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// 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
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.GraphDef)
GraphDefOrBuilder {
// Use GraphDef.newBuilder() to construct.
private GraphDef(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private GraphDef() {
node_ = java.util.Collections.emptyList();
version_ = 0;
}
@java.lang.Override
public final com.google.protobuf.UnknownFieldSet
getUnknownFields() {
return com.google.protobuf.UnknownFieldSet.getDefaultInstance();
}
private GraphDef(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
this();
int mutable_bitField0_ = 0;
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
default: {
if (!input.skipField(tag)) {
done = true;
}
break;
}
case 10: {
if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) {
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;
}
}
}
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(
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if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) {
node_ = java.util.Collections.unmodifiableList(node_);
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makeExtensionsImmutable();
}
}
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor;
}
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_fieldAccessorTable
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org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class);
}
private int bitField0_;
public static final int NODE_FIELD_NUMBER = 1;
private java.util.List node_;
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public java.util.List getNodeList() {
return node_;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public java.util.List
getNodeOrBuilderList() {
return node_;
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public int getNodeCount() {
return node_.size();
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
public org.tensorflow.framework.NodeDef getNode(int index) {
return node_.get(index);
}
/**
* repeated .tensorflow.NodeDef node = 1;
*/
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.
*
* 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.
*
* 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() {
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];
*/
@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.
*
* 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.
*
* 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() {
return getLibrary();
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized == 1) return true;
if (isInitialized == 0) return false;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.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());
}
}
public int getSerializedSize() {
int size = memoizedSize;
if (size != -1) return size;
size = 0;
for (int i = 0; i < node_.size(); i++) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(1, node_.get(i));
}
if (library_ != null) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(2, getLibrary());
}
if (version_ != 0) {
size += com.google.protobuf.CodedOutputStream
.computeInt32Size(3, version_);
}
if (versions_ != null) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(4, getVersions());
}
memoizedSize = size;
return size;
}
private static final long serialVersionUID = 0L;
@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;
boolean result = true;
result = result && getNodeList()
.equals(other.getNodeList());
result = result && (hasVersions() == other.hasVersions());
if (hasVersions()) {
result = result && getVersions()
.equals(other.getVersions());
}
result = result && (getVersion()
== other.getVersion());
result = result && (hasLibrary() == other.hasLibrary());
if (hasLibrary()) {
result = result && getLibrary()
.equals(other.getLibrary());
}
return result;
}
@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 com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef parseFrom(
java.nio.ByteBuffer data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.GraphDef parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.GraphDef parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.GraphDef parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.GraphDef parseFrom(java.io.InputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphDef parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input, extensionRegistry);
}
public static org.tensorflow.framework.GraphDef parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphDef parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input, extensionRegistry);
}
public static org.tensorflow.framework.GraphDef parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
}
public static org.tensorflow.framework.GraphDef parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.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.GraphDef 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(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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];
*/
@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.
*
* Deprecated single version field; use versions above instead. Since all
* GraphDef changes before "versions" was introduced were forward
* compatible, this field is entirely ignored.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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.
*
* 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 com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder>
getLibraryFieldBuilder() {
if (libraryBuilder_ == null) {
libraryBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.FunctionDefLibrary, org.tensorflow.framework.FunctionDefLibrary.Builder, org.tensorflow.framework.FunctionDefLibraryOrBuilder>(
getLibrary(),
getParentForChildren(),
isClean());
library_ = null;
}
return libraryBuilder_;
}
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return this;
}
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return this;
}
// @@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 com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
public GraphDef parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new GraphDef(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
public org.tensorflow.framework.GraphDef getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
