RunMetadata.Builder

classe final estática pública RunMetadata.Builder

 Metadata output (i.e., non-Tensor) for a single Run() call.
 
Tipo de protobuf tensorflow.RunMetadata

Métodos Públicos

RunMetadata.Builder
addAllFunctionGraphs (Iterable<? estende RunMetadata.FunctionGraphs > valores)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addAllPartitionGraphs (Iterable<? estende GraphDef > valores)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (valor RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder construtorForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
addFunctionGraphsBuilder (índice interno)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
addFunctionGraphsBuilder ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addPartitionGraphs (índice int, valor GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs ( GraphDef.Builder construtorForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (valor GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (índice int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
GraphDef.Builder
addPartitionGraphsBuilder (índice interno)
 Graphs of the partitions executed by executors.
GraphDef.Builder
addPartitionGraphsBuilder ()
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)
ExecutarMetadados
ExecutarMetadados
RunMetadata.Builder
claro ()
RunMetadata.Builder
limparCostGraph ()
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
clearField (campo com.google.protobuf.Descriptors.FieldDescriptor)
RunMetadata.Builder
clearFunctionGraphs ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
clearOneof (com.google.protobuf.Descriptors.OneofDescriptor umof)
RunMetadata.Builder
limparPartitionGraphs ()
 Graphs of the partitions executed by executors.
RunMetadata.Builder
limparStepStats ()
 Statistics traced for this step.
RunMetadata.Builder
clonar ()
CostGraphDef
getCostGraph ()
 The cost graph for the computation defined by the run call.
CostGraphDef.Builder
getCostGraphBuilder ()
 The cost graph for the computation defined by the run call.
CostGraphDefOrBuilder
getCostGraphOrBuilder ()
 The cost graph for the computation defined by the run call.
ExecutarMetadados
final estático com.google.protobuf.Descriptors.Descriptor
com.google.protobuf.Descriptors.Descriptor
RunMetadata.FunctionGraphs
getFunctionGraphs (índice interno)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
getFunctionGraphsBuilder (índice interno)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Lista< RunMetadata.FunctionGraphs.Builder >
getFunctionGraphsBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
interno
getFunctionGraphsCount ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Lista< RunMetadata.FunctionGraphs >
getFunctionGraphsList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphsOrBuilder
getFunctionGraphsOrBuilder (índice interno)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Lista<? estende RunMetadata.FunctionGraphsOrBuilder >
getFunctionGraphsOrBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
GráficoDef
getPartitionGraphs (índice interno)
 Graphs of the partitions executed by executors.
GraphDef.Builder
getPartitionGraphsBuilder (índice interno)
 Graphs of the partitions executed by executors.
Lista< GraphDef.Builder >
getPartitionGraphsBuilderList ()
 Graphs of the partitions executed by executors.
interno
getPartitionGraphsCount ()
 Graphs of the partitions executed by executors.
Lista< GraphDef >
getPartitionGraphsList ()
 Graphs of the partitions executed by executors.
GraphDefOrBuilder
getPartitionGraphsOrBuilder (índice interno)
 Graphs of the partitions executed by executors.
Lista<? estende GraphDefOrBuilder >
getPartitionGraphsOrBuilderList ()
 Graphs of the partitions executed by executors.
StepStats
getStepStats ()
 Statistics traced for this step.
StepStats.Builder
getStepStatsBuilder ()
 Statistics traced for this step.
StepStatsOrBuilder
getStepStatsOrBuilder ()
 Statistics traced for this step.
booleano
hasCostGraph ()
 The cost graph for the computation defined by the run call.
booleano
hasStepStats ()
 Statistics traced for this step.
booleano final
RunMetadata.Builder
mergeCostGraph (valor CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
mergeFrom (com.google.protobuf.Message outro)
RunMetadata.Builder
mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
RunMetadata.Builder
mergeStepStats (valor StepStats )
 Statistics traced for this step.
RunMetadata.Builder final
mesclarUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)
RunMetadata.Builder
removeFunctionGraphs (índice interno)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
removePartitionGraphs (índice interno)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setCostGraph (valor CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setCostGraph ( CostGraphDef.Builder construtorForValue)
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)
RunMetadata.Builder
setFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setPartitionGraphs (índice int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setPartitionGraphs (índice int, valor GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor do objeto)
RunMetadata.Builder
setStepStats ( StepStats.Builder construtorForValue)
 Statistics traced for this step.
RunMetadata.Builder
setStepStats (valor StepStats )
 Statistics traced for this step.
RunMetadata.Builder final
setUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)

Métodos herdados

Métodos Públicos

public RunMetadata.Builder addAllFunctionGraphs (Iterable<? estende RunMetadata.FunctionGraphs > valores)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.Builder addAllPartitionGraphs (Iterable<? estende GraphDef > valores)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público RunMetadata.Builder addFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.Builder addFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.Builder addFunctionGraphs (valor RunMetadata.FunctionGraphs )

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.Builder addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (índice int)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.Builder addPartitionGraphs (índice int, valor GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public RunMetadata.Builder addPartitionGraphs ( GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public RunMetadata.Builder addPartitionGraphs (valor GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public RunMetadata.Builder addPartitionGraphs (índice int, GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público GraphDef.Builder addPartitionGraphsBuilder (índice int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público GraphDef.Builder addPartitionGraphsBuilder ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public RunMetadata.Builder addRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)

compilação RunMetadata pública ()

public RunMetadata buildPartial ()

público RunMetadata.Builder claro ()

público RunMetadata.Builder clearCostGraph ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

public RunMetadata.Builder clearField (campo com.google.protobuf.Descriptors.FieldDescriptor)

public RunMetadata.Builder clearFunctionGraphs ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.Builder clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

público RunMetadata.Builder clearPartitionGraphs ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público RunMetadata.Builder clearStepStats ()

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

Clone RunMetadata.Builder público ()

public CostGraphDef getCostGraph ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

public CostGraphDef.Builder getCostGraphBuilder ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

public CostGraphDefOrBuilder getCostGraphOrBuilder ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

público RunMetadata getDefaultInstanceForType ()

final estático público com.google.protobuf.Descriptors.Descriptor getDescriptor ()

público com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()

público RunMetadata.FunctionGraphs getFunctionGraphs (índice int)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder (índice int)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

Lista pública< RunMetadata.FunctionGraphs.Builder > getFunctionGraphsBuilderList ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público int getFunctionGraphsCount ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

Lista pública< RunMetadata.FunctionGraphs > getFunctionGraphsList ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder (índice int)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

Lista pública<? estende RunMetadata.FunctionGraphsOrBuilder > getFunctionGraphsOrBuilderList ()

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público GraphDef getPartitionGraphs (índice int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público GraphDef.Builder getPartitionGraphsBuilder (índice int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

Lista pública< GraphDef.Builder > getPartitionGraphsBuilderList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público int getPartitionGraphsCount ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

Lista pública< GraphDef > getPartitionGraphsList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público GraphDefOrBuilder getPartitionGraphsOrBuilder (índice int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

Lista pública<? estende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

StepStats públicos getStepStats ()

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público StepStats.BuildergetStepStatsBuilder ( )

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público StepStatsOrBuildergetStepStatsOrBuilder ( )

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

hasCostGraph booleano público ()

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

hasStepStats booleano público ()

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público final booleano isInitialized ()

público RunMetadata.Builder mergeCostGraph (valor CostGraphDef )

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

público RunMetadata.Builder mergeFrom (com.google.protobuf.Message outro)

public RunMetadata.Builder mergeFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lança
IOException

público RunMetadata.Builder mergeStepStats (valor StepStats )

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público final RunMetadata.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)

público RunMetadata.Builder removeFunctionGraphs (índice int)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

public RunMetadata.Builder removePartitionGraphs (índice int)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público RunMetadata.Builder setCostGraph (valor CostGraphDef )

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

público RunMetadata.Builder setCostGraph ( CostGraphDef.Builder builderForValue)

 The cost graph for the computation defined by the run call.
 
.tensorflow.CostGraphDef cost_graph = 2;

public RunMetadata.Builder setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valor do objeto)

público RunMetadata.Builder setFunctionGraphs (índice int, valor RunMetadata.FunctionGraphs )

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.Builder setFunctionGraphs (índice int, RunMetadata.FunctionGraphs.Builder builderForValue)

 This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
 
repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

público RunMetadata.Builder setPartitionGraphs (índice int, GraphDef.Builder builderForValue)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

público RunMetadata.Builder setPartitionGraphs (índice int, valor GraphDef )

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public RunMetadata.Builder setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, índice int, valor do objeto)

público RunMetadata.Builder setStepStats ( StepStats.Builder builderForValue)

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público RunMetadata.Builder setStepStats (valor StepStats )

 Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
 
.tensorflow.StepStats step_stats = 1;

público final RunMetadata.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet desconhecidoFields)