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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/meta_graph.proto
package org.tensorflow.framework;
/**
*
* Information about a Tensor necessary for feeding or retrieval.
*
*
* Protobuf type {@code tensorflow.TensorInfo}
*/
public final class TensorInfo extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.TensorInfo)
TensorInfoOrBuilder {
private static final long serialVersionUID = 0L;
// Use TensorInfo.newBuilder() to construct.
private TensorInfo(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
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public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
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}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
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org.nd4j.shade.protobuf.MessageOrBuilder {
/**
*
* The shape of the values Tensor is [?]. Its dtype must be the dtype of
* the SparseTensor as a whole, given in the enclosing TensorInfo.
*
* The dynamic logical shape represented by the SparseTensor is recorded in
* the Tensor referenced here. It must have dtype int64 and shape [?].
*
*
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public Builder setDenseShapeTensorNameBytes(
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checkByteStringIsUtf8(value);
denseShapeTensorName_ = value;
onChanged();
return this;
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public final Builder setUnknownFields(
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public final Builder mergeUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
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// @@protoc_insertion_point(builder_scope:tensorflow.TensorInfo.CooSparse)
}
// @@protoc_insertion_point(class_scope:tensorflow.TensorInfo.CooSparse)
private static final org.tensorflow.framework.TensorInfo.CooSparse DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.TensorInfo.CooSparse();
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public static org.tensorflow.framework.TensorInfo.CooSparse getDefaultInstance() {
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private static final org.nd4j.shade.protobuf.Parser
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org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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public org.tensorflow.framework.TensorInfo.CooSparse getDefaultInstanceForType() {
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/**
*
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*
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/**
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
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org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf(dtype_);
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private org.tensorflow.framework.TensorShapeProto tensorShape_;
/**
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
*
* .tensorflow.TensorShapeProto tensor_shape = 3;
*/
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public static org.tensorflow.framework.TensorInfo parseFrom(
java.nio.ByteBuffer data)
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return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.TensorInfo 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.TensorInfo parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.TensorInfo 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.TensorInfo parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.TensorInfo 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.TensorInfo parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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}
public static org.tensorflow.framework.TensorInfo parseFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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}
public static org.tensorflow.framework.TensorInfo parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
}
public static org.tensorflow.framework.TensorInfo parseDelimitedFrom(
java.io.InputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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}
public static org.tensorflow.framework.TensorInfo 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.TensorInfo parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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}
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(org.tensorflow.framework.TensorInfo prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
? new Builder() : new Builder().mergeFrom(this);
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@java.lang.Override
protected Builder newBuilderForType(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* Information about a Tensor necessary for feeding or retrieval.
*
*
* Protobuf type {@code tensorflow.TensorInfo}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.TensorInfo)
org.tensorflow.framework.TensorInfoOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.MetaGraphProtos.internal_static_tensorflow_TensorInfo_descriptor;
}
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onBuilt();
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public Builder clone() {
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public Builder setField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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public Builder clearField(
org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) {
return (Builder) super.clearField(field);
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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public final boolean isInitialized() {
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public Builder mergeFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.TensorInfo parsedMessage = null;
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public Builder clearEncoding() {
encodingCase_ = 0;
encoding_ = null;
onChanged();
return this;
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/**
*
* For dense `Tensor`s, the name of the tensor in the graph.
*
*
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*/
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/**
*
* For dense `Tensor`s, the name of the tensor in the graph.
*
*
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/**
*
* For dense `Tensor`s, the name of the tensor in the graph.
*
*
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*/
public Builder setName(
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throw new NullPointerException();
}
encodingCase_ = 1;
encoding_ = value;
onChanged();
return this;
}
/**
*
* For dense `Tensor`s, the name of the tensor in the graph.
*
*
* string name = 1;
*/
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if (encodingCase_ == 1) {
encodingCase_ = 0;
encoding_ = null;
onChanged();
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return this;
}
/**
*
* For dense `Tensor`s, the name of the tensor in the graph.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* There are many possible encodings of sparse matrices
* (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow
* uses only the COO encoding. This is supported and documented in the
* SparseTensor Python class.
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
*
* .tensorflow.TensorShapeProto tensor_shape = 3;
*/
public Builder setTensorShape(org.tensorflow.framework.TensorShapeProto value) {
if (tensorShapeBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
tensorShape_ = value;
onChanged();
} else {
tensorShapeBuilder_.setMessage(value);
}
return this;
}
/**
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
* The static shape should be recorded here, to the extent that it can
* be known in advance. In the case of a SparseTensor, this field describes
* the logical shape of the represented tensor (aka dense_shape).
*
*
* .tensorflow.TensorShapeProto tensor_shape = 3;
*/
private org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder>
getTensorShapeFieldBuilder() {
if (tensorShapeBuilder_ == null) {
tensorShapeBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.TensorShapeProto, org.tensorflow.framework.TensorShapeProto.Builder, org.tensorflow.framework.TensorShapeProtoOrBuilder>(
getTensorShape(),
getParentForChildren(),
isClean());
tensorShape_ = null;
}
return tensorShapeBuilder_;
}
public final Builder setUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
public final Builder mergeUnknownFields(
final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.TensorInfo)
}
// @@protoc_insertion_point(class_scope:tensorflow.TensorInfo)
private static final org.tensorflow.framework.TensorInfo DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.TensorInfo();
}
public static org.tensorflow.framework.TensorInfo getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
public TensorInfo parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new TensorInfo(input, extensionRegistry);
}
};
public static org.nd4j.shade.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public org.nd4j.shade.protobuf.Parser getParserForType() {
return PARSER;
}
public org.tensorflow.framework.TensorInfo getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}