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automl_image_classification.pb.go
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automl_image_classification.pb.go
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// 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 v1.25.0-devel
// protoc v3.13.0
// source: google/cloud/aiplatform/v1/schema/trainingjob/definition/automl_image_classification.proto
package definition
import (
reflect "reflect"
sync "sync"
proto "github.com/golang/protobuf/proto"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
)
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)
)
// This is a compile-time assertion that a sufficiently up-to-date version
// of the legacy proto package is being used.
const _ = proto.ProtoPackageIsVersion4
type AutoMlImageClassificationInputs_ModelType int32
const (
// Should not be set.
AutoMlImageClassificationInputs_MODEL_TYPE_UNSPECIFIED AutoMlImageClassificationInputs_ModelType = 0
// A Model best tailored to be used within Google Cloud, and which cannot
// be exported.
// Default.
AutoMlImageClassificationInputs_CLOUD AutoMlImageClassificationInputs_ModelType = 1
// A model that, in addition to being available within Google
// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
// or Core ML model and used on a mobile or edge device afterwards.
// Expected to have low latency, but may have lower prediction
// quality than other mobile models.
AutoMlImageClassificationInputs_MOBILE_TF_LOW_LATENCY_1 AutoMlImageClassificationInputs_ModelType = 2
// A model that, in addition to being available within Google
// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
// or Core ML model and used on a mobile or edge device with afterwards.
AutoMlImageClassificationInputs_MOBILE_TF_VERSATILE_1 AutoMlImageClassificationInputs_ModelType = 3
// A model that, in addition to being available within Google
// Cloud, can also be exported (see ModelService.ExportModel) as TensorFlow
// or Core ML model and used on a mobile or edge device afterwards.
// Expected to have a higher latency, but should also have a higher
// prediction quality than other mobile models.
AutoMlImageClassificationInputs_MOBILE_TF_HIGH_ACCURACY_1 AutoMlImageClassificationInputs_ModelType = 4
)
// Enum value maps for AutoMlImageClassificationInputs_ModelType.
var (
AutoMlImageClassificationInputs_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "CLOUD",
2: "MOBILE_TF_LOW_LATENCY_1",
3: "MOBILE_TF_VERSATILE_1",
4: "MOBILE_TF_HIGH_ACCURACY_1",
}
AutoMlImageClassificationInputs_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"CLOUD": 1,
"MOBILE_TF_LOW_LATENCY_1": 2,
"MOBILE_TF_VERSATILE_1": 3,
"MOBILE_TF_HIGH_ACCURACY_1": 4,
}
)
func (x AutoMlImageClassificationInputs_ModelType) Enum() *AutoMlImageClassificationInputs_ModelType {
p := new(AutoMlImageClassificationInputs_ModelType)
*p = x
return p
}
func (x AutoMlImageClassificationInputs_ModelType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (AutoMlImageClassificationInputs_ModelType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes[0].Descriptor()
}
func (AutoMlImageClassificationInputs_ModelType) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes[0]
}
func (x AutoMlImageClassificationInputs_ModelType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use AutoMlImageClassificationInputs_ModelType.Descriptor instead.
func (AutoMlImageClassificationInputs_ModelType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP(), []int{1, 0}
}
type AutoMlImageClassificationMetadata_SuccessfulStopReason int32
const (
// Should not be set.
AutoMlImageClassificationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED AutoMlImageClassificationMetadata_SuccessfulStopReason = 0
// The inputs.budgetMilliNodeHours had been reached.
AutoMlImageClassificationMetadata_BUDGET_REACHED AutoMlImageClassificationMetadata_SuccessfulStopReason = 1
// Further training of the Model ceased to increase its quality, since it
// already has converged.
AutoMlImageClassificationMetadata_MODEL_CONVERGED AutoMlImageClassificationMetadata_SuccessfulStopReason = 2
)
// Enum value maps for AutoMlImageClassificationMetadata_SuccessfulStopReason.
var (
AutoMlImageClassificationMetadata_SuccessfulStopReason_name = map[int32]string{
0: "SUCCESSFUL_STOP_REASON_UNSPECIFIED",
1: "BUDGET_REACHED",
2: "MODEL_CONVERGED",
}
AutoMlImageClassificationMetadata_SuccessfulStopReason_value = map[string]int32{
"SUCCESSFUL_STOP_REASON_UNSPECIFIED": 0,
"BUDGET_REACHED": 1,
"MODEL_CONVERGED": 2,
}
)
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Enum() *AutoMlImageClassificationMetadata_SuccessfulStopReason {
p := new(AutoMlImageClassificationMetadata_SuccessfulStopReason)
*p = x
return p
}
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes[1].Descriptor()
}
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) Type() protoreflect.EnumType {
return &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes[1]
}
func (x AutoMlImageClassificationMetadata_SuccessfulStopReason) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use AutoMlImageClassificationMetadata_SuccessfulStopReason.Descriptor instead.
func (AutoMlImageClassificationMetadata_SuccessfulStopReason) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP(), []int{2, 0}
}
// A TrainingJob that trains and uploads an AutoML Image Classification Model.
type AutoMlImageClassification struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The input parameters of this TrainingJob.
Inputs *AutoMlImageClassificationInputs `protobuf:"bytes,1,opt,name=inputs,proto3" json:"inputs,omitempty"`
// The metadata information.
Metadata *AutoMlImageClassificationMetadata `protobuf:"bytes,2,opt,name=metadata,proto3" json:"metadata,omitempty"`
}
func (x *AutoMlImageClassification) Reset() {
*x = AutoMlImageClassification{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *AutoMlImageClassification) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*AutoMlImageClassification) ProtoMessage() {}
func (x *AutoMlImageClassification) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_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 AutoMlImageClassification.ProtoReflect.Descriptor instead.
func (*AutoMlImageClassification) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP(), []int{0}
}
func (x *AutoMlImageClassification) GetInputs() *AutoMlImageClassificationInputs {
if x != nil {
return x.Inputs
}
return nil
}
func (x *AutoMlImageClassification) GetMetadata() *AutoMlImageClassificationMetadata {
if x != nil {
return x.Metadata
}
return nil
}
type AutoMlImageClassificationInputs struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
ModelType AutoMlImageClassificationInputs_ModelType `protobuf:"varint,1,opt,name=model_type,json=modelType,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs_ModelType" json:"model_type,omitempty"`
// The ID of the `base` model. If it is specified, the new model will be
// trained based on the `base` model. Otherwise, the new model will be
// trained from scratch. The `base` model must be in the same
// Project and Location as the new Model to train, and have the same
// modelType.
BaseModelId string `protobuf:"bytes,2,opt,name=base_model_id,json=baseModelId,proto3" json:"base_model_id,omitempty"`
// The training budget of creating this model, expressed in milli node
// hours i.e. 1,000 value in this field means 1 node hour. The actual
// metadata.costMilliNodeHours will be equal or less than this value.
// If further model training ceases to provide any improvements, it will
// stop without using the full budget and the metadata.successfulStopReason
// will be `model-converged`.
// Note, node_hour = actual_hour * number_of_nodes_involved.
// For modelType `cloud`(default), the budget must be between 8,000
// and 800,000 milli node hours, inclusive. The default value is 192,000
// which represents one day in wall time, considering 8 nodes are used.
// For model types `mobile-tf-low-latency-1`, `mobile-tf-versatile-1`,
// `mobile-tf-high-accuracy-1`, the training budget must be between
// 1,000 and 100,000 milli node hours, inclusive.
// The default value is 24,000 which represents one day in wall time on a
// single node that is used.
BudgetMilliNodeHours int64 `protobuf:"varint,3,opt,name=budget_milli_node_hours,json=budgetMilliNodeHours,proto3" json:"budget_milli_node_hours,omitempty"`
// Use the entire training budget. This disables the early stopping feature.
// When false the early stopping feature is enabled, which means that
// AutoML Image Classification might stop training before the entire
// training budget has been used.
DisableEarlyStopping bool `protobuf:"varint,4,opt,name=disable_early_stopping,json=disableEarlyStopping,proto3" json:"disable_early_stopping,omitempty"`
// If false, a single-label (multi-class) Model will be trained (i.e.
// assuming that for each image just up to one annotation may be
// applicable). If true, a multi-label Model will be trained (i.e.
// assuming that for each image multiple annotations may be applicable).
MultiLabel bool `protobuf:"varint,5,opt,name=multi_label,json=multiLabel,proto3" json:"multi_label,omitempty"`
}
func (x *AutoMlImageClassificationInputs) Reset() {
*x = AutoMlImageClassificationInputs{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *AutoMlImageClassificationInputs) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*AutoMlImageClassificationInputs) ProtoMessage() {}
func (x *AutoMlImageClassificationInputs) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_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 AutoMlImageClassificationInputs.ProtoReflect.Descriptor instead.
func (*AutoMlImageClassificationInputs) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP(), []int{1}
}
func (x *AutoMlImageClassificationInputs) GetModelType() AutoMlImageClassificationInputs_ModelType {
if x != nil {
return x.ModelType
}
return AutoMlImageClassificationInputs_MODEL_TYPE_UNSPECIFIED
}
func (x *AutoMlImageClassificationInputs) GetBaseModelId() string {
if x != nil {
return x.BaseModelId
}
return ""
}
func (x *AutoMlImageClassificationInputs) GetBudgetMilliNodeHours() int64 {
if x != nil {
return x.BudgetMilliNodeHours
}
return 0
}
func (x *AutoMlImageClassificationInputs) GetDisableEarlyStopping() bool {
if x != nil {
return x.DisableEarlyStopping
}
return false
}
func (x *AutoMlImageClassificationInputs) GetMultiLabel() bool {
if x != nil {
return x.MultiLabel
}
return false
}
type AutoMlImageClassificationMetadata struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The actual training cost of creating this model, expressed in
// milli node hours, i.e. 1,000 value in this field means 1 node hour.
// Guaranteed to not exceed inputs.budgetMilliNodeHours.
CostMilliNodeHours int64 `protobuf:"varint,1,opt,name=cost_milli_node_hours,json=costMilliNodeHours,proto3" json:"cost_milli_node_hours,omitempty"`
// For successful job completions, this is the reason why the job has
// finished.
SuccessfulStopReason AutoMlImageClassificationMetadata_SuccessfulStopReason `protobuf:"varint,2,opt,name=successful_stop_reason,json=successfulStopReason,proto3,enum=google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata_SuccessfulStopReason" json:"successful_stop_reason,omitempty"`
}
func (x *AutoMlImageClassificationMetadata) Reset() {
*x = AutoMlImageClassificationMetadata{}
if protoimpl.UnsafeEnabled {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *AutoMlImageClassificationMetadata) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*AutoMlImageClassificationMetadata) ProtoMessage() {}
func (x *AutoMlImageClassificationMetadata) ProtoReflect() protoreflect.Message {
mi := &file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_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 AutoMlImageClassificationMetadata.ProtoReflect.Descriptor instead.
func (*AutoMlImageClassificationMetadata) Descriptor() ([]byte, []int) {
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP(), []int{2}
}
func (x *AutoMlImageClassificationMetadata) GetCostMilliNodeHours() int64 {
if x != nil {
return x.CostMilliNodeHours
}
return 0
}
func (x *AutoMlImageClassificationMetadata) GetSuccessfulStopReason() AutoMlImageClassificationMetadata_SuccessfulStopReason {
if x != nil {
return x.SuccessfulStopReason
}
return AutoMlImageClassificationMetadata_SUCCESSFUL_STOP_REASON_UNSPECIFIED
}
var File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto protoreflect.FileDescriptor
var file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDesc = []byte{
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}
var (
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescOnce sync.Once
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescData = file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDesc
)
func file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescGZIP() []byte {
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescOnce.Do(func() {
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescData)
})
return file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDescData
}
var file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes = make([]protoimpl.EnumInfo, 2)
var file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes = make([]protoimpl.MessageInfo, 3)
var file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_goTypes = []interface{}{
(AutoMlImageClassificationInputs_ModelType)(0), // 0: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType
(AutoMlImageClassificationMetadata_SuccessfulStopReason)(0), // 1: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata.SuccessfulStopReason
(*AutoMlImageClassification)(nil), // 2: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassification
(*AutoMlImageClassificationInputs)(nil), // 3: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs
(*AutoMlImageClassificationMetadata)(nil), // 4: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata
}
var file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_depIdxs = []int32{
3, // 0: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassification.inputs:type_name -> google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs
4, // 1: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassification.metadata:type_name -> google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata
0, // 2: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.model_type:type_name -> google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationInputs.ModelType
1, // 3: google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata.successful_stop_reason:type_name -> google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlImageClassificationMetadata.SuccessfulStopReason
4, // [4:4] is the sub-list for method output_type
4, // [4:4] is the sub-list for method input_type
4, // [4:4] is the sub-list for extension type_name
4, // [4:4] is the sub-list for extension extendee
0, // [0:4] is the sub-list for field type_name
}
func init() {
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_init()
}
func file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_init() {
if File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto != nil {
return
}
if !protoimpl.UnsafeEnabled {
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*AutoMlImageClassification); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*AutoMlImageClassificationInputs); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*AutoMlImageClassificationMetadata); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDesc,
NumEnums: 2,
NumMessages: 3,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_goTypes,
DependencyIndexes: file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_depIdxs,
EnumInfos: file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_enumTypes,
MessageInfos: file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_msgTypes,
}.Build()
File_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto = out.File
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_rawDesc = nil
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_goTypes = nil
file_google_cloud_aiplatform_v1_schema_trainingjob_definition_automl_image_classification_proto_depIdxs = nil
}