Package com.google.cloud.aiplatform.v1

Class ExplanationMetadata.InputMetadata.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderT>
com.google.protobuf.GeneratedMessage.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.GeneratedMessage.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 Details

    • getDescriptor

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

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessage.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessage.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.GeneratedMessage.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.GeneratedMessage.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

    • 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.GeneratedMessage.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.

    • getEncodedBaselinesList

      public List<com.google.protobuf.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;
      Specified by:
      getEncodedBaselinesList in interface ExplanationMetadata.InputMetadataOrBuilder

    • 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;
      Specified by:
      getEncodedBaselinesCount in interface ExplanationMetadata.InputMetadataOrBuilder

    • getEncodedBaselines

      public com.google.protobuf.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;
      Specified by:
      getEncodedBaselines in interface ExplanationMetadata.InputMetadataOrBuilder

    • setEncodedBaselines

      public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, com.google.protobuf.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;

    • setEncodedBaselines

      public ExplanationMetadata.InputMetadata.Builder setEncodedBaselines(int index, com.google.protobuf.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;

    • addEncodedBaselines

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

    • addEncodedBaselines

      public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, com.google.protobuf.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;

    • addEncodedBaselines

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

    • addEncodedBaselines

      public ExplanationMetadata.InputMetadata.Builder addEncodedBaselines(int index, com.google.protobuf.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;

    • addAllEncodedBaselines

      public ExplanationMetadata.InputMetadata.Builder addAllEncodedBaselines(Iterable<? extends com.google.protobuf.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;

    • 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;

    • removeEncodedBaselines

      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;

    • getEncodedBaselinesBuilder

      public com.google.protobuf.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;

    • getEncodedBaselinesOrBuilder

      public com.google.protobuf.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;
      Specified by:
      getEncodedBaselinesOrBuilder in interface ExplanationMetadata.InputMetadataOrBuilder

    • getEncodedBaselinesOrBuilderList

      public List<? extends com.google.protobuf.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;
      Specified by:
      getEncodedBaselinesOrBuilderList in interface ExplanationMetadata.InputMetadataOrBuilder

    • addEncodedBaselinesBuilder

      public com.google.protobuf.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;

    • addEncodedBaselinesBuilder

      public com.google.protobuf.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;

    • getEncodedBaselinesBuilderList

      public List<com.google.protobuf.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;

    • hasVisualization

      public boolean hasVisualization()
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
      Specified by:
      hasVisualization in interface ExplanationMetadata.InputMetadataOrBuilder
      Returns:
      Whether the visualization field is set.

    • getVisualization

      public ExplanationMetadata.InputMetadata.Visualization getVisualization()
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
      Specified by:
      getVisualization in interface ExplanationMetadata.InputMetadataOrBuilder
      Returns:
      The visualization.

    • setVisualization

      public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization value)
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

    • setVisualization

      public ExplanationMetadata.InputMetadata.Builder setVisualization(ExplanationMetadata.InputMetadata.Visualization.Builder builderForValue)
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

    • mergeVisualization

      public ExplanationMetadata.InputMetadata.Builder mergeVisualization(ExplanationMetadata.InputMetadata.Visualization value)
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

    • clearVisualization

      public ExplanationMetadata.InputMetadata.Builder clearVisualization()
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

    • getVisualizationBuilder

      public ExplanationMetadata.InputMetadata.Visualization.Builder getVisualizationBuilder()
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;

    • getVisualizationOrBuilder

      public ExplanationMetadata.InputMetadata.VisualizationOrBuilder getVisualizationOrBuilder()
       Visualization configurations for image explanation.
      .google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata.Visualization visualization = 11;
      Specified by:
      getVisualizationOrBuilder in interface ExplanationMetadata.InputMetadataOrBuilder

    • getGroupName

      public String getGroupName()
       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][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
      string group_name = 12;
      Specified by:
      getGroupName in interface ExplanationMetadata.InputMetadataOrBuilder
      Returns:
      The groupName.

    • getGroupNameBytes

      public com.google.protobuf.ByteString getGroupNameBytes()
       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][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
      string group_name = 12;
      Specified by:
      getGroupNameBytes in interface ExplanationMetadata.InputMetadataOrBuilder
      Returns:
      The bytes for groupName.

    • setGroupName

      public ExplanationMetadata.InputMetadata.Builder setGroupName(String value)
       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][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
      string group_name = 12;
      Parameters:
      value - The groupName to set.
      Returns:
      This builder for chaining.

    • 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][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
      string group_name = 12;
      Returns:
      This builder for chaining.

    • setGroupNameBytes

      public ExplanationMetadata.InputMetadata.Builder setGroupNameBytes(com.google.protobuf.ByteString value)
       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][google.cloud.aiplatform.v1.Attribution.feature_attributions],
 keyed by the group name.
      string group_name = 12;
      Parameters:
      value - The bytes for groupName to set.
      Returns:
      This builder for chaining.