Class ExplanationMetadata.InputMetadata.Builder (3.44.0)

public static final class ExplanationMetadata.InputMetadata.Builder extends GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder> implements ExplanationMetadata.InputMetadataOrBuilder

Metadata of the input of a feature.

Fields other than InputMetadata.input_baselines are applicable only for Models that are using Vertex AI-provided images for Tensorflow.

Protobuf type google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ExplanationMetadata.InputMetadata.Builder

Implements

ExplanationMetadata.InputMetadataOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
`Descriptor`

Methods

addAllEncodedBaselines(Iterable<? extends Value> values)

public ExplanationMetadata.InputMetadata.Builder addAllEncodedBaselines(Iterable<? extends Value> values)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`values`	`Iterable<? extends com.google.protobuf.Value>`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addAllIndexFeatureMapping(Iterable<String> values)

public ExplanationMetadata.InputMetadata.Builder addAllIndexFeatureMapping(Iterable<String> values)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter
Name	Description
`values`	`Iterable<String>` The indexFeatureMapping to add.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

addAllInputBaselines(Iterable<? extends Value> values)

public ExplanationMetadata.InputMetadata.Builder addAllInputBaselines(Iterable<? extends Value> values)

Baseline inputs for this feature.

If no baseline is specified, Vertex AI chooses the baseline for this feature. If multiple baselines are specified, Vertex AI returns the average attributions across them in Attribution.feature_attributions.

For Vertex AI-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor.

For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`values`	`Iterable<? extends com.google.protobuf.Value>`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addEncodedBaselines(Value value)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value value)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addEncodedBaselines(Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(Value.Builder builderForValue)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addEncodedBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value value)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameters
Name	Description
`index`	`int`
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addEncodedBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, Value.Builder builderForValue)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameters
Name	Description
`index`	`int`
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addEncodedBaselinesBuilder()

public Value.Builder addEncodedBaselinesBuilder()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`Builder`

addEncodedBaselinesBuilder(int index)

public Value.Builder addEncodedBaselinesBuilder(int index)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Builder`

addIndexFeatureMapping(String value)

public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMapping(String value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter
Name	Description
`value`	`String` The indexFeatureMapping to add.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

addIndexFeatureMappingBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMappingBytes(ByteString value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter
Name	Description
`value`	`ByteString` The bytes of the indexFeatureMapping to add.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

addInputBaselines(Value value)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value value)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addInputBaselines(Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(Value.Builder builderForValue)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addInputBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value value)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameters
Name	Description
`index`	`int`
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addInputBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(int index, Value.Builder builderForValue)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameters
Name	Description
`index`	`int`
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

addInputBaselinesBuilder()

public Value.Builder addInputBaselinesBuilder()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`Builder`

addInputBaselinesBuilder(int index)

public Value.Builder addInputBaselinesBuilder(int index)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Builder`

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ExplanationMetadata.InputMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ExplanationMetadata.InputMetadata build()

Returns
Type	Description
`ExplanationMetadata.InputMetadata`

buildPartial()

public ExplanationMetadata.InputMetadata buildPartial()

Returns
Type	Description
`ExplanationMetadata.InputMetadata`

clear()

public ExplanationMetadata.InputMetadata.Builder clear()

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearDenseShapeTensorName()

public ExplanationMetadata.InputMetadata.Builder clearDenseShapeTensorName()

Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.

string dense_shape_tensor_name = 7;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearEncodedBaselines()

public ExplanationMetadata.InputMetadata.Builder clearEncodedBaselines()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

clearEncodedTensorName()

public ExplanationMetadata.InputMetadata.Builder clearEncodedTensorName()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

string encoded_tensor_name = 9;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearEncoding()

public ExplanationMetadata.InputMetadata.Builder clearEncoding()

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearFeatureValueDomain()

public ExplanationMetadata.InputMetadata.Builder clearFeatureValueDomain()

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

clearField(Descriptors.FieldDescriptor field)

public ExplanationMetadata.InputMetadata.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
`field`	`FieldDescriptor`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearGroupName()

public ExplanationMetadata.InputMetadata.Builder clearGroupName()

Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.

string group_name = 12;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearIndexFeatureMapping()

public ExplanationMetadata.InputMetadata.Builder clearIndexFeatureMapping()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearIndicesTensorName()

public ExplanationMetadata.InputMetadata.Builder clearIndicesTensorName()

Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.

string indices_tensor_name = 6;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearInputBaselines()

public ExplanationMetadata.InputMetadata.Builder clearInputBaselines()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

clearInputTensorName()

public ExplanationMetadata.InputMetadata.Builder clearInputTensorName()

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

string input_tensor_name = 2;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearModality()

public ExplanationMetadata.InputMetadata.Builder clearModality()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ExplanationMetadata.InputMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
`oneof`	`OneofDescriptor`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearVisualization()

public ExplanationMetadata.InputMetadata.Builder clearVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

clone()

public ExplanationMetadata.InputMetadata.Builder clone()

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getDefaultInstanceForType()

public ExplanationMetadata.InputMetadata getDefaultInstanceForType()

Returns
Type	Description
`ExplanationMetadata.InputMetadata`

getDenseShapeTensorName()

public String getDenseShapeTensorName()

string dense_shape_tensor_name = 7;

Returns
Type	Description
`String`	The denseShapeTensorName.

getDenseShapeTensorNameBytes()

public ByteString getDenseShapeTensorNameBytes()

string dense_shape_tensor_name = 7;

Returns
Type	Description
`ByteString`	The bytes for denseShapeTensorName.

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
`Descriptor`

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getEncodedBaselines(int index)

public Value getEncodedBaselines(int index)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Value`

getEncodedBaselinesBuilder(int index)

public Value.Builder getEncodedBaselinesBuilder(int index)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Builder`

getEncodedBaselinesBuilderList()

public List<Value.Builder> getEncodedBaselinesBuilderList()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`List<Builder>`

getEncodedBaselinesCount()

public int getEncodedBaselinesCount()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`int`

getEncodedBaselinesList()

public List<Value> getEncodedBaselinesList()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`List<Value>`

getEncodedBaselinesOrBuilder(int index)

public ValueOrBuilder getEncodedBaselinesOrBuilder(int index)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`ValueOrBuilder`

getEncodedBaselinesOrBuilderList()

public List<? extends ValueOrBuilder> getEncodedBaselinesOrBuilderList()

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Returns
Type	Description
`List<? extends com.google.protobuf.ValueOrBuilder>`

getEncodedTensorName()

public String getEncodedTensorName()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

string encoded_tensor_name = 9;

Returns
Type	Description
`String`	The encodedTensorName.

getEncodedTensorNameBytes()

public ByteString getEncodedTensorNameBytes()

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

string encoded_tensor_name = 9;

Returns
Type	Description
`ByteString`	The bytes for encodedTensorName.

getEncoding()

public ExplanationMetadata.InputMetadata.Encoding getEncoding()

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Encoding`	The encoding.

getEncodingValue()

public int getEncodingValue()

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Returns
Type	Description
`int`	The enum numeric value on the wire for encoding.

getFeatureValueDomain()

public ExplanationMetadata.InputMetadata.FeatureValueDomain getFeatureValueDomain()

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.FeatureValueDomain`	The featureValueDomain.

getFeatureValueDomainBuilder()

public ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder getFeatureValueDomainBuilder()

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder`

getFeatureValueDomainOrBuilder()

public ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder getFeatureValueDomainOrBuilder()

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder`

getGroupName()

public String getGroupName()

string group_name = 12;

Returns
Type	Description
`String`	The groupName.

getGroupNameBytes()

public ByteString getGroupNameBytes()

string group_name = 12;

Returns
Type	Description
`ByteString`	The bytes for groupName.

getIndexFeatureMapping(int index)

public String getIndexFeatureMapping(int index)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter
Name	Description
`index`	`int` The index of the element to return.

Returns
Type	Description
`String`	The indexFeatureMapping at the given index.

getIndexFeatureMappingBytes(int index)

public ByteString getIndexFeatureMappingBytes(int index)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameter
Name	Description
`index`	`int` The index of the value to return.

Returns
Type	Description
`ByteString`	The bytes of the indexFeatureMapping at the given index.

getIndexFeatureMappingCount()

public int getIndexFeatureMappingCount()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns
Type	Description
`int`	The count of indexFeatureMapping.

getIndexFeatureMappingList()

public ProtocolStringList getIndexFeatureMappingList()

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns
Type	Description
`ProtocolStringList`	A list containing the indexFeatureMapping.

getIndicesTensorName()

public String getIndicesTensorName()

string indices_tensor_name = 6;

Returns
Type	Description
`String`	The indicesTensorName.

getIndicesTensorNameBytes()

public ByteString getIndicesTensorNameBytes()

string indices_tensor_name = 6;

Returns
Type	Description
`ByteString`	The bytes for indicesTensorName.

getInputBaselines(int index)

public Value getInputBaselines(int index)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Value`

getInputBaselinesBuilder(int index)

public Value.Builder getInputBaselinesBuilder(int index)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`Builder`

getInputBaselinesBuilderList()

public List<Value.Builder> getInputBaselinesBuilderList()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`List<Builder>`

getInputBaselinesCount()

public int getInputBaselinesCount()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`int`

getInputBaselinesList()

public List<Value> getInputBaselinesList()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`List<Value>`

getInputBaselinesOrBuilder(int index)

public ValueOrBuilder getInputBaselinesOrBuilder(int index)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`ValueOrBuilder`

getInputBaselinesOrBuilderList()

public List<? extends ValueOrBuilder> getInputBaselinesOrBuilderList()

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Returns
Type	Description
`List<? extends com.google.protobuf.ValueOrBuilder>`

getInputTensorName()

public String getInputTensorName()

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

string input_tensor_name = 2;

Returns
Type	Description
`String`	The inputTensorName.

getInputTensorNameBytes()

public ByteString getInputTensorNameBytes()

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

string input_tensor_name = 2;

Returns
Type	Description
`ByteString`	The bytes for inputTensorName.

getModality()

public String getModality()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns
Type	Description
`String`	The modality.

getModalityBytes()

public ByteString getModalityBytes()

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Returns
Type	Description
`ByteString`	The bytes for modality.

getVisualization()

public ExplanationMetadata.InputMetadata.Visualization getVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Visualization`	The visualization.

getVisualizationBuilder()

public ExplanationMetadata.InputMetadata.Visualization.Builder getVisualizationBuilder()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Visualization.Builder`

getVisualizationOrBuilder()

public ExplanationMetadata.InputMetadata.VisualizationOrBuilder getVisualizationOrBuilder()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns
Type	Description
`ExplanationMetadata.InputMetadata.VisualizationOrBuilder`

hasFeatureValueDomain()

public boolean hasFeatureValueDomain()

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Returns
Type	Description
`boolean`	Whether the featureValueDomain field is set.

hasVisualization()

public boolean hasVisualization()

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Returns
Type	Description
`boolean`	Whether the visualization field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
`FieldAccessorTable`

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
`boolean`

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

public ExplanationMetadata.InputMetadata.Builder mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Parameter
Name	Description
`value`	`ExplanationMetadata.InputMetadata.FeatureValueDomain`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

mergeFrom(ExplanationMetadata.InputMetadata other)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(ExplanationMetadata.InputMetadata other)

Parameter
Name	Description
`other`	`ExplanationMetadata.InputMetadata`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`CodedInputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
`IOException`

mergeFrom(Message other)

public ExplanationMetadata.InputMetadata.Builder mergeFrom(Message other)

Parameter
Name	Description
`other`	`Message`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ExplanationMetadata.InputMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)

public ExplanationMetadata.InputMetadata.Builder mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter
Name	Description
`value`	`ExplanationMetadata.InputMetadata.Visualization`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

removeEncodedBaselines(int index)

public ExplanationMetadata.InputMetadata.Builder removeEncodedBaselines(int index)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

removeInputBaselines(int index)

public ExplanationMetadata.InputMetadata.Builder removeInputBaselines(int index)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameter
Name	Description
`index`	`int`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setDenseShapeTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorName(String value)

string dense_shape_tensor_name = 7;

Parameter
Name	Description
`value`	`String` The denseShapeTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setDenseShapeTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorNameBytes(ByteString value)

string dense_shape_tensor_name = 7;

Parameter
Name	Description
`value`	`ByteString` The bytes for denseShapeTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setEncodedBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value value)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameters
Name	Description
`index`	`int`
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setEncodedBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, Value.Builder builderForValue)

A list of baselines for the encoded tensor.

The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Vertex AI broadcasts to the same shape as the encoded tensor.

repeated .google.protobuf.Value encoded_baselines = 10;

Parameters
Name	Description
`index`	`int`
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setEncodedTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorName(String value)

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

string encoded_tensor_name = 9;

Parameter
Name	Description
`value`	`String` The encodedTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setEncodedTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorNameBytes(ByteString value)

Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable.

An encoded tensor is generated if the input tensor is encoded by a lookup table.

string encoded_tensor_name = 9;

Parameter
Name	Description
`value`	`ByteString` The bytes for encodedTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setEncoding(ExplanationMetadata.InputMetadata.Encoding value)

public ExplanationMetadata.InputMetadata.Builder setEncoding(ExplanationMetadata.InputMetadata.Encoding value)

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Parameter
Name	Description
`value`	`ExplanationMetadata.InputMetadata.Encoding` The encoding to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setEncodingValue(int value)

public ExplanationMetadata.InputMetadata.Builder setEncodingValue(int value)

Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Parameter
Name	Description
`value`	`int` The enum numeric value on the wire for encoding to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Parameter
Name	Description
`value`	`ExplanationMetadata.InputMetadata.FeatureValueDomain`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)

The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

Parameter
Name	Description
`builderForValue`	`ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setField(Descriptors.FieldDescriptor field, Object value)

public ExplanationMetadata.InputMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setGroupName(String value)

public ExplanationMetadata.InputMetadata.Builder setGroupName(String value)

string group_name = 12;

Parameter
Name	Description
`value`	`String` The groupName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setGroupNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setGroupNameBytes(ByteString value)

string group_name = 12;

Parameter
Name	Description
`value`	`ByteString` The bytes for groupName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setIndexFeatureMapping(int index, String value)

public ExplanationMetadata.InputMetadata.Builder setIndexFeatureMapping(int index, String value)

A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters
Name	Description
`index`	`int` The index to set the value at.
`value`	`String` The indexFeatureMapping to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setIndicesTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorName(String value)

string indices_tensor_name = 6;

Parameter
Name	Description
`value`	`String` The indicesTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setIndicesTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorNameBytes(ByteString value)

string indices_tensor_name = 6;

Parameter
Name	Description
`value`	`ByteString` The bytes for indicesTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setInputBaselines(int index, Value value)

public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value value)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameters
Name	Description
`index`	`int`
`value`	`Value`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setInputBaselines(int index, Value.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setInputBaselines(int index, Value.Builder builderForValue)

Baseline inputs for this feature.

repeated .google.protobuf.Value input_baselines = 1;

Parameters
Name	Description
`index`	`int`
`builderForValue`	`Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setInputTensorName(String value)

public ExplanationMetadata.InputMetadata.Builder setInputTensorName(String value)

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

string input_tensor_name = 2;

Parameter
Name	Description
`value`	`String` The inputTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setInputTensorNameBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setInputTensorNameBytes(ByteString value)

Name of the input tensor for this feature. Required and is only applicable to Vertex AI-provided images for Tensorflow.

string input_tensor_name = 2;

Parameter
Name	Description
`value`	`ByteString` The bytes for inputTensorName to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setModality(String value)

public ExplanationMetadata.InputMetadata.Builder setModality(String value)

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Parameter
Name	Description
`value`	`String` The modality to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setModalityBytes(ByteString value)

public ExplanationMetadata.InputMetadata.Builder setModalityBytes(ByteString value)

Modality of the feature. Valid values are: numeric, image. Defaults to numeric.

string modality = 4;

Parameter
Name	Description
`value`	`ByteString` The bytes for modality to set.

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ExplanationMetadata.InputMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`index`	`int`
`value`	`Object`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setUnknownFields(UnknownFieldSet unknownFields)

public final ExplanationMetadata.InputMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)

setVisualization(ExplanationMetadata.InputMetadata.Visualization value)

public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization value)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter
Name	Description
`value`	`ExplanationMetadata.InputMetadata.Visualization`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`

setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)

public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)

Visualization configurations for image explanation.

.google.cloud.aiplatform.v1beta1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

Parameter
Name	Description
`builderForValue`	`ExplanationMetadata.InputMetadata.Visualization.Builder`

Returns
Type	Description
`ExplanationMetadata.InputMetadata.Builder`