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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
/**
*
* Options for a single Run() call.
*
*
* Protobuf type {@code tensorflow.RunOptions}
*/
public final class RunOptions extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RunOptions)
RunOptionsOrBuilder {
private static final long serialVersionUID = 0L;
// Use RunOptions.newBuilder() to construct.
private RunOptions(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private RunOptions() {
traceLevel_ = 0;
timeoutInMs_ = 0L;
interOpThreadPool_ = 0;
outputPartitionGraphs_ = false;
reportTensorAllocationsUponOom_ = false;
}
@java.lang.Override
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getUnknownFields() {
return this.unknownFields;
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private RunOptions(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
this();
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
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int mutable_bitField0_ = 0;
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throw e.setUnfinishedMessage(this);
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throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException(
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makeExtensionsImmutable();
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public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
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/**
*
* TODO(pbar) Turn this into a TraceOptions proto which allows
* tracing to be controlled in a more orthogonal manner?
*
*
* Protobuf enum {@code tensorflow.RunOptions.TraceLevel}
*/
public enum TraceLevel
implements org.nd4j.shade.protobuf.ProtocolMessageEnum {
/**
* NO_TRACE = 0;
*/
NO_TRACE(0),
/**
* SOFTWARE_TRACE = 1;
*/
SOFTWARE_TRACE(1),
/**
* HARDWARE_TRACE = 2;
*/
HARDWARE_TRACE(2),
/**
* FULL_TRACE = 3;
*/
FULL_TRACE(3),
UNRECOGNIZED(-1),
;
/**
* NO_TRACE = 0;
*/
public static final int NO_TRACE_VALUE = 0;
/**
* SOFTWARE_TRACE = 1;
*/
public static final int SOFTWARE_TRACE_VALUE = 1;
/**
* HARDWARE_TRACE = 2;
*/
public static final int HARDWARE_TRACE_VALUE = 2;
/**
* FULL_TRACE = 3;
*/
public static final int FULL_TRACE_VALUE = 3;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static TraceLevel valueOf(int value) {
return forNumber(value);
}
public static TraceLevel forNumber(int value) {
switch (value) {
case 0: return NO_TRACE;
case 1: return SOFTWARE_TRACE;
case 2: return HARDWARE_TRACE;
case 3: return FULL_TRACE;
default: return null;
}
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internalGetValueMap() {
return internalValueMap;
}
private static final org.nd4j.shade.protobuf.Internal.EnumLiteMap<
TraceLevel> internalValueMap =
new org.nd4j.shade.protobuf.Internal.EnumLiteMap() {
public TraceLevel findValueByNumber(int number) {
return TraceLevel.forNumber(number);
}
};
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getValueDescriptor() {
return getDescriptor().getValues().get(ordinal());
}
public final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptorForType() {
return getDescriptor();
}
public static final org.nd4j.shade.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return org.tensorflow.framework.RunOptions.getDescriptor().getEnumTypes().get(0);
}
private static final TraceLevel[] VALUES = values();
public static TraceLevel valueOf(
org.nd4j.shade.protobuf.Descriptors.EnumValueDescriptor desc) {
if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
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if (desc.getIndex() == -1) {
return UNRECOGNIZED;
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private final int value;
private TraceLevel(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.RunOptions.TraceLevel)
}
public interface ExperimentalOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.RunOptions.Experimental)
org.nd4j.shade.protobuf.MessageOrBuilder {
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
* Everything inside Experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* Protobuf type {@code tensorflow.RunOptions.Experimental}
*/
public static final class Experimental extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RunOptions.Experimental)
ExperimentalOrBuilder {
private static final long serialVersionUID = 0L;
// Use Experimental.newBuilder() to construct.
private Experimental(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
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public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
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return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_fieldAccessorTable
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public static final int COLLECTIVE_GRAPH_KEY_FIELD_NUMBER = 1;
private long collectiveGraphKey_;
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
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public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions.Experimental 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.RunOptions.Experimental parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
org.nd4j.shade.protobuf.ByteString data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RunOptions.Experimental parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions.Experimental 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.RunOptions.Experimental parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RunOptions.Experimental 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.RunOptions.Experimental parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RunOptions.Experimental parseDelimitedFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RunOptions.Experimental 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.RunOptions.Experimental parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
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public static Builder newBuilder(org.tensorflow.framework.RunOptions.Experimental prototype) {
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}
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
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@java.lang.Override
protected Builder newBuilderForType(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* Everything inside Experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* Protobuf type {@code tensorflow.RunOptions.Experimental}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RunOptions.Experimental)
org.tensorflow.framework.RunOptions.ExperimentalOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_fieldAccessorTable
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private Builder() {
maybeForceBuilderInitialization();
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public Builder clear() {
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collectiveGraphKey_ = 0L;
return this;
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public org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_descriptor;
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org.tensorflow.framework.RunOptions.Experimental result = buildPartial();
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onBuilt();
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public Builder clone() {
return (Builder) super.clone();
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public Builder setField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.setField(field, value);
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public Builder clearField(
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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public Builder addRepeatedField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.addRepeatedField(field, value);
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org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.RunOptions.Experimental parsedMessage = null;
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private long collectiveGraphKey_ ;
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public long getCollectiveGraphKey() {
return collectiveGraphKey_;
}
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public Builder clearCollectiveGraphKey() {
collectiveGraphKey_ = 0L;
onChanged();
return this;
}
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.RunOptions.Experimental)
}
// @@protoc_insertion_point(class_scope:tensorflow.RunOptions.Experimental)
private static final org.tensorflow.framework.RunOptions.Experimental DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.RunOptions.Experimental();
}
public static org.tensorflow.framework.RunOptions.Experimental getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
public Experimental parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new Experimental(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.RunOptions.Experimental getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
public static final int TRACE_LEVEL_FIELD_NUMBER = 1;
private int traceLevel_;
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public int getTraceLevelValue() {
return traceLevel_;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public org.tensorflow.framework.RunOptions.TraceLevel getTraceLevel() {
org.tensorflow.framework.RunOptions.TraceLevel result = org.tensorflow.framework.RunOptions.TraceLevel.valueOf(traceLevel_);
return result == null ? org.tensorflow.framework.RunOptions.TraceLevel.UNRECOGNIZED : result;
}
public static final int TIMEOUT_IN_MS_FIELD_NUMBER = 2;
private long timeoutInMs_;
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public long getTimeoutInMs() {
return timeoutInMs_;
}
public static final int INTER_OP_THREAD_POOL_FIELD_NUMBER = 3;
private int interOpThreadPool_;
/**
*
* The thread pool to use, if session_inter_op_thread_pool is configured.
*
*
* int32 inter_op_thread_pool = 3;
*/
public int getInterOpThreadPool() {
return interOpThreadPool_;
}
public static final int OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER = 5;
private boolean outputPartitionGraphs_;
/**
*
* Whether the partition graph(s) executed by the executor(s) should be
* outputted via RunMetadata.
*
*
* bool output_partition_graphs = 5;
*/
public boolean getOutputPartitionGraphs() {
return outputPartitionGraphs_;
}
public static final int DEBUG_OPTIONS_FIELD_NUMBER = 6;
private org.tensorflow.framework.DebugOptions debugOptions_;
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptionsOrBuilder getDebugOptionsOrBuilder() {
return getDebugOptions();
}
public static final int REPORT_TENSOR_ALLOCATIONS_UPON_OOM_FIELD_NUMBER = 7;
private boolean reportTensorAllocationsUponOom_;
/**
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
*
* bool report_tensor_allocations_upon_oom = 7;
*/
public boolean getReportTensorAllocationsUponOom() {
return reportTensorAllocationsUponOom_;
}
public static final int EXPERIMENTAL_FIELD_NUMBER = 8;
private org.tensorflow.framework.RunOptions.Experimental experimental_;
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public boolean hasExperimental() {
return experimental_ != null;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.Experimental getExperimental() {
return experimental_ == null ? org.tensorflow.framework.RunOptions.Experimental.getDefaultInstance() : experimental_;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.ExperimentalOrBuilder getExperimentalOrBuilder() {
return getExperimental();
}
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byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output)
throws java.io.IOException {
if (traceLevel_ != org.tensorflow.framework.RunOptions.TraceLevel.NO_TRACE.getNumber()) {
output.writeEnum(1, traceLevel_);
}
if (timeoutInMs_ != 0L) {
output.writeInt64(2, timeoutInMs_);
}
if (interOpThreadPool_ != 0) {
output.writeInt32(3, interOpThreadPool_);
}
if (outputPartitionGraphs_ != false) {
output.writeBool(5, outputPartitionGraphs_);
}
if (debugOptions_ != null) {
output.writeMessage(6, getDebugOptions());
}
if (reportTensorAllocationsUponOom_ != false) {
output.writeBool(7, reportTensorAllocationsUponOom_);
}
if (experimental_ != null) {
output.writeMessage(8, getExperimental());
}
unknownFields.writeTo(output);
}
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
if (traceLevel_ != org.tensorflow.framework.RunOptions.TraceLevel.NO_TRACE.getNumber()) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeEnumSize(1, traceLevel_);
}
if (timeoutInMs_ != 0L) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeInt64Size(2, timeoutInMs_);
}
if (interOpThreadPool_ != 0) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeInt32Size(3, interOpThreadPool_);
}
if (outputPartitionGraphs_ != false) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeBoolSize(5, outputPartitionGraphs_);
}
if (debugOptions_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(6, getDebugOptions());
}
if (reportTensorAllocationsUponOom_ != false) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeBoolSize(7, reportTensorAllocationsUponOom_);
}
if (experimental_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(8, getExperimental());
}
size += unknownFields.getSerializedSize();
memoizedSize = size;
return size;
}
@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof org.tensorflow.framework.RunOptions)) {
return super.equals(obj);
}
org.tensorflow.framework.RunOptions other = (org.tensorflow.framework.RunOptions) obj;
boolean result = true;
result = result && traceLevel_ == other.traceLevel_;
result = result && (getTimeoutInMs()
== other.getTimeoutInMs());
result = result && (getInterOpThreadPool()
== other.getInterOpThreadPool());
result = result && (getOutputPartitionGraphs()
== other.getOutputPartitionGraphs());
result = result && (hasDebugOptions() == other.hasDebugOptions());
if (hasDebugOptions()) {
result = result && getDebugOptions()
.equals(other.getDebugOptions());
}
result = result && (getReportTensorAllocationsUponOom()
== other.getReportTensorAllocationsUponOom());
result = result && (hasExperimental() == other.hasExperimental());
if (hasExperimental()) {
result = result && getExperimental()
.equals(other.getExperimental());
}
result = result && unknownFields.equals(other.unknownFields);
return result;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + TRACE_LEVEL_FIELD_NUMBER;
hash = (53 * hash) + traceLevel_;
hash = (37 * hash) + TIMEOUT_IN_MS_FIELD_NUMBER;
hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashLong(
getTimeoutInMs());
hash = (37 * hash) + INTER_OP_THREAD_POOL_FIELD_NUMBER;
hash = (53 * hash) + getInterOpThreadPool();
hash = (37 * hash) + OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER;
hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean(
getOutputPartitionGraphs());
if (hasDebugOptions()) {
hash = (37 * hash) + DEBUG_OPTIONS_FIELD_NUMBER;
hash = (53 * hash) + getDebugOptions().hashCode();
}
hash = (37 * hash) + REPORT_TENSOR_ALLOCATIONS_UPON_OOM_FIELD_NUMBER;
hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean(
getReportTensorAllocationsUponOom());
if (hasExperimental()) {
hash = (37 * hash) + EXPERIMENTAL_FIELD_NUMBER;
hash = (53 * hash) + getExperimental().hashCode();
}
hash = (29 * hash) + unknownFields.hashCode();
memoizedHashCode = hash;
return hash;
}
public static org.tensorflow.framework.RunOptions parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions parseFrom(
java.nio.ByteBuffer data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RunOptions parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions parseFrom(
org.nd4j.shade.protobuf.ByteString data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RunOptions parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions parseFrom(
byte[] data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RunOptions parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.RunOptions 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.RunOptions parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
}
public static org.tensorflow.framework.RunOptions 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.RunOptions 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.RunOptions 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.RunOptions 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;
}
/**
*
* Options for a single Run() call.
*
*
* Protobuf type {@code tensorflow.RunOptions}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RunOptions)
org.tensorflow.framework.RunOptionsOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_fieldAccessorTable
.ensureFieldAccessorsInitialized(
org.tensorflow.framework.RunOptions.class, org.tensorflow.framework.RunOptions.Builder.class);
}
// Construct using org.tensorflow.framework.RunOptions.newBuilder()
private Builder() {
maybeForceBuilderInitialization();
}
private Builder(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
}
private void maybeForceBuilderInitialization() {
if (org.nd4j.shade.protobuf.GeneratedMessageV3
.alwaysUseFieldBuilders) {
}
}
public Builder clear() {
super.clear();
traceLevel_ = 0;
timeoutInMs_ = 0L;
interOpThreadPool_ = 0;
outputPartitionGraphs_ = false;
if (debugOptionsBuilder_ == null) {
debugOptions_ = null;
} else {
debugOptions_ = null;
debugOptionsBuilder_ = null;
}
reportTensorAllocationsUponOom_ = false;
if (experimentalBuilder_ == null) {
experimental_ = null;
} else {
experimental_ = null;
experimentalBuilder_ = null;
}
return this;
}
public org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
public org.tensorflow.framework.RunOptions getDefaultInstanceForType() {
return org.tensorflow.framework.RunOptions.getDefaultInstance();
}
public org.tensorflow.framework.RunOptions build() {
org.tensorflow.framework.RunOptions result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
public org.tensorflow.framework.RunOptions buildPartial() {
org.tensorflow.framework.RunOptions result = new org.tensorflow.framework.RunOptions(this);
result.traceLevel_ = traceLevel_;
result.timeoutInMs_ = timeoutInMs_;
result.interOpThreadPool_ = interOpThreadPool_;
result.outputPartitionGraphs_ = outputPartitionGraphs_;
if (debugOptionsBuilder_ == null) {
result.debugOptions_ = debugOptions_;
} else {
result.debugOptions_ = debugOptionsBuilder_.build();
}
result.reportTensorAllocationsUponOom_ = reportTensorAllocationsUponOom_;
if (experimentalBuilder_ == null) {
result.experimental_ = experimental_;
} else {
result.experimental_ = experimentalBuilder_.build();
}
onBuilt();
return result;
}
public Builder clone() {
return (Builder) super.clone();
}
public Builder setField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.setField(field, value);
}
public Builder clearField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) {
return (Builder) super.clearField(field);
}
public Builder clearOneof(
org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) {
return (Builder) super.clearOneof(oneof);
}
public Builder setRepeatedField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
int index, java.lang.Object value) {
return (Builder) super.setRepeatedField(field, index, value);
}
public Builder addRepeatedField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.addRepeatedField(field, value);
}
public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) {
if (other instanceof org.tensorflow.framework.RunOptions) {
return mergeFrom((org.tensorflow.framework.RunOptions)other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(org.tensorflow.framework.RunOptions other) {
if (other == org.tensorflow.framework.RunOptions.getDefaultInstance()) return this;
if (other.traceLevel_ != 0) {
setTraceLevelValue(other.getTraceLevelValue());
}
if (other.getTimeoutInMs() != 0L) {
setTimeoutInMs(other.getTimeoutInMs());
}
if (other.getInterOpThreadPool() != 0) {
setInterOpThreadPool(other.getInterOpThreadPool());
}
if (other.getOutputPartitionGraphs() != false) {
setOutputPartitionGraphs(other.getOutputPartitionGraphs());
}
if (other.hasDebugOptions()) {
mergeDebugOptions(other.getDebugOptions());
}
if (other.getReportTensorAllocationsUponOom() != false) {
setReportTensorAllocationsUponOom(other.getReportTensorAllocationsUponOom());
}
if (other.hasExperimental()) {
mergeExperimental(other.getExperimental());
}
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.RunOptions parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
parsedMessage = (org.tensorflow.framework.RunOptions) e.getUnfinishedMessage();
throw e.unwrapIOException();
} finally {
if (parsedMessage != null) {
mergeFrom(parsedMessage);
}
}
return this;
}
private int traceLevel_ = 0;
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public int getTraceLevelValue() {
return traceLevel_;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder setTraceLevelValue(int value) {
traceLevel_ = value;
onChanged();
return this;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public org.tensorflow.framework.RunOptions.TraceLevel getTraceLevel() {
org.tensorflow.framework.RunOptions.TraceLevel result = org.tensorflow.framework.RunOptions.TraceLevel.valueOf(traceLevel_);
return result == null ? org.tensorflow.framework.RunOptions.TraceLevel.UNRECOGNIZED : result;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder setTraceLevel(org.tensorflow.framework.RunOptions.TraceLevel value) {
if (value == null) {
throw new NullPointerException();
}
traceLevel_ = value.getNumber();
onChanged();
return this;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder clearTraceLevel() {
traceLevel_ = 0;
onChanged();
return this;
}
private long timeoutInMs_ ;
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public long getTimeoutInMs() {
return timeoutInMs_;
}
/**
*
* Time to wait for operation to complete in milliseconds.
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
