org.tensorflow.framework.GraphDef Maven / Gradle / Ivy
The newest version!
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/framework/graph.proto
package org.tensorflow.framework;
/**
*
* Represents the graph of operations
*
*
* Protobuf type {@code tensorflow.GraphDef}
*/
public final class GraphDef extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.GraphDef)
GraphDefOrBuilder {
private static final long serialVersionUID = 0L;
// Use GraphDef.newBuilder() to construct.
private GraphDef(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private GraphDef() {
node_ = java.util.Collections.emptyList();
}
@java.lang.Override
@SuppressWarnings({"unused"})
protected java.lang.Object newInstance(
UnusedPrivateParameter unused) {
return new GraphDef();
}
@java.lang.Override
public final org.nd4j.shade.protobuf.UnknownFieldSet
getUnknownFields() {
return this.unknownFields;
}
private GraphDef(
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();
}
int mutable_bitField0_ = 0;
org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields =
org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder();
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 10: {
if (!((mutable_bitField0_ & 0x00000001) != 0)) {
node_ = new java.util.ArrayList();
mutable_bitField0_ |= 0x00000001;
}
node_.add(
input.readMessage(org.tensorflow.framework.NodeDef.parser(), extensionRegistry));
break;
}
case 18: {
org.tensorflow.framework.FunctionDefLibrary.Builder subBuilder = null;
if (library_ != null) {
subBuilder = library_.toBuilder();
}
library_ = input.readMessage(org.tensorflow.framework.FunctionDefLibrary.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(library_);
library_ = subBuilder.buildPartial();
}
break;
}
case 24: {
version_ = input.readInt32();
break;
}
case 34: {
org.tensorflow.framework.VersionDef.Builder subBuilder = null;
if (versions_ != null) {
subBuilder = versions_.toBuilder();
}
versions_ = input.readMessage(org.tensorflow.framework.VersionDef.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(versions_);
versions_ = subBuilder.buildPartial();
}
break;
}
default: {
if (!parseUnknownField(
input, unknownFields, extensionRegistry, tag)) {
done = true;
}
break;
}
}
}
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (org.nd4j.shade.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException(
e).setUnfinishedMessage(this);
} finally {
if (((mutable_bitField0_ & 0x00000001) != 0)) {
node_ = java.util.Collections.unmodifiableList(node_);
}
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor;
}
@java.lang.Override
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_fieldAccessorTable
.ensureFieldAccessorsInitialized(
org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class);
}
public static final int NODE_FIELD_NUMBER = 1;
private java.util.List node_;
/**
* repeated .tensorflow.NodeDef node = 1;
*/
@java.lang.Override
public java.util.List getNodeList() {
return node_;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
@java.lang.Override
public java.util.List
getNodeOrBuilderList() {
return node_;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
@java.lang.Override
public int getNodeCount() {
return node_.size();
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
@java.lang.Override
public org.tensorflow.framework.NodeDef getNode(int index) {
return node_.get(index);
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
@java.lang.Override
public org.tensorflow.framework.NodeDefOrBuilder getNodeOrBuilder(
int index) {
return node_.get(index);
}
public static final int VERSIONS_FIELD_NUMBER = 4;
private org.tensorflow.framework.VersionDef versions_;
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
* @return Whether the versions field is set.
*/
@java.lang.Override
public boolean hasVersions() {
return versions_ != null;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
* @return The versions.
*/
@java.lang.Override
public org.tensorflow.framework.VersionDef getVersions() {
return versions_ == null ? org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
@java.lang.Override
public org.tensorflow.framework.VersionDefOrBuilder getVersionsOrBuilder() {
return getVersions();
}
public static final int VERSION_FIELD_NUMBER = 3;
private int version_;
/**
*
* Deprecated single version field; use versions above instead. Since all
* GraphDef changes before "versions" was introduced were forward
* compatible, this field is entirely ignored.
*
*
* int32 version = 3 [deprecated = true];
* @deprecated tensorflow.GraphDef.version is deprecated.
* See tensorflow/core/framework/graph.proto;l=24
* @return The version.
*/
@java.lang.Override
@java.lang.Deprecated public int getVersion() {
return version_;
}
public static final int LIBRARY_FIELD_NUMBER = 2;
private org.tensorflow.framework.FunctionDefLibrary library_;
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
* @return Whether the library field is set.
*/
@java.lang.Override
public boolean hasLibrary() {
return library_ != null;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
* @return The library.
*/
@java.lang.Override
public org.tensorflow.framework.FunctionDefLibrary getLibrary() {
return library_ == null ? org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
@java.lang.Override
public org.tensorflow.framework.FunctionDefLibraryOrBuilder getLibraryOrBuilder() {
return getLibrary();
}
private byte memoizedIsInitialized = -1;
@java.lang.Override
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
@java.lang.Override
public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output)
throws java.io.IOException {
for (int i = 0; i < node_.size(); i++) {
output.writeMessage(1, node_.get(i));
}
if (library_ != null) {
output.writeMessage(2, getLibrary());
}
if (version_ != 0) {
output.writeInt32(3, version_);
}
if (versions_ != null) {
output.writeMessage(4, getVersions());
}
unknownFields.writeTo(output);
}
@java.lang.Override
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
for (int i = 0; i < node_.size(); i++) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(1, node_.get(i));
}
if (library_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(2, getLibrary());
}
if (version_ != 0) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeInt32Size(3, version_);
}
if (versions_ != null) {
size += org.nd4j.shade.protobuf.CodedOutputStream
.computeMessageSize(4, getVersions());
}
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.GraphDef)) {
return super.equals(obj);
}
org.tensorflow.framework.GraphDef other = (org.tensorflow.framework.GraphDef) obj;
if (!getNodeList()
.equals(other.getNodeList())) return false;
if (hasVersions() != other.hasVersions()) return false;
if (hasVersions()) {
if (!getVersions()
.equals(other.getVersions())) return false;
}
if (getVersion()
!= other.getVersion()) return false;
if (hasLibrary() != other.hasLibrary()) return false;
if (hasLibrary()) {
if (!getLibrary()
.equals(other.getLibrary())) return false;
}
if (!unknownFields.equals(other.unknownFields)) return false;
return true;
}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
return memoizedHashCode;
}
int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
if (getNodeCount() > 0) {
hash = (37 * hash) + NODE_FIELD_NUMBER;
hash = (53 * hash) + getNodeList().hashCode();
}
if (hasVersions()) {
hash = (37 * hash) + VERSIONS_FIELD_NUMBER;
hash = (53 * hash) + getVersions().hashCode();
}
hash = (37 * hash) + VERSION_FIELD_NUMBER;
hash = (53 * hash) + getVersion();
if (hasLibrary()) {
hash = (37 * hash) + LIBRARY_FIELD_NUMBER;
hash = (53 * hash) + getLibrary().hashCode();
}
hash = (29 * hash) + unknownFields.hashCode();
memoizedHashCode = hash;
return hash;
}
public static org.tensorflow.framework.GraphDef parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef 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.GraphDef parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef 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.GraphDef parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef 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.GraphDef parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphDef 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.GraphDef parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphDef 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.GraphDef 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.GraphDef 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);
}
@java.lang.Override
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(org.tensorflow.framework.GraphDef prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
@java.lang.Override
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;
}
/**
*
* Represents the graph of operations
*
*
* Protobuf type {@code tensorflow.GraphDef}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.GraphDef)
org.tensorflow.framework.GraphDefOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor;
}
@java.lang.Override
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_fieldAccessorTable
.ensureFieldAccessorsInitialized(
org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class);
}
// Construct using org.tensorflow.framework.GraphDef.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) {
getNodeFieldBuilder();
}
}
@java.lang.Override
public Builder clear() {
super.clear();
if (nodeBuilder_ == null) {
node_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
} else {
nodeBuilder_.clear();
}
if (versionsBuilder_ == null) {
versions_ = null;
} else {
versions_ = null;
versionsBuilder_ = null;
}
version_ = 0;
if (libraryBuilder_ == null) {
library_ = null;
} else {
library_ = null;
libraryBuilder_ = null;
}
return this;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor;
}
@java.lang.Override
public org.tensorflow.framework.GraphDef getDefaultInstanceForType() {
return org.tensorflow.framework.GraphDef.getDefaultInstance();
}
@java.lang.Override
public org.tensorflow.framework.GraphDef build() {
org.tensorflow.framework.GraphDef result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
@java.lang.Override
public org.tensorflow.framework.GraphDef buildPartial() {
org.tensorflow.framework.GraphDef result = new org.tensorflow.framework.GraphDef(this);
int from_bitField0_ = bitField0_;
if (nodeBuilder_ == null) {
if (((bitField0_ & 0x00000001) != 0)) {
node_ = java.util.Collections.unmodifiableList(node_);
bitField0_ = (bitField0_ & ~0x00000001);
}
result.node_ = node_;
} else {
result.node_ = nodeBuilder_.build();
}
if (versionsBuilder_ == null) {
result.versions_ = versions_;
} else {
result.versions_ = versionsBuilder_.build();
}
result.version_ = version_;
if (libraryBuilder_ == null) {
result.library_ = library_;
} else {
result.library_ = libraryBuilder_.build();
}
onBuilt();
return result;
}
@java.lang.Override
public Builder clone() {
return super.clone();
}
@java.lang.Override
public Builder setField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return super.setField(field, value);
}
@java.lang.Override
public Builder clearField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) {
return super.clearField(field);
}
@java.lang.Override
public Builder clearOneof(
org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) {
return super.clearOneof(oneof);
}
@java.lang.Override
public Builder setRepeatedField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
int index, java.lang.Object value) {
return super.setRepeatedField(field, index, value);
}
@java.lang.Override
public Builder addRepeatedField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return super.addRepeatedField(field, value);
}
@java.lang.Override
public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) {
if (other instanceof org.tensorflow.framework.GraphDef) {
return mergeFrom((org.tensorflow.framework.GraphDef)other);
} else {
super.mergeFrom(other);
return this;
}
}
public Builder mergeFrom(org.tensorflow.framework.GraphDef other) {
if (other == org.tensorflow.framework.GraphDef.getDefaultInstance()) return this;
if (nodeBuilder_ == null) {
if (!other.node_.isEmpty()) {
if (node_.isEmpty()) {
node_ = other.node_;
bitField0_ = (bitField0_ & ~0x00000001);
} else {
ensureNodeIsMutable();
node_.addAll(other.node_);
}
onChanged();
}
} else {
if (!other.node_.isEmpty()) {
if (nodeBuilder_.isEmpty()) {
nodeBuilder_.dispose();
nodeBuilder_ = null;
node_ = other.node_;
bitField0_ = (bitField0_ & ~0x00000001);
nodeBuilder_ =
org.nd4j.shade.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ?
getNodeFieldBuilder() : null;
} else {
nodeBuilder_.addAllMessages(other.node_);
}
}
}
if (other.hasVersions()) {
mergeVersions(other.getVersions());
}
if (other.getVersion() != 0) {
setVersion(other.getVersion());
}
if (other.hasLibrary()) {
mergeLibrary(other.getLibrary());
}
this.mergeUnknownFields(other.unknownFields);
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.GraphDef parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
} catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) {
parsedMessage = (org.tensorflow.framework.GraphDef) e.getUnfinishedMessage();
throw e.unwrapIOException();
} finally {
if (parsedMessage != null) {
mergeFrom(parsedMessage);
}
}
return this;
}
private int bitField0_;
private java.util.List node_ =
java.util.Collections.emptyList();
private void ensureNodeIsMutable() {
if (!((bitField0_ & 0x00000001) != 0)) {
node_ = new java.util.ArrayList(node_);
bitField0_ |= 0x00000001;
}
}
private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> nodeBuilder_;
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public java.util.List getNodeList() {
if (nodeBuilder_ == null) {
return java.util.Collections.unmodifiableList(node_);
} else {
return nodeBuilder_.getMessageList();
}
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public int getNodeCount() {
if (nodeBuilder_ == null) {
return node_.size();
} else {
return nodeBuilder_.getCount();
}
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDef getNode(int index) {
if (nodeBuilder_ == null) {
return node_.get(index);
} else {
return nodeBuilder_.getMessage(index);
}
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder setNode(
int index, org.tensorflow.framework.NodeDef value) {
if (nodeBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureNodeIsMutable();
node_.set(index, value);
onChanged();
} else {
nodeBuilder_.setMessage(index, value);
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder setNode(
int index, org.tensorflow.framework.NodeDef.Builder builderForValue) {
if (nodeBuilder_ == null) {
ensureNodeIsMutable();
node_.set(index, builderForValue.build());
onChanged();
} else {
nodeBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder addNode(org.tensorflow.framework.NodeDef value) {
if (nodeBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureNodeIsMutable();
node_.add(value);
onChanged();
} else {
nodeBuilder_.addMessage(value);
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder addNode(
int index, org.tensorflow.framework.NodeDef value) {
if (nodeBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureNodeIsMutable();
node_.add(index, value);
onChanged();
} else {
nodeBuilder_.addMessage(index, value);
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder addNode(
org.tensorflow.framework.NodeDef.Builder builderForValue) {
if (nodeBuilder_ == null) {
ensureNodeIsMutable();
node_.add(builderForValue.build());
onChanged();
} else {
nodeBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder addNode(
int index, org.tensorflow.framework.NodeDef.Builder builderForValue) {
if (nodeBuilder_ == null) {
ensureNodeIsMutable();
node_.add(index, builderForValue.build());
onChanged();
} else {
nodeBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder addAllNode(
java.lang.Iterable values) {
if (nodeBuilder_ == null) {
ensureNodeIsMutable();
org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll(
values, node_);
onChanged();
} else {
nodeBuilder_.addAllMessages(values);
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder clearNode() {
if (nodeBuilder_ == null) {
node_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
} else {
nodeBuilder_.clear();
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public Builder removeNode(int index) {
if (nodeBuilder_ == null) {
ensureNodeIsMutable();
node_.remove(index);
onChanged();
} else {
nodeBuilder_.remove(index);
}
return this;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDef.Builder getNodeBuilder(
int index) {
return getNodeFieldBuilder().getBuilder(index);
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDefOrBuilder getNodeOrBuilder(
int index) {
if (nodeBuilder_ == null) {
return node_.get(index); } else {
return nodeBuilder_.getMessageOrBuilder(index);
}
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public java.util.List
getNodeOrBuilderList() {
if (nodeBuilder_ != null) {
return nodeBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(node_);
}
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDef.Builder addNodeBuilder() {
return getNodeFieldBuilder().addBuilder(
org.tensorflow.framework.NodeDef.getDefaultInstance());
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDef.Builder addNodeBuilder(
int index) {
return getNodeFieldBuilder().addBuilder(
index, org.tensorflow.framework.NodeDef.getDefaultInstance());
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public java.util.List
getNodeBuilderList() {
return getNodeFieldBuilder().getBuilderList();
}
private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder>
getNodeFieldBuilder() {
if (nodeBuilder_ == null) {
nodeBuilder_ = new org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder>(
node_,
((bitField0_ & 0x00000001) != 0),
getParentForChildren(),
isClean());
node_ = null;
}
return nodeBuilder_;
}
private org.tensorflow.framework.VersionDef versions_;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder> versionsBuilder_;
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
* @return Whether the versions field is set.
*/
public boolean hasVersions() {
return versionsBuilder_ != null || versions_ != null;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
* @return The versions.
*/
public org.tensorflow.framework.VersionDef getVersions() {
if (versionsBuilder_ == null) {
return versions_ == null ? org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_;
} else {
return versionsBuilder_.getMessage();
}
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public Builder setVersions(org.tensorflow.framework.VersionDef value) {
if (versionsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
versions_ = value;
onChanged();
} else {
versionsBuilder_.setMessage(value);
}
return this;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public Builder setVersions(
org.tensorflow.framework.VersionDef.Builder builderForValue) {
if (versionsBuilder_ == null) {
versions_ = builderForValue.build();
onChanged();
} else {
versionsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public Builder mergeVersions(org.tensorflow.framework.VersionDef value) {
if (versionsBuilder_ == null) {
if (versions_ != null) {
versions_ =
org.tensorflow.framework.VersionDef.newBuilder(versions_).mergeFrom(value).buildPartial();
} else {
versions_ = value;
}
onChanged();
} else {
versionsBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public Builder clearVersions() {
if (versionsBuilder_ == null) {
versions_ = null;
onChanged();
} else {
versions_ = null;
versionsBuilder_ = null;
}
return this;
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public org.tensorflow.framework.VersionDef.Builder getVersionsBuilder() {
onChanged();
return getVersionsFieldBuilder().getBuilder();
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
public org.tensorflow.framework.VersionDefOrBuilder getVersionsOrBuilder() {
if (versionsBuilder_ != null) {
return versionsBuilder_.getMessageOrBuilder();
} else {
return versions_ == null ?
org.tensorflow.framework.VersionDef.getDefaultInstance() : versions_;
}
}
/**
*
* Compatibility versions of the graph. See core/public/version.h for version
* history. The GraphDef version is distinct from the TensorFlow version, and
* each release of TensorFlow will support a range of GraphDef versions.
*
*
* .tensorflow.VersionDef versions = 4;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder>
getVersionsFieldBuilder() {
if (versionsBuilder_ == null) {
versionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder>(
getVersions(),
getParentForChildren(),
isClean());
versions_ = null;
}
return versionsBuilder_;
}
private int version_ ;
/**
*
* Deprecated single version field; use versions above instead. Since all
* GraphDef changes before "versions" was introduced were forward
* compatible, this field is entirely ignored.
*
*
* int32 version = 3 [deprecated = true];
* @deprecated tensorflow.GraphDef.version is deprecated.
* See tensorflow/core/framework/graph.proto;l=24
* @return The version.
*/
@java.lang.Override
@java.lang.Deprecated public int getVersion() {
return version_;
}
/**
*
* Deprecated single version field; use versions above instead. Since all
* GraphDef changes before "versions" was introduced were forward
* compatible, this field is entirely ignored.
*
*
* int32 version = 3 [deprecated = true];
* @deprecated tensorflow.GraphDef.version is deprecated.
* See tensorflow/core/framework/graph.proto;l=24
* @param value The version to set.
* @return This builder for chaining.
*/
@java.lang.Deprecated public Builder setVersion(int value) {
version_ = value;
onChanged();
return this;
}
/**
*
* Deprecated single version field; use versions above instead. Since all
* GraphDef changes before "versions" was introduced were forward
* compatible, this field is entirely ignored.
*
*
* int32 version = 3 [deprecated = true];
* @deprecated tensorflow.GraphDef.version is deprecated.
* See tensorflow/core/framework/graph.proto;l=24
* @return This builder for chaining.
*/
@java.lang.Deprecated public Builder clearVersion() {
version_ = 0;
onChanged();
return this;
}
private org.tensorflow.framework.FunctionDefLibrary library_;
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder> libraryBuilder_;
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
* @return Whether the library field is set.
*/
public boolean hasLibrary() {
return libraryBuilder_ != null || library_ != null;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
* @return The library.
*/
public org.tensorflow.framework.FunctionDefLibrary getLibrary() {
if (libraryBuilder_ == null) {
return library_ == null ? org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_;
} else {
return libraryBuilder_.getMessage();
}
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public Builder setLibrary(org.tensorflow.framework.FunctionDefLibrary value) {
if (libraryBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
library_ = value;
onChanged();
} else {
libraryBuilder_.setMessage(value);
}
return this;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public Builder setLibrary(
org.tensorflow.framework.FunctionDefLibrary.Builder builderForValue) {
if (libraryBuilder_ == null) {
library_ = builderForValue.build();
onChanged();
} else {
libraryBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public Builder mergeLibrary(org.tensorflow.framework.FunctionDefLibrary value) {
if (libraryBuilder_ == null) {
if (library_ != null) {
library_ =
org.tensorflow.framework.FunctionDefLibrary.newBuilder(library_).mergeFrom(value).buildPartial();
} else {
library_ = value;
}
onChanged();
} else {
libraryBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public Builder clearLibrary() {
if (libraryBuilder_ == null) {
library_ = null;
onChanged();
} else {
library_ = null;
libraryBuilder_ = null;
}
return this;
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public org.tensorflow.framework.FunctionDefLibrary.Builder getLibraryBuilder() {
onChanged();
return getLibraryFieldBuilder().getBuilder();
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
public org.tensorflow.framework.FunctionDefLibraryOrBuilder getLibraryOrBuilder() {
if (libraryBuilder_ != null) {
return libraryBuilder_.getMessageOrBuilder();
} else {
return library_ == null ?
org.tensorflow.framework.FunctionDefLibrary.getDefaultInstance() : library_;
}
}
/**
*
* EXPERIMENTAL. DO NOT USE OR DEPEND ON THIS YET.
* "library" provides user-defined functions.
* Naming:
* * library.function.name are in a flat namespace.
* NOTE: We may need to change it to be hierarchical to support
* different orgs. E.g.,
* { "/google/nn", { ... }},
* { "/google/vision", { ... }}
* { "/org_foo/module_bar", { ... }}
* map<string, FunctionDefLib> named_lib;
* * If node[i].op is the name of one function in "library",
* node[i] is deemed as a function call. Otherwise, node[i].op
* must be a primitive operation supported by the runtime.
* Function call semantics:
* * The callee may start execution as soon as some of its inputs
* are ready. The caller may want to use Tuple() mechanism to
* ensure all inputs are ready in the same time.
* * The consumer of return values may start executing as soon as
* the return values the consumer depends on are ready. The
* consumer may want to use Tuple() mechanism to ensure the
* consumer does not start until all return values of the callee
* function are ready.
*
*
* .tensorflow.FunctionDefLibrary library = 2;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder>
getLibraryFieldBuilder() {
if (libraryBuilder_ == null) {
libraryBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder>(
getLibrary(),
getParentForChildren(),
isClean());
library_ = null;
}
return libraryBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.GraphDef)
}
// @@protoc_insertion_point(class_scope:tensorflow.GraphDef)
private static final org.tensorflow.framework.GraphDef DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.GraphDef();
}
public static org.tensorflow.framework.GraphDef getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
@java.lang.Override
public GraphDef parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new GraphDef(input, extensionRegistry);
}
};
public static org.nd4j.shade.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.framework.GraphDef getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}