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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/framework/function.proto
package org.tensorflow.framework;
/**
*
* GradientDef defines the gradient function of a function defined in
* a function library.
* A gradient function g (specified by gradient_func) for a function f
* (specified by function_name) must follow the following:
* The function 'f' must be a numerical function which takes N inputs
* and produces M outputs. Its gradient function 'g', which is a
* function taking N + M inputs and produces N outputs.
* I.e. if we have
* (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
* then, g is
* (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
* dL/dy1, dL/dy2, ..., dL/dy_M),
* where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
* loss function). dL/dx_i is the partial derivative of L with respect
* to x_i.
*
*
* Protobuf type {@code tensorflow.GradientDef}
*/
public final class GradientDef extends
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// @@protoc_insertion_point(message_implements:tensorflow.GradientDef)
GradientDefOrBuilder {
private static final long serialVersionUID = 0L;
// Use GradientDef.newBuilder() to construct.
private GradientDef(com.github.os72.protobuf351.GeneratedMessageV3.Builder builder) {
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private GradientDef() {
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gradientFunc_ = "";
}
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public static final com.github.os72.protobuf351.Descriptors.Descriptor
getDescriptor() {
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*
*
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com.github.os72.protobuf351.GeneratedMessageV3.writeString(output, 1, functionName_);
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return super.equals(obj);
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org.tensorflow.framework.GradientDef other = (org.tensorflow.framework.GradientDef) obj;
boolean result = true;
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@java.lang.Override
public int hashCode() {
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int hash = 41;
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hash = (37 * hash) + FUNCTION_NAME_FIELD_NUMBER;
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hash = (37 * hash) + GRADIENT_FUNC_FIELD_NUMBER;
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public static org.tensorflow.framework.GradientDef parseFrom(
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return PARSER.parseFrom(data);
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public static org.tensorflow.framework.GradientDef parseFrom(
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com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws com.github.os72.protobuf351.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GradientDef parseFrom(
com.github.os72.protobuf351.ByteString data)
throws com.github.os72.protobuf351.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static org.tensorflow.framework.GradientDef parseFrom(
com.github.os72.protobuf351.ByteString data,
com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws com.github.os72.protobuf351.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GradientDef parseFrom(byte[] data)
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return PARSER.parseFrom(data);
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public static org.tensorflow.framework.GradientDef parseFrom(
byte[] data,
com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws com.github.os72.protobuf351.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.GradientDef parseFrom(
java.io.InputStream input,
com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.github.os72.protobuf351.GeneratedMessageV3
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public static org.tensorflow.framework.GradientDef parseDelimitedFrom(java.io.InputStream input)
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public static org.tensorflow.framework.GradientDef parseDelimitedFrom(
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com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
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public static org.tensorflow.framework.GradientDef parseFrom(
com.github.os72.protobuf351.CodedInputStream input)
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public static org.tensorflow.framework.GradientDef parseFrom(
com.github.os72.protobuf351.CodedInputStream input,
com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.github.os72.protobuf351.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.framework.GradientDef 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.github.os72.protobuf351.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* GradientDef defines the gradient function of a function defined in
* a function library.
* A gradient function g (specified by gradient_func) for a function f
* (specified by function_name) must follow the following:
* The function 'f' must be a numerical function which takes N inputs
* and produces M outputs. Its gradient function 'g', which is a
* function taking N + M inputs and produces N outputs.
* I.e. if we have
* (y1, y2, ..., y_M) = f(x1, x2, ..., x_N),
* then, g is
* (dL/dx1, dL/dx2, ..., dL/dx_N) = g(x1, x2, ..., x_N,
* dL/dy1, dL/dy2, ..., dL/dy_M),
* where L is a scalar-value function of (x1, x2, ..., xN) (e.g., the
* loss function). dL/dx_i is the partial derivative of L with respect
* to x_i.
*
*
* Protobuf type {@code tensorflow.GradientDef}
*/
public static final class Builder extends
com.github.os72.protobuf351.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.GradientDef)
org.tensorflow.framework.GradientDefOrBuilder {
public static final com.github.os72.protobuf351.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.FunctionProtos.internal_static_tensorflow_GradientDef_descriptor;
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protected com.github.os72.protobuf351.GeneratedMessageV3.FieldAccessorTable
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private Builder(
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public Builder clear() {
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functionName_ = "";
gradientFunc_ = "";
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org.tensorflow.framework.GradientDef result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
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result.functionName_ = functionName_;
result.gradientFunc_ = gradientFunc_;
onBuilt();
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com.github.os72.protobuf351.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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com.github.os72.protobuf351.Descriptors.FieldDescriptor field) {
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com.github.os72.protobuf351.Descriptors.OneofDescriptor oneof) {
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com.github.os72.protobuf351.Descriptors.FieldDescriptor field,
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com.github.os72.protobuf351.Descriptors.FieldDescriptor field,
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public Builder mergeFrom(org.tensorflow.framework.GradientDef other) {
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com.github.os72.protobuf351.ExtensionRegistryLite extensionRegistry)
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private java.lang.Object functionName_ = "";
/**
*
