Class RunMetadata.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<RunMetadata.Builder>
com.google.protobuf.GeneratedMessageV3.Builder<RunMetadata.Builder>
org.tensorflow.proto.RunMetadata.Builder
All Implemented Interfaces:
Message.Builder, MessageLite.Builder, MessageLiteOrBuilder, MessageOrBuilder, Cloneable, RunMetadataOrBuilder
Enclosing class:
RunMetadata
public static final class RunMetadata.Builder extends GeneratedMessageV3.Builder<RunMetadata.Builder> implements RunMetadataOrBuilder
Metadata output (i.e., non-Tensor) for a single Run() call.
Protobuf type tensorflow.RunMetadata

  • Method Details

    • getDescriptor

      public static final Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • clear

      public RunMetadata.Builder clear()
      Specified by:
      clear in interface Message.Builder
      Specified by:
      clear in interface MessageLite.Builder
      Overrides:
      clear in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • getDescriptorForType

      public Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface Message.Builder
      Specified by:
      getDescriptorForType in interface MessageOrBuilder
      Overrides:
      getDescriptorForType in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • getDefaultInstanceForType

      public RunMetadata getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface MessageOrBuilder

    • build

      public RunMetadata build()
      Specified by:
      build in interface Message.Builder
      Specified by:
      build in interface MessageLite.Builder

    • buildPartial

      public RunMetadata buildPartial()
      Specified by:
      buildPartial in interface Message.Builder
      Specified by:
      buildPartial in interface MessageLite.Builder

    • clone

      public RunMetadata.Builder clone()
      Specified by:
      clone in interface Message.Builder
      Specified by:
      clone in interface MessageLite.Builder
      Overrides:
      clone in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • setField

      public RunMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)
      Specified by:
      setField in interface Message.Builder
      Overrides:
      setField in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • clearField

      public RunMetadata.Builder clearField(Descriptors.FieldDescriptor field)
      Specified by:
      clearField in interface Message.Builder
      Overrides:
      clearField in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • clearOneof

      public RunMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)
      Specified by:
      clearOneof in interface Message.Builder
      Overrides:
      clearOneof in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • setRepeatedField

      public RunMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)
      Specified by:
      setRepeatedField in interface Message.Builder
      Overrides:
      setRepeatedField in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • addRepeatedField

      public RunMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)
      Specified by:
      addRepeatedField in interface Message.Builder
      Overrides:
      addRepeatedField in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • mergeFrom

      public RunMetadata.Builder mergeFrom(Message other)
      Specified by:
      mergeFrom in interface Message.Builder
      Overrides:
      mergeFrom in class AbstractMessage.Builder<RunMetadata.Builder>

    • mergeFrom

      public RunMetadata.Builder mergeFrom(RunMetadata other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface MessageLiteOrBuilder
      Overrides:
      isInitialized in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • mergeFrom

      public RunMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface Message.Builder
      Specified by:
      mergeFrom in interface MessageLite.Builder
      Overrides:
      mergeFrom in class AbstractMessage.Builder<RunMetadata.Builder>
      Throws:
      IOException

    • hasStepStats

      public boolean hasStepStats()
      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;
      Specified by:
      hasStepStats in interface RunMetadataOrBuilder
      Returns:
      Whether the stepStats field is set.

    • getStepStats

      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;
      Specified by:
      getStepStats in interface RunMetadataOrBuilder
      Returns:
      The stepStats.

    • setStepStats

      public RunMetadata.Builder setStepStats(StepStats value)
      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;

    • setStepStats

      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;

    • mergeStepStats

      public RunMetadata.Builder mergeStepStats(StepStats value)
      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;

    • clearStepStats

      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;

    • getStepStatsBuilder

      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;

    • getStepStatsOrBuilder

      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;
      Specified by:
      getStepStatsOrBuilder in interface RunMetadataOrBuilder

    • hasCostGraph

      public boolean hasCostGraph()
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;
      Specified by:
      hasCostGraph in interface RunMetadataOrBuilder
      Returns:
      Whether the costGraph field is set.

    • getCostGraph

      public CostGraphDef getCostGraph()
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;
      Specified by:
      getCostGraph in interface RunMetadataOrBuilder
      Returns:
      The costGraph.

    • setCostGraph

      public RunMetadata.Builder setCostGraph(CostGraphDef value)
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • setCostGraph

      public RunMetadata.Builder setCostGraph(CostGraphDef.Builder builderForValue)
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • mergeCostGraph

      public RunMetadata.Builder mergeCostGraph(CostGraphDef value)
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • clearCostGraph

      public RunMetadata.Builder clearCostGraph()
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • getCostGraphBuilder

      public CostGraphDef.Builder getCostGraphBuilder()
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • getCostGraphOrBuilder

      public CostGraphDefOrBuilder getCostGraphOrBuilder()
      The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;
      Specified by:
      getCostGraphOrBuilder in interface RunMetadataOrBuilder

    • getPartitionGraphsList

      public List<GraphDef> getPartitionGraphsList()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;
      Specified by:
      getPartitionGraphsList in interface RunMetadataOrBuilder

    • getPartitionGraphsCount

      public int getPartitionGraphsCount()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;
      Specified by:
      getPartitionGraphsCount in interface RunMetadataOrBuilder

    • getPartitionGraphs

      public GraphDef getPartitionGraphs(int index)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;
      Specified by:
      getPartitionGraphs in interface RunMetadataOrBuilder

    • setPartitionGraphs

      public RunMetadata.Builder setPartitionGraphs(int index, GraphDef value)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • setPartitionGraphs

      public RunMetadata.Builder setPartitionGraphs(int index, GraphDef.Builder builderForValue)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addPartitionGraphs

      public RunMetadata.Builder addPartitionGraphs(GraphDef value)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addPartitionGraphs

      public RunMetadata.Builder addPartitionGraphs(int index, GraphDef value)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addPartitionGraphs

      public RunMetadata.Builder addPartitionGraphs(GraphDef.Builder builderForValue)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addPartitionGraphs

      public RunMetadata.Builder addPartitionGraphs(int index, GraphDef.Builder builderForValue)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addAllPartitionGraphs

      public RunMetadata.Builder addAllPartitionGraphs(Iterable<? extends GraphDef> values)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • clearPartitionGraphs

      public RunMetadata.Builder clearPartitionGraphs()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • removePartitionGraphs

      public RunMetadata.Builder removePartitionGraphs(int index)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsBuilder

      public GraphDef.Builder getPartitionGraphsBuilder(int index)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsOrBuilder

      public GraphDefOrBuilder getPartitionGraphsOrBuilder(int index)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;
      Specified by:
      getPartitionGraphsOrBuilder in interface RunMetadataOrBuilder

    • getPartitionGraphsOrBuilderList

      public List<? extends GraphDefOrBuilder> getPartitionGraphsOrBuilderList()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;
      Specified by:
      getPartitionGraphsOrBuilderList in interface RunMetadataOrBuilder

    • addPartitionGraphsBuilder

      public GraphDef.Builder addPartitionGraphsBuilder()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • addPartitionGraphsBuilder

      public GraphDef.Builder addPartitionGraphsBuilder(int index)
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsBuilderList

      public List<GraphDef.Builder> getPartitionGraphsBuilderList()
      Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getFunctionGraphsList

      public List<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;
      Specified by:
      getFunctionGraphsList in interface RunMetadataOrBuilder

    • getFunctionGraphsCount

      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;
      Specified by:
      getFunctionGraphsCount in interface RunMetadataOrBuilder

    • getFunctionGraphs

      public RunMetadata.FunctionGraphs getFunctionGraphs(int index)
      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;
      Specified by:
      getFunctionGraphs in interface RunMetadataOrBuilder

    • setFunctionGraphs

      public RunMetadata.Builder setFunctionGraphs(int index, RunMetadata.FunctionGraphs value)
      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;

    • setFunctionGraphs

      public RunMetadata.Builder setFunctionGraphs(int index, 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;

    • addFunctionGraphs

      public RunMetadata.Builder addFunctionGraphs(RunMetadata.FunctionGraphs value)
      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;

    • addFunctionGraphs

      public RunMetadata.Builder addFunctionGraphs(int index, RunMetadata.FunctionGraphs value)
      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;

    • addFunctionGraphs

      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;

    • addFunctionGraphs

      public RunMetadata.Builder addFunctionGraphs(int index, 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;

    • addAllFunctionGraphs

      public RunMetadata.Builder addAllFunctionGraphs(Iterable<? extends RunMetadata.FunctionGraphs> values)
      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;

    • clearFunctionGraphs

      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;

    • removeFunctionGraphs

      public RunMetadata.Builder removeFunctionGraphs(int index)
      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;

    • getFunctionGraphsBuilder

      public RunMetadata.FunctionGraphs.Builder getFunctionGraphsBuilder(int index)
      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;

    • getFunctionGraphsOrBuilder

      public RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder(int index)
      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;
      Specified by:
      getFunctionGraphsOrBuilder in interface RunMetadataOrBuilder

    • getFunctionGraphsOrBuilderList

      public List<? extends 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;
      Specified by:
      getFunctionGraphsOrBuilderList in interface RunMetadataOrBuilder

    • addFunctionGraphsBuilder

      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;

    • addFunctionGraphsBuilder

      public RunMetadata.FunctionGraphs.Builder addFunctionGraphsBuilder(int index)
      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;

    • getFunctionGraphsBuilderList

      public List<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;

    • hasSessionMetadata

      public boolean hasSessionMetadata()
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;
      Specified by:
      hasSessionMetadata in interface RunMetadataOrBuilder
      Returns:
      Whether the sessionMetadata field is set.

    • getSessionMetadata

      public SessionMetadata getSessionMetadata()
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;
      Specified by:
      getSessionMetadata in interface RunMetadataOrBuilder
      Returns:
      The sessionMetadata.

    • setSessionMetadata

      public RunMetadata.Builder setSessionMetadata(SessionMetadata value)
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;

    • setSessionMetadata

      public RunMetadata.Builder setSessionMetadata(SessionMetadata.Builder builderForValue)
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;

    • mergeSessionMetadata

      public RunMetadata.Builder mergeSessionMetadata(SessionMetadata value)
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;

    • clearSessionMetadata

      public RunMetadata.Builder clearSessionMetadata()
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;

    • getSessionMetadataBuilder

      public SessionMetadata.Builder getSessionMetadataBuilder()
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;

    • getSessionMetadataOrBuilder

      public SessionMetadataOrBuilder getSessionMetadataOrBuilder()
      Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;
      Specified by:
      getSessionMetadataOrBuilder in interface RunMetadataOrBuilder

    • setUnknownFields

      public final RunMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)
      Specified by:
      setUnknownFields in interface Message.Builder
      Overrides:
      setUnknownFields in class GeneratedMessageV3.Builder<RunMetadata.Builder>

    • mergeUnknownFields

      public final RunMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)
      Specified by:
      mergeUnknownFields in interface Message.Builder
      Overrides:
      mergeUnknownFields in class GeneratedMessageV3.Builder<RunMetadata.Builder>