ExplanationMetadata.InputMetadata.Builder (proto-google-cloud-aiplatform-v1 3.44.0 API)

java.lang.Object
- com.google.protobuf.AbstractMessageLite.Builder
- - com.google.protobuf.AbstractMessage.Builder<BuilderT>
  - - com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>
    - - com.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Builder

All Implemented Interfaces:: ExplanationMetadata.InputMetadataOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable

Enclosing class:: ExplanationMetadata.InputMetadata

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

 Metadata of the input of a feature.

 Fields other than
 [InputMetadata.input_baselines][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.input_baselines]
 are applicable only for Models that are using Vertex AI-provided images for
 Tensorflow.

Protobuf type google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`ExplanationMetadata.InputMetadata.Builder`	`addAllEncodedBaselines(Iterable<? extends com.google.protobuf.Value> values)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addAllIndexFeatureMapping(Iterable<String> values)` A list of feature names for each index in the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addAllInputBaselines(Iterable<? extends com.google.protobuf.Value> values)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`addEncodedBaselines(int index, com.google.protobuf.Value.Builder builderForValue)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addEncodedBaselines(int index, com.google.protobuf.Value value)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addEncodedBaselines(com.google.protobuf.Value.Builder builderForValue)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addEncodedBaselines(com.google.protobuf.Value value)` A list of baselines for the encoded tensor.
`com.google.protobuf.Value.Builder`	`addEncodedBaselinesBuilder()` A list of baselines for the encoded tensor.
`com.google.protobuf.Value.Builder`	`addEncodedBaselinesBuilder(int index)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addIndexFeatureMapping(String value)` A list of feature names for each index in the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addIndexFeatureMappingBytes(com.google.protobuf.ByteString value)` A list of feature names for each index in the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`addInputBaselines(int index, com.google.protobuf.Value.Builder builderForValue)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`addInputBaselines(int index, com.google.protobuf.Value value)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`addInputBaselines(com.google.protobuf.Value.Builder builderForValue)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`addInputBaselines(com.google.protobuf.Value value)` Baseline inputs for this feature.
`com.google.protobuf.Value.Builder`	`addInputBaselinesBuilder()` Baseline inputs for this feature.
`com.google.protobuf.Value.Builder`	`addInputBaselinesBuilder(int index)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)`
`ExplanationMetadata.InputMetadata`	`build()`
`ExplanationMetadata.InputMetadata`	`buildPartial()`
`ExplanationMetadata.InputMetadata.Builder`	`clear()`
`ExplanationMetadata.InputMetadata.Builder`	`clearDenseShapeTensorName()` Specifies the shape of the values of the input if the input is a sparse representation.
`ExplanationMetadata.InputMetadata.Builder`	`clearEncodedBaselines()` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`clearEncodedTensorName()` Encoded tensor is a transformation of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`clearEncoding()` Defines how the feature is encoded into the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`clearFeatureValueDomain()` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.Builder`	`clearField(com.google.protobuf.Descriptors.FieldDescriptor field)`
`ExplanationMetadata.InputMetadata.Builder`	`clearGroupName()` Name of the group that the input belongs to.
`ExplanationMetadata.InputMetadata.Builder`	`clearIndexFeatureMapping()` A list of feature names for each index in the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`clearIndicesTensorName()` Specifies the index of the values of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`clearInputBaselines()` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`clearInputTensorName()` Name of the input tensor for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`clearModality()` Modality of the feature.
`ExplanationMetadata.InputMetadata.Builder`	`clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)`
`ExplanationMetadata.InputMetadata.Builder`	`clearVisualization()` Visualization configurations for image explanation.
`ExplanationMetadata.InputMetadata.Builder`	`clone()`
`ExplanationMetadata.InputMetadata`	`getDefaultInstanceForType()`
`String`	`getDenseShapeTensorName()` Specifies the shape of the values of the input if the input is a sparse representation.
`com.google.protobuf.ByteString`	`getDenseShapeTensorNameBytes()` Specifies the shape of the values of the input if the input is a sparse representation.
`static com.google.protobuf.Descriptors.Descriptor`	`getDescriptor()`
`com.google.protobuf.Descriptors.Descriptor`	`getDescriptorForType()`
`com.google.protobuf.Value`	`getEncodedBaselines(int index)` A list of baselines for the encoded tensor.
`com.google.protobuf.Value.Builder`	`getEncodedBaselinesBuilder(int index)` A list of baselines for the encoded tensor.
`List<com.google.protobuf.Value.Builder>`	`getEncodedBaselinesBuilderList()` A list of baselines for the encoded tensor.
`int`	`getEncodedBaselinesCount()` A list of baselines for the encoded tensor.
`List<com.google.protobuf.Value>`	`getEncodedBaselinesList()` A list of baselines for the encoded tensor.
`com.google.protobuf.ValueOrBuilder`	`getEncodedBaselinesOrBuilder(int index)` A list of baselines for the encoded tensor.
`List<? extends com.google.protobuf.ValueOrBuilder>`	`getEncodedBaselinesOrBuilderList()` A list of baselines for the encoded tensor.
`String`	`getEncodedTensorName()` Encoded tensor is a transformation of the input tensor.
`com.google.protobuf.ByteString`	`getEncodedTensorNameBytes()` Encoded tensor is a transformation of the input tensor.
`ExplanationMetadata.InputMetadata.Encoding`	`getEncoding()` Defines how the feature is encoded into the input tensor.
`int`	`getEncodingValue()` Defines how the feature is encoded into the input tensor.
`ExplanationMetadata.InputMetadata.FeatureValueDomain`	`getFeatureValueDomain()` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder`	`getFeatureValueDomainBuilder()` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.FeatureValueDomainOrBuilder`	`getFeatureValueDomainOrBuilder()` The domain details of the input feature value.
`String`	`getGroupName()` Name of the group that the input belongs to.
`com.google.protobuf.ByteString`	`getGroupNameBytes()` Name of the group that the input belongs to.
`String`	`getIndexFeatureMapping(int index)` A list of feature names for each index in the input tensor.
`com.google.protobuf.ByteString`	`getIndexFeatureMappingBytes(int index)` A list of feature names for each index in the input tensor.
`int`	`getIndexFeatureMappingCount()` A list of feature names for each index in the input tensor.
`com.google.protobuf.ProtocolStringList`	`getIndexFeatureMappingList()` A list of feature names for each index in the input tensor.
`String`	`getIndicesTensorName()` Specifies the index of the values of the input tensor.
`com.google.protobuf.ByteString`	`getIndicesTensorNameBytes()` Specifies the index of the values of the input tensor.
`com.google.protobuf.Value`	`getInputBaselines(int index)` Baseline inputs for this feature.
`com.google.protobuf.Value.Builder`	`getInputBaselinesBuilder(int index)` Baseline inputs for this feature.
`List<com.google.protobuf.Value.Builder>`	`getInputBaselinesBuilderList()` Baseline inputs for this feature.
`int`	`getInputBaselinesCount()` Baseline inputs for this feature.
`List<com.google.protobuf.Value>`	`getInputBaselinesList()` Baseline inputs for this feature.
`com.google.protobuf.ValueOrBuilder`	`getInputBaselinesOrBuilder(int index)` Baseline inputs for this feature.
`List<? extends com.google.protobuf.ValueOrBuilder>`	`getInputBaselinesOrBuilderList()` Baseline inputs for this feature.
`String`	`getInputTensorName()` Name of the input tensor for this feature.
`com.google.protobuf.ByteString`	`getInputTensorNameBytes()` Name of the input tensor for this feature.
`String`	`getModality()` Modality of the feature.
`com.google.protobuf.ByteString`	`getModalityBytes()` Modality of the feature.
`ExplanationMetadata.InputMetadata.Visualization`	`getVisualization()` Visualization configurations for image explanation.
`ExplanationMetadata.InputMetadata.Visualization.Builder`	`getVisualizationBuilder()` Visualization configurations for image explanation.
`ExplanationMetadata.InputMetadata.VisualizationOrBuilder`	`getVisualizationOrBuilder()` Visualization configurations for image explanation.
`boolean`	`hasFeatureValueDomain()` The domain details of the input feature value.
`boolean`	`hasVisualization()` Visualization configurations for image explanation.
`protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable`	`internalGetFieldAccessorTable()`
`boolean`	`isInitialized()`
`ExplanationMetadata.InputMetadata.Builder`	`mergeFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.Builder`	`mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)`
`ExplanationMetadata.InputMetadata.Builder`	`mergeFrom(ExplanationMetadata.InputMetadata other)`
`ExplanationMetadata.InputMetadata.Builder`	`mergeFrom(com.google.protobuf.Message other)`
`ExplanationMetadata.InputMetadata.Builder`	`mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)`
`ExplanationMetadata.InputMetadata.Builder`	`mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)` Visualization configurations for image explanation.
`ExplanationMetadata.InputMetadata.Builder`	`removeEncodedBaselines(int index)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`removeInputBaselines(int index)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`setDenseShapeTensorName(String value)` Specifies the shape of the values of the input if the input is a sparse representation.
`ExplanationMetadata.InputMetadata.Builder`	`setDenseShapeTensorNameBytes(com.google.protobuf.ByteString value)` Specifies the shape of the values of the input if the input is a sparse representation.
`ExplanationMetadata.InputMetadata.Builder`	`setEncodedBaselines(int index, com.google.protobuf.Value.Builder builderForValue)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setEncodedBaselines(int index, com.google.protobuf.Value value)` A list of baselines for the encoded tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setEncodedTensorName(String value)` Encoded tensor is a transformation of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setEncodedTensorNameBytes(com.google.protobuf.ByteString value)` Encoded tensor is a transformation of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setEncoding(ExplanationMetadata.InputMetadata.Encoding value)` Defines how the feature is encoded into the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setEncodingValue(int value)` Defines how the feature is encoded into the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain.Builder builderForValue)` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.Builder`	`setFeatureValueDomain(ExplanationMetadata.InputMetadata.FeatureValueDomain value)` The domain details of the input feature value.
`ExplanationMetadata.InputMetadata.Builder`	`setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)`
`ExplanationMetadata.InputMetadata.Builder`	`setGroupName(String value)` Name of the group that the input belongs to.
`ExplanationMetadata.InputMetadata.Builder`	`setGroupNameBytes(com.google.protobuf.ByteString value)` Name of the group that the input belongs to.
`ExplanationMetadata.InputMetadata.Builder`	`setIndexFeatureMapping(int index, String value)` A list of feature names for each index in the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setIndicesTensorName(String value)` Specifies the index of the values of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setIndicesTensorNameBytes(com.google.protobuf.ByteString value)` Specifies the index of the values of the input tensor.
`ExplanationMetadata.InputMetadata.Builder`	`setInputBaselines(int index, com.google.protobuf.Value.Builder builderForValue)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`setInputBaselines(int index, com.google.protobuf.Value value)` Baseline inputs for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`setInputTensorName(String value)` Name of the input tensor for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`setInputTensorNameBytes(com.google.protobuf.ByteString value)` Name of the input tensor for this feature.
`ExplanationMetadata.InputMetadata.Builder`	`setModality(String value)` Modality of the feature.
`ExplanationMetadata.InputMetadata.Builder`	`setModalityBytes(com.google.protobuf.ByteString value)` Modality of the feature.
`ExplanationMetadata.InputMetadata.Builder`	`setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)`
`ExplanationMetadata.InputMetadata.Builder`	`setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)`
`ExplanationMetadata.InputMetadata.Builder`	`setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)` Visualization configurations for image explanation.
`ExplanationMetadata.InputMetadata.Builder`	`setVisualization(ExplanationMetadata.InputMetadata.Visualization value)` Visualization configurations for image explanation.

Methods inherited from class com.google.protobuf.GeneratedMessageV3.Builder
getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, getUnknownFieldSetBuilder, hasField, hasOneof, internalGetMapField, internalGetMapFieldReflection, internalGetMutableMapField, internalGetMutableMapFieldReflection, isClean, markClean, mergeUnknownLengthDelimitedField, mergeUnknownVarintField, newBuilderForField, onBuilt, onChanged, parseUnknownField, setUnknownFieldSetBuilder, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder
findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder
addAll, addAll, mergeDelimitedFrom, mergeDelimitedFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.Message.Builder
mergeDelimitedFrom, mergeDelimitedFrom

Method Detail

getDescriptor

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

internalGetFieldAccessorTable
```
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
```
Specified by:

internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

clear
```
public ExplanationMetadata.InputMetadata.Builder clear()
```
Specified by:

clear in interface com.google.protobuf.Message.Builder

Specified by:

clear in interface com.google.protobuf.MessageLite.Builder

Overrides:

clear in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

getDescriptorForType
```
public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
```
Specified by:

getDescriptorForType in interface com.google.protobuf.Message.Builder

Specified by:

getDescriptorForType in interface com.google.protobuf.MessageOrBuilder

Overrides:

getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

getDefaultInstanceForType
```
public ExplanationMetadata.InputMetadata getDefaultInstanceForType()
```
Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

build
```
public ExplanationMetadata.InputMetadata build()
```
Specified by:

build in interface com.google.protobuf.Message.Builder

Specified by:

build in interface com.google.protobuf.MessageLite.Builder

buildPartial
```
public ExplanationMetadata.InputMetadata buildPartial()
```
Specified by:

buildPartial in interface com.google.protobuf.Message.Builder

Specified by:

buildPartial in interface com.google.protobuf.MessageLite.Builder

clone
```
public ExplanationMetadata.InputMetadata.Builder clone()
```
Specified by:

clone in interface com.google.protobuf.Message.Builder

Specified by:

clone in interface com.google.protobuf.MessageLite.Builder

Overrides:

clone in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

setField

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

Specified by:: setField in interface com.google.protobuf.Message.Builder
Overrides:: setField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

clearField
```
public ExplanationMetadata.InputMetadata.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
```
Specified by:

clearField in interface com.google.protobuf.Message.Builder

Overrides:

clearField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

clearOneof
```
public ExplanationMetadata.InputMetadata.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
```
Specified by:

clearOneof in interface com.google.protobuf.Message.Builder

Overrides:

clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

setRepeatedField

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

Specified by:: setRepeatedField in interface com.google.protobuf.Message.Builder
Overrides:: setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

addRepeatedField

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

Specified by:: addRepeatedField in interface com.google.protobuf.Message.Builder
Overrides:: addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

mergeFrom
```
public ExplanationMetadata.InputMetadata.Builder mergeFrom(com.google.protobuf.Message other)
```
Specified by:

mergeFrom in interface com.google.protobuf.Message.Builder

Overrides:

mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ExplanationMetadata.InputMetadata.Builder>

mergeFrom

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

isInitialized
```
public final boolean isInitialized()
```
Specified by:

isInitialized in interface com.google.protobuf.MessageLiteOrBuilder

Overrides:

isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<ExplanationMetadata.InputMetadata.Builder>

mergeFrom

public ExplanationMetadata.InputMetadata.Builder mergeFrom(com.google.protobuf.CodedInputStream input,
                                                           com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                                    throws IOException

Specified by:: mergeFrom in interface com.google.protobuf.Message.Builder
Specified by:: mergeFrom in interface com.google.protobuf.MessageLite.Builder
Overrides:: mergeFrom in class com.google.protobuf.AbstractMessage.Builder<ExplanationMetadata.InputMetadata.Builder>
Throws:: IOException

getInputBaselinesList

public List<com.google.protobuf.Value> getInputBaselinesList()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

Specified by:: getInputBaselinesList in interface ExplanationMetadata.InputMetadataOrBuilder

getInputBaselinesCount

public int getInputBaselinesCount()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

Specified by:: getInputBaselinesCount in interface ExplanationMetadata.InputMetadataOrBuilder

getInputBaselines

public com.google.protobuf.Value getInputBaselines(int index)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

Specified by:: getInputBaselines in interface ExplanationMetadata.InputMetadataOrBuilder

setInputBaselines

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

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

setInputBaselines

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

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addInputBaselines

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(com.google.protobuf.Value value)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addInputBaselines

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

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addInputBaselines

public ExplanationMetadata.InputMetadata.Builder addInputBaselines(com.google.protobuf.Value.Builder builderForValue)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addInputBaselines

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

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addAllInputBaselines

public ExplanationMetadata.InputMetadata.Builder addAllInputBaselines(Iterable<? extends com.google.protobuf.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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

clearInputBaselines

public ExplanationMetadata.InputMetadata.Builder clearInputBaselines()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

removeInputBaselines

public ExplanationMetadata.InputMetadata.Builder removeInputBaselines(int index)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

getInputBaselinesBuilder

public com.google.protobuf.Value.Builder getInputBaselinesBuilder(int index)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

getInputBaselinesOrBuilder

public com.google.protobuf.ValueOrBuilder getInputBaselinesOrBuilder(int index)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

Specified by:: getInputBaselinesOrBuilder in interface ExplanationMetadata.InputMetadataOrBuilder

getInputBaselinesOrBuilderList

public List<? extends com.google.protobuf.ValueOrBuilder> getInputBaselinesOrBuilderList()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

Specified by:: getInputBaselinesOrBuilderList in interface ExplanationMetadata.InputMetadataOrBuilder

addInputBaselinesBuilder

public com.google.protobuf.Value.Builder addInputBaselinesBuilder()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

addInputBaselinesBuilder

public com.google.protobuf.Value.Builder addInputBaselinesBuilder(int index)

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

getInputBaselinesBuilderList

public List<com.google.protobuf.Value.Builder> getInputBaselinesBuilderList()

 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][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.ExplainRequest.instances][]. The
 schema of any single instance may be specified via Endpoint's
 DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model]
 [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata]
 [instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].

repeated .google.protobuf.Value input_baselines = 1;

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;

Specified by:: getInputTensorName in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The inputTensorName.

getInputTensorNameBytes

public com.google.protobuf.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;

Specified by:: getInputTensorNameBytes in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The bytes for inputTensorName.

setInputTensorName

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;

Parameters:: value - The inputTensorName to set.
Returns:: This builder for chaining.

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:: This builder for chaining.

setInputTensorNameBytes

public ExplanationMetadata.InputMetadata.Builder setInputTensorNameBytes(com.google.protobuf.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;

Parameters:: value - The bytes for inputTensorName to set.
Returns:: This builder for chaining.

getEncodingValue
```
public int getEncodingValue()
```
```
 Defines how the feature is encoded into the input tensor. Defaults to
 IDENTITY.
 
```
.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
Specified by:

getEncodingValue in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

The enum numeric value on the wire for encoding.

setEncodingValue
```
public ExplanationMetadata.InputMetadata.Builder setEncodingValue(int value)
```
```
 Defines how the feature is encoded into the input tensor. Defaults to
 IDENTITY.
 
```
.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
Parameters:

value - The enum numeric value on the wire for encoding to set.

Returns:

This builder for chaining.

getEncoding
```
public ExplanationMetadata.InputMetadata.Encoding getEncoding()
```
```
 Defines how the feature is encoded into the input tensor. Defaults to
 IDENTITY.
 
```
.google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;
Specified by:

getEncoding in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

The encoding.

setEncoding

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.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Parameters:: value - The encoding to set.
Returns:: 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.v1.ExplanationMetadata.InputMetadata.Encoding encoding = 3;

Returns:: This builder for chaining.

getModality
```
public String getModality()
```
```
 Modality of the feature. Valid values are: numeric, image. Defaults to
 numeric.
 
```
string modality = 4;
Specified by:

getModality in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

The modality.

getModalityBytes
```
public com.google.protobuf.ByteString getModalityBytes()
```
```
 Modality of the feature. Valid values are: numeric, image. Defaults to
 numeric.
 
```
string modality = 4;
Specified by:

getModalityBytes in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

The bytes for modality.

setModality

public ExplanationMetadata.InputMetadata.Builder setModality(String value)

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

string modality = 4;

Parameters:: value - The modality to set.
Returns:: 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:: This builder for chaining.

setModalityBytes

public ExplanationMetadata.InputMetadata.Builder setModalityBytes(com.google.protobuf.ByteString value)

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

string modality = 4;

Parameters:: value - The bytes for modality to set.
Returns:: This builder for chaining.

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
Specified by:

hasFeatureValueDomain in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

Whether the featureValueDomain field is set.

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
Specified by:

getFeatureValueDomain in interface ExplanationMetadata.InputMetadataOrBuilder

Returns:

The featureValueDomain.

setFeatureValueDomain

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

setFeatureValueDomain

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

mergeFeatureValueDomain

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;

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.v1.ExplanationMetadata.InputMetadata.FeatureValueDomain feature_value_domain = 5;
Specified by:

getFeatureValueDomainOrBuilder in interface ExplanationMetadata.InputMetadataOrBuilder

getIndicesTensorName

public String getIndicesTensorName()

 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;

Specified by:: getIndicesTensorName in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The indicesTensorName.

getIndicesTensorNameBytes

public com.google.protobuf.ByteString getIndicesTensorNameBytes()

 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;

Specified by:: getIndicesTensorNameBytes in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The bytes for indicesTensorName.

setIndicesTensorName

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorName(String value)

 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;

Parameters:: value - The indicesTensorName to set.
Returns:: 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:: This builder for chaining.

setIndicesTensorNameBytes

public ExplanationMetadata.InputMetadata.Builder setIndicesTensorNameBytes(com.google.protobuf.ByteString value)

 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;

Parameters:: value - The bytes for indicesTensorName to set.
Returns:: This builder for chaining.

getDenseShapeTensorName

public String getDenseShapeTensorName()

 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;

Specified by:: getDenseShapeTensorName in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The denseShapeTensorName.

getDenseShapeTensorNameBytes

public com.google.protobuf.ByteString getDenseShapeTensorNameBytes()

 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;

Specified by:: getDenseShapeTensorNameBytes in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The bytes for denseShapeTensorName.

setDenseShapeTensorName

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorName(String value)

 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;

Parameters:: value - The denseShapeTensorName to set.
Returns:: This builder for chaining.

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:: This builder for chaining.

setDenseShapeTensorNameBytes

public ExplanationMetadata.InputMetadata.Builder setDenseShapeTensorNameBytes(com.google.protobuf.ByteString value)

 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;

Parameters:: value - The bytes for denseShapeTensorName to set.
Returns:: This builder for chaining.

getIndexFeatureMappingList

public com.google.protobuf.ProtocolStringList getIndexFeatureMappingList()

 A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Specified by:: getIndexFeatureMappingList in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: A list containing the indexFeatureMapping.

getIndexFeatureMappingCount

public int getIndexFeatureMappingCount()

 A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Specified by:: getIndexFeatureMappingCount in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The count of indexFeatureMapping.

getIndexFeatureMapping

public String getIndexFeatureMapping(int index)

 A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Specified by:: getIndexFeatureMapping in interface ExplanationMetadata.InputMetadataOrBuilder
Parameters:: index - The index of the element to return.
Returns:: The indexFeatureMapping at the given index.

getIndexFeatureMappingBytes

public com.google.protobuf.ByteString getIndexFeatureMappingBytes(int index)

 A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Specified by:: getIndexFeatureMappingBytes in interface ExplanationMetadata.InputMetadataOrBuilder
Parameters:: index - The index of the value to return.
Returns:: The bytes of the indexFeatureMapping at the given index.

setIndexFeatureMapping

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][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters:: index - The index to set the value at.; value - The indexFeatureMapping to set.
Returns:: This builder for chaining.

addIndexFeatureMapping

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][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters:: value - The indexFeatureMapping to add.
Returns:: This builder for chaining.

addAllIndexFeatureMapping

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][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters:: values - The indexFeatureMapping to add.
Returns:: 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][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Returns:: This builder for chaining.

addIndexFeatureMappingBytes

public ExplanationMetadata.InputMetadata.Builder addIndexFeatureMappingBytes(com.google.protobuf.ByteString value)

 A list of feature names for each index in the input tensor.
 Required when the input
 [InputMetadata.encoding][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.encoding]
 is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.

repeated string index_feature_mapping = 8;

Parameters:: value - The bytes of the indexFeatureMapping to add.
Returns:: This builder for chaining.

getEncodedTensorName

public String getEncodedTensorName()

 Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.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;

Specified by:: getEncodedTensorName in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The encodedTensorName.

getEncodedTensorNameBytes

public com.google.protobuf.ByteString getEncodedTensorNameBytes()

 Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.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;

Specified by:: getEncodedTensorNameBytes in interface ExplanationMetadata.InputMetadataOrBuilder
Returns:: The bytes for encodedTensorName.

setEncodedTensorName

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorName(String value)

 Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.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;

Parameters:: value - The encodedTensorName to set.
Returns:: This builder for chaining.

clearEncodedTensorName

public ExplanationMetadata.InputMetadata.Builder clearEncodedTensorName()

 Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.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:: This builder for chaining.

setEncodedTensorNameBytes

public ExplanationMetadata.InputMetadata.Builder setEncodedTensorNameBytes(com.google.protobuf.ByteString value)

 Encoded tensor is a transformation of the input tensor. Must be provided
 if choosing
 [Integrated Gradients
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution]
 or [XRAI
 attribution][google.cloud.aiplatform.v1.ExplanationParameters.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;

Parameters:: value - The bytes for encodedTensorName to set.
Returns:: This builder for chaining.