b&intjMXCDOBH Ǟ m `U%V, YY  F ݞ}V+ba+ybc-z.x/yb o0low0 ;1 o1 D3 +4% , y, 8$b XintjMCDOB, Ǟ m ` Y cVd% Fg ݞ}l+$ba+yc-z.x/y o0low0 ;1 o1 D3 +43 : y:8intjMXCD Ǟ m ` Y  F ݞ}$y$88b$TintjMXCD@:OBH 1DǞ m ` Y  F ݞ}+8b$a+c-Pz.yPx/ o0@low0f ;1 o1 D3@ +4% , y,8\b BintjMXCDOBH Ǟ GFm ` Y  F ݞ}+\b a+yc-z.yx/y o0ylow0y ;1y o1y , y,\8hb 'intjMXCDOBH Ǟ m `V, eXY  F ݞ}+hb a+yc-z. x/y o0?low0  ;1 o1 , y,8tb XintjMCDOB3 Ǟ m ` Y d% fF ݞ}+tba+yjc-z.x/yj o0low0 ;1 o1 A yA8intjMXCD Ǟ m ` Y  F ݞ}$y$ 8bWintjMXCDOBH Ǟ m `H Y  F ݞ}+ba+yc-Pz.yPx/y o0y low0y# ;1y o1y D3y  +4% , y,8bEintjMXCDOBH Ǟ m `V, Y Z F ݞ}+ba+yEc-z.fx/yE o0low0f ;1 o1 D3 +4% , y,v8b*XintjMCDOB, Ǟ m ` Y d% F ݞh} +ba+yc-z.x/y o0low0 ;1 o1 D3 +43 : y:#8intjMXCD Ǟ m ` Y  F ݞ}$y$Z8b sintjMXCD?3nC Ǟ m `U%V, Y  F ݞ}#+b a+c-Pz.yPx/ o0%low0k ;1 o1 D3% +4% , y, 8b(aintjMXCDAA ǞE m `U%V, Y  F ݞ}9+b(a+c-Pz.yPx/ o0low07 ;1 o1 D3 +4% , y,8cOintjMXCD Ǟ m `U%V,͟W Y  F ݞ}O+ca+c-Pz.Px/ o0low0 ;1 o1 D3 +4% , y,8 cL8XintjMCD Ǟ m `V3 Y cVd%e F ݞ}e+ cLa+Dc-RSz.RSx/D o0elow0 ;1 o1 D3e +4,? 3 y3C83intjMXCD Ǟ m ` Y  F ݞ}$y$z8lc(intjMXCDAA Ǟ mG `U%V, Y  F ݞ}{+lc(a+c-Pz.yPx/ o0low0: ;1 o1 D3 +4% , y,88c intjMXCD Ǟ m `U%V, YY  F ݞ}+c a+c-Pz.Px/ o0low0 ;1 o1 D3 +4% , y,8crXintjMCD Ǟ m `V, Y cVd% Fg ݞ}+ca+Rc-z.sx/R o0low0s ;1 o1 D3 +43 , y,8XintjMXCD Ǟ m ` Y  F ݞ}$y$8c intjMXCD@: 1DǞ m `V, Y  F ݞ}+c a+c-Pz.yPx/ o0low0& ;1 o1 D3 +4% , y,8c(intjMXCDOBH Ǟ GFm `V, Y  F ݞ}+c(a+rc-Pz.yPx/r o0low0 ;1 o1 D3 +4% , y,G8d intjMXCD Ǟ m `V, eXY  F ݞ}+d a+>c-z._x/> o0>low0_ ;1 o1 , y,8d|XintjMCD Ǟ m `V3 Y d% fF ݞ}+da+c-z.x/ o0low0 ;1 o1 3 y38dintjMXCD Ǟ m ` Y  F ݞ}$y$80d(intjMXCDOBH Ǟ m `HV, Y  F ݞ}+0d(a+ c-Pz.yPx/  o0Elow0 ;1 o1 D3E +4% , y,8XdintjMXCD Ǟ m `V, Y Z F ݞ}0+Xda+c-Pz.Px/ o0low0 ;1 o1 D3 +4% , y,r8hdXintjMCD Ǟ m `V, Y d% F ݞh}G+hda+Qc-z.ox/Q o0low0o ;1 o1 D3 +43 , y,8intjMXCD Ǟ m ` Y  F ݞ}$y$V8|d4XintjMCD?:nC Ǟ m ` Y cVd% F ݞ}^+|d4a+c-Rz.yRx/ o0 low0 ;1 o1 D3 +4, 3 y38dDXintjMCDAH ǞE m ` Y cVd% F ݞ}t+dDa+c-Rz.yRx/  o0 low0 ;1 o1 D3 +4, 3 y38d<XintjMCD Ǟ m `U,͟W Y cVd% F ݞ}+d<a+#c-Rz.Rx/K o0 low0 ;1 o1 D3 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U/4: ; I04: ; I11RUX Y 2131X Y 4151617B8 U9B1: ;.?n4<<.?4<=.?: ; <% U$ > $ > 9: ;  : ; I8 42 .?: ; nIL <dI4 .?: ;nL M<d  : ;  .?: ; nL M<d : ; I I I 9: ;  : ; I8 2 .?: ; nI2 <d.?: ; nIL 2 <d.?: ; nIL M2 <d.?nI42 <dI&I I.G dI4.1n@dB1 1!1"B#.G@dB$I4%&.?n4<% U$ > : ; I$ >   I : ; n : ; I8 : ; I8 9: ;  : ;  < .?: ; n<.: ; I<I.?: ; I<.: ; I<&I&I.?: ; I<.?: ; <.: ; I<.: ; <9: ;  : ; .?: ; I<: ;: ; I : ; n ( ! : ; "I#!I/ $% : ; &:: ; '.?: ; nI<d(I4).?: ; nI<d*.: ;I +: ;I,.: ; I - .4: ; I/.G d0I41.4 2: ; I3.1n@dB4151X Y 67.: ; @B8: ; I9 U:4: ; I;4: ; I<1=B1>B?.G@B@ A UB1X Y C D1RUX Y B EF1G.4@BH1RUX Y I1 J1K1LB1M4: ; IN4I?4<O.?n4<P.?4<Q.?I4<% U$ > : ; I$ >   I : ; n : ; I8 : ; I8 9: ;  : ;  < &I.?: ; n<.: ; I<I.?: ; I<.: ; I<&I.?: ; I<.?: ; <.: ; I<.: ; <9: ;  : ;  : ; ( .: ;I<.: ;< .?: ;I<!.?: ; <": ;#: ; I$ : ; n% : ; &<'I(!I/ )*:: ; +.: ; I ,: ; I-: ; I. /4: ; I0.G 1: ;I24: ; I3.: ; 4 I5.: ; I@B6: ; I7: ; I8 94: ; I:: ; I; U<4: ; I= >4: ; I?1@BA1X Y B1C1D41E1F : ; G : ; IHB1IB1J.: ;I K: ;IL UM1X Y N1O1RUX Y P.G@BQ: ;IR : ;S : ;T4: ;IU4: ;IV4: ;IW4: ;I X4: ;IY1RUX YZ41[1X Y\1X YB ]1RUX Y^1RUX Y _1RUX YB ` : ;a.?: ; <b.?I4<% U9: ; 9: ;  : ;  : ; I8 : ; I2 .?: ; nI<cdI4 I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d.?: ; nI2 <cd.?: ; nI2 <d&I.?: ; nI<.?: ; nI<: ; : ; I<.?: ; nI<.?: ; n<.?: ; n<  : ; d I$ >  I; B I!$ > " : ;n# : ;I8 $: ;I%: ; I& : ; n'( ( : ; ) : ; I8 *I+!I/ ,-.: ;I<..: ; I</.: ;<0.?: ;<1.?: ;I<2.?: ;I<3: ;45:: ; 697.G 8: ;I9: ; I:.G: ; d;I4< =4: ; I>: ;I?.G: ; @dB@I4A.: ; @BB: ; IC: ; ID UE4: ; IF1X Y G1H1IBJKB1L1M.1n@dBN1O P1Q: ; IR.1n@dBS UT41UI4V: ; IW4: ; IXY.G@BZ: ; I[4: ; I\1RUX Y ]1RUX Y^4: ; I_.?: ; I<% $ > $ > 9: ; 4: ; nI?<: ;9: ; : ; I  I  4G% U9: ; 9: ;  : ;  : ; I8 .?: ; nI<cdI4I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d .?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I<.?: ; nI<.?: ; nI<.?: ; n<  I I;B I$ > $ >  : ;n : ;I8 : ;I  : ; n! : ; I8 ".: ; I<#.?: ; I<$%.: ; I<&&'I(.?: ; I<).?: ; <*.: ; I<+.: ; <,.?: ; n<-4: ; nI?<.: ;/:: ; 0.G@B1: ; I2B31415B6 74: ; I8 U94: ; I:.?: ; <;.?I4<<.?4<=.?n4<>.?4<% U9: ; 9: ;  : ;  : ; I8 .?: ; nI<cdI4I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d .?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I<.?: ; nI<.?: ; n<  I I;B I$ > $ >  : ;n : ;I8 : ;I: ; I  : ; n!( " : ; # : ; $ : ; I8 %I&!I/ '(.: ; I<).: ;<*.?: ; <+.?: ; <,.?: ; n<-.?: ;<..?: ;I</.?: ;I<0: ;12:: ; 3.G 4: ; I5.: ; 6: ; I7 84: ; I9: ;I:.1@B;1< U=41>1? 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"4: ; I#1X Y $1%1% $ > $ > 9: ; 4: ; nI?<: ;9: ; : ; I  I  4G% U$ > $ > 9: ; <&I< : ;2 ( 9 :: ;  4: ; I<  : ; I8 2 .?: ;nI2 <cdI4I.?: ; nIL 2 <d I: ;9: ;  .G dI4.1n@dB1B14G .?n4<% U$ > $ > 9: ; <&I< : ;2 ( 9 :: ;  4: ; I<  : ; I8 2 .?: ; nI2 <cdI4I.?: ; nIL 2 <d.?: ; nIL M2 <d I: ;9: ;  .G@dBI4.G dI4.1n@dB1B !1"4G #.?n4<% U$ > $ > 9: ; <&I< : ;2 ( 9 :: ;  4: ; I<  : ; I8 2 .?: ; nI2 <cdI4I.?: ; nIL 2 <d I: ;9: ;  .G dI4.1n@dB1B14G .?n4<% U$ > $ > ;9: ; 9: ;  : ;  : ; I8 .?: ; nI<cd I4 I .?: ; n<d .?: ; nI<d.?: ; nI2 <d.?: ; n2 <d.?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I<<  I IB I9.: ; I<:: ; .: ; I<.: ; < .: ; <!.?: ;I<".?: ;<#.?: ;<$: ;%& : ; '( (<).?: ;nI2 <d*4: ; I< + : ; ,.?: ; nI<d-.G d.I4/.G 0: ; I1.?: ; nI 2 34: ; I4.: ; I 54: ; I6: ; I7 8 : ; 9 : ; I:.I4<d;.I4<d<I=!I/ >.?: ; n ?.G @.G@BA: ;IB UC4: ;ID1RUX YE1F1G1X YH I41J4: ;IK4: ;IL1M UN4: ;IO1PBQ1X YB R1X Y S1X Y T1RUX Y B U1V1X YB W X4G Y.?: ; <Z.?: ; I<[\.?I4<].?4<^.?n4<_.?4<% U$ > $ > 9: ; : ;9: ;  : ;I8 2 .?: ;nI2 <d I4 .?: ; nIL 2 <d 9: ;  < I .G dI4&I.1n@dB1B1.?n4<I% U9.: ; I<I:: ; 9: ; : ; I.?: ; nI< &I .?: ; nI< $ >   I .G : ; I 4: ; I&.G@B: ; I: ; I U4: ; I4: ; I4: ; I1RUX Y 141.G@B% U9: ; 9: ;  : ;  : ; I8 .?: ; nI<cdI4I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d .?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I< : ;  I8 42 .?: ; nIL 2 <d  I I;B I$ > $ > I I .G d!I4".1n@dB#1$%B&'1(.?n4<% U9: ; 9: ;  : ;  : ; I8 .?: ; nI<cdI4I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d .?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I< : ; .?: ; nI<.?: ; nI<4: ; nI?<   I I;B I$ > $ > 9 4: ; I<!:: ; ".G@B#: ; I$ U%4: ; I& '4: ; I(4G% U$ > : ; I$ > ;  I : ; n : ; I8 : ; I8 9: ;  : ;  9: ;  : ; .?: ; nI<cdI4I.?: ; n<d.?: ; nI<d.?: ; nI2 <d.?: ; n2 <d.?: ; nI2 <cd.?: ; nI2 <d&I<<.?: ; nI<.: ; I<.?: ; I<.: ; I< &!I".?: ; I<#.?: ; <$.: ; I<%.: ; <& I'B I(.G d)I4*.?: ; nI@B+: ; I, U-4: ; I. U/4: ; I01RUX Y B 1121314B516.?I4<7.?4<8% U9: ; 9: ;  : ;  : ; I8 .?: ; nI<cdI4I .?: ; n<d .?: ; nI<d .?: ; nI2 <d .?: ; n2 <d .?: ; nI2 <cd.?: ; nI2 <d&I: ; : ; I< : ; .?: ; nI<.?n4<  I I;B I$ > $ > .?: ; nI@B: ; I I! U"4: ; I#4: ; I$ %4I&1'1(B)1*.?I4<+.?4<,.?: ; I<% U9: ; : ; I.?: ; nI@B: ; I1B1  $ >  .?: ; nI< I .?n4<.?4<% U9: ;  : ; : ; I&I.?: ; nI@B: ; I: ; I B1 B  $ >   I.?: ; nI<I% U$ > $ > 9: ;  : ; : ;2 ( I8 2 : ;I8 2 .?: ;nI2 <cd I4 I .?: ; nIL 2 <d.?: ;nI2 <d.?: ; nIL M2 <d.?: ; nIL M2 <d<&I< : ;2 9:: ; 4: ; I<  I: ;9: ; .?: ; nI2 <d.?: ; nI2 <d  I.G d I4!: ; I" #4: ; I$.G@dB%I4&: ; I'B(B).1n@dB*1+,-1. U/4101X Y B 11213 U44154G 6.?n4<7.?I4<% U$ > $ > 9: ; <.?: ; nIL M2 <dI4I < &I  : ;2 ( 9:: ; 4: ; I<  : ;I8 2  : ;I8 2 .?: ;nI2 <cd.?: ; nIL 2 <d.?: ;nI2 <d I: ;9: ; <.?: ; nI2 <d.?: ; nI2 <d  I.G dI4 : ; I! "4: ; I#.1n@dB$1%&B'(1) U*41+1RUX Y ,1- U.41/B04G 1.?n4<% U$ > $ > 9: ; <.?: ; nIL M2 <dI4I < &I 9 :: ;  4: ; I<  : ;I8 2 .?: ;nI2 <cd.?: ; nIL 2 <d.?: ; nIL M2 <d I: ;9: ; <.?: ; nI2 <d .G dI4: ; I.G@dBI4.1n@dB1 !B"#1$.1n@d%1RUX Y & '1(4G ).?n4<* I% U$ > : ; I$ > 9: ; .?: ; <: ;9: ;  .?: ; n< .G@B 1 B 1.?: ; I<I I&% U: ; I$ > $ > 9: ; < : ;2 (  : ; 2  : ; I8 .?: ; nI<d I4 I.?: ; nI<d.?: ; nI2 <d.?: ; nI2 <d&I.?: ;nIL M2 <d : ;2 9:: ; .: ; I</4: ; I<  : ;I8 2  : ;I8 2 .?: ;nI2 <cd.?: ; nIL 2 <d.?: ;nI2 <d.?: ; nIL M2 <d .?: ; nIL M2 <d! I": ;#9: ; $&% I& '.G d(I4): ; I*.G +: ; I,.1n@dB-1./B0112 U3B41X Y 5164G 7.?n4<% U$ > $ > 9: ; <&I< : ;2 ( 9 :: ;  4: ; I<  I: ;9: ;  : ;  I8 42  : ; I8 2 .?: ; nIL 2 <dI4.?: ; nI2 <dI.?: ; nIL M2 <d.?: ; nI2 <cd.?: ; nI<d.?: ; nI<dI I  I .G d!I4".1n@dB#1$.G@dB%I4&I'1(1)B*: ; I+: ; I,1RUX Y -1X Y B .1/104G 1.?n4<2.?nI4<% U$ > $ > 9: ; <&I9:: ;  4: ; I<  I : ; 9: ;  <.?: ; nI2 <dI4.?: ; nI2 <dI: ; .G dI4 I.G@dBI4: ; I1X Y 11X Y B 1114G .?nI4<% U$ > $ > 9: ;  : ;  : ; I8 9 : ; I8 .: ; I<d I4 .: ; I2 <d I .: ; I2 <d&I.: ; I<:: ; .?: ;<: ; I.?: ; I<.?: ; <.?: ;I<.?: ;I<: ;9: ; <<.?: ; nI2 <d.?: ; n<: ; I : ; n(  : ; !I"!I/ # I$% &' I(.G d)I4*.G +: ; I, -4: ; I..G; d/.1@dB011 U241314I5.G@B6: ;I7 84: ;I9:B;: ; I< U=4: ; I> ?4: ; I@1X Y B A1B1C1DE.G@BF4: ; IG1RUX Y H1RUX Y B I1X Y JBKBLBM1X YB N4: ;IO4: ;IP1X YQ9: ;R.?: ;I<S.?: ;<T: ;IUB1V.?I4<W.?4<X.?n4<Y.?4<% U: ; I$ > $ > 9: ;  : ; : ;2 ( : ;I8 2 .?: ;nI2 <d I4 .?: ;nI2 <d < : ; 2  : ; I8 .?: ; nI<dI.?: ; nI<d.?: ; nI2 <d.?: ; nI2 <d&I.?: ;nIL M2 <d : ;I8 2 .?: ;nI2 <cd.?: ; nIL 2 <d.?: ; nIL M2 <d9:: ; .: ; I</I /!4: ; I< " I#: ;$9: ; %&& I'I(!I/ ) *.G +: ; I,.G d-I4. /4: ; I04: ; I1 : ; 2 3: ; I4: ;I5: ;I64: ;I74: ;I8.1n@dB91:;B<=1> U? U@41A41B41C1X Y D1E F1X Y G 1H 1I1RUX Y JBK1RUX Y L M1RUX YN1X YO1P4G Q.?n4<% U$ > : ; I$ >   I : ; n : ; I8 : ; I8 9: ;  : ;  < .?: ; nI2 <dI4&I<.: ; I<I.?: ; I<.: ; I<&I.?: ; I<.?: ; <.: ; I<.: ; <9: ; .?: ;I<: ; : ;  I!!I/ ".?: ; I<#.?: ;I<$:: ; %.G d&I4'.?: ; n<(.G@B) U*4: ; I+4: ; I,4: ; I-1.B/101RUX Y 112 314 I5!64: ; I?<7.?nI4<8.?I4<9.?4<:.?: ; <;.?n4<% U9: ; 9: ;  : ; : ; I : ; I8 2 .?: ; nI2 <d I4 I .?: ; nI2 <d &I 4: ; nI?<9: ; : ; : ; I2 .?: ; n2 <d/I$ > $ > :: ;   I& I.G dI4.1n@dB1: ; I.1n@dB% U: ; I$ > 9: ; .?: ; nI<I.?: ; n< I 5I .G@B : ; I : ; I .G@B% U$ > $ > : ; I : ;  I8 4  I : ; I8 : ; I8 I I &II!I/ 9: ; : ; 9: ;  : ; ( <: ;I2 : ; I2  : ;I?2 <  : ;I?2 <  : ;I?2 < : ; .?: ; nI2 <dI4.?: ; nI2 <d.?: ; nI2 <d  : ;! : ;I?< ".?: ;nI<#.?: ;nI<$.?: ; <%.?: ; I<&.?: ; I<'.?: ;I<( : ; ) : ; n*5I+ : ; n,-9: ; .:: ; / : ; 0&192.: ; I<3.: ; I<4.G: ; d5I46.G 7.G: ; d8.G@B9: ; I: U;: ; I< =4: ; I>1?B@1A.1n@dBB1C D4: ;IE1F1RUX Y G UH41I.G: ; @dBJI4KIL1X Y M UN O1P.G: ;@dBQ: ;IR4: ;IS1X YTB1U.G V: ; IW4: ; IX: ;IY1RUX YZ1RUX YB [41\B1]4: ;I^4: ;I_.?n4<`.?4<% U9: ; 9: ; : ; I&I : ;  : ; I8 .?: ; nI<d I4 I /I .?nI4<d /I : ;I8 .?: ;nI<d<.?: ; nI2 <d.?: ; nI<.?: ;nI<<.?: ; n<9: ; :: ; $ > $ >   I : ; n5I  : ; !( " : ; #.?: ; nI2 <d$.?: ; n2 <d%: ; I2 &.?: ; nI2 <cd': ; ( : ; ).?: ; n<d*.?: ; nI2 <d+ : ;,: ;I- : ;I8 ..?: ;nI2 <d/.?: ;n2 <d0I8 2 1: ;I2 2.?: ;n<3.?: ;n<4.?: ;nI<d5.?: ;n<d6.?nI42 <d7 : ;I?<8 : ;90I : : ; ;.?: ; nIL M2 <d<.?nIL 42 <d=4: ; I< > I?&@IA!I/ B.: ; I C D4: ; IE.G: ; dFI4G.?: ; nI H: ; II.G dJ.: ;I K: ;IL4: ;nI?M4: ;IN.G O: ; IP Q.G: ; dR: ;IS4: ;IT.4 U.G@dBVI4W.1n@dBX1YZB[1RUX Y \1].G@B^1RUX Y B _1`1abB1c1d: ; Ie: ; If1X Y g h4: ; Ii j1k1X Y B lB1mI4n1X Y o: ; Ip1RUX Y q.1n@Br Us41t.G: ; @dBu Uv41w1RUX YB x.G: ; @dBy1X Y B z4I{1RUX Y|1RUX Y}41~1X Y1X YB 1X Y1RUX YB 1X YB : ;II: ;I: ;I.4@B1 11 4G 4Gn.?: ; n<.?I4<.?4<.?n4<.?4<% U9: ; : ; 9: ;  : ; : ; I.?: ; n<I &I .?: ; nI< .?: ;nI< .?: ;nI< .?: ;nI< : ; I8 2 : ; I2 .?: ; nI2 <dI4.?: ; nI2 <d : ;I8  : ;I8 .?: ;nI<d : ;I?2 < : ; I8  : ; I?<.?: ; nI<.?: ; nI<d.?: ; n<d.?: ;n<d.?: ;nI<d .?: ;n<!.?: ;nI<".?: ;nI2 <d#.?: ; nI2 <cd$.?: ;nI2 <d%.?: ;n2 <d&.?: ; nI<'/I(/I)<* : ; I?2 < + : ; I?2 < , : ;2 - : ;I?<..?: ;n<d/<0.?: ; n2 <1.?: ; nI2 <2.?: ; nI2 <3 : ;2 4.?: ;nI<d5.?: ;nI2 <d6.?: ;nI2 <7.?: ;nI2 <8.?: ;nI<cd9.?: ;n<: : ; ;( <: ;I2 = : ;I?2 < > : ;I?2 < ? 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].?: ; n2 <d^/I_ : ;I8 2 ` : ; a : ; b.?nI42 <dc.?: ; nIL M2 <dd.?nIL 42 <de.?: ; n<f4: ; I< g:: ; h Ii5Ijk!l9m4: ; I<n.: ; I o p4: ; Iq.G drI4s.G: ; dt.G u: ; Iv4: ; Iw: ; Ix.?: ; nI y.?: ; n z: ;I{.: ;I |.G: ; d}.G: ; ~.G@dBI4.1n@dB1B1RUX Y 1.G@B1RUX Y B 11: ;I4: ;I 4: ;I U411X YB 1RUX YB 1RUX Y1RUX Y1RUX Y .1n@B1X Y111X Y.G: ; @B: ; I4: ; I4: ; I41 1X Y  U1 1X Y 1X Y B .G: ;@B414G!I/4G 4G: ;n.?I4<.?4<.?n4<.?4<% U : ; 9: ; : ; 9: ;  : ; : ; I.?: ; n< I &I .?: ; nI< .?: ;nI< .?: ;nI<.?: ;nI< : ; I8 2 : ; I2 .?: ; nI2 <dI4.?: ; nI2 <d : ;I8  : ;I8 .?: ;nI<d.?nI4<d : ;I?2 < : ; I8  : ; I?<.?: ; nI<.?: ; nI<d.?: ; n<d .?: ;n<d!.?: ;nI<d".?: ;n<#.?: ;nI<$.?: ;nI2 <d%.?: ; nI2 <cd&.?: ;nI2 <d'.?: ;n2 <d(.?: ; nI<)/I*/I+<, : ;-( . : ; I?2 < / : ; I?2 < 0 : ;2 1 : ;I?<2.?: ;n<d3<4.?: ;nI2 <cd5.?: ;nI2 <6.?: ;n2 <7.?: ;n2 <8 : ;2 9.?: ;nI<d:.?: ;nI2 <d;.?: ;nI2 <<.?: ;nI<cd=.?: ;n<>.?nI42 <d? : ;@ : ;2 A:: ; B : ;I?2 <C : ;I?2 <D.?: ;n2 <dE0I F.?: ; n<G.?: ; I<H II$ > J$ > K.?: ; I<L I8 4M N : ; 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PKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE13_Rb_tree_implISF_Lb0EEC1Ev_ZN9__gnu_cxx13new_allocatorISt13_Rb_tree_nodeISt4pairIKN6google8protobuf11StringPieceENS4_4util8StatusOrIPKNS4_4EnumEEEEEED2Ev_ZNSaISt13_Rb_tree_nodeISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4EnumEEEEEED1Ev_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE8_S_rightEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE7_S_leftEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE15_M_destroy_nodeEPSt13_Rb_tree_nodeISB_E_ZNSt8_Rb_treeIPKN6google8protobuf4TypeES4_St9_IdentityIS4_ESt4lessIS4_ESaIS4_EEC2Ev_ZNSt8_Rb_treeIPKN6google8protobuf4TypeES4_St9_IdentityIS4_ESt4lessIS4_ESaIS4_EE13_Rb_tree_implIS8_Lb0EEC1Ev_ZN9__gnu_cxx13new_allocatorISt13_Rb_tree_nodeIPKN6google8protobuf4TypeEEED2Ev_ZNSaISt13_Rb_tree_nodeIPKN6google8protobuf4TypeEEED1Ev_ZNSt8_Rb_treeIPKN6google8protobuf4TypeES4_St9_IdentityIS4_ESt4lessIS4_ESaIS4_EE8_S_rightEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIPKN6google8protobuf4TypeES4_St9_IdentityIS4_ESt4lessIS4_ESaIS4_EE7_S_leftEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIPKN6google8protobuf4TypeES4_St9_IdentityIS4_ESt4lessIS4_ESaIS4_EE15_M_destroy_nodeEPSt13_Rb_tree_nodeIS4_E_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_S2_ESt10_Select1stIS5_ESt4lessIS2_ESaIS5_EEC2Ev_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_S2_ESt10_Select1stIS5_ESt4lessIS2_ESaIS5_EE13_Rb_tree_implIS9_Lb0EEC1Ev_ZN9__gnu_cxx13new_allocatorISt13_Rb_tree_nodeISt4pairIKN6google8protobuf11StringPieceES5_EEED2Ev_ZNSaISt13_Rb_tree_nodeISt4pairIKN6google8protobuf11StringPieceES3_EEED1Ev_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_S2_ESt10_Select1stIS5_ESt4lessIS2_ESaIS5_EE8_S_rightEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_S2_ESt10_Select1stIS5_ESt4lessIS2_ESaIS5_EE7_S_leftEPSt18_Rb_tree_node_base_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_S2_ESt10_Select1stIS5_ESt4lessIS2_ESaIS5_EE15_M_destroy_nodeEPSt13_Rb_tree_nodeIS5_E_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE5beginEv_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE5beginEv_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE6_M_endEv_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE14_M_lower_boundEPSt13_Rb_tree_nodeISB_ESK_RS4__ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE6_S_keyEPKSt18_Rb_tree_node_base_ZNSt17_Rb_tree_iteratorISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4TypeEEEEEC1EPSt18_Rb_tree_node_base_ZSt11__addressofISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4TypeEEEEEPT_RSC__ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE11lower_boundERS4__ZNKSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE8key_compEv_ZN6google8protobuf4util8StatusOrIPKNS0_4TypeEEC2Ev_ZNSt4pairIKN6google8protobuf11StringPieceENS1_4util8StatusOrIPKNS1_4TypeEEEEC2ERS3_RKS9__ZNSt23_Rb_tree_const_iteratorISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4TypeEEEEEC1ERKSt17_Rb_tree_iteratorISB_E_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4TypeEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE17_M_insert_unique_ESt23_Rb_tree_const_iteratorISB_ERKSB__ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE6_M_endEv_ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE14_M_lower_boundEPSt13_Rb_tree_nodeISB_ESK_RS4__ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE6_S_keyEPKSt18_Rb_tree_node_base_ZNSt17_Rb_tree_iteratorISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4EnumEEEEEC1EPSt18_Rb_tree_node_base_ZSt11__addressofISt4pairIKN6google8protobuf11StringPieceENS2_4util8StatusOrIPKNS2_4EnumEEEEEPT_RSC__ZNSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1stISB_ESt4lessIS2_ESaISB_EE11lower_boundERS4__ZNKSt8_Rb_treeIN6google8protobuf11StringPieceESt4pairIKS2_NS1_4util8StatusOrIPKNS1_4EnumEEEESt10_Select1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