org.tensorflow.framework.GraphOptions Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
/**
* Protobuf type {@code tensorflow.GraphOptions}
*/
public final class GraphOptions extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.GraphOptions)
GraphOptionsOrBuilder {
private static final long serialVersionUID = 0L;
// Use GraphOptions.newBuilder() to construct.
private GraphOptions(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private GraphOptions() {
enableRecvScheduling_ = false;
buildCostModel_ = 0L;
buildCostModelAfter_ = 0L;
inferShapes_ = false;
placePrunedGraph_ = false;
enableBfloat16Sendrecv_ = false;
timelineStep_ = 0;
}
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org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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org.tensorflow.framework.OptimizerOptions.Builder subBuilder = null;
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case 32: {
buildCostModel_ = input.readInt64();
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case 40: {
inferShapes_ = input.readBool();
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placePrunedGraph_ = input.readBool();
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timelineStep_ = input.readInt32();
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public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_GraphOptions_descriptor;
}
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public static final int ENABLE_RECV_SCHEDULING_FIELD_NUMBER = 2;
private boolean enableRecvScheduling_;
/**
*
* If true, use control flow to schedule the activation of Recv nodes.
* (Currently ignored.)
*
*
* bool enable_recv_scheduling = 2;
*/
public boolean getEnableRecvScheduling() {
return enableRecvScheduling_;
}
public static final int OPTIMIZER_OPTIONS_FIELD_NUMBER = 3;
private org.tensorflow.framework.OptimizerOptions optimizerOptions_;
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public boolean hasOptimizerOptions() {
return optimizerOptions_ != null;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public org.tensorflow.framework.OptimizerOptions getOptimizerOptions() {
return optimizerOptions_ == null ? org.tensorflow.framework.OptimizerOptions.getDefaultInstance() : optimizerOptions_;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public org.tensorflow.framework.OptimizerOptionsOrBuilder getOptimizerOptionsOrBuilder() {
return getOptimizerOptions();
}
public static final int BUILD_COST_MODEL_FIELD_NUMBER = 4;
private long buildCostModel_;
/**
*
* The number of steps to run before returning a cost model detailing
* the memory usage and performance of each node of the graph. 0 means
* no cost model.
*
*
* int64 build_cost_model = 4;
*/
public long getBuildCostModel() {
return buildCostModel_;
}
public static final int BUILD_COST_MODEL_AFTER_FIELD_NUMBER = 9;
private long buildCostModelAfter_;
/**
*
* The number of steps to skip before collecting statistics for the
* cost model.
*
*
* int64 build_cost_model_after = 9;
*/
public long getBuildCostModelAfter() {
return buildCostModelAfter_;
}
public static final int INFER_SHAPES_FIELD_NUMBER = 5;
private boolean inferShapes_;
/**
*
* Annotate each Node with Op output shape data, to the extent it can
* be statically inferred.
*
*
* bool infer_shapes = 5;
*/
public boolean getInferShapes() {
return inferShapes_;
}
public static final int PLACE_PRUNED_GRAPH_FIELD_NUMBER = 6;
private boolean placePrunedGraph_;
/**
*
* Only place the subgraphs that are run, rather than the entire graph.
* This is useful for interactive graph building, where one might
* produce graphs that cannot be placed during the debugging
* process. In particular, it allows the client to continue work in
* a session after adding a node to a graph whose placement
* constraints are unsatisfiable.
*
*
* bool place_pruned_graph = 6;
*/
public boolean getPlacePrunedGraph() {
return placePrunedGraph_;
}
public static final int ENABLE_BFLOAT16_SENDRECV_FIELD_NUMBER = 7;
private boolean enableBfloat16Sendrecv_;
/**
*
* If true, transfer float values between processes as bfloat16.
*
*
* bool enable_bfloat16_sendrecv = 7;
*/
public boolean getEnableBfloat16Sendrecv() {
return enableBfloat16Sendrecv_;
}
public static final int TIMELINE_STEP_FIELD_NUMBER = 8;
private int timelineStep_;
/**
*
* If > 0, record a timeline every this many steps.
* EXPERIMENTAL: This currently has no effect in MasterSession.
*
*
* int32 timeline_step = 8;
*/
public int getTimelineStep() {
return timelineStep_;
}
public static final int REWRITE_OPTIONS_FIELD_NUMBER = 10;
private org.tensorflow.framework.RewriterConfig rewriteOptions_;
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public boolean hasRewriteOptions() {
return rewriteOptions_ != null;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public org.tensorflow.framework.RewriterConfig getRewriteOptions() {
return rewriteOptions_ == null ? org.tensorflow.framework.RewriterConfig.getDefaultInstance() : rewriteOptions_;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public org.tensorflow.framework.RewriterConfigOrBuilder getRewriteOptionsOrBuilder() {
return getRewriteOptions();
}
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public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output)
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if (enableRecvScheduling_ != false) {
output.writeBool(2, enableRecvScheduling_);
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output.writeBool(5, inferShapes_);
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@java.lang.Override
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org.tensorflow.framework.GraphOptions other = (org.tensorflow.framework.GraphOptions) obj;
boolean result = true;
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result = result && (hasOptimizerOptions() == other.hasOptimizerOptions());
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result = result && (getBuildCostModelAfter()
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result = result && (getInferShapes()
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result = result && (getEnableBfloat16Sendrecv()
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result = result && (getTimelineStep()
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result = result && (hasRewriteOptions() == other.hasRewriteOptions());
if (hasRewriteOptions()) {
result = result && getRewriteOptions()
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result = result && unknownFields.equals(other.unknownFields);
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}
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public static org.tensorflow.framework.GraphOptions parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphOptions parseFrom(
java.nio.ByteBuffer data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GraphOptions parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphOptions parseFrom(
org.nd4j.shade.protobuf.ByteString data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GraphOptions parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static org.tensorflow.framework.GraphOptions parseFrom(
byte[] data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GraphOptions parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphOptions parseFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input, extensionRegistry);
}
public static org.tensorflow.framework.GraphOptions parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphOptions parseDelimitedFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input, extensionRegistry);
}
public static org.tensorflow.framework.GraphOptions parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphOptions parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input, extensionRegistry);
}
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(org.tensorflow.framework.GraphOptions prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code tensorflow.GraphOptions}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.GraphOptions)
org.tensorflow.framework.GraphOptionsOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_GraphOptions_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_GraphOptions_fieldAccessorTable
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org.tensorflow.framework.GraphOptions.class, org.tensorflow.framework.GraphOptions.Builder.class);
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// Construct using org.tensorflow.framework.GraphOptions.newBuilder()
private Builder() {
maybeForceBuilderInitialization();
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org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
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}
}
public Builder clear() {
super.clear();
enableRecvScheduling_ = false;
if (optimizerOptionsBuilder_ == null) {
optimizerOptions_ = null;
} else {
optimizerOptions_ = null;
optimizerOptionsBuilder_ = null;
}
buildCostModel_ = 0L;
buildCostModelAfter_ = 0L;
inferShapes_ = false;
placePrunedGraph_ = false;
enableBfloat16Sendrecv_ = false;
timelineStep_ = 0;
if (rewriteOptionsBuilder_ == null) {
rewriteOptions_ = null;
} else {
rewriteOptions_ = null;
rewriteOptionsBuilder_ = null;
}
return this;
}
public org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_GraphOptions_descriptor;
}
public org.tensorflow.framework.GraphOptions getDefaultInstanceForType() {
return org.tensorflow.framework.GraphOptions.getDefaultInstance();
}
public org.tensorflow.framework.GraphOptions build() {
org.tensorflow.framework.GraphOptions result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
public org.tensorflow.framework.GraphOptions buildPartial() {
org.tensorflow.framework.GraphOptions result = new org.tensorflow.framework.GraphOptions(this);
result.enableRecvScheduling_ = enableRecvScheduling_;
if (optimizerOptionsBuilder_ == null) {
result.optimizerOptions_ = optimizerOptions_;
} else {
result.optimizerOptions_ = optimizerOptionsBuilder_.build();
}
result.buildCostModel_ = buildCostModel_;
result.buildCostModelAfter_ = buildCostModelAfter_;
result.inferShapes_ = inferShapes_;
result.placePrunedGraph_ = placePrunedGraph_;
result.enableBfloat16Sendrecv_ = enableBfloat16Sendrecv_;
result.timelineStep_ = timelineStep_;
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onBuilt();
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) {
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org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) {
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) {
if (other instanceof org.tensorflow.framework.GraphOptions) {
return mergeFrom((org.tensorflow.framework.GraphOptions)other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(org.tensorflow.framework.GraphOptions other) {
if (other == org.tensorflow.framework.GraphOptions.getDefaultInstance()) return this;
if (other.getEnableRecvScheduling() != false) {
setEnableRecvScheduling(other.getEnableRecvScheduling());
}
if (other.hasOptimizerOptions()) {
mergeOptimizerOptions(other.getOptimizerOptions());
}
if (other.getBuildCostModel() != 0L) {
setBuildCostModel(other.getBuildCostModel());
}
if (other.getBuildCostModelAfter() != 0L) {
setBuildCostModelAfter(other.getBuildCostModelAfter());
}
if (other.getInferShapes() != false) {
setInferShapes(other.getInferShapes());
}
if (other.getPlacePrunedGraph() != false) {
setPlacePrunedGraph(other.getPlacePrunedGraph());
}
if (other.getEnableBfloat16Sendrecv() != false) {
setEnableBfloat16Sendrecv(other.getEnableBfloat16Sendrecv());
}
if (other.getTimelineStep() != 0) {
setTimelineStep(other.getTimelineStep());
}
if (other.hasRewriteOptions()) {
mergeRewriteOptions(other.getRewriteOptions());
}
this.mergeUnknownFields(other.unknownFields);
onChanged();
return this;
}
public final boolean isInitialized() {
return true;
}
public Builder mergeFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.GraphOptions parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
parsedMessage = (org.tensorflow.framework.GraphOptions) e.getUnfinishedMessage();
throw e.unwrapIOException();
} finally {
if (parsedMessage != null) {
mergeFrom(parsedMessage);
}
}
return this;
}
private boolean enableRecvScheduling_ ;
/**
*
* If true, use control flow to schedule the activation of Recv nodes.
* (Currently ignored.)
*
*
* bool enable_recv_scheduling = 2;
*/
public boolean getEnableRecvScheduling() {
return enableRecvScheduling_;
}
/**
*
* If true, use control flow to schedule the activation of Recv nodes.
* (Currently ignored.)
*
*
* bool enable_recv_scheduling = 2;
*/
public Builder setEnableRecvScheduling(boolean value) {
enableRecvScheduling_ = value;
onChanged();
return this;
}
/**
*
* If true, use control flow to schedule the activation of Recv nodes.
* (Currently ignored.)
*
*
* bool enable_recv_scheduling = 2;
*/
public Builder clearEnableRecvScheduling() {
enableRecvScheduling_ = false;
onChanged();
return this;
}
private org.tensorflow.framework.OptimizerOptions optimizerOptions_ = null;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.OptimizerOptions, org.tensorflow.framework.OptimizerOptions.Builder, org.tensorflow.framework.OptimizerOptionsOrBuilder> optimizerOptionsBuilder_;
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public boolean hasOptimizerOptions() {
return optimizerOptionsBuilder_ != null || optimizerOptions_ != null;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public org.tensorflow.framework.OptimizerOptions getOptimizerOptions() {
if (optimizerOptionsBuilder_ == null) {
return optimizerOptions_ == null ? org.tensorflow.framework.OptimizerOptions.getDefaultInstance() : optimizerOptions_;
} else {
return optimizerOptionsBuilder_.getMessage();
}
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public Builder setOptimizerOptions(org.tensorflow.framework.OptimizerOptions value) {
if (optimizerOptionsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
optimizerOptions_ = value;
onChanged();
} else {
optimizerOptionsBuilder_.setMessage(value);
}
return this;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public Builder setOptimizerOptions(
org.tensorflow.framework.OptimizerOptions.Builder builderForValue) {
if (optimizerOptionsBuilder_ == null) {
optimizerOptions_ = builderForValue.build();
onChanged();
} else {
optimizerOptionsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public Builder mergeOptimizerOptions(org.tensorflow.framework.OptimizerOptions value) {
if (optimizerOptionsBuilder_ == null) {
if (optimizerOptions_ != null) {
optimizerOptions_ =
org.tensorflow.framework.OptimizerOptions.newBuilder(optimizerOptions_).mergeFrom(value).buildPartial();
} else {
optimizerOptions_ = value;
}
onChanged();
} else {
optimizerOptionsBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public Builder clearOptimizerOptions() {
if (optimizerOptionsBuilder_ == null) {
optimizerOptions_ = null;
onChanged();
} else {
optimizerOptions_ = null;
optimizerOptionsBuilder_ = null;
}
return this;
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public org.tensorflow.framework.OptimizerOptions.Builder getOptimizerOptionsBuilder() {
onChanged();
return getOptimizerOptionsFieldBuilder().getBuilder();
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
public org.tensorflow.framework.OptimizerOptionsOrBuilder getOptimizerOptionsOrBuilder() {
if (optimizerOptionsBuilder_ != null) {
return optimizerOptionsBuilder_.getMessageOrBuilder();
} else {
return optimizerOptions_ == null ?
org.tensorflow.framework.OptimizerOptions.getDefaultInstance() : optimizerOptions_;
}
}
/**
*
* Options controlling how graph is optimized.
*
*
* .tensorflow.OptimizerOptions optimizer_options = 3;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.OptimizerOptions, org.tensorflow.framework.OptimizerOptions.Builder, org.tensorflow.framework.OptimizerOptionsOrBuilder>
getOptimizerOptionsFieldBuilder() {
if (optimizerOptionsBuilder_ == null) {
optimizerOptionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.OptimizerOptions, org.tensorflow.framework.OptimizerOptions.Builder, org.tensorflow.framework.OptimizerOptionsOrBuilder>(
getOptimizerOptions(),
getParentForChildren(),
isClean());
optimizerOptions_ = null;
}
return optimizerOptionsBuilder_;
}
private long buildCostModel_ ;
/**
*
* The number of steps to run before returning a cost model detailing
* the memory usage and performance of each node of the graph. 0 means
* no cost model.
*
*
* int64 build_cost_model = 4;
*/
public long getBuildCostModel() {
return buildCostModel_;
}
/**
*
* The number of steps to run before returning a cost model detailing
* the memory usage and performance of each node of the graph. 0 means
* no cost model.
*
*
* int64 build_cost_model = 4;
*/
public Builder setBuildCostModel(long value) {
buildCostModel_ = value;
onChanged();
return this;
}
/**
*
* The number of steps to run before returning a cost model detailing
* the memory usage and performance of each node of the graph. 0 means
* no cost model.
*
*
* int64 build_cost_model = 4;
*/
public Builder clearBuildCostModel() {
buildCostModel_ = 0L;
onChanged();
return this;
}
private long buildCostModelAfter_ ;
/**
*
* The number of steps to skip before collecting statistics for the
* cost model.
*
*
* int64 build_cost_model_after = 9;
*/
public long getBuildCostModelAfter() {
return buildCostModelAfter_;
}
/**
*
* The number of steps to skip before collecting statistics for the
* cost model.
*
*
* int64 build_cost_model_after = 9;
*/
public Builder setBuildCostModelAfter(long value) {
buildCostModelAfter_ = value;
onChanged();
return this;
}
/**
*
* The number of steps to skip before collecting statistics for the
* cost model.
*
*
* int64 build_cost_model_after = 9;
*/
public Builder clearBuildCostModelAfter() {
buildCostModelAfter_ = 0L;
onChanged();
return this;
}
private boolean inferShapes_ ;
/**
*
* Annotate each Node with Op output shape data, to the extent it can
* be statically inferred.
*
*
* bool infer_shapes = 5;
*/
public boolean getInferShapes() {
return inferShapes_;
}
/**
*
* Annotate each Node with Op output shape data, to the extent it can
* be statically inferred.
*
*
* bool infer_shapes = 5;
*/
public Builder setInferShapes(boolean value) {
inferShapes_ = value;
onChanged();
return this;
}
/**
*
* Annotate each Node with Op output shape data, to the extent it can
* be statically inferred.
*
*
* bool infer_shapes = 5;
*/
public Builder clearInferShapes() {
inferShapes_ = false;
onChanged();
return this;
}
private boolean placePrunedGraph_ ;
/**
*
* Only place the subgraphs that are run, rather than the entire graph.
* This is useful for interactive graph building, where one might
* produce graphs that cannot be placed during the debugging
* process. In particular, it allows the client to continue work in
* a session after adding a node to a graph whose placement
* constraints are unsatisfiable.
*
*
* bool place_pruned_graph = 6;
*/
public boolean getPlacePrunedGraph() {
return placePrunedGraph_;
}
/**
*
* Only place the subgraphs that are run, rather than the entire graph.
* This is useful for interactive graph building, where one might
* produce graphs that cannot be placed during the debugging
* process. In particular, it allows the client to continue work in
* a session after adding a node to a graph whose placement
* constraints are unsatisfiable.
*
*
* bool place_pruned_graph = 6;
*/
public Builder setPlacePrunedGraph(boolean value) {
placePrunedGraph_ = value;
onChanged();
return this;
}
/**
*
* Only place the subgraphs that are run, rather than the entire graph.
* This is useful for interactive graph building, where one might
* produce graphs that cannot be placed during the debugging
* process. In particular, it allows the client to continue work in
* a session after adding a node to a graph whose placement
* constraints are unsatisfiable.
*
*
* bool place_pruned_graph = 6;
*/
public Builder clearPlacePrunedGraph() {
placePrunedGraph_ = false;
onChanged();
return this;
}
private boolean enableBfloat16Sendrecv_ ;
/**
*
* If true, transfer float values between processes as bfloat16.
*
*
* bool enable_bfloat16_sendrecv = 7;
*/
public boolean getEnableBfloat16Sendrecv() {
return enableBfloat16Sendrecv_;
}
/**
*
* If true, transfer float values between processes as bfloat16.
*
*
* bool enable_bfloat16_sendrecv = 7;
*/
public Builder setEnableBfloat16Sendrecv(boolean value) {
enableBfloat16Sendrecv_ = value;
onChanged();
return this;
}
/**
*
* If true, transfer float values between processes as bfloat16.
*
*
* bool enable_bfloat16_sendrecv = 7;
*/
public Builder clearEnableBfloat16Sendrecv() {
enableBfloat16Sendrecv_ = false;
onChanged();
return this;
}
private int timelineStep_ ;
/**
*
* If > 0, record a timeline every this many steps.
* EXPERIMENTAL: This currently has no effect in MasterSession.
*
*
* int32 timeline_step = 8;
*/
public int getTimelineStep() {
return timelineStep_;
}
/**
*
* If > 0, record a timeline every this many steps.
* EXPERIMENTAL: This currently has no effect in MasterSession.
*
*
* int32 timeline_step = 8;
*/
public Builder setTimelineStep(int value) {
timelineStep_ = value;
onChanged();
return this;
}
/**
*
* If > 0, record a timeline every this many steps.
* EXPERIMENTAL: This currently has no effect in MasterSession.
*
*
* int32 timeline_step = 8;
*/
public Builder clearTimelineStep() {
timelineStep_ = 0;
onChanged();
return this;
}
private org.tensorflow.framework.RewriterConfig rewriteOptions_ = null;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RewriterConfig, org.tensorflow.framework.RewriterConfig.Builder, org.tensorflow.framework.RewriterConfigOrBuilder> rewriteOptionsBuilder_;
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public boolean hasRewriteOptions() {
return rewriteOptionsBuilder_ != null || rewriteOptions_ != null;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public org.tensorflow.framework.RewriterConfig getRewriteOptions() {
if (rewriteOptionsBuilder_ == null) {
return rewriteOptions_ == null ? org.tensorflow.framework.RewriterConfig.getDefaultInstance() : rewriteOptions_;
} else {
return rewriteOptionsBuilder_.getMessage();
}
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public Builder setRewriteOptions(org.tensorflow.framework.RewriterConfig value) {
if (rewriteOptionsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
rewriteOptions_ = value;
onChanged();
} else {
rewriteOptionsBuilder_.setMessage(value);
}
return this;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public Builder setRewriteOptions(
org.tensorflow.framework.RewriterConfig.Builder builderForValue) {
if (rewriteOptionsBuilder_ == null) {
rewriteOptions_ = builderForValue.build();
onChanged();
} else {
rewriteOptionsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public Builder mergeRewriteOptions(org.tensorflow.framework.RewriterConfig value) {
if (rewriteOptionsBuilder_ == null) {
if (rewriteOptions_ != null) {
rewriteOptions_ =
org.tensorflow.framework.RewriterConfig.newBuilder(rewriteOptions_).mergeFrom(value).buildPartial();
} else {
rewriteOptions_ = value;
}
onChanged();
} else {
rewriteOptionsBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public Builder clearRewriteOptions() {
if (rewriteOptionsBuilder_ == null) {
rewriteOptions_ = null;
onChanged();
} else {
rewriteOptions_ = null;
rewriteOptionsBuilder_ = null;
}
return this;
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public org.tensorflow.framework.RewriterConfig.Builder getRewriteOptionsBuilder() {
onChanged();
return getRewriteOptionsFieldBuilder().getBuilder();
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
public org.tensorflow.framework.RewriterConfigOrBuilder getRewriteOptionsOrBuilder() {
if (rewriteOptionsBuilder_ != null) {
return rewriteOptionsBuilder_.getMessageOrBuilder();
} else {
return rewriteOptions_ == null ?
org.tensorflow.framework.RewriterConfig.getDefaultInstance() : rewriteOptions_;
}
}
/**
*
* Options that control the type and amount of graph rewriting.
* Not currently configurable via the public Python API (i.e. there is no API
* stability guarantee if you import RewriterConfig explicitly).
*
*
* .tensorflow.RewriterConfig rewrite_options = 10;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RewriterConfig, org.tensorflow.framework.RewriterConfig.Builder, org.tensorflow.framework.RewriterConfigOrBuilder>
getRewriteOptionsFieldBuilder() {
if (rewriteOptionsBuilder_ == null) {
rewriteOptionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RewriterConfig, org.tensorflow.framework.RewriterConfig.Builder, org.tensorflow.framework.RewriterConfigOrBuilder>(
getRewriteOptions(),
getParentForChildren(),
isClean());
rewriteOptions_ = null;
}
return rewriteOptionsBuilder_;
}
public final Builder setUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
public final Builder mergeUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.GraphOptions)
}
// @@protoc_insertion_point(class_scope:tensorflow.GraphOptions)
private static final org.tensorflow.framework.GraphOptions DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.GraphOptions();
}
public static org.tensorflow.framework.GraphOptions getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
public GraphOptions parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new GraphOptions(input, extensionRegistry);
}
};
public static org.nd4j.shade.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Parser getParserForType() {
return PARSER;
}
public org.tensorflow.framework.GraphOptions getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}