RunMetadata.Builder

classe finale statica pubblica RunMetadata.Builder

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

Metodi pubblici

RunMetadata.Builder
addAllFunctionGraphs (Iterable<? estende RunMetadata.FunctionGraphs > valori)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addAllPartitionGraphs (Iterable<? estende GraphDef > valori)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addFunctionGraphs (indice int, valore RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (indice int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs (valore RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
addFunctionGraphs ( RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
addFunctionGraphsBuilder (indice int)
 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 (indice int, valore GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs ( GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (valore GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
addPartitionGraphs (indice int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
GraphDef.Builder
addPartitionGraphsBuilder (indice int)
 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, valore oggetto)
RunMetadata
RunMetadata
RunMetadata.Builder
chiaro ()
RunMetadata.Builder
clearCostGraph ()
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
clearField (campo com.google.protobuf.Descriptors.FieldDescriptor)
RunMetadata.Builder
CancellaFunzioniGrafici ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)
RunMetadata.Builder
clearPartitionGraphs ()
 Graphs of the partitions executed by executors.
RunMetadata.Builder
clearStepStats ()
 Statistics traced for this step.
RunMetadata.Builder
clone ()
CostoGraphDef
getGraficoCosti ()
 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.
RunMetadata
com.google.protobuf.Descriptors.Descriptor statico finale
com.google.protobuf.Descriptors.Descriptor
RunMetadata.FunctionGraphs
getFunctionGraphs (indice int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphs.Builder
getFunctionGraphsBuilder (indice int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Elenco< RunMetadata.FunctionGraphs.Builder >
getFunctionGraphsBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
int
getFunctionGraphsCount ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Elenco< RunMetadata.FunctionGraphs >
getFunctionGraphsList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.FunctionGraphsOrBuilder
getFunctionGraphsOrBuilder (indice int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
Elenco<? estende RunMetadata.FunctionGraphsOrBuilder >
getFunctionGraphsOrBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
GraficoDef
getPartitionGraphs (indice int)
 Graphs of the partitions executed by executors.
GraphDef.Builder
getPartitionGraphsBuilder (indice int)
 Graphs of the partitions executed by executors.
Elenco< GraphDef.Builder >
getPartitionGraphsBuilderList ()
 Graphs of the partitions executed by executors.
int
getPartitionGraphsCount ()
 Graphs of the partitions executed by executors.
Elenco <GraphDef>
getPartitionGraphsList ()
 Graphs of the partitions executed by executors.
GraphDefOrBuilder
getPartitionGraphsOrBuilder (indice int)
 Graphs of the partitions executed by executors.
Elenco<? 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 finale
RunMetadata.Builder
mergeCostGraph (valore CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
mergeFrom (com.google.protobuf.Message altro)
RunMetadata.Builder
mergeFrom (input com.google.protobuf.CodedInputStream, estensione com.google.protobuf.ExtensionRegistryLiteRegistry)
RunMetadata.Builder
mergeStepStats (valore StepStats )
 Statistics traced for this step.
finale RunMetadata.Builder
mergeUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields)
RunMetadata.Builder
rimuoviFunctionGraphs (indice int)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
rimuoviPartitionGraphs (indice int)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setCostGraph (valore CostGraphDef )
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setCostGraph ( CostGraphDef.Builder builderForValue)
 The cost graph for the computation defined by the run call.
RunMetadata.Builder
setField (campo com.google.protobuf.Descriptors.FieldDescriptor, valore oggetto)
RunMetadata.Builder
setFunctionGraphs (indice int, valore RunMetadata.FunctionGraphs )
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setFunctionGraphs (indice int, RunMetadata.FunctionGraphs.Builder builderForValue)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
RunMetadata.Builder
setPartitionGraphs (indice int, GraphDef.Builder builderForValue)
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setPartitionGraphs (indice int, valore GraphDef )
 Graphs of the partitions executed by executors.
RunMetadata.Builder
setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, indice int, valore oggetto)
RunMetadata.Builder
setStepStats ( StepStats.Builder builderForValue)
 Statistics traced for this step.
RunMetadata.Builder
setStepStats (valore StepStats )
 Statistics traced for this step.
finale RunMetadata.Builder
setUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields)

Metodi ereditati

Metodi pubblici

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

 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 > valori)

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

public RunMetadata.Builder addFunctionGraphs (indice int, valore 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 (indice 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;

public RunMetadata.Builder addFunctionGraphs (valore 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;

public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder (indice 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.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 (indice int, valore 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 (valore GraphDef )

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

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

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

public GraphDef.Builder addPartitionGraphsBuilder (indice int)

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

public 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, valore oggetto)

build RunMetadata pubblica ()

public RunMetadata buildPartial ()

public RunMetadata.Builder cancella ()

pubblico 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;

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

public RunMetadata.Builder clearPartitionGraphs ()

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

public 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 pubblico RunMetadata.Builder ()

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;

public RunMetadata getDefaultInstanceForType ()

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

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

public RunMetadata.FunctionGraphs getFunctionGraphs (indice 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.FunctionGraphs.Builder getFunctionGraphsBuilder (indice 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;

Elenco pubblico< 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;

public 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;

Elenco pubblico< 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 (indice 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;

Elenco pubblico<? 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;

public GraphDef getPartitionGraphs (indice int)

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

public GraphDef.Builder getPartitionGraphsBuilder (indice int)

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

Elenco pubblico< GraphDef.Builder > getPartitionGraphsBuilderList ()

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

public int getPartitionGraphsCount ()

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

Elenco pubblico < GraphDef > getPartitionGraphsList ()

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

public GraphDefOrBuilder getPartitionGraphsOrBuilder (indice int)

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

Elenco pubblico<? estende GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()

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

public StepStats 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;

public StepStats.Builder getStepStatsBuilder ()

 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;

public StepStatsOrBuilder getStepStatsOrBuilder ()

 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;

pubblico booleano hasCostGraph ()

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

hasStepStats booleano pubblico ()

 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;

public final booleano isInitialized ()

public RunMetadata.Builder mergeCostGraph (valore CostGraphDef )

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

pubblico RunMetadata.Builder mergeFrom (com.google.protobuf.Message altro)

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

Lancia
IOException

pubblico RunMetadata.Builder mergeStepStats (valore 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;

pubblico finale RunMetadata.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet UnknownFields)

public RunMetadata.BuilderremoveFunctionGraphs ( indice 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 rimuoviPartitionGraphs (indice int)

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

public RunMetadata.Builder setCostGraph (valore CostGraphDef )

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

public 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, valore oggetto)

public RunMetadata.Builder setFunctionGraphs (indice int, valore 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 setFunctionGraphs (indice 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;

public RunMetadata.Builder setPartitionGraphs (indice int, GraphDef.Builder builderForValue)

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

public RunMetadata.Builder setPartitionGraphs (indice int, valore GraphDef )

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

public RunMetadata.Builder setRepeatedField (campo com.google.protobuf.Descriptors.FieldDescriptor, indice int, valore oggetto)

public 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;

pubblico RunMetadata.Builder setStepStats (valore 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;

pubblico finale RunMetadata.Builder setUnknownFields (com.google.protobuf.UnknownFieldSetknownFields)