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// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: 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( e).setUnfinishedMessage(this); } finally { if (((mutable_bitField0_ & 0x00000001) == 0x00000001)) { node_ = java.util.Collections.unmodifiableList(node_); } 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 .ensureFieldAccessorsInitialized( 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.
   * 
* * optional .tensorflow.VersionDef versions = 4; */ 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.
   * 
* * optional .tensorflow.VersionDef versions = 4; */ 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.
   * 
* * optional .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.
   * 
* * optional 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.
   * 
* * optional .tensorflow.FunctionDefLibrary library = 2; */ 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.
   * 
* * optional .tensorflow.FunctionDefLibrary library = 2; */ 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.
   * 
* * optional .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) + getDescriptorForType().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( 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; } /** *
   * Represents the graph of operations
   * 
* * Protobuf type {@code tensorflow.GraphDef} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.GraphDef) org.tensorflow.framework.GraphDefOrBuilder { 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 .ensureFieldAccessorsInitialized( org.tensorflow.framework.GraphDef.class, org.tensorflow.framework.GraphDef.Builder.class); } // Construct using org.tensorflow.framework.GraphDef.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (com.google.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getNodeFieldBuilder(); } } 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; } public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.framework.GraphProtos.internal_static_tensorflow_GraphDef_descriptor; } public org.tensorflow.framework.GraphDef getDefaultInstanceForType() { return org.tensorflow.framework.GraphDef.getDefaultInstance(); } public org.tensorflow.framework.GraphDef build() { org.tensorflow.framework.GraphDef result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } public org.tensorflow.framework.GraphDef buildPartial() { org.tensorflow.framework.GraphDef result = new org.tensorflow.framework.GraphDef(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; if (nodeBuilder_ == null) { if (((bitField0_ & 0x00000001) == 0x00000001)) { 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(); } result.bitField0_ = to_bitField0_; onBuilt(); return result; } public Builder clone() { return (Builder) super.clone(); } public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, Object value) { return (Builder) super.setField(field, value); } public Builder clearField( com.google.protobuf.Descriptors.FieldDescriptor field) { return (Builder) super.clearField(field); } public Builder clearOneof( com.google.protobuf.Descriptors.OneofDescriptor oneof) { return (Builder) super.clearOneof(oneof); } public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(com.google.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_ = com.google.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()); } onChanged(); return this; } public final boolean isInitialized() { return true; } public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { org.tensorflow.framework.GraphDef parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (com.google.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) == 0x00000001)) { node_ = new java.util.ArrayList(node_); bitField0_ |= 0x00000001; } } private com.google.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(); com.google.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 com.google.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder> getNodeFieldBuilder() { if (nodeBuilder_ == null) { nodeBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.NodeDef, org.tensorflow.framework.NodeDef.Builder, org.tensorflow.framework.NodeDefOrBuilder>( node_, ((bitField0_ & 0x00000001) == 0x00000001), getParentForChildren(), isClean()); node_ = null; } return nodeBuilder_; } private org.tensorflow.framework.VersionDef versions_ = null; private com.google.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.
     * 
* * optional .tensorflow.VersionDef versions = 4; */ 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.
     * 
* * optional .tensorflow.VersionDef versions = 4; */ 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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .tensorflow.VersionDef versions = 4; */ private com.google.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.VersionDef, org.tensorflow.framework.VersionDef.Builder, org.tensorflow.framework.VersionDefOrBuilder> getVersionsFieldBuilder() { if (versionsBuilder_ == null) { versionsBuilder_ = new com.google.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.
     * 
* * optional 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.
     * 
* * optional int32 version = 3 [deprecated = true]; */ @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.
     * 
* * optional int32 version = 3 [deprecated = true]; */ @java.lang.Deprecated public Builder clearVersion() { version_ = 0; onChanged(); return this; } private org.tensorflow.framework.FunctionDefLibrary library_ = null; private com.google.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.
     * 
* * optional .tensorflow.FunctionDefLibrary library = 2; */ 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.
     * 
* * optional .tensorflow.FunctionDefLibrary library = 2; */ 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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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.
     * 
* * optional .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; } }




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