org.tensorflow.metadata.v0.ProblemStatement Maven / Gradle / Ivy
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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow_metadata/proto/v0/problem_statement.proto
// Protobuf Java Version: 3.25.4
package org.tensorflow.metadata.v0;
/**
* Protobuf type {@code tensorflow.metadata.v0.ProblemStatement}
*/
public final class ProblemStatement extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.metadata.v0.ProblemStatement)
ProblemStatementOrBuilder {
private static final long serialVersionUID = 0L;
// Use ProblemStatement.newBuilder() to construct.
private ProblemStatement(com.google.protobuf.GeneratedMessageV3.Builder builder) {
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owner_ =
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tasks_ = java.util.Collections.emptyList();
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/**
*
* Description of the problem statement. For example, should describe how
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/**
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* the problem statement was arrived at: what experiments were run, what
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*
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/**
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/**
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/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
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*
*
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*/
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/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
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*
*
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/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
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*/
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/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
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*/
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/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
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public static final int MULTI_OBJECTIVE_FIELD_NUMBER = 8;
private boolean multiObjective_ = false;
/**
* bool multi_objective = 8 [deprecated = true];
* @deprecated tensorflow.metadata.v0.ProblemStatement.multi_objective is deprecated.
* See tensorflow_metadata/proto/v0/problem_statement.proto;l=356
* @return The multiObjective.
*/
@java.lang.Override
@java.lang.Deprecated public boolean getMultiObjective() {
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@SuppressWarnings("serial")
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/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
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/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
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/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
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/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
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}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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// @@protoc_insertion_point(builder_implements:tensorflow.metadata.v0.ProblemStatement)
org.tensorflow.metadata.v0.ProblemStatementOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
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if (!other.tasks_.isEmpty()) {
if (tasksBuilder_.isEmpty()) {
tasksBuilder_.dispose();
tasksBuilder_ = null;
tasks_ = other.tasks_;
bitField0_ = (bitField0_ & ~0x00000020);
tasksBuilder_ =
com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ?
getTasksFieldBuilder() : null;
} else {
tasksBuilder_.addAllMessages(other.tasks_);
}
}
}
this.mergeUnknownFields(other.getUnknownFields());
onChanged();
return this;
}
@java.lang.Override
public final boolean isInitialized() {
return true;
}
@java.lang.Override
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
if (extensionRegistry == null) {
throw new java.lang.NullPointerException();
}
try {
boolean done = false;
while (!done) {
int tag = input.readTag();
switch (tag) {
case 0:
done = true;
break;
case 18: {
description_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000001;
break;
} // case 18
case 26: {
java.lang.String s = input.readStringRequireUtf8();
ensureOwnerIsMutable();
owner_.add(s);
break;
} // case 26
case 34: {
environment_ = input.readStringRequireUtf8();
bitField0_ |= 0x00000004;
break;
} // case 34
case 58: {
org.tensorflow.metadata.v0.MetaOptimizationTarget m =
input.readMessage(
org.tensorflow.metadata.v0.MetaOptimizationTarget.parser(),
extensionRegistry);
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.add(m);
} else {
metaOptimizationTargetBuilder_.addMessage(m);
}
break;
} // case 58
case 64: {
multiObjective_ = input.readBool();
bitField0_ |= 0x00000010;
break;
} // case 64
case 74: {
org.tensorflow.metadata.v0.Task m =
input.readMessage(
org.tensorflow.metadata.v0.Task.parser(),
extensionRegistry);
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
tasks_.add(m);
} else {
tasksBuilder_.addMessage(m);
}
break;
} // case 74
default: {
if (!super.parseUnknownField(input, extensionRegistry, tag)) {
done = true; // was an endgroup tag
}
break;
} // default:
} // switch (tag)
} // while (!done)
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.unwrapIOException();
} finally {
onChanged();
} // finally
return this;
}
private int bitField0_;
private java.lang.Object description_ = "";
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @return The description.
*/
public java.lang.String getDescription() {
java.lang.Object ref = description_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
description_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @return The bytes for description.
*/
public com.google.protobuf.ByteString
getDescriptionBytes() {
java.lang.Object ref = description_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
description_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @param value The description to set.
* @return This builder for chaining.
*/
public Builder setDescription(
java.lang.String value) {
if (value == null) { throw new NullPointerException(); }
description_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @return This builder for chaining.
*/
public Builder clearDescription() {
description_ = getDefaultInstance().getDescription();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
/**
*
* Description of the problem statement. For example, should describe how
* the problem statement was arrived at: what experiments were run, what
* side-by-sides were considered.
*
*
* string description = 2;
* @param value The bytes for description to set.
* @return This builder for chaining.
*/
public Builder setDescriptionBytes(
com.google.protobuf.ByteString value) {
if (value == null) { throw new NullPointerException(); }
checkByteStringIsUtf8(value);
description_ = value;
bitField0_ |= 0x00000001;
onChanged();
return this;
}
private com.google.protobuf.LazyStringArrayList owner_ =
com.google.protobuf.LazyStringArrayList.emptyList();
private void ensureOwnerIsMutable() {
if (!owner_.isModifiable()) {
owner_ = new com.google.protobuf.LazyStringArrayList(owner_);
}
bitField0_ |= 0x00000002;
}
/**
* repeated string owner = 3;
* @return A list containing the owner.
*/
public com.google.protobuf.ProtocolStringList
getOwnerList() {
owner_.makeImmutable();
return owner_;
}
/**
* repeated string owner = 3;
* @return The count of owner.
*/
public int getOwnerCount() {
return owner_.size();
}
/**
* repeated string owner = 3;
* @param index The index of the element to return.
* @return The owner at the given index.
*/
public java.lang.String getOwner(int index) {
return owner_.get(index);
}
/**
* repeated string owner = 3;
* @param index The index of the value to return.
* @return The bytes of the owner at the given index.
*/
public com.google.protobuf.ByteString
getOwnerBytes(int index) {
return owner_.getByteString(index);
}
/**
* repeated string owner = 3;
* @param index The index to set the value at.
* @param value The owner to set.
* @return This builder for chaining.
*/
public Builder setOwner(
int index, java.lang.String value) {
if (value == null) { throw new NullPointerException(); }
ensureOwnerIsMutable();
owner_.set(index, value);
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
* repeated string owner = 3;
* @param value The owner to add.
* @return This builder for chaining.
*/
public Builder addOwner(
java.lang.String value) {
if (value == null) { throw new NullPointerException(); }
ensureOwnerIsMutable();
owner_.add(value);
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
* repeated string owner = 3;
* @param values The owner to add.
* @return This builder for chaining.
*/
public Builder addAllOwner(
java.lang.Iterable values) {
ensureOwnerIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, owner_);
bitField0_ |= 0x00000002;
onChanged();
return this;
}
/**
* repeated string owner = 3;
* @return This builder for chaining.
*/
public Builder clearOwner() {
owner_ =
com.google.protobuf.LazyStringArrayList.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);;
onChanged();
return this;
}
/**
* repeated string owner = 3;
* @param value The bytes of the owner to add.
* @return This builder for chaining.
*/
public Builder addOwnerBytes(
com.google.protobuf.ByteString value) {
if (value == null) { throw new NullPointerException(); }
checkByteStringIsUtf8(value);
ensureOwnerIsMutable();
owner_.add(value);
bitField0_ |= 0x00000002;
onChanged();
return this;
}
private java.lang.Object environment_ = "";
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @return The environment.
*/
public java.lang.String getEnvironment() {
java.lang.Object ref = environment_;
if (!(ref instanceof java.lang.String)) {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
environment_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @return The bytes for environment.
*/
public com.google.protobuf.ByteString
getEnvironmentBytes() {
java.lang.Object ref = environment_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
environment_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @param value The environment to set.
* @return This builder for chaining.
*/
public Builder setEnvironment(
java.lang.String value) {
if (value == null) { throw new NullPointerException(); }
environment_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @return This builder for chaining.
*/
public Builder clearEnvironment() {
environment_ = getDefaultInstance().getEnvironment();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
/**
*
* The environment of the ProblemStatement (optional). Specifies an
* environment string in the SchemaProto.
*
*
* string environment = 4;
* @param value The bytes for environment to set.
* @return This builder for chaining.
*/
public Builder setEnvironmentBytes(
com.google.protobuf.ByteString value) {
if (value == null) { throw new NullPointerException(); }
checkByteStringIsUtf8(value);
environment_ = value;
bitField0_ |= 0x00000004;
onChanged();
return this;
}
private java.util.List metaOptimizationTarget_ =
java.util.Collections.emptyList();
private void ensureMetaOptimizationTargetIsMutable() {
if (!((bitField0_ & 0x00000008) != 0)) {
metaOptimizationTarget_ = new java.util.ArrayList(metaOptimizationTarget_);
bitField0_ |= 0x00000008;
}
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.MetaOptimizationTarget, org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder, org.tensorflow.metadata.v0.MetaOptimizationTargetOrBuilder> metaOptimizationTargetBuilder_;
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public java.util.List getMetaOptimizationTargetList() {
if (metaOptimizationTargetBuilder_ == null) {
return java.util.Collections.unmodifiableList(metaOptimizationTarget_);
} else {
return metaOptimizationTargetBuilder_.getMessageList();
}
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public int getMetaOptimizationTargetCount() {
if (metaOptimizationTargetBuilder_ == null) {
return metaOptimizationTarget_.size();
} else {
return metaOptimizationTargetBuilder_.getCount();
}
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public org.tensorflow.metadata.v0.MetaOptimizationTarget getMetaOptimizationTarget(int index) {
if (metaOptimizationTargetBuilder_ == null) {
return metaOptimizationTarget_.get(index);
} else {
return metaOptimizationTargetBuilder_.getMessage(index);
}
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder setMetaOptimizationTarget(
int index, org.tensorflow.metadata.v0.MetaOptimizationTarget value) {
if (metaOptimizationTargetBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.set(index, value);
onChanged();
} else {
metaOptimizationTargetBuilder_.setMessage(index, value);
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder setMetaOptimizationTarget(
int index, org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder builderForValue) {
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.set(index, builderForValue.build());
onChanged();
} else {
metaOptimizationTargetBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder addMetaOptimizationTarget(org.tensorflow.metadata.v0.MetaOptimizationTarget value) {
if (metaOptimizationTargetBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.add(value);
onChanged();
} else {
metaOptimizationTargetBuilder_.addMessage(value);
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder addMetaOptimizationTarget(
int index, org.tensorflow.metadata.v0.MetaOptimizationTarget value) {
if (metaOptimizationTargetBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.add(index, value);
onChanged();
} else {
metaOptimizationTargetBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder addMetaOptimizationTarget(
org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder builderForValue) {
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.add(builderForValue.build());
onChanged();
} else {
metaOptimizationTargetBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder addMetaOptimizationTarget(
int index, org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder builderForValue) {
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.add(index, builderForValue.build());
onChanged();
} else {
metaOptimizationTargetBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder addAllMetaOptimizationTarget(
java.lang.Iterable values) {
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, metaOptimizationTarget_);
onChanged();
} else {
metaOptimizationTargetBuilder_.addAllMessages(values);
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder clearMetaOptimizationTarget() {
if (metaOptimizationTargetBuilder_ == null) {
metaOptimizationTarget_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000008);
onChanged();
} else {
metaOptimizationTargetBuilder_.clear();
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public Builder removeMetaOptimizationTarget(int index) {
if (metaOptimizationTargetBuilder_ == null) {
ensureMetaOptimizationTargetIsMutable();
metaOptimizationTarget_.remove(index);
onChanged();
} else {
metaOptimizationTargetBuilder_.remove(index);
}
return this;
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder getMetaOptimizationTargetBuilder(
int index) {
return getMetaOptimizationTargetFieldBuilder().getBuilder(index);
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public org.tensorflow.metadata.v0.MetaOptimizationTargetOrBuilder getMetaOptimizationTargetOrBuilder(
int index) {
if (metaOptimizationTargetBuilder_ == null) {
return metaOptimizationTarget_.get(index); } else {
return metaOptimizationTargetBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public java.util.List
getMetaOptimizationTargetOrBuilderList() {
if (metaOptimizationTargetBuilder_ != null) {
return metaOptimizationTargetBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(metaOptimizationTarget_);
}
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder addMetaOptimizationTargetBuilder() {
return getMetaOptimizationTargetFieldBuilder().addBuilder(
org.tensorflow.metadata.v0.MetaOptimizationTarget.getDefaultInstance());
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder addMetaOptimizationTargetBuilder(
int index) {
return getMetaOptimizationTargetFieldBuilder().addBuilder(
index, org.tensorflow.metadata.v0.MetaOptimizationTarget.getDefaultInstance());
}
/**
*
* The target used for meta-optimization. This is used to compare multiple
* solutions for this problem. For example, if two solutions have different
* candidates, a tuning tool can use meta_optimization_target to decide which
* candidate performs the best.
* A repeated meta-optimization target implies the weighted sum of the
* meta_optimization targets of any non-thresholded metrics.
*
*
* repeated .tensorflow.metadata.v0.MetaOptimizationTarget meta_optimization_target = 7;
*/
public java.util.List
getMetaOptimizationTargetBuilderList() {
return getMetaOptimizationTargetFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.MetaOptimizationTarget, org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder, org.tensorflow.metadata.v0.MetaOptimizationTargetOrBuilder>
getMetaOptimizationTargetFieldBuilder() {
if (metaOptimizationTargetBuilder_ == null) {
metaOptimizationTargetBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.MetaOptimizationTarget, org.tensorflow.metadata.v0.MetaOptimizationTarget.Builder, org.tensorflow.metadata.v0.MetaOptimizationTargetOrBuilder>(
metaOptimizationTarget_,
((bitField0_ & 0x00000008) != 0),
getParentForChildren(),
isClean());
metaOptimizationTarget_ = null;
}
return metaOptimizationTargetBuilder_;
}
private boolean multiObjective_ ;
/**
* bool multi_objective = 8 [deprecated = true];
* @deprecated tensorflow.metadata.v0.ProblemStatement.multi_objective is deprecated.
* See tensorflow_metadata/proto/v0/problem_statement.proto;l=356
* @return The multiObjective.
*/
@java.lang.Override
@java.lang.Deprecated public boolean getMultiObjective() {
return multiObjective_;
}
/**
* bool multi_objective = 8 [deprecated = true];
* @deprecated tensorflow.metadata.v0.ProblemStatement.multi_objective is deprecated.
* See tensorflow_metadata/proto/v0/problem_statement.proto;l=356
* @param value The multiObjective to set.
* @return This builder for chaining.
*/
@java.lang.Deprecated public Builder setMultiObjective(boolean value) {
multiObjective_ = value;
bitField0_ |= 0x00000010;
onChanged();
return this;
}
/**
* bool multi_objective = 8 [deprecated = true];
* @deprecated tensorflow.metadata.v0.ProblemStatement.multi_objective is deprecated.
* See tensorflow_metadata/proto/v0/problem_statement.proto;l=356
* @return This builder for chaining.
*/
@java.lang.Deprecated public Builder clearMultiObjective() {
bitField0_ = (bitField0_ & ~0x00000010);
multiObjective_ = false;
onChanged();
return this;
}
private java.util.List tasks_ =
java.util.Collections.emptyList();
private void ensureTasksIsMutable() {
if (!((bitField0_ & 0x00000020) != 0)) {
tasks_ = new java.util.ArrayList(tasks_);
bitField0_ |= 0x00000020;
}
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.Task, org.tensorflow.metadata.v0.Task.Builder, org.tensorflow.metadata.v0.TaskOrBuilder> tasksBuilder_;
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public java.util.List getTasksList() {
if (tasksBuilder_ == null) {
return java.util.Collections.unmodifiableList(tasks_);
} else {
return tasksBuilder_.getMessageList();
}
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public int getTasksCount() {
if (tasksBuilder_ == null) {
return tasks_.size();
} else {
return tasksBuilder_.getCount();
}
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public org.tensorflow.metadata.v0.Task getTasks(int index) {
if (tasksBuilder_ == null) {
return tasks_.get(index);
} else {
return tasksBuilder_.getMessage(index);
}
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder setTasks(
int index, org.tensorflow.metadata.v0.Task value) {
if (tasksBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTasksIsMutable();
tasks_.set(index, value);
onChanged();
} else {
tasksBuilder_.setMessage(index, value);
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder setTasks(
int index, org.tensorflow.metadata.v0.Task.Builder builderForValue) {
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
tasks_.set(index, builderForValue.build());
onChanged();
} else {
tasksBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder addTasks(org.tensorflow.metadata.v0.Task value) {
if (tasksBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTasksIsMutable();
tasks_.add(value);
onChanged();
} else {
tasksBuilder_.addMessage(value);
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder addTasks(
int index, org.tensorflow.metadata.v0.Task value) {
if (tasksBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureTasksIsMutable();
tasks_.add(index, value);
onChanged();
} else {
tasksBuilder_.addMessage(index, value);
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder addTasks(
org.tensorflow.metadata.v0.Task.Builder builderForValue) {
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
tasks_.add(builderForValue.build());
onChanged();
} else {
tasksBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder addTasks(
int index, org.tensorflow.metadata.v0.Task.Builder builderForValue) {
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
tasks_.add(index, builderForValue.build());
onChanged();
} else {
tasksBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder addAllTasks(
java.lang.Iterable values) {
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
com.google.protobuf.AbstractMessageLite.Builder.addAll(
values, tasks_);
onChanged();
} else {
tasksBuilder_.addAllMessages(values);
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder clearTasks() {
if (tasksBuilder_ == null) {
tasks_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000020);
onChanged();
} else {
tasksBuilder_.clear();
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public Builder removeTasks(int index) {
if (tasksBuilder_ == null) {
ensureTasksIsMutable();
tasks_.remove(index);
onChanged();
} else {
tasksBuilder_.remove(index);
}
return this;
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public org.tensorflow.metadata.v0.Task.Builder getTasksBuilder(
int index) {
return getTasksFieldBuilder().getBuilder(index);
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public org.tensorflow.metadata.v0.TaskOrBuilder getTasksOrBuilder(
int index) {
if (tasksBuilder_ == null) {
return tasks_.get(index); } else {
return tasksBuilder_.getMessageOrBuilder(index);
}
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public java.util.List
getTasksOrBuilderList() {
if (tasksBuilder_ != null) {
return tasksBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(tasks_);
}
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public org.tensorflow.metadata.v0.Task.Builder addTasksBuilder() {
return getTasksFieldBuilder().addBuilder(
org.tensorflow.metadata.v0.Task.getDefaultInstance());
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public org.tensorflow.metadata.v0.Task.Builder addTasksBuilder(
int index) {
return getTasksFieldBuilder().addBuilder(
index, org.tensorflow.metadata.v0.Task.getDefaultInstance());
}
/**
*
* Tasks for heads of the generated model. This field is repeated because some
* models are multi-task models. Each task should have a unique name.
* If you wish to directly optimize this problem statement, you need
* to specify the objective in the task.
*
*
* repeated .tensorflow.metadata.v0.Task tasks = 9;
*/
public java.util.List
getTasksBuilderList() {
return getTasksFieldBuilder().getBuilderList();
}
private com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.Task, org.tensorflow.metadata.v0.Task.Builder, org.tensorflow.metadata.v0.TaskOrBuilder>
getTasksFieldBuilder() {
if (tasksBuilder_ == null) {
tasksBuilder_ = new com.google.protobuf.RepeatedFieldBuilderV3<
org.tensorflow.metadata.v0.Task, org.tensorflow.metadata.v0.Task.Builder, org.tensorflow.metadata.v0.TaskOrBuilder>(
tasks_,
((bitField0_ & 0x00000020) != 0),
getParentForChildren(),
isClean());
tasks_ = null;
}
return tasksBuilder_;
}
@java.lang.Override
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFields(unknownFields);
}
@java.lang.Override
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.metadata.v0.ProblemStatement)
}
// @@protoc_insertion_point(class_scope:tensorflow.metadata.v0.ProblemStatement)
private static final org.tensorflow.metadata.v0.ProblemStatement DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.metadata.v0.ProblemStatement();
}
public static org.tensorflow.metadata.v0.ProblemStatement getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
@java.lang.Override
public ProblemStatement parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
Builder builder = newBuilder();
try {
builder.mergeFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(builder.buildPartial());
} catch (com.google.protobuf.UninitializedMessageException e) {
throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial());
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(e)
.setUnfinishedMessage(builder.buildPartial());
}
return builder.buildPartial();
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
@java.lang.Override
public org.tensorflow.metadata.v0.ProblemStatement getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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