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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/tensor_bundle.proto
package org.tensorflow.util;
/**
*
* Describes the metadata related to a checkpointed tensor.
*
*
* Protobuf type {@code tensorflow.BundleEntryProto}
*/
public final class BundleEntryProto extends
org.nd4j.shade.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.BundleEntryProto)
BundleEntryProtoOrBuilder {
private static final long serialVersionUID = 0L;
// Use BundleEntryProto.newBuilder() to construct.
private BundleEntryProto(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private BundleEntryProto() {
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shardId_ = 0;
offset_ = 0L;
size_ = 0L;
crc32C_ = 0;
slices_ = java.util.Collections.emptyList();
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@java.lang.Override
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private BundleEntryProto(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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this();
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default: {
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case 8: {
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dtype_ = rawValue;
break;
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case 18: {
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subBuilder = shape_.toBuilder();
}
shape_ = input.readMessage(org.tensorflow.framework.TensorShapeProto.parser(), extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(shape_);
shape_ = subBuilder.buildPartial();
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break;
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case 24: {
shardId_ = input.readInt32();
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case 32: {
offset_ = input.readInt64();
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case 40: {
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public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.util.TensorBundleProtos.internal_static_tensorflow_BundleEntryProto_descriptor;
}
protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
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private int bitField0_;
public static final int DTYPE_FIELD_NUMBER = 1;
private int dtype_;
/**
*
* The tensor dtype and shape.
*
*
* .tensorflow.DataType dtype = 1;
*/
public int getDtypeValue() {
return dtype_;
}
/**
*
* The tensor dtype and shape.
*
*
* .tensorflow.DataType dtype = 1;
*/
public org.tensorflow.framework.DataType getDtype() {
org.tensorflow.framework.DataType result = org.tensorflow.framework.DataType.valueOf(dtype_);
return result == null ? org.tensorflow.framework.DataType.UNRECOGNIZED : result;
}
public static final int SHAPE_FIELD_NUMBER = 2;
private org.tensorflow.framework.TensorShapeProto shape_;
/**
* .tensorflow.TensorShapeProto shape = 2;
*/
public boolean hasShape() {
return shape_ != null;
}
/**
* .tensorflow.TensorShapeProto shape = 2;
*/
public org.tensorflow.framework.TensorShapeProto getShape() {
return shape_ == null ? org.tensorflow.framework.TensorShapeProto.getDefaultInstance() : shape_;
}
/**
* .tensorflow.TensorShapeProto shape = 2;
*/
public org.tensorflow.framework.TensorShapeProtoOrBuilder getShapeOrBuilder() {
return getShape();
}
public static final int SHARD_ID_FIELD_NUMBER = 3;
private int shardId_;
/**
*
* The binary content of the tensor lies in:
* File "shard_id": bytes [offset, offset + size).
*
*
* int32 shard_id = 3;
*/
public int getShardId() {
return shardId_;
}
public static final int OFFSET_FIELD_NUMBER = 4;
private long offset_;
/**
* int64 offset = 4;
*/
public long getOffset() {
return offset_;
}
public static final int SIZE_FIELD_NUMBER = 5;
private long size_;
/**
* int64 size = 5;
*/
public long getSize() {
return size_;
}
public static final int CRC32C_FIELD_NUMBER = 6;
private int crc32C_;
/**
*
* The CRC32C checksum of the tensor bytes.
*
*
* fixed32 crc32c = 6;
*/
public int getCrc32C() {
return crc32C_;
}
public static final int SLICES_FIELD_NUMBER = 7;
private java.util.List slices_;
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public int getSlicesCount() {
return slices_.size();
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public org.tensorflow.framework.TensorSliceProtoOrBuilder getSlicesOrBuilder(
int index) {
return slices_.get(index);
}
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unknownFields.writeTo(output);
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@java.lang.Override
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int hash = 41;
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hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashLong(
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public static org.tensorflow.util.BundleEntryProto parseFrom(
java.nio.ByteBuffer data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static org.tensorflow.util.BundleEntryProto parseFrom(
java.nio.ByteBuffer data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.util.BundleEntryProto parseFrom(
org.nd4j.shade.protobuf.ByteString data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.util.BundleEntryProto 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.util.BundleEntryProto parseFrom(byte[] data)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.util.BundleEntryProto parseFrom(
byte[] data,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
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public static org.tensorflow.util.BundleEntryProto parseFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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public static org.tensorflow.util.BundleEntryProto 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.util.BundleEntryProto parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input);
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public static org.tensorflow.util.BundleEntryProto 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.util.BundleEntryProto parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input);
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public static org.tensorflow.util.BundleEntryProto parseFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return org.nd4j.shade.protobuf.GeneratedMessageV3
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}
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
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public static Builder newBuilder(org.tensorflow.util.BundleEntryProto prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* Describes the metadata related to a checkpointed tensor.
*
*
* Protobuf type {@code tensorflow.BundleEntryProto}
*/
public static final class Builder extends
org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.BundleEntryProto)
org.tensorflow.util.BundleEntryProtoOrBuilder {
public static final org.nd4j.shade.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.util.TensorBundleProtos.internal_static_tensorflow_BundleEntryProto_descriptor;
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protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable
internalGetFieldAccessorTable() {
return org.tensorflow.util.TensorBundleProtos.internal_static_tensorflow_BundleEntryProto_fieldAccessorTable
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// Construct using org.tensorflow.util.BundleEntryProto.newBuilder()
private Builder() {
maybeForceBuilderInitialization();
}
private Builder(
org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
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private void maybeForceBuilderInitialization() {
if (org.nd4j.shade.protobuf.GeneratedMessageV3
.alwaysUseFieldBuilders) {
getSlicesFieldBuilder();
}
}
public Builder clear() {
super.clear();
dtype_ = 0;
if (shapeBuilder_ == null) {
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shapeBuilder_ = null;
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size_ = 0L;
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org.tensorflow.util.BundleEntryProto result = buildPartial();
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throw newUninitializedMessageException(result);
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) {
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org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) {
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field,
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public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) {
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org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
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org.tensorflow.util.BundleEntryProto parsedMessage = null;
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throw e.unwrapIOException();
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mergeFrom(parsedMessage);
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private int bitField0_;
private int dtype_ = 0;
/**
*
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*
*
* .tensorflow.DataType dtype = 1;
*/
public int getDtypeValue() {
return dtype_;
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public int getSlicesCount() {
if (slicesBuilder_ == null) {
return slices_.size();
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return slicesBuilder_.getCount();
}
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public Builder setSlices(
int index, org.tensorflow.framework.TensorSliceProto value) {
if (slicesBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureSlicesIsMutable();
slices_.set(index, value);
onChanged();
} else {
slicesBuilder_.setMessage(index, value);
}
return this;
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public Builder addSlices(org.tensorflow.framework.TensorSliceProto value) {
if (slicesBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureSlicesIsMutable();
slices_.add(value);
onChanged();
} else {
slicesBuilder_.addMessage(value);
}
return this;
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public Builder addSlices(
int index, org.tensorflow.framework.TensorSliceProto value) {
if (slicesBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureSlicesIsMutable();
slices_.add(index, value);
onChanged();
} else {
slicesBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public org.tensorflow.framework.TensorSliceProto.Builder getSlicesBuilder(
int index) {
return getSlicesFieldBuilder().getBuilder(index);
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public org.tensorflow.framework.TensorSliceProtoOrBuilder getSlicesOrBuilder(
int index) {
if (slicesBuilder_ == null) {
return slices_.get(index); } else {
return slicesBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
* Iff present, this entry represents a partitioned tensor. The previous
* fields are interpreted as follows:
* "dtype", "shape": describe the full tensor.
* "shard_id", "offset", "size", "crc32c": all IGNORED.
* These information for each slice can be looked up in their own
* BundleEntryProto, keyed by each "slice_name".
*
*
* repeated .tensorflow.TensorSliceProto slices = 7;
*/
public java.util.List
getSlicesBuilderList() {
return getSlicesFieldBuilder().getBuilderList();
}
private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TensorSliceProto, org.tensorflow.framework.TensorSliceProto.Builder, org.tensorflow.framework.TensorSliceProtoOrBuilder>
getSlicesFieldBuilder() {
if (slicesBuilder_ == null) {
slicesBuilder_ = new org.nd4j.shade.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.framework.TensorSliceProto, org.tensorflow.framework.TensorSliceProto.Builder, org.tensorflow.framework.TensorSliceProtoOrBuilder>(
slices_,
((bitField0_ & 0x00000040) == 0x00000040),
getParentForChildren(),
isClean());
slices_ = null;
}
return slicesBuilder_;
}
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.BundleEntryProto)
}
// @@protoc_insertion_point(class_scope:tensorflow.BundleEntryProto)
private static final org.tensorflow.util.BundleEntryProto DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.util.BundleEntryProto();
}
public static org.tensorflow.util.BundleEntryProto getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final org.nd4j.shade.protobuf.Parser
PARSER = new org.nd4j.shade.protobuf.AbstractParser() {
public BundleEntryProto parsePartialFrom(
org.nd4j.shade.protobuf.CodedInputStream input,
org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
throws org.nd4j.shade.protobuf.InvalidProtocolBufferException {
return new BundleEntryProto(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.util.BundleEntryProto getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
