// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// 	protoc-gen-go v1.26.0
// 	protoc        v3.12.2
// source: google/cloud/aiplatform/v1/explanation.proto

package aiplatform

import (
	reflect "reflect"
	sync "sync"

	_ "google.golang.org/genproto/googleapis/api/annotations"
	protoreflect "google.golang.org/protobuf/reflect/protoreflect"
	protoimpl "google.golang.org/protobuf/runtime/protoimpl"
	structpb "google.golang.org/protobuf/types/known/structpb"
)

const (
	// Verify that this generated code is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
	// Verify that runtime/protoimpl is sufficiently up-to-date.
	_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)

// Explanation of a prediction (provided in [PredictResponse.predictions][google.cloud.aiplatform.v1.PredictResponse.predictions])
// produced by the Model on a given [instance][google.cloud.aiplatform.v1.ExplainRequest.instances].
type Explanation struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Output only. Feature attributions grouped by predicted outputs.
	//
	// For Models that predict only one output, such as regression Models that
	// predict only one score, there is only one attibution that explains the
	// predicted output. For Models that predict multiple outputs, such as
	// multiclass Models that predict multiple classes, each element explains one
	// specific item. [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] can be used to identify which
	// output this attribution is explaining.
	//
	// If users set [ExplanationParameters.top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k], the attributions are sorted
	// by [instance_output_value][Attributions.instance_output_value] in
	// descending order. If [ExplanationParameters.output_indices][google.cloud.aiplatform.v1.ExplanationParameters.output_indices] is specified,
	// the attributions are stored by [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] in the same
	// order as they appear in the output_indices.
	Attributions []*Attribution `protobuf:"bytes,1,rep,name=attributions,proto3" json:"attributions,omitempty"`
}

func (x *Explanation) Reset() {
	*x = Explanation{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[0]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *Explanation) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*Explanation) ProtoMessage() {}

func (x *Explanation) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[0]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use Explanation.ProtoReflect.Descriptor instead.
func (*Explanation) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{0}
}

func (x *Explanation) GetAttributions() []*Attribution {
	if x != nil {
		return x.Attributions
	}
	return nil
}

// Aggregated explanation metrics for a Model over a set of instances.
type ModelExplanation struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Output only. Aggregated attributions explaining the Model's prediction outputs over the
	// set of instances. The attributions are grouped by outputs.
	//
	// For Models that predict only one output, such as regression Models that
	// predict only one score, there is only one attibution that explains the
	// predicted output. For Models that predict multiple outputs, such as
	// multiclass Models that predict multiple classes, each element explains one
	// specific item. [Attribution.output_index][google.cloud.aiplatform.v1.Attribution.output_index] can be used to identify which
	// output this attribution is explaining.
	//
	// The [baselineOutputValue][google.cloud.aiplatform.v1.Attribution.baseline_output_value],
	// [instanceOutputValue][google.cloud.aiplatform.v1.Attribution.instance_output_value] and
	// [featureAttributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] fields are
	// averaged over the test data.
	//
	// NOTE: Currently AutoML tabular classification Models produce only one
	// attribution, which averages attributions over all the classes it predicts.
	// [Attribution.approximation_error][google.cloud.aiplatform.v1.Attribution.approximation_error] is not populated.
	MeanAttributions []*Attribution `protobuf:"bytes,1,rep,name=mean_attributions,json=meanAttributions,proto3" json:"mean_attributions,omitempty"`
}

func (x *ModelExplanation) Reset() {
	*x = ModelExplanation{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[1]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ModelExplanation) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ModelExplanation) ProtoMessage() {}

func (x *ModelExplanation) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[1]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ModelExplanation.ProtoReflect.Descriptor instead.
func (*ModelExplanation) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{1}
}

func (x *ModelExplanation) GetMeanAttributions() []*Attribution {
	if x != nil {
		return x.MeanAttributions
	}
	return nil
}

// Attribution that explains a particular prediction output.
type Attribution struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Output only. Model predicted output if the input instance is constructed from the
	// baselines of all the features defined in [ExplanationMetadata.inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
	// The field name of the output is determined by the key in
	// [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].
	//
	// If the Model's predicted output has multiple dimensions (rank > 1), this is
	// the value in the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].
	//
	// If there are multiple baselines, their output values are averaged.
	BaselineOutputValue float64 `protobuf:"fixed64,1,opt,name=baseline_output_value,json=baselineOutputValue,proto3" json:"baseline_output_value,omitempty"`
	// Output only. Model predicted output on the corresponding [explanation
	// instance][ExplainRequest.instances]. The field name of the output is
	// determined by the key in [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].
	//
	// If the Model predicted output has multiple dimensions, this is the value in
	// the output located by [output_index][google.cloud.aiplatform.v1.Attribution.output_index].
	InstanceOutputValue float64 `protobuf:"fixed64,2,opt,name=instance_output_value,json=instanceOutputValue,proto3" json:"instance_output_value,omitempty"`
	// Output only. Attributions of each explained feature. Features are extracted from
	// the [prediction instances][google.cloud.aiplatform.v1.ExplainRequest.instances] according to
	// [explanation metadata for inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
	//
	// The value is a struct, whose keys are the name of the feature. The values
	// are how much the feature in the [instance][google.cloud.aiplatform.v1.ExplainRequest.instances]
	// contributed to the predicted result.
	//
	// The format of the value is determined by the feature's input format:
	//
	//   * If the feature is a scalar value, the attribution value is a
	//     [floating number][google.protobuf.Value.number_value].
	//
	//   * If the feature is an array of scalar values, the attribution value is
	//     an [array][google.protobuf.Value.list_value].
	//
	//   * If the feature is a struct, the attribution value is a
	//     [struct][google.protobuf.Value.struct_value]. The keys in the
	//     attribution value struct are the same as the keys in the feature
	//     struct. The formats of the values in the attribution struct are
	//     determined by the formats of the values in the feature struct.
	//
	// The [ExplanationMetadata.feature_attributions_schema_uri][google.cloud.aiplatform.v1.ExplanationMetadata.feature_attributions_schema_uri] field,
	// pointed to by the [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] field of the
	// [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models] object, points to the schema file that
	// describes the features and their attribution values (if it is populated).
	FeatureAttributions *structpb.Value `protobuf:"bytes,3,opt,name=feature_attributions,json=featureAttributions,proto3" json:"feature_attributions,omitempty"`
	// Output only. The index that locates the explained prediction output.
	//
	// If the prediction output is a scalar value, output_index is not populated.
	// If the prediction output has multiple dimensions, the length of the
	// output_index list is the same as the number of dimensions of the output.
	// The i-th element in output_index is the element index of the i-th dimension
	// of the output vector. Indices start from 0.
	OutputIndex []int32 `protobuf:"varint,4,rep,packed,name=output_index,json=outputIndex,proto3" json:"output_index,omitempty"`
	// Output only. The display name of the output identified by [output_index][google.cloud.aiplatform.v1.Attribution.output_index]. For example,
	// the predicted class name by a multi-classification Model.
	//
	// This field is only populated iff the Model predicts display names as a
	// separate field along with the explained output. The predicted display name
	// must has the same shape of the explained output, and can be located using
	// output_index.
	OutputDisplayName string `protobuf:"bytes,5,opt,name=output_display_name,json=outputDisplayName,proto3" json:"output_display_name,omitempty"`
	// Output only. Error of [feature_attributions][google.cloud.aiplatform.v1.Attribution.feature_attributions] caused by approximation used in the
	// explanation method. Lower value means more precise attributions.
	//
	// * For Sampled Shapley
	// [attribution][google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution],
	// increasing [path_count][google.cloud.aiplatform.v1.SampledShapleyAttribution.path_count] might reduce
	// the error.
	// * For Integrated Gradients
	// [attribution][google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution],
	// increasing [step_count][google.cloud.aiplatform.v1.IntegratedGradientsAttribution.step_count] might
	// reduce the error.
	// * For [XRAI attribution][google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution],
	// increasing
	// [step_count][google.cloud.aiplatform.v1.XraiAttribution.step_count] might reduce the error.
	//
	// See [this introduction](/vertex-ai/docs/explainable-ai/overview)
	// for more information.
	ApproximationError float64 `protobuf:"fixed64,6,opt,name=approximation_error,json=approximationError,proto3" json:"approximation_error,omitempty"`
	// Output only. Name of the explain output. Specified as the key in
	// [ExplanationMetadata.outputs][google.cloud.aiplatform.v1.ExplanationMetadata.outputs].
	OutputName string `protobuf:"bytes,7,opt,name=output_name,json=outputName,proto3" json:"output_name,omitempty"`
}

func (x *Attribution) Reset() {
	*x = Attribution{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[2]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *Attribution) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*Attribution) ProtoMessage() {}

func (x *Attribution) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[2]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use Attribution.ProtoReflect.Descriptor instead.
func (*Attribution) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{2}
}

func (x *Attribution) GetBaselineOutputValue() float64 {
	if x != nil {
		return x.BaselineOutputValue
	}
	return 0
}

func (x *Attribution) GetInstanceOutputValue() float64 {
	if x != nil {
		return x.InstanceOutputValue
	}
	return 0
}

func (x *Attribution) GetFeatureAttributions() *structpb.Value {
	if x != nil {
		return x.FeatureAttributions
	}
	return nil
}

func (x *Attribution) GetOutputIndex() []int32 {
	if x != nil {
		return x.OutputIndex
	}
	return nil
}

func (x *Attribution) GetOutputDisplayName() string {
	if x != nil {
		return x.OutputDisplayName
	}
	return ""
}

func (x *Attribution) GetApproximationError() float64 {
	if x != nil {
		return x.ApproximationError
	}
	return 0
}

func (x *Attribution) GetOutputName() string {
	if x != nil {
		return x.OutputName
	}
	return ""
}

// Specification of Model explanation.
type ExplanationSpec struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. Parameters that configure explaining of the Model's predictions.
	Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"`
	// Required. Metadata describing the Model's input and output for explanation.
	Metadata *ExplanationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
}

func (x *ExplanationSpec) Reset() {
	*x = ExplanationSpec{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[3]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ExplanationSpec) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ExplanationSpec) ProtoMessage() {}

func (x *ExplanationSpec) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[3]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ExplanationSpec.ProtoReflect.Descriptor instead.
func (*ExplanationSpec) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{3}
}

func (x *ExplanationSpec) GetParameters() *ExplanationParameters {
	if x != nil {
		return x.Parameters
	}
	return nil
}

func (x *ExplanationSpec) GetMetadata() *ExplanationMetadata {
	if x != nil {
		return x.Metadata
	}
	return nil
}

// Parameters to configure explaining for Model's predictions.
type ExplanationParameters struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Types that are assignable to Method:
	//	*ExplanationParameters_SampledShapleyAttribution
	//	*ExplanationParameters_IntegratedGradientsAttribution
	//	*ExplanationParameters_XraiAttribution
	Method isExplanationParameters_Method `protobuf_oneof:"method"`
	// If populated, returns attributions for top K indices of outputs
	// (defaults to 1). Only applies to Models that predicts more than one outputs
	// (e,g, multi-class Models). When set to -1, returns explanations for all
	// outputs.
	TopK int32 `protobuf:"varint,4,opt,name=top_k,json=topK,proto3" json:"top_k,omitempty"`
	// If populated, only returns attributions that have
	// [output_index][google.cloud.aiplatform.v1.Attribution.output_index] contained in output_indices. It
	// must be an ndarray of integers, with the same shape of the output it's
	// explaining.
	//
	// If not populated, returns attributions for [top_k][google.cloud.aiplatform.v1.ExplanationParameters.top_k] indices of outputs.
	// If neither top_k nor output_indeices is populated, returns the argmax
	// index of the outputs.
	//
	// Only applicable to Models that predict multiple outputs (e,g, multi-class
	// Models that predict multiple classes).
	OutputIndices *structpb.ListValue `protobuf:"bytes,5,opt,name=output_indices,json=outputIndices,proto3" json:"output_indices,omitempty"`
}

func (x *ExplanationParameters) Reset() {
	*x = ExplanationParameters{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ExplanationParameters) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ExplanationParameters) ProtoMessage() {}

func (x *ExplanationParameters) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ExplanationParameters.ProtoReflect.Descriptor instead.
func (*ExplanationParameters) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{4}
}

func (m *ExplanationParameters) GetMethod() isExplanationParameters_Method {
	if m != nil {
		return m.Method
	}
	return nil
}

func (x *ExplanationParameters) GetSampledShapleyAttribution() *SampledShapleyAttribution {
	if x, ok := x.GetMethod().(*ExplanationParameters_SampledShapleyAttribution); ok {
		return x.SampledShapleyAttribution
	}
	return nil
}

func (x *ExplanationParameters) GetIntegratedGradientsAttribution() *IntegratedGradientsAttribution {
	if x, ok := x.GetMethod().(*ExplanationParameters_IntegratedGradientsAttribution); ok {
		return x.IntegratedGradientsAttribution
	}
	return nil
}

func (x *ExplanationParameters) GetXraiAttribution() *XraiAttribution {
	if x, ok := x.GetMethod().(*ExplanationParameters_XraiAttribution); ok {
		return x.XraiAttribution
	}
	return nil
}

func (x *ExplanationParameters) GetTopK() int32 {
	if x != nil {
		return x.TopK
	}
	return 0
}

func (x *ExplanationParameters) GetOutputIndices() *structpb.ListValue {
	if x != nil {
		return x.OutputIndices
	}
	return nil
}

type isExplanationParameters_Method interface {
	isExplanationParameters_Method()
}

type ExplanationParameters_SampledShapleyAttribution struct {
	// An attribution method that approximates Shapley values for features that
	// contribute to the label being predicted. A sampling strategy is used to
	// approximate the value rather than considering all subsets of features.
	// Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
	SampledShapleyAttribution *SampledShapleyAttribution `protobuf:"bytes,1,opt,name=sampled_shapley_attribution,json=sampledShapleyAttribution,proto3,oneof"`
}

type ExplanationParameters_IntegratedGradientsAttribution struct {
	// An attribution method that computes Aumann-Shapley values taking
	// advantage of the model's fully differentiable structure. Refer to this
	// paper for more details: https://arxiv.org/abs/1703.01365
	IntegratedGradientsAttribution *IntegratedGradientsAttribution `protobuf:"bytes,2,opt,name=integrated_gradients_attribution,json=integratedGradientsAttribution,proto3,oneof"`
}

type ExplanationParameters_XraiAttribution struct {
	// An attribution method that redistributes Integrated Gradients
	// attribution to segmented regions, taking advantage of the model's fully
	// differentiable structure. Refer to this paper for
	// more details: https://arxiv.org/abs/1906.02825
	//
	// XRAI currently performs better on natural images, like a picture of a
	// house or an animal. If the images are taken in artificial environments,
	// like a lab or manufacturing line, or from diagnostic equipment, like
	// x-rays or quality-control cameras, use Integrated Gradients instead.
	XraiAttribution *XraiAttribution `protobuf:"bytes,3,opt,name=xrai_attribution,json=xraiAttribution,proto3,oneof"`
}

func (*ExplanationParameters_SampledShapleyAttribution) isExplanationParameters_Method() {}

func (*ExplanationParameters_IntegratedGradientsAttribution) isExplanationParameters_Method() {}

func (*ExplanationParameters_XraiAttribution) isExplanationParameters_Method() {}

// An attribution method that approximates Shapley values for features that
// contribute to the label being predicted. A sampling strategy is used to
// approximate the value rather than considering all subsets of features.
type SampledShapleyAttribution struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. The number of feature permutations to consider when approximating the
	// Shapley values.
	//
	// Valid range of its value is [1, 50], inclusively.
	PathCount int32 `protobuf:"varint,1,opt,name=path_count,json=pathCount,proto3" json:"path_count,omitempty"`
}

func (x *SampledShapleyAttribution) Reset() {
	*x = SampledShapleyAttribution{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *SampledShapleyAttribution) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*SampledShapleyAttribution) ProtoMessage() {}

func (x *SampledShapleyAttribution) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use SampledShapleyAttribution.ProtoReflect.Descriptor instead.
func (*SampledShapleyAttribution) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{5}
}

func (x *SampledShapleyAttribution) GetPathCount() int32 {
	if x != nil {
		return x.PathCount
	}
	return 0
}

// An attribution method that computes the Aumann-Shapley value taking advantage
// of the model's fully differentiable structure. Refer to this paper for
// more details: https://arxiv.org/abs/1703.01365
type IntegratedGradientsAttribution struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. The number of steps for approximating the path integral.
	// A good value to start is 50 and gradually increase until the
	// sum to diff property is within the desired error range.
	//
	// Valid range of its value is [1, 100], inclusively.
	StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"`
	// Config for SmoothGrad approximation of gradients.
	//
	// When enabled, the gradients are approximated by averaging the gradients
	// from noisy samples in the vicinity of the inputs. Adding
	// noise can help improve the computed gradients. Refer to this paper for more
	// details: https://arxiv.org/pdf/1706.03825.pdf
	SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"`
	// Config for IG with blur baseline.
	//
	// When enabled, a linear path from the maximally blurred image to the input
	// image is created. Using a blurred baseline instead of zero (black image) is
	// motivated by the BlurIG approach explained here:
	// https://arxiv.org/abs/2004.03383
	BlurBaselineConfig *BlurBaselineConfig `protobuf:"bytes,3,opt,name=blur_baseline_config,json=blurBaselineConfig,proto3" json:"blur_baseline_config,omitempty"`
}

func (x *IntegratedGradientsAttribution) Reset() {
	*x = IntegratedGradientsAttribution{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[6]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *IntegratedGradientsAttribution) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*IntegratedGradientsAttribution) ProtoMessage() {}

func (x *IntegratedGradientsAttribution) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[6]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use IntegratedGradientsAttribution.ProtoReflect.Descriptor instead.
func (*IntegratedGradientsAttribution) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{6}
}

func (x *IntegratedGradientsAttribution) GetStepCount() int32 {
	if x != nil {
		return x.StepCount
	}
	return 0
}

func (x *IntegratedGradientsAttribution) GetSmoothGradConfig() *SmoothGradConfig {
	if x != nil {
		return x.SmoothGradConfig
	}
	return nil
}

func (x *IntegratedGradientsAttribution) GetBlurBaselineConfig() *BlurBaselineConfig {
	if x != nil {
		return x.BlurBaselineConfig
	}
	return nil
}

// An explanation method that redistributes Integrated Gradients
// attributions to segmented regions, taking advantage of the model's fully
// differentiable structure. Refer to this paper for more details:
// https://arxiv.org/abs/1906.02825
//
// Supported only by image Models.
type XraiAttribution struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. The number of steps for approximating the path integral.
	// A good value to start is 50 and gradually increase until the
	// sum to diff property is met within the desired error range.
	//
	// Valid range of its value is [1, 100], inclusively.
	StepCount int32 `protobuf:"varint,1,opt,name=step_count,json=stepCount,proto3" json:"step_count,omitempty"`
	// Config for SmoothGrad approximation of gradients.
	//
	// When enabled, the gradients are approximated by averaging the gradients
	// from noisy samples in the vicinity of the inputs. Adding
	// noise can help improve the computed gradients. Refer to this paper for more
	// details: https://arxiv.org/pdf/1706.03825.pdf
	SmoothGradConfig *SmoothGradConfig `protobuf:"bytes,2,opt,name=smooth_grad_config,json=smoothGradConfig,proto3" json:"smooth_grad_config,omitempty"`
	// Config for XRAI with blur baseline.
	//
	// When enabled, a linear path from the maximally blurred image to the input
	// image is created. Using a blurred baseline instead of zero (black image) is
	// motivated by the BlurIG approach explained here:
	// https://arxiv.org/abs/2004.03383
	BlurBaselineConfig *BlurBaselineConfig `protobuf:"bytes,3,opt,name=blur_baseline_config,json=blurBaselineConfig,proto3" json:"blur_baseline_config,omitempty"`
}

func (x *XraiAttribution) Reset() {
	*x = XraiAttribution{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[7]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *XraiAttribution) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*XraiAttribution) ProtoMessage() {}

func (x *XraiAttribution) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[7]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use XraiAttribution.ProtoReflect.Descriptor instead.
func (*XraiAttribution) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{7}
}

func (x *XraiAttribution) GetStepCount() int32 {
	if x != nil {
		return x.StepCount
	}
	return 0
}

func (x *XraiAttribution) GetSmoothGradConfig() *SmoothGradConfig {
	if x != nil {
		return x.SmoothGradConfig
	}
	return nil
}

func (x *XraiAttribution) GetBlurBaselineConfig() *BlurBaselineConfig {
	if x != nil {
		return x.BlurBaselineConfig
	}
	return nil
}

// Config for SmoothGrad approximation of gradients.
//
// When enabled, the gradients are approximated by averaging the gradients from
// noisy samples in the vicinity of the inputs. Adding noise can help improve
// the computed gradients. Refer to this paper for more details:
// https://arxiv.org/pdf/1706.03825.pdf
type SmoothGradConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Represents the standard deviation of the gaussian kernel
	// that will be used to add noise to the interpolated inputs
	// prior to computing gradients.
	//
	// Types that are assignable to GradientNoiseSigma:
	//	*SmoothGradConfig_NoiseSigma
	//	*SmoothGradConfig_FeatureNoiseSigma
	GradientNoiseSigma isSmoothGradConfig_GradientNoiseSigma `protobuf_oneof:"GradientNoiseSigma"`
	// The number of gradient samples to use for
	// approximation. The higher this number, the more accurate the gradient
	// is, but the runtime complexity increases by this factor as well.
	// Valid range of its value is [1, 50]. Defaults to 3.
	NoisySampleCount int32 `protobuf:"varint,3,opt,name=noisy_sample_count,json=noisySampleCount,proto3" json:"noisy_sample_count,omitempty"`
}

func (x *SmoothGradConfig) Reset() {
	*x = SmoothGradConfig{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *SmoothGradConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*SmoothGradConfig) ProtoMessage() {}

func (x *SmoothGradConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use SmoothGradConfig.ProtoReflect.Descriptor instead.
func (*SmoothGradConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{8}
}

func (m *SmoothGradConfig) GetGradientNoiseSigma() isSmoothGradConfig_GradientNoiseSigma {
	if m != nil {
		return m.GradientNoiseSigma
	}
	return nil
}

func (x *SmoothGradConfig) GetNoiseSigma() float32 {
	if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_NoiseSigma); ok {
		return x.NoiseSigma
	}
	return 0
}

func (x *SmoothGradConfig) GetFeatureNoiseSigma() *FeatureNoiseSigma {
	if x, ok := x.GetGradientNoiseSigma().(*SmoothGradConfig_FeatureNoiseSigma); ok {
		return x.FeatureNoiseSigma
	}
	return nil
}

func (x *SmoothGradConfig) GetNoisySampleCount() int32 {
	if x != nil {
		return x.NoisySampleCount
	}
	return 0
}

type isSmoothGradConfig_GradientNoiseSigma interface {
	isSmoothGradConfig_GradientNoiseSigma()
}

type SmoothGradConfig_NoiseSigma struct {
	// This is a single float value and will be used to add noise to all the
	// features. Use this field when all features are normalized to have the
	// same distribution: scale to range [0, 1], [-1, 1] or z-scoring, where
	// features are normalized to have 0-mean and 1-variance. Learn more about
	// [normalization](https://developers.google.com/machine-learning/data-prep/transform/normalization).
	//
	// For best results the recommended value is about 10% - 20% of the standard
	// deviation of the input feature. Refer to section 3.2 of the SmoothGrad
	// paper: https://arxiv.org/pdf/1706.03825.pdf. Defaults to 0.1.
	//
	// If the distribution is different per feature, set
	// [feature_noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma] instead
	// for each feature.
	NoiseSigma float32 `protobuf:"fixed32,1,opt,name=noise_sigma,json=noiseSigma,proto3,oneof"`
}

type SmoothGradConfig_FeatureNoiseSigma struct {
	// This is similar to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma], but
	// provides additional flexibility. A separate noise sigma can be provided
	// for each feature, which is useful if their distributions are different.
	// No noise is added to features that are not set. If this field is unset,
	// [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] will be used for all
	// features.
	FeatureNoiseSigma *FeatureNoiseSigma `protobuf:"bytes,2,opt,name=feature_noise_sigma,json=featureNoiseSigma,proto3,oneof"`
}

func (*SmoothGradConfig_NoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {}

func (*SmoothGradConfig_FeatureNoiseSigma) isSmoothGradConfig_GradientNoiseSigma() {}

// Noise sigma by features. Noise sigma represents the standard deviation of the
// gaussian kernel that will be used to add noise to interpolated inputs prior
// to computing gradients.
type FeatureNoiseSigma struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Noise sigma per feature. No noise is added to features that are not set.
	NoiseSigma []*FeatureNoiseSigma_NoiseSigmaForFeature `protobuf:"bytes,1,rep,name=noise_sigma,json=noiseSigma,proto3" json:"noise_sigma,omitempty"`
}

func (x *FeatureNoiseSigma) Reset() {
	*x = FeatureNoiseSigma{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *FeatureNoiseSigma) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*FeatureNoiseSigma) ProtoMessage() {}

func (x *FeatureNoiseSigma) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use FeatureNoiseSigma.ProtoReflect.Descriptor instead.
func (*FeatureNoiseSigma) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{9}
}

func (x *FeatureNoiseSigma) GetNoiseSigma() []*FeatureNoiseSigma_NoiseSigmaForFeature {
	if x != nil {
		return x.NoiseSigma
	}
	return nil
}

// Config for blur baseline.
//
// When enabled, a linear path from the maximally blurred image to the input
// image is created. Using a blurred baseline instead of zero (black image) is
// motivated by the BlurIG approach explained here:
// https://arxiv.org/abs/2004.03383
type BlurBaselineConfig struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The standard deviation of the blur kernel for the blurred baseline. The
	// same blurring parameter is used for both the height and the width
	// dimension. If not set, the method defaults to the zero (i.e. black for
	// images) baseline.
	MaxBlurSigma float32 `protobuf:"fixed32,1,opt,name=max_blur_sigma,json=maxBlurSigma,proto3" json:"max_blur_sigma,omitempty"`
}

func (x *BlurBaselineConfig) Reset() {
	*x = BlurBaselineConfig{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *BlurBaselineConfig) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*BlurBaselineConfig) ProtoMessage() {}

func (x *BlurBaselineConfig) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use BlurBaselineConfig.ProtoReflect.Descriptor instead.
func (*BlurBaselineConfig) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{10}
}

func (x *BlurBaselineConfig) GetMaxBlurSigma() float32 {
	if x != nil {
		return x.MaxBlurSigma
	}
	return 0
}

// The [ExplanationSpec][google.cloud.aiplatform.v1.ExplanationSpec] entries that can be overridden at
// [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.
type ExplanationSpecOverride struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The parameters to be overridden. Note that the
	// [method][google.cloud.aiplatform.v1.ExplanationParameters.method] cannot be changed. If not specified,
	// no parameter is overridden.
	Parameters *ExplanationParameters `protobuf:"bytes,1,opt,name=parameters,proto3" json:"parameters,omitempty"`
	// The metadata to be overridden. If not specified, no metadata is overridden.
	Metadata *ExplanationMetadataOverride `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
}

func (x *ExplanationSpecOverride) Reset() {
	*x = ExplanationSpecOverride{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ExplanationSpecOverride) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ExplanationSpecOverride) ProtoMessage() {}

func (x *ExplanationSpecOverride) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ExplanationSpecOverride.ProtoReflect.Descriptor instead.
func (*ExplanationSpecOverride) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{11}
}

func (x *ExplanationSpecOverride) GetParameters() *ExplanationParameters {
	if x != nil {
		return x.Parameters
	}
	return nil
}

func (x *ExplanationSpecOverride) GetMetadata() *ExplanationMetadataOverride {
	if x != nil {
		return x.Metadata
	}
	return nil
}

// The [ExplanationMetadata][google.cloud.aiplatform.v1.ExplanationMetadata] entries that can be overridden at
// [online explanation][google.cloud.aiplatform.v1.PredictionService.Explain] time.
type ExplanationMetadataOverride struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Required. Overrides the [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.inputs] of the features.
	// The key is the name of the feature to be overridden. The keys specified
	// here must exist in the input metadata to be overridden. If a feature is
	// not specified here, the corresponding feature's input metadata is not
	// overridden.
	Inputs map[string]*ExplanationMetadataOverride_InputMetadataOverride `protobuf:"bytes,1,rep,name=inputs,proto3" json:"inputs,omitempty" protobuf_key:"bytes,1,opt,name=key,proto3" protobuf_val:"bytes,2,opt,name=value,proto3"`
}

func (x *ExplanationMetadataOverride) Reset() {
	*x = ExplanationMetadataOverride{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ExplanationMetadataOverride) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ExplanationMetadataOverride) ProtoMessage() {}

func (x *ExplanationMetadataOverride) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ExplanationMetadataOverride.ProtoReflect.Descriptor instead.
func (*ExplanationMetadataOverride) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{12}
}

func (x *ExplanationMetadataOverride) GetInputs() map[string]*ExplanationMetadataOverride_InputMetadataOverride {
	if x != nil {
		return x.Inputs
	}
	return nil
}

// Noise sigma for a single feature.
type FeatureNoiseSigma_NoiseSigmaForFeature struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// The name of the input feature for which noise sigma is provided. The
	// features are defined in
	// [explanation metadata inputs][google.cloud.aiplatform.v1.ExplanationMetadata.inputs].
	Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
	// This represents the standard deviation of the Gaussian kernel that will
	// be used to add noise to the feature prior to computing gradients. Similar
	// to [noise_sigma][google.cloud.aiplatform.v1.SmoothGradConfig.noise_sigma] but represents the
	// noise added to the current feature. Defaults to 0.1.
	Sigma float32 `protobuf:"fixed32,2,opt,name=sigma,proto3" json:"sigma,omitempty"`
}

func (x *FeatureNoiseSigma_NoiseSigmaForFeature) Reset() {
	*x = FeatureNoiseSigma_NoiseSigmaForFeature{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *FeatureNoiseSigma_NoiseSigmaForFeature) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*FeatureNoiseSigma_NoiseSigmaForFeature) ProtoMessage() {}

func (x *FeatureNoiseSigma_NoiseSigmaForFeature) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use FeatureNoiseSigma_NoiseSigmaForFeature.ProtoReflect.Descriptor instead.
func (*FeatureNoiseSigma_NoiseSigmaForFeature) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{9, 0}
}

func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetName() string {
	if x != nil {
		return x.Name
	}
	return ""
}

func (x *FeatureNoiseSigma_NoiseSigmaForFeature) GetSigma() float32 {
	if x != nil {
		return x.Sigma
	}
	return 0
}

// The [input metadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata] entries to be
// overridden.
type ExplanationMetadataOverride_InputMetadataOverride struct {
	state         protoimpl.MessageState
	sizeCache     protoimpl.SizeCache
	unknownFields protoimpl.UnknownFields

	// Baseline inputs for this feature.
	//
	// This overrides the `input_baseline` field of the
	// [ExplanationMetadata.InputMetadata][google.cloud.aiplatform.v1.ExplanationMetadata.InputMetadata]
	// object of the corresponding feature's input metadata. If it's not
	// specified, the original baselines are not overridden.
	InputBaselines []*structpb.Value `protobuf:"bytes,1,rep,name=input_baselines,json=inputBaselines,proto3" json:"input_baselines,omitempty"`
}

func (x *ExplanationMetadataOverride_InputMetadataOverride) Reset() {
	*x = ExplanationMetadataOverride_InputMetadataOverride{}
	if protoimpl.UnsafeEnabled {
		mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[14]
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		ms.StoreMessageInfo(mi)
	}
}

func (x *ExplanationMetadataOverride_InputMetadataOverride) String() string {
	return protoimpl.X.MessageStringOf(x)
}

func (*ExplanationMetadataOverride_InputMetadataOverride) ProtoMessage() {}

func (x *ExplanationMetadataOverride_InputMetadataOverride) ProtoReflect() protoreflect.Message {
	mi := &file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[14]
	if protoimpl.UnsafeEnabled && x != nil {
		ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
		if ms.LoadMessageInfo() == nil {
			ms.StoreMessageInfo(mi)
		}
		return ms
	}
	return mi.MessageOf(x)
}

// Deprecated: Use ExplanationMetadataOverride_InputMetadataOverride.ProtoReflect.Descriptor instead.
func (*ExplanationMetadataOverride_InputMetadataOverride) Descriptor() ([]byte, []int) {
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP(), []int{12, 0}
}

func (x *ExplanationMetadataOverride_InputMetadataOverride) GetInputBaselines() []*structpb.Value {
	if x != nil {
		return x.InputBaselines
	}
	return nil
}

var File_google_cloud_aiplatform_v1_explanation_proto protoreflect.FileDescriptor

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}

var (
	file_google_cloud_aiplatform_v1_explanation_proto_rawDescOnce sync.Once
	file_google_cloud_aiplatform_v1_explanation_proto_rawDescData = file_google_cloud_aiplatform_v1_explanation_proto_rawDesc
)

func file_google_cloud_aiplatform_v1_explanation_proto_rawDescGZIP() []byte {
	file_google_cloud_aiplatform_v1_explanation_proto_rawDescOnce.Do(func() {
		file_google_cloud_aiplatform_v1_explanation_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1_explanation_proto_rawDescData)
	})
	return file_google_cloud_aiplatform_v1_explanation_proto_rawDescData
}

var file_google_cloud_aiplatform_v1_explanation_proto_msgTypes = make([]protoimpl.MessageInfo, 16)
var file_google_cloud_aiplatform_v1_explanation_proto_goTypes = []interface{}{
	(*Explanation)(nil),                                       // 0: google.cloud.aiplatform.v1.Explanation
	(*ModelExplanation)(nil),                                  // 1: google.cloud.aiplatform.v1.ModelExplanation
	(*Attribution)(nil),                                       // 2: google.cloud.aiplatform.v1.Attribution
	(*ExplanationSpec)(nil),                                   // 3: google.cloud.aiplatform.v1.ExplanationSpec
	(*ExplanationParameters)(nil),                             // 4: google.cloud.aiplatform.v1.ExplanationParameters
	(*SampledShapleyAttribution)(nil),                         // 5: google.cloud.aiplatform.v1.SampledShapleyAttribution
	(*IntegratedGradientsAttribution)(nil),                    // 6: google.cloud.aiplatform.v1.IntegratedGradientsAttribution
	(*XraiAttribution)(nil),                                   // 7: google.cloud.aiplatform.v1.XraiAttribution
	(*SmoothGradConfig)(nil),                                  // 8: google.cloud.aiplatform.v1.SmoothGradConfig
	(*FeatureNoiseSigma)(nil),                                 // 9: google.cloud.aiplatform.v1.FeatureNoiseSigma
	(*BlurBaselineConfig)(nil),                                // 10: google.cloud.aiplatform.v1.BlurBaselineConfig
	(*ExplanationSpecOverride)(nil),                           // 11: google.cloud.aiplatform.v1.ExplanationSpecOverride
	(*ExplanationMetadataOverride)(nil),                       // 12: google.cloud.aiplatform.v1.ExplanationMetadataOverride
	(*FeatureNoiseSigma_NoiseSigmaForFeature)(nil),            // 13: google.cloud.aiplatform.v1.FeatureNoiseSigma.NoiseSigmaForFeature
	(*ExplanationMetadataOverride_InputMetadataOverride)(nil), // 14: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride
	nil,                         // 15: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry
	(*structpb.Value)(nil),      // 16: google.protobuf.Value
	(*ExplanationMetadata)(nil), // 17: google.cloud.aiplatform.v1.ExplanationMetadata
	(*structpb.ListValue)(nil),  // 18: google.protobuf.ListValue
}
var file_google_cloud_aiplatform_v1_explanation_proto_depIdxs = []int32{
	2,  // 0: google.cloud.aiplatform.v1.Explanation.attributions:type_name -> google.cloud.aiplatform.v1.Attribution
	2,  // 1: google.cloud.aiplatform.v1.ModelExplanation.mean_attributions:type_name -> google.cloud.aiplatform.v1.Attribution
	16, // 2: google.cloud.aiplatform.v1.Attribution.feature_attributions:type_name -> google.protobuf.Value
	4,  // 3: google.cloud.aiplatform.v1.ExplanationSpec.parameters:type_name -> google.cloud.aiplatform.v1.ExplanationParameters
	17, // 4: google.cloud.aiplatform.v1.ExplanationSpec.metadata:type_name -> google.cloud.aiplatform.v1.ExplanationMetadata
	5,  // 5: google.cloud.aiplatform.v1.ExplanationParameters.sampled_shapley_attribution:type_name -> google.cloud.aiplatform.v1.SampledShapleyAttribution
	6,  // 6: google.cloud.aiplatform.v1.ExplanationParameters.integrated_gradients_attribution:type_name -> google.cloud.aiplatform.v1.IntegratedGradientsAttribution
	7,  // 7: google.cloud.aiplatform.v1.ExplanationParameters.xrai_attribution:type_name -> google.cloud.aiplatform.v1.XraiAttribution
	18, // 8: google.cloud.aiplatform.v1.ExplanationParameters.output_indices:type_name -> google.protobuf.ListValue
	8,  // 9: google.cloud.aiplatform.v1.IntegratedGradientsAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1.SmoothGradConfig
	10, // 10: google.cloud.aiplatform.v1.IntegratedGradientsAttribution.blur_baseline_config:type_name -> google.cloud.aiplatform.v1.BlurBaselineConfig
	8,  // 11: google.cloud.aiplatform.v1.XraiAttribution.smooth_grad_config:type_name -> google.cloud.aiplatform.v1.SmoothGradConfig
	10, // 12: google.cloud.aiplatform.v1.XraiAttribution.blur_baseline_config:type_name -> google.cloud.aiplatform.v1.BlurBaselineConfig
	9,  // 13: google.cloud.aiplatform.v1.SmoothGradConfig.feature_noise_sigma:type_name -> google.cloud.aiplatform.v1.FeatureNoiseSigma
	13, // 14: google.cloud.aiplatform.v1.FeatureNoiseSigma.noise_sigma:type_name -> google.cloud.aiplatform.v1.FeatureNoiseSigma.NoiseSigmaForFeature
	4,  // 15: google.cloud.aiplatform.v1.ExplanationSpecOverride.parameters:type_name -> google.cloud.aiplatform.v1.ExplanationParameters
	12, // 16: google.cloud.aiplatform.v1.ExplanationSpecOverride.metadata:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride
	15, // 17: google.cloud.aiplatform.v1.ExplanationMetadataOverride.inputs:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry
	16, // 18: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride.input_baselines:type_name -> google.protobuf.Value
	14, // 19: google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputsEntry.value:type_name -> google.cloud.aiplatform.v1.ExplanationMetadataOverride.InputMetadataOverride
	20, // [20:20] is the sub-list for method output_type
	20, // [20:20] is the sub-list for method input_type
	20, // [20:20] is the sub-list for extension type_name
	20, // [20:20] is the sub-list for extension extendee
	0,  // [0:20] is the sub-list for field type_name
}

func init() { file_google_cloud_aiplatform_v1_explanation_proto_init() }
func file_google_cloud_aiplatform_v1_explanation_proto_init() {
	if File_google_cloud_aiplatform_v1_explanation_proto != nil {
		return
	}
	file_google_cloud_aiplatform_v1_explanation_metadata_proto_init()
	file_google_cloud_aiplatform_v1_io_proto_init()
	if !protoimpl.UnsafeEnabled {
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*Explanation); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ModelExplanation); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*Attribution); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ExplanationSpec); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ExplanationParameters); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*SampledShapleyAttribution); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*IntegratedGradientsAttribution); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*XraiAttribution); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*SmoothGradConfig); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[9].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*FeatureNoiseSigma); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[10].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*BlurBaselineConfig); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[11].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ExplanationSpecOverride); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[12].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ExplanationMetadataOverride); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[13].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*FeatureNoiseSigma_NoiseSigmaForFeature); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
		file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[14].Exporter = func(v interface{}, i int) interface{} {
			switch v := v.(*ExplanationMetadataOverride_InputMetadataOverride); i {
			case 0:
				return &v.state
			case 1:
				return &v.sizeCache
			case 2:
				return &v.unknownFields
			default:
				return nil
			}
		}
	}
	file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[4].OneofWrappers = []interface{}{
		(*ExplanationParameters_SampledShapleyAttribution)(nil),
		(*ExplanationParameters_IntegratedGradientsAttribution)(nil),
		(*ExplanationParameters_XraiAttribution)(nil),
	}
	file_google_cloud_aiplatform_v1_explanation_proto_msgTypes[8].OneofWrappers = []interface{}{
		(*SmoothGradConfig_NoiseSigma)(nil),
		(*SmoothGradConfig_FeatureNoiseSigma)(nil),
	}
	type x struct{}
	out := protoimpl.TypeBuilder{
		File: protoimpl.DescBuilder{
			GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
			RawDescriptor: file_google_cloud_aiplatform_v1_explanation_proto_rawDesc,
			NumEnums:      0,
			NumMessages:   16,
			NumExtensions: 0,
			NumServices:   0,
		},
		GoTypes:           file_google_cloud_aiplatform_v1_explanation_proto_goTypes,
		DependencyIndexes: file_google_cloud_aiplatform_v1_explanation_proto_depIdxs,
		MessageInfos:      file_google_cloud_aiplatform_v1_explanation_proto_msgTypes,
	}.Build()
	File_google_cloud_aiplatform_v1_explanation_proto = out.File
	file_google_cloud_aiplatform_v1_explanation_proto_rawDesc = nil
	file_google_cloud_aiplatform_v1_explanation_proto_goTypes = nil
	file_google_cloud_aiplatform_v1_explanation_proto_depIdxs = nil
}
