Package org.tensorflow.framework

Class RunMetadata.Builder

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

    • Method Detail

      • getDescriptor

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

      • internalGetFieldAccessorTable

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

      • clear

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

      • getDescriptorForType

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

      • getDefaultInstanceForType

        public RunMetadata getDefaultInstanceForType()
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
        Specified by:
        getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

      • build

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

      • buildPartial

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

      • clone

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

      • setField

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

      • clearField

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

      • clearOneof

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

      • setRepeatedField

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

      • addRepeatedField

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

      • mergeFrom

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

      • isInitialized

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

      • mergeFrom

        public RunMetadata.Builder mergeFrom​(com.google.protobuf.CodedInputStream input,
                                     com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                              throws java.io.IOException
        Specified by:
        mergeFrom in interface com.google.protobuf.Message.Builder
        Specified by:
        mergeFrom in interface com.google.protobuf.MessageLite.Builder
        Overrides:
        mergeFrom in class com.google.protobuf.AbstractMessage.Builder<RunMetadata.Builder>
        Throws:
        java.io.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;

      • getPartitionGraphsList

        public java.util.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​(java.lang.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;

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

      • getFunctionGraphsList

        public java.util.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​(java.lang.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 java.util.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 java.util.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;

      • setUnknownFields

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

      • mergeUnknownFields

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