<?php
# Generated by the protocol buffer compiler.  DO NOT EDIT!
# source: google/cloud/aiplatform/v1/model.proto

namespace Google\Cloud\AIPlatform\V1;

use Google\Protobuf\Internal\GPBType;
use Google\Protobuf\Internal\RepeatedField;
use Google\Protobuf\Internal\GPBUtil;

/**
 * A trained machine learning Model.
 *
 * Generated from protobuf message <code>google.cloud.aiplatform.v1.Model</code>
 */
class Model extends \Google\Protobuf\Internal\Message
{
    /**
     * The resource name of the Model.
     *
     * Generated from protobuf field <code>string name = 1;</code>
     */
    private $name = '';
    /**
     * Output only. Immutable. The version ID of the model.
     * A new version is committed when a new model version is uploaded or
     * trained under an existing model id. It is an auto-incrementing decimal
     * number in string representation.
     *
     * Generated from protobuf field <code>string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $version_id = '';
    /**
     * User provided version aliases so that a model version can be referenced via
     * alias (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_alias}`
     * instead of auto-generated version id (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_id})`.
     * The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
     * version_id. A default version alias will be created for the first version
     * of the model, and there must be exactly one default version alias for a
     * model.
     *
     * Generated from protobuf field <code>repeated string version_aliases = 29;</code>
     */
    private $version_aliases;
    /**
     * Output only. Timestamp when this version was created.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $version_create_time = null;
    /**
     * Output only. Timestamp when this version was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $version_update_time = null;
    /**
     * Required. The display name of the Model.
     * The name can be up to 128 characters long and can consist of any UTF-8
     * characters.
     *
     * Generated from protobuf field <code>string display_name = 2 [(.google.api.field_behavior) = REQUIRED];</code>
     */
    private $display_name = '';
    /**
     * The description of the Model.
     *
     * Generated from protobuf field <code>string description = 3;</code>
     */
    private $description = '';
    /**
     * The description of this version.
     *
     * Generated from protobuf field <code>string version_description = 30;</code>
     */
    private $version_description = '';
    /**
     * The schemata that describe formats of the Model's predictions and
     * explanations as given and returned via
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * and
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;</code>
     */
    private $predict_schemata = null;
    /**
     * Immutable. Points to a YAML file stored on Google Cloud Storage describing
     * additional information about the Model, that is specific to it. Unset if
     * the Model does not have any additional information. The schema is defined
     * as an OpenAPI 3.0.2 [Schema
     * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
     * AutoML Models always have this field populated by Vertex AI, if no
     * additional metadata is needed, this field is set to an empty string.
     * Note: The URI given on output will be immutable and probably different,
     * including the URI scheme, than the one given on input. The output URI will
     * point to a location where the user only has a read access.
     *
     * Generated from protobuf field <code>string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];</code>
     */
    private $metadata_schema_uri = '';
    /**
     * Immutable. An additional information about the Model; the schema of the
     * metadata can be found in
     * [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
     * Unset if the Model does not have any additional information.
     *
     * Generated from protobuf field <code>.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];</code>
     */
    private $metadata = null;
    /**
     * Output only. The formats in which this Model may be exported. If empty,
     * this Model is not available for export.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $supported_export_formats;
    /**
     * Output only. The resource name of the TrainingPipeline that uploaded this
     * Model, if any.
     *
     * Generated from protobuf field <code>string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = {</code>
     */
    private $training_pipeline = '';
    /**
     * Input only. The specification of the container that is to be used when
     * deploying this Model. The specification is ingested upon
     * [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
     * and all binaries it contains are copied and stored internally by Vertex AI.
     * Not present for AutoML Models.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];</code>
     */
    private $container_spec = null;
    /**
     * Immutable. The path to the directory containing the Model artifact and any
     * of its supporting files. Not present for AutoML Models.
     *
     * Generated from protobuf field <code>string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];</code>
     */
    private $artifact_uri = '';
    /**
     * Output only. When this Model is deployed, its prediction resources are
     * described by the `prediction_resources` field of the
     * [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
     * object. Because not all Models support all resource configuration types,
     * the configuration types this Model supports are listed here. If no
     * configuration types are listed, the Model cannot be deployed to an
     * [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
     * online predictions
     * ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
     * Such a Model can serve predictions by using a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
     * has at least one entry each in
     * [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
     * and
     * [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $supported_deployment_resources_types;
    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
     * If
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * exists, the instances should be given as per that schema.
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each instance is a single line. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `csv`
     * The CSV format, where each instance is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record`
     * The TFRecord format, where each instance is a single record in tfrecord
     * syntax. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record-gzip`
     * Similar to `tf-record`, but the file is gzipped. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `bigquery`
     * Each instance is a single row in BigQuery. Uses
     * [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
     * * `file-list`
     * Each line of the file is the location of an instance to process, uses
     * `gcs_source` field of the
     * [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
     * object.
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $supported_input_storage_formats;
    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
     * If both
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * and
     * [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
     * exist, the predictions are returned together with their instances. In other
     * words, the prediction has the original instance data first, followed by the
     * actual prediction content (as per the schema).
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each prediction is a single line. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `csv`
     * The CSV format, where each prediction is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `bigquery`
     * Each prediction is a single row in a BigQuery table, uses
     * [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
     * .
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $supported_output_storage_formats;
    /**
     * Output only. Timestamp when this Model was uploaded into Vertex AI.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $create_time = null;
    /**
     * Output only. Timestamp when this Model was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $update_time = null;
    /**
     * Output only. The pointers to DeployedModels created from this Model. Note
     * that Model could have been deployed to Endpoints in different Locations.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $deployed_models;
    /**
     * The default explanation specification for this Model.
     * The Model can be used for [requesting
     * explanation][PredictionService.Explain] after being
     * [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is
     * populated. The Model can be used for [batch
     * explanation][BatchPredictionJob.generate_explanation] if it is populated.
     * All fields of the explanation_spec can be overridden by
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
     * or
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * If the default explanation specification is not set for this Model, this
     * Model can still be used for [requesting
     * explanation][PredictionService.Explain] by setting
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
     * and for [batch explanation][BatchPredictionJob.generate_explanation] by
     * setting
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;</code>
     */
    private $explanation_spec = null;
    /**
     * Used to perform consistent read-modify-write updates. If not set, a blind
     * "overwrite" update happens.
     *
     * Generated from protobuf field <code>string etag = 16;</code>
     */
    private $etag = '';
    /**
     * The labels with user-defined metadata to organize your Models.
     * Label keys and values can be no longer than 64 characters
     * (Unicode codepoints), can only contain lowercase letters, numeric
     * characters, underscores and dashes. International characters are allowed.
     * See https://goo.gl/xmQnxf for more information and examples of labels.
     *
     * Generated from protobuf field <code>map<string, string> labels = 17;</code>
     */
    private $labels;
    /**
     * Customer-managed encryption key spec for a Model. If set, this
     * Model and all sub-resources of this Model will be secured by this key.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;</code>
     */
    private $encryption_spec = null;
    /**
     * Output only. Source of a model. It can either be automl training pipeline,
     * custom training pipeline, BigQuery ML, or existing Vertex AI Model.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $model_source_info = null;
    /**
     * Output only. If this Model is a copy of another Model, this contains info
     * about the original.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $original_model_info = null;
    /**
     * Output only. The resource name of the Artifact that was created in
     * MetadataStore when creating the Model. The Artifact resource name pattern
     * is
     * `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     *
     * Generated from protobuf field <code>string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     */
    private $metadata_artifact = '';

    /**
     * Constructor.
     *
     * @param array $data {
     *     Optional. Data for populating the Message object.
     *
     *     @type string $name
     *           The resource name of the Model.
     *     @type string $version_id
     *           Output only. Immutable. The version ID of the model.
     *           A new version is committed when a new model version is uploaded or
     *           trained under an existing model id. It is an auto-incrementing decimal
     *           number in string representation.
     *     @type array<string>|\Google\Protobuf\Internal\RepeatedField $version_aliases
     *           User provided version aliases so that a model version can be referenced via
     *           alias (i.e.
     *           `projects/{project}/locations/{location}/models/{model_id}&#64;{version_alias}`
     *           instead of auto-generated version id (i.e.
     *           `projects/{project}/locations/{location}/models/{model_id}&#64;{version_id})`.
     *           The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
     *           version_id. A default version alias will be created for the first version
     *           of the model, and there must be exactly one default version alias for a
     *           model.
     *     @type \Google\Protobuf\Timestamp $version_create_time
     *           Output only. Timestamp when this version was created.
     *     @type \Google\Protobuf\Timestamp $version_update_time
     *           Output only. Timestamp when this version was most recently updated.
     *     @type string $display_name
     *           Required. The display name of the Model.
     *           The name can be up to 128 characters long and can consist of any UTF-8
     *           characters.
     *     @type string $description
     *           The description of the Model.
     *     @type string $version_description
     *           The description of this version.
     *     @type \Google\Cloud\AIPlatform\V1\PredictSchemata $predict_schemata
     *           The schemata that describe formats of the Model's predictions and
     *           explanations as given and returned via
     *           [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     *           and
     *           [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *     @type string $metadata_schema_uri
     *           Immutable. Points to a YAML file stored on Google Cloud Storage describing
     *           additional information about the Model, that is specific to it. Unset if
     *           the Model does not have any additional information. The schema is defined
     *           as an OpenAPI 3.0.2 [Schema
     *           Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
     *           AutoML Models always have this field populated by Vertex AI, if no
     *           additional metadata is needed, this field is set to an empty string.
     *           Note: The URI given on output will be immutable and probably different,
     *           including the URI scheme, than the one given on input. The output URI will
     *           point to a location where the user only has a read access.
     *     @type \Google\Protobuf\Value $metadata
     *           Immutable. An additional information about the Model; the schema of the
     *           metadata can be found in
     *           [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
     *           Unset if the Model does not have any additional information.
     *     @type array<\Google\Cloud\AIPlatform\V1\Model\ExportFormat>|\Google\Protobuf\Internal\RepeatedField $supported_export_formats
     *           Output only. The formats in which this Model may be exported. If empty,
     *           this Model is not available for export.
     *     @type string $training_pipeline
     *           Output only. The resource name of the TrainingPipeline that uploaded this
     *           Model, if any.
     *     @type \Google\Cloud\AIPlatform\V1\ModelContainerSpec $container_spec
     *           Input only. The specification of the container that is to be used when
     *           deploying this Model. The specification is ingested upon
     *           [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
     *           and all binaries it contains are copied and stored internally by Vertex AI.
     *           Not present for AutoML Models.
     *     @type string $artifact_uri
     *           Immutable. The path to the directory containing the Model artifact and any
     *           of its supporting files. Not present for AutoML Models.
     *     @type array<int>|\Google\Protobuf\Internal\RepeatedField $supported_deployment_resources_types
     *           Output only. When this Model is deployed, its prediction resources are
     *           described by the `prediction_resources` field of the
     *           [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
     *           object. Because not all Models support all resource configuration types,
     *           the configuration types this Model supports are listed here. If no
     *           configuration types are listed, the Model cannot be deployed to an
     *           [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
     *           online predictions
     *           ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     *           or
     *           [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
     *           Such a Model can serve predictions by using a
     *           [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
     *           has at least one entry each in
     *           [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
     *           and
     *           [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
     *     @type array<string>|\Google\Protobuf\Internal\RepeatedField $supported_input_storage_formats
     *           Output only. The formats this Model supports in
     *           [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
     *           If
     *           [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     *           exists, the instances should be given as per that schema.
     *           The possible formats are:
     *           * `jsonl`
     *           The JSON Lines format, where each instance is a single line. Uses
     *           [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     *           * `csv`
     *           The CSV format, where each instance is a single comma-separated line.
     *           The first line in the file is the header, containing comma-separated field
     *           names. Uses
     *           [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     *           * `tf-record`
     *           The TFRecord format, where each instance is a single record in tfrecord
     *           syntax. Uses
     *           [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     *           * `tf-record-gzip`
     *           Similar to `tf-record`, but the file is gzipped. Uses
     *           [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     *           * `bigquery`
     *           Each instance is a single row in BigQuery. Uses
     *           [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
     *           * `file-list`
     *           Each line of the file is the location of an instance to process, uses
     *           `gcs_source` field of the
     *           [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
     *           object.
     *           If this Model doesn't support any of these formats it means it cannot be
     *           used with a
     *           [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *           However, if it has
     *           [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     *           it could serve online predictions by using
     *           [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     *           or
     *           [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *     @type array<string>|\Google\Protobuf\Internal\RepeatedField $supported_output_storage_formats
     *           Output only. The formats this Model supports in
     *           [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
     *           If both
     *           [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     *           and
     *           [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
     *           exist, the predictions are returned together with their instances. In other
     *           words, the prediction has the original instance data first, followed by the
     *           actual prediction content (as per the schema).
     *           The possible formats are:
     *           * `jsonl`
     *           The JSON Lines format, where each prediction is a single line. Uses
     *           [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     *           * `csv`
     *           The CSV format, where each prediction is a single comma-separated line.
     *           The first line in the file is the header, containing comma-separated field
     *           names. Uses
     *           [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     *           * `bigquery`
     *           Each prediction is a single row in a BigQuery table, uses
     *           [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
     *           .
     *           If this Model doesn't support any of these formats it means it cannot be
     *           used with a
     *           [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *           However, if it has
     *           [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     *           it could serve online predictions by using
     *           [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     *           or
     *           [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *     @type \Google\Protobuf\Timestamp $create_time
     *           Output only. Timestamp when this Model was uploaded into Vertex AI.
     *     @type \Google\Protobuf\Timestamp $update_time
     *           Output only. Timestamp when this Model was most recently updated.
     *     @type array<\Google\Cloud\AIPlatform\V1\DeployedModelRef>|\Google\Protobuf\Internal\RepeatedField $deployed_models
     *           Output only. The pointers to DeployedModels created from this Model. Note
     *           that Model could have been deployed to Endpoints in different Locations.
     *     @type \Google\Cloud\AIPlatform\V1\ExplanationSpec $explanation_spec
     *           The default explanation specification for this Model.
     *           The Model can be used for [requesting
     *           explanation][PredictionService.Explain] after being
     *           [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is
     *           populated. The Model can be used for [batch
     *           explanation][BatchPredictionJob.generate_explanation] if it is populated.
     *           All fields of the explanation_spec can be overridden by
     *           [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     *           of
     *           [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
     *           or
     *           [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     *           of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *           If the default explanation specification is not set for this Model, this
     *           Model can still be used for [requesting
     *           explanation][PredictionService.Explain] by setting
     *           [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     *           of
     *           [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
     *           and for [batch explanation][BatchPredictionJob.generate_explanation] by
     *           setting
     *           [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     *           of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *     @type string $etag
     *           Used to perform consistent read-modify-write updates. If not set, a blind
     *           "overwrite" update happens.
     *     @type array|\Google\Protobuf\Internal\MapField $labels
     *           The labels with user-defined metadata to organize your Models.
     *           Label keys and values can be no longer than 64 characters
     *           (Unicode codepoints), can only contain lowercase letters, numeric
     *           characters, underscores and dashes. International characters are allowed.
     *           See https://goo.gl/xmQnxf for more information and examples of labels.
     *     @type \Google\Cloud\AIPlatform\V1\EncryptionSpec $encryption_spec
     *           Customer-managed encryption key spec for a Model. If set, this
     *           Model and all sub-resources of this Model will be secured by this key.
     *     @type \Google\Cloud\AIPlatform\V1\ModelSourceInfo $model_source_info
     *           Output only. Source of a model. It can either be automl training pipeline,
     *           custom training pipeline, BigQuery ML, or existing Vertex AI Model.
     *     @type \Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo $original_model_info
     *           Output only. If this Model is a copy of another Model, this contains info
     *           about the original.
     *     @type string $metadata_artifact
     *           Output only. The resource name of the Artifact that was created in
     *           MetadataStore when creating the Model. The Artifact resource name pattern
     *           is
     *           `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     * }
     */
    public function __construct($data = NULL) {
        \GPBMetadata\Google\Cloud\Aiplatform\V1\Model::initOnce();
        parent::__construct($data);
    }

    /**
     * The resource name of the Model.
     *
     * Generated from protobuf field <code>string name = 1;</code>
     * @return string
     */
    public function getName()
    {
        return $this->name;
    }

    /**
     * The resource name of the Model.
     *
     * Generated from protobuf field <code>string name = 1;</code>
     * @param string $var
     * @return $this
     */
    public function setName($var)
    {
        GPBUtil::checkString($var, True);
        $this->name = $var;

        return $this;
    }

    /**
     * Output only. Immutable. The version ID of the model.
     * A new version is committed when a new model version is uploaded or
     * trained under an existing model id. It is an auto-incrementing decimal
     * number in string representation.
     *
     * Generated from protobuf field <code>string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return string
     */
    public function getVersionId()
    {
        return $this->version_id;
    }

    /**
     * Output only. Immutable. The version ID of the model.
     * A new version is committed when a new model version is uploaded or
     * trained under an existing model id. It is an auto-incrementing decimal
     * number in string representation.
     *
     * Generated from protobuf field <code>string version_id = 28 [(.google.api.field_behavior) = IMMUTABLE, (.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param string $var
     * @return $this
     */
    public function setVersionId($var)
    {
        GPBUtil::checkString($var, True);
        $this->version_id = $var;

        return $this;
    }

    /**
     * User provided version aliases so that a model version can be referenced via
     * alias (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_alias}`
     * instead of auto-generated version id (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_id})`.
     * The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
     * version_id. A default version alias will be created for the first version
     * of the model, and there must be exactly one default version alias for a
     * model.
     *
     * Generated from protobuf field <code>repeated string version_aliases = 29;</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getVersionAliases()
    {
        return $this->version_aliases;
    }

    /**
     * User provided version aliases so that a model version can be referenced via
     * alias (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_alias}`
     * instead of auto-generated version id (i.e.
     * `projects/{project}/locations/{location}/models/{model_id}&#64;{version_id})`.
     * The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from
     * version_id. A default version alias will be created for the first version
     * of the model, and there must be exactly one default version alias for a
     * model.
     *
     * Generated from protobuf field <code>repeated string version_aliases = 29;</code>
     * @param array<string>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setVersionAliases($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::STRING);
        $this->version_aliases = $arr;

        return $this;
    }

    /**
     * Output only. Timestamp when this version was created.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Timestamp|null
     */
    public function getVersionCreateTime()
    {
        return $this->version_create_time;
    }

    public function hasVersionCreateTime()
    {
        return isset($this->version_create_time);
    }

    public function clearVersionCreateTime()
    {
        unset($this->version_create_time);
    }

    /**
     * Output only. Timestamp when this version was created.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_create_time = 31 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Protobuf\Timestamp $var
     * @return $this
     */
    public function setVersionCreateTime($var)
    {
        GPBUtil::checkMessage($var, \Google\Protobuf\Timestamp::class);
        $this->version_create_time = $var;

        return $this;
    }

    /**
     * Output only. Timestamp when this version was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Timestamp|null
     */
    public function getVersionUpdateTime()
    {
        return $this->version_update_time;
    }

    public function hasVersionUpdateTime()
    {
        return isset($this->version_update_time);
    }

    public function clearVersionUpdateTime()
    {
        unset($this->version_update_time);
    }

    /**
     * Output only. Timestamp when this version was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp version_update_time = 32 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Protobuf\Timestamp $var
     * @return $this
     */
    public function setVersionUpdateTime($var)
    {
        GPBUtil::checkMessage($var, \Google\Protobuf\Timestamp::class);
        $this->version_update_time = $var;

        return $this;
    }

    /**
     * Required. The display name of the Model.
     * The name can be up to 128 characters long and can consist of any UTF-8
     * characters.
     *
     * Generated from protobuf field <code>string display_name = 2 [(.google.api.field_behavior) = REQUIRED];</code>
     * @return string
     */
    public function getDisplayName()
    {
        return $this->display_name;
    }

    /**
     * Required. The display name of the Model.
     * The name can be up to 128 characters long and can consist of any UTF-8
     * characters.
     *
     * Generated from protobuf field <code>string display_name = 2 [(.google.api.field_behavior) = REQUIRED];</code>
     * @param string $var
     * @return $this
     */
    public function setDisplayName($var)
    {
        GPBUtil::checkString($var, True);
        $this->display_name = $var;

        return $this;
    }

    /**
     * The description of the Model.
     *
     * Generated from protobuf field <code>string description = 3;</code>
     * @return string
     */
    public function getDescription()
    {
        return $this->description;
    }

    /**
     * The description of the Model.
     *
     * Generated from protobuf field <code>string description = 3;</code>
     * @param string $var
     * @return $this
     */
    public function setDescription($var)
    {
        GPBUtil::checkString($var, True);
        $this->description = $var;

        return $this;
    }

    /**
     * The description of this version.
     *
     * Generated from protobuf field <code>string version_description = 30;</code>
     * @return string
     */
    public function getVersionDescription()
    {
        return $this->version_description;
    }

    /**
     * The description of this version.
     *
     * Generated from protobuf field <code>string version_description = 30;</code>
     * @param string $var
     * @return $this
     */
    public function setVersionDescription($var)
    {
        GPBUtil::checkString($var, True);
        $this->version_description = $var;

        return $this;
    }

    /**
     * The schemata that describe formats of the Model's predictions and
     * explanations as given and returned via
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * and
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;</code>
     * @return \Google\Cloud\AIPlatform\V1\PredictSchemata|null
     */
    public function getPredictSchemata()
    {
        return $this->predict_schemata;
    }

    public function hasPredictSchemata()
    {
        return isset($this->predict_schemata);
    }

    public function clearPredictSchemata()
    {
        unset($this->predict_schemata);
    }

    /**
     * The schemata that describe formats of the Model's predictions and
     * explanations as given and returned via
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * and
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.PredictSchemata predict_schemata = 4;</code>
     * @param \Google\Cloud\AIPlatform\V1\PredictSchemata $var
     * @return $this
     */
    public function setPredictSchemata($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\PredictSchemata::class);
        $this->predict_schemata = $var;

        return $this;
    }

    /**
     * Immutable. Points to a YAML file stored on Google Cloud Storage describing
     * additional information about the Model, that is specific to it. Unset if
     * the Model does not have any additional information. The schema is defined
     * as an OpenAPI 3.0.2 [Schema
     * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
     * AutoML Models always have this field populated by Vertex AI, if no
     * additional metadata is needed, this field is set to an empty string.
     * Note: The URI given on output will be immutable and probably different,
     * including the URI scheme, than the one given on input. The output URI will
     * point to a location where the user only has a read access.
     *
     * Generated from protobuf field <code>string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @return string
     */
    public function getMetadataSchemaUri()
    {
        return $this->metadata_schema_uri;
    }

    /**
     * Immutable. Points to a YAML file stored on Google Cloud Storage describing
     * additional information about the Model, that is specific to it. Unset if
     * the Model does not have any additional information. The schema is defined
     * as an OpenAPI 3.0.2 [Schema
     * Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
     * AutoML Models always have this field populated by Vertex AI, if no
     * additional metadata is needed, this field is set to an empty string.
     * Note: The URI given on output will be immutable and probably different,
     * including the URI scheme, than the one given on input. The output URI will
     * point to a location where the user only has a read access.
     *
     * Generated from protobuf field <code>string metadata_schema_uri = 5 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @param string $var
     * @return $this
     */
    public function setMetadataSchemaUri($var)
    {
        GPBUtil::checkString($var, True);
        $this->metadata_schema_uri = $var;

        return $this;
    }

    /**
     * Immutable. An additional information about the Model; the schema of the
     * metadata can be found in
     * [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
     * Unset if the Model does not have any additional information.
     *
     * Generated from protobuf field <code>.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @return \Google\Protobuf\Value|null
     */
    public function getMetadata()
    {
        return $this->metadata;
    }

    public function hasMetadata()
    {
        return isset($this->metadata);
    }

    public function clearMetadata()
    {
        unset($this->metadata);
    }

    /**
     * Immutable. An additional information about the Model; the schema of the
     * metadata can be found in
     * [metadata_schema][google.cloud.aiplatform.v1.Model.metadata_schema_uri].
     * Unset if the Model does not have any additional information.
     *
     * Generated from protobuf field <code>.google.protobuf.Value metadata = 6 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @param \Google\Protobuf\Value $var
     * @return $this
     */
    public function setMetadata($var)
    {
        GPBUtil::checkMessage($var, \Google\Protobuf\Value::class);
        $this->metadata = $var;

        return $this;
    }

    /**
     * Output only. The formats in which this Model may be exported. If empty,
     * this Model is not available for export.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getSupportedExportFormats()
    {
        return $this->supported_export_formats;
    }

    /**
     * Output only. The formats in which this Model may be exported. If empty,
     * this Model is not available for export.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.ExportFormat supported_export_formats = 20 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param array<\Google\Cloud\AIPlatform\V1\Model\ExportFormat>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setSupportedExportFormats($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::MESSAGE, \Google\Cloud\AIPlatform\V1\Model\ExportFormat::class);
        $this->supported_export_formats = $arr;

        return $this;
    }

    /**
     * Output only. The resource name of the TrainingPipeline that uploaded this
     * Model, if any.
     *
     * Generated from protobuf field <code>string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = {</code>
     * @return string
     */
    public function getTrainingPipeline()
    {
        return $this->training_pipeline;
    }

    /**
     * Output only. The resource name of the TrainingPipeline that uploaded this
     * Model, if any.
     *
     * Generated from protobuf field <code>string training_pipeline = 7 [(.google.api.field_behavior) = OUTPUT_ONLY, (.google.api.resource_reference) = {</code>
     * @param string $var
     * @return $this
     */
    public function setTrainingPipeline($var)
    {
        GPBUtil::checkString($var, True);
        $this->training_pipeline = $var;

        return $this;
    }

    /**
     * Input only. The specification of the container that is to be used when
     * deploying this Model. The specification is ingested upon
     * [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
     * and all binaries it contains are copied and stored internally by Vertex AI.
     * Not present for AutoML Models.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];</code>
     * @return \Google\Cloud\AIPlatform\V1\ModelContainerSpec|null
     */
    public function getContainerSpec()
    {
        return $this->container_spec;
    }

    public function hasContainerSpec()
    {
        return isset($this->container_spec);
    }

    public function clearContainerSpec()
    {
        unset($this->container_spec);
    }

    /**
     * Input only. The specification of the container that is to be used when
     * deploying this Model. The specification is ingested upon
     * [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel],
     * and all binaries it contains are copied and stored internally by Vertex AI.
     * Not present for AutoML Models.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelContainerSpec container_spec = 9 [(.google.api.field_behavior) = INPUT_ONLY];</code>
     * @param \Google\Cloud\AIPlatform\V1\ModelContainerSpec $var
     * @return $this
     */
    public function setContainerSpec($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\ModelContainerSpec::class);
        $this->container_spec = $var;

        return $this;
    }

    /**
     * Immutable. The path to the directory containing the Model artifact and any
     * of its supporting files. Not present for AutoML Models.
     *
     * Generated from protobuf field <code>string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @return string
     */
    public function getArtifactUri()
    {
        return $this->artifact_uri;
    }

    /**
     * Immutable. The path to the directory containing the Model artifact and any
     * of its supporting files. Not present for AutoML Models.
     *
     * Generated from protobuf field <code>string artifact_uri = 26 [(.google.api.field_behavior) = IMMUTABLE];</code>
     * @param string $var
     * @return $this
     */
    public function setArtifactUri($var)
    {
        GPBUtil::checkString($var, True);
        $this->artifact_uri = $var;

        return $this;
    }

    /**
     * Output only. When this Model is deployed, its prediction resources are
     * described by the `prediction_resources` field of the
     * [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
     * object. Because not all Models support all resource configuration types,
     * the configuration types this Model supports are listed here. If no
     * configuration types are listed, the Model cannot be deployed to an
     * [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
     * online predictions
     * ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
     * Such a Model can serve predictions by using a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
     * has at least one entry each in
     * [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
     * and
     * [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getSupportedDeploymentResourcesTypes()
    {
        return $this->supported_deployment_resources_types;
    }

    /**
     * Output only. When this Model is deployed, its prediction resources are
     * described by the `prediction_resources` field of the
     * [Endpoint.deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models]
     * object. Because not all Models support all resource configuration types,
     * the configuration types this Model supports are listed here. If no
     * configuration types are listed, the Model cannot be deployed to an
     * [Endpoint][google.cloud.aiplatform.v1.Endpoint] and does not support
     * online predictions
     * ([PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain]).
     * Such a Model can serve predictions by using a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob], if it
     * has at least one entry each in
     * [supported_input_storage_formats][google.cloud.aiplatform.v1.Model.supported_input_storage_formats]
     * and
     * [supported_output_storage_formats][google.cloud.aiplatform.v1.Model.supported_output_storage_formats].
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.Model.DeploymentResourcesType supported_deployment_resources_types = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param array<int>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setSupportedDeploymentResourcesTypes($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::ENUM, \Google\Cloud\AIPlatform\V1\Model\DeploymentResourcesType::class);
        $this->supported_deployment_resources_types = $arr;

        return $this;
    }

    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
     * If
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * exists, the instances should be given as per that schema.
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each instance is a single line. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `csv`
     * The CSV format, where each instance is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record`
     * The TFRecord format, where each instance is a single record in tfrecord
     * syntax. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record-gzip`
     * Similar to `tf-record`, but the file is gzipped. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `bigquery`
     * Each instance is a single row in BigQuery. Uses
     * [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
     * * `file-list`
     * Each line of the file is the location of an instance to process, uses
     * `gcs_source` field of the
     * [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
     * object.
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getSupportedInputStorageFormats()
    {
        return $this->supported_input_storage_formats;
    }

    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.input_config][google.cloud.aiplatform.v1.BatchPredictionJob.input_config].
     * If
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * exists, the instances should be given as per that schema.
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each instance is a single line. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `csv`
     * The CSV format, where each instance is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record`
     * The TFRecord format, where each instance is a single record in tfrecord
     * syntax. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `tf-record-gzip`
     * Similar to `tf-record`, but the file is gzipped. Uses
     * [GcsSource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.gcs_source].
     * * `bigquery`
     * Each instance is a single row in BigQuery. Uses
     * [BigQuerySource][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig.bigquery_source].
     * * `file-list`
     * Each line of the file is the location of an instance to process, uses
     * `gcs_source` field of the
     * [InputConfig][google.cloud.aiplatform.v1.BatchPredictionJob.InputConfig]
     * object.
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_input_storage_formats = 11 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param array<string>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setSupportedInputStorageFormats($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::STRING);
        $this->supported_input_storage_formats = $arr;

        return $this;
    }

    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
     * If both
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * and
     * [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
     * exist, the predictions are returned together with their instances. In other
     * words, the prediction has the original instance data first, followed by the
     * actual prediction content (as per the schema).
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each prediction is a single line. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `csv`
     * The CSV format, where each prediction is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `bigquery`
     * Each prediction is a single row in a BigQuery table, uses
     * [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
     * .
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getSupportedOutputStorageFormats()
    {
        return $this->supported_output_storage_formats;
    }

    /**
     * Output only. The formats this Model supports in
     * [BatchPredictionJob.output_config][google.cloud.aiplatform.v1.BatchPredictionJob.output_config].
     * If both
     * [PredictSchemata.instance_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri]
     * and
     * [PredictSchemata.prediction_schema_uri][google.cloud.aiplatform.v1.PredictSchemata.prediction_schema_uri]
     * exist, the predictions are returned together with their instances. In other
     * words, the prediction has the original instance data first, followed by the
     * actual prediction content (as per the schema).
     * The possible formats are:
     * * `jsonl`
     * The JSON Lines format, where each prediction is a single line. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `csv`
     * The CSV format, where each prediction is a single comma-separated line.
     * The first line in the file is the header, containing comma-separated field
     * names. Uses
     * [GcsDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.gcs_destination].
     * * `bigquery`
     * Each prediction is a single row in a BigQuery table, uses
     * [BigQueryDestination][google.cloud.aiplatform.v1.BatchPredictionJob.OutputConfig.bigquery_destination]
     * .
     * If this Model doesn't support any of these formats it means it cannot be
     * used with a
     * [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * However, if it has
     * [supported_deployment_resources_types][google.cloud.aiplatform.v1.Model.supported_deployment_resources_types],
     * it could serve online predictions by using
     * [PredictionService.Predict][google.cloud.aiplatform.v1.PredictionService.Predict]
     * or
     * [PredictionService.Explain][google.cloud.aiplatform.v1.PredictionService.Explain].
     *
     * Generated from protobuf field <code>repeated string supported_output_storage_formats = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param array<string>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setSupportedOutputStorageFormats($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::STRING);
        $this->supported_output_storage_formats = $arr;

        return $this;
    }

    /**
     * Output only. Timestamp when this Model was uploaded into Vertex AI.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Timestamp|null
     */
    public function getCreateTime()
    {
        return $this->create_time;
    }

    public function hasCreateTime()
    {
        return isset($this->create_time);
    }

    public function clearCreateTime()
    {
        unset($this->create_time);
    }

    /**
     * Output only. Timestamp when this Model was uploaded into Vertex AI.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp create_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Protobuf\Timestamp $var
     * @return $this
     */
    public function setCreateTime($var)
    {
        GPBUtil::checkMessage($var, \Google\Protobuf\Timestamp::class);
        $this->create_time = $var;

        return $this;
    }

    /**
     * Output only. Timestamp when this Model was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Timestamp|null
     */
    public function getUpdateTime()
    {
        return $this->update_time;
    }

    public function hasUpdateTime()
    {
        return isset($this->update_time);
    }

    public function clearUpdateTime()
    {
        unset($this->update_time);
    }

    /**
     * Output only. Timestamp when this Model was most recently updated.
     *
     * Generated from protobuf field <code>.google.protobuf.Timestamp update_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Protobuf\Timestamp $var
     * @return $this
     */
    public function setUpdateTime($var)
    {
        GPBUtil::checkMessage($var, \Google\Protobuf\Timestamp::class);
        $this->update_time = $var;

        return $this;
    }

    /**
     * Output only. The pointers to DeployedModels created from this Model. Note
     * that Model could have been deployed to Endpoints in different Locations.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Protobuf\Internal\RepeatedField
     */
    public function getDeployedModels()
    {
        return $this->deployed_models;
    }

    /**
     * Output only. The pointers to DeployedModels created from this Model. Note
     * that Model could have been deployed to Endpoints in different Locations.
     *
     * Generated from protobuf field <code>repeated .google.cloud.aiplatform.v1.DeployedModelRef deployed_models = 15 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param array<\Google\Cloud\AIPlatform\V1\DeployedModelRef>|\Google\Protobuf\Internal\RepeatedField $var
     * @return $this
     */
    public function setDeployedModels($var)
    {
        $arr = GPBUtil::checkRepeatedField($var, \Google\Protobuf\Internal\GPBType::MESSAGE, \Google\Cloud\AIPlatform\V1\DeployedModelRef::class);
        $this->deployed_models = $arr;

        return $this;
    }

    /**
     * The default explanation specification for this Model.
     * The Model can be used for [requesting
     * explanation][PredictionService.Explain] after being
     * [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is
     * populated. The Model can be used for [batch
     * explanation][BatchPredictionJob.generate_explanation] if it is populated.
     * All fields of the explanation_spec can be overridden by
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
     * or
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * If the default explanation specification is not set for this Model, this
     * Model can still be used for [requesting
     * explanation][PredictionService.Explain] by setting
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
     * and for [batch explanation][BatchPredictionJob.generate_explanation] by
     * setting
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;</code>
     * @return \Google\Cloud\AIPlatform\V1\ExplanationSpec|null
     */
    public function getExplanationSpec()
    {
        return $this->explanation_spec;
    }

    public function hasExplanationSpec()
    {
        return isset($this->explanation_spec);
    }

    public function clearExplanationSpec()
    {
        unset($this->explanation_spec);
    }

    /**
     * The default explanation specification for this Model.
     * The Model can be used for [requesting
     * explanation][PredictionService.Explain] after being
     * [deployed][google.cloud.aiplatform.v1.EndpointService.DeployModel] if it is
     * populated. The Model can be used for [batch
     * explanation][BatchPredictionJob.generate_explanation] if it is populated.
     * All fields of the explanation_spec can be overridden by
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model],
     * or
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     * If the default explanation specification is not set for this Model, this
     * Model can still be used for [requesting
     * explanation][PredictionService.Explain] by setting
     * [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec]
     * of
     * [DeployModelRequest.deployed_model][google.cloud.aiplatform.v1.DeployModelRequest.deployed_model]
     * and for [batch explanation][BatchPredictionJob.generate_explanation] by
     * setting
     * [explanation_spec][google.cloud.aiplatform.v1.BatchPredictionJob.explanation_spec]
     * of [BatchPredictionJob][google.cloud.aiplatform.v1.BatchPredictionJob].
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ExplanationSpec explanation_spec = 23;</code>
     * @param \Google\Cloud\AIPlatform\V1\ExplanationSpec $var
     * @return $this
     */
    public function setExplanationSpec($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\ExplanationSpec::class);
        $this->explanation_spec = $var;

        return $this;
    }

    /**
     * Used to perform consistent read-modify-write updates. If not set, a blind
     * "overwrite" update happens.
     *
     * Generated from protobuf field <code>string etag = 16;</code>
     * @return string
     */
    public function getEtag()
    {
        return $this->etag;
    }

    /**
     * Used to perform consistent read-modify-write updates. If not set, a blind
     * "overwrite" update happens.
     *
     * Generated from protobuf field <code>string etag = 16;</code>
     * @param string $var
     * @return $this
     */
    public function setEtag($var)
    {
        GPBUtil::checkString($var, True);
        $this->etag = $var;

        return $this;
    }

    /**
     * The labels with user-defined metadata to organize your Models.
     * Label keys and values can be no longer than 64 characters
     * (Unicode codepoints), can only contain lowercase letters, numeric
     * characters, underscores and dashes. International characters are allowed.
     * See https://goo.gl/xmQnxf for more information and examples of labels.
     *
     * Generated from protobuf field <code>map<string, string> labels = 17;</code>
     * @return \Google\Protobuf\Internal\MapField
     */
    public function getLabels()
    {
        return $this->labels;
    }

    /**
     * The labels with user-defined metadata to organize your Models.
     * Label keys and values can be no longer than 64 characters
     * (Unicode codepoints), can only contain lowercase letters, numeric
     * characters, underscores and dashes. International characters are allowed.
     * See https://goo.gl/xmQnxf for more information and examples of labels.
     *
     * Generated from protobuf field <code>map<string, string> labels = 17;</code>
     * @param array|\Google\Protobuf\Internal\MapField $var
     * @return $this
     */
    public function setLabels($var)
    {
        $arr = GPBUtil::checkMapField($var, \Google\Protobuf\Internal\GPBType::STRING, \Google\Protobuf\Internal\GPBType::STRING);
        $this->labels = $arr;

        return $this;
    }

    /**
     * Customer-managed encryption key spec for a Model. If set, this
     * Model and all sub-resources of this Model will be secured by this key.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;</code>
     * @return \Google\Cloud\AIPlatform\V1\EncryptionSpec|null
     */
    public function getEncryptionSpec()
    {
        return $this->encryption_spec;
    }

    public function hasEncryptionSpec()
    {
        return isset($this->encryption_spec);
    }

    public function clearEncryptionSpec()
    {
        unset($this->encryption_spec);
    }

    /**
     * Customer-managed encryption key spec for a Model. If set, this
     * Model and all sub-resources of this Model will be secured by this key.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 24;</code>
     * @param \Google\Cloud\AIPlatform\V1\EncryptionSpec $var
     * @return $this
     */
    public function setEncryptionSpec($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\EncryptionSpec::class);
        $this->encryption_spec = $var;

        return $this;
    }

    /**
     * Output only. Source of a model. It can either be automl training pipeline,
     * custom training pipeline, BigQuery ML, or existing Vertex AI Model.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Cloud\AIPlatform\V1\ModelSourceInfo|null
     */
    public function getModelSourceInfo()
    {
        return $this->model_source_info;
    }

    public function hasModelSourceInfo()
    {
        return isset($this->model_source_info);
    }

    public function clearModelSourceInfo()
    {
        unset($this->model_source_info);
    }

    /**
     * Output only. Source of a model. It can either be automl training pipeline,
     * custom training pipeline, BigQuery ML, or existing Vertex AI Model.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.ModelSourceInfo model_source_info = 38 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Cloud\AIPlatform\V1\ModelSourceInfo $var
     * @return $this
     */
    public function setModelSourceInfo($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\ModelSourceInfo::class);
        $this->model_source_info = $var;

        return $this;
    }

    /**
     * Output only. If this Model is a copy of another Model, this contains info
     * about the original.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return \Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo|null
     */
    public function getOriginalModelInfo()
    {
        return $this->original_model_info;
    }

    public function hasOriginalModelInfo()
    {
        return isset($this->original_model_info);
    }

    public function clearOriginalModelInfo()
    {
        unset($this->original_model_info);
    }

    /**
     * Output only. If this Model is a copy of another Model, this contains info
     * about the original.
     *
     * Generated from protobuf field <code>.google.cloud.aiplatform.v1.Model.OriginalModelInfo original_model_info = 34 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param \Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo $var
     * @return $this
     */
    public function setOriginalModelInfo($var)
    {
        GPBUtil::checkMessage($var, \Google\Cloud\AIPlatform\V1\Model\OriginalModelInfo::class);
        $this->original_model_info = $var;

        return $this;
    }

    /**
     * Output only. The resource name of the Artifact that was created in
     * MetadataStore when creating the Model. The Artifact resource name pattern
     * is
     * `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     *
     * Generated from protobuf field <code>string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @return string
     */
    public function getMetadataArtifact()
    {
        return $this->metadata_artifact;
    }

    /**
     * Output only. The resource name of the Artifact that was created in
     * MetadataStore when creating the Model. The Artifact resource name pattern
     * is
     * `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
     *
     * Generated from protobuf field <code>string metadata_artifact = 44 [(.google.api.field_behavior) = OUTPUT_ONLY];</code>
     * @param string $var
     * @return $this
     */
    public function setMetadataArtifact($var)
    {
        GPBUtil::checkString($var, True);
        $this->metadata_artifact = $var;

        return $this;
    }

}