# Vertex AI API . datasets

## Instance Methods

Returns the datasetVersions Resource.

Close httplib2 connections.

Creates a Dataset.

Deletes a Dataset.

Gets a Dataset.

Lists Datasets in a Location.

Retrieves the next page of results.

Updates a Dataset.

### Method Details

`close()`

    Close httplib2 connections.

`create(body=None, parent=None, x__xgafv=None)`

    Creates a Dataset.

    Args:
      body: object, The request body.
        The object takes the form of:

    { # A collection of DataItems and Annotations on them.
      "createTime": "A String", # Output only. Timestamp when this Dataset was created.
      "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
      "description": "A String", # The description of the Dataset.
      "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
        "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
      "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
        "a_key": "A String",
      },
      "metadata": "", # Required. Additional information about the Dataset.
      "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
      "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
      "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
      "satisfiesPzi": True or False, # Output only. Reserved for future use.
      "satisfiesPzs": True or False, # Output only. Reserved for future use.
      "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
        { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
          "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
          "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
          "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
          "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
          "metadata": "", # Some additional information about the SavedQuery.
          "name": "A String", # Output only. Resource name of the SavedQuery.
          "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
          "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
          "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
        },
      ],
      "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
    }

      parent: string, Required. The resource name of the Location to create the Dataset in. Format: `projects/{project}/locations/{location}`
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
      "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
      "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }

`delete(name, x__xgafv=None)`

    Deletes a Dataset.

    Args:
      name: string, Required. The resource name of the Dataset to delete. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
      "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
      },
      "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
      "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }

`get(name, readMask=None, x__xgafv=None)`

    Gets a Dataset.

    Args:
      name: string, Required. The name of the Dataset resource. (required)
      readMask: string, Mask specifying which fields to read.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A collection of DataItems and Annotations on them.
      "createTime": "A String", # Output only. Timestamp when this Dataset was created.
      "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
      "description": "A String", # The description of the Dataset.
      "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
        "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
      "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
        "a_key": "A String",
      },
      "metadata": "", # Required. Additional information about the Dataset.
      "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
      "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
      "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
      "satisfiesPzi": True or False, # Output only. Reserved for future use.
      "satisfiesPzs": True or False, # Output only. Reserved for future use.
      "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
        { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
          "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
          "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
          "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
          "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
          "metadata": "", # Some additional information about the SavedQuery.
          "name": "A String", # Output only. Resource name of the SavedQuery.
          "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
          "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
          "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
        },
      ],
      "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
    }

`list(filter=None, orderBy=None, pageSize=None, pageToken=None, parent=None, readMask=None, x__xgafv=None)`

    Lists Datasets in a Location.

    Args:
      filter: string, An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `display_name`: supports = and != * `metadata_schema_uri`: supports = and != * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. Some examples: * `displayName="myDisplayName"` * `labels.myKey="myValue"`
      orderBy: string, A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time`
      pageSize: integer, The standard list page size.
      pageToken: string, The standard list page token.
      parent: string, Required. The name of the Dataset's parent resource. Format: `projects/{project}/locations/{location}`
      readMask: string, Mask specifying which fields to read.
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # Response message for DatasetService.ListDatasets.
      "datasets": [ # A list of Datasets that matches the specified filter in the request.
        { # A collection of DataItems and Annotations on them.
          "createTime": "A String", # Output only. Timestamp when this Dataset was created.
          "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
          "description": "A String", # The description of the Dataset.
          "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
            "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
          },
          "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
          "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
            "a_key": "A String",
          },
          "metadata": "", # Required. Additional information about the Dataset.
          "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
          "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
          "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
          "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
          "satisfiesPzi": True or False, # Output only. Reserved for future use.
          "satisfiesPzs": True or False, # Output only. Reserved for future use.
          "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
            { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
              "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
              "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
              "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
              "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
              "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
              "metadata": "", # Some additional information about the SavedQuery.
              "name": "A String", # Output only. Resource name of the SavedQuery.
              "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
              "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
              "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
            },
          ],
          "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
        },
      ],
      "nextPageToken": "A String", # The standard List next-page token.
    }

`list_next()`

    Retrieves the next page of results.

            Args:
              previous_request: The request for the previous page. (required)
              previous_response: The response from the request for the previous page. (required)

            Returns:
              A request object that you can call 'execute()' on to request the next
              page. Returns None if there are no more items in the collection.

`patch(name, body=None, updateMask=None, x__xgafv=None)`

    Updates a Dataset.

    Args:
      name: string, Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
      body: object, The request body.
        The object takes the form of:

    { # A collection of DataItems and Annotations on them.
      "createTime": "A String", # Output only. Timestamp when this Dataset was created.
      "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
      "description": "A String", # The description of the Dataset.
      "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
        "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
      "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
        "a_key": "A String",
      },
      "metadata": "", # Required. Additional information about the Dataset.
      "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
      "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
      "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
      "satisfiesPzi": True or False, # Output only. Reserved for future use.
      "satisfiesPzs": True or False, # Output only. Reserved for future use.
      "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
        { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
          "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
          "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
          "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
          "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
          "metadata": "", # Some additional information about the SavedQuery.
          "name": "A String", # Output only. Resource name of the SavedQuery.
          "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
          "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
          "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
        },
      ],
      "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
    }

      updateMask: string, Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name` * `description` * `labels`
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A collection of DataItems and Annotations on them.
      "createTime": "A String", # Output only. Timestamp when this Dataset was created.
      "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
      "description": "A String", # The description of the Dataset.
      "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
        "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
      "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
        "a_key": "A String",
      },
      "metadata": "", # Required. Additional information about the Dataset.
      "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
      "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
      "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
      "satisfiesPzi": True or False, # Output only. Reserved for future use.
      "satisfiesPzs": True or False, # Output only. Reserved for future use.
      "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
        { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
          "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
          "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
          "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
          "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
          "metadata": "", # Some additional information about the SavedQuery.
          "name": "A String", # Output only. Resource name of the SavedQuery.
          "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
          "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
          "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
        },
      ],
      "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
    }