@datafire/google_automl

Client library for Cloud AutoML API

Installation and Usage

npm install --save @datafire/google_automl

let google_automl = require('@datafire/google_automl').create({
  access_token: "",
  refresh_token: "",
  client_id: "",
  client_secret: "",
  redirect_uri: ""
});

.then(data => {
  console.log(data);
});

Description

Train high-quality custom machine learning models with minimum effort and machine learning expertise.

Actions

oauthCallback

Exchange the code passed to your redirect URI for an access_token

google_automl.oauthCallback({
  "code": ""
}, context)

Input

• input object
  • code required string

Output

• output object
  • access_token string
  • refresh_token string
  • token_type string
  • scope string
  • expiration string

oauthRefresh

Exchange a refresh_token for an access_token

google_automl.oauthRefresh(null, context)

Input

This action has no parameters

Output

• output object
  • access_token string
  • refresh_token string
  • token_type string
  • scope string
  • expiration string

automl.projects.locations.operations.delete

Deletes a long-running operation. This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

google_automl.automl.projects.locations.operations.delete({
  "name": ""
}, context)

Input

• input object
  • name required string: The name of the operation resource to be deleted.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Empty

automl.projects.locations.operations.get

Gets the latest state of a long-running operation. Clients can use this method to poll the operation result at intervals as recommended by the API service.

google_automl.automl.projects.locations.operations.get({
  "name": ""
}, context)

Input

• input object
  • name required string: The name of the operation resource.
  • fieldMask string: Mask specifying which fields to read.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.datasets.tableSpecs.columnSpecs.patch

Updates a column spec.

google_automl.automl.projects.locations.datasets.tableSpecs.columnSpecs.patch({
  "name": ""
}, context)

Input

• input object
  • name required string: Output only. The resource name of the column specs. Form: projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/tableSpecs/{table_spec_id}/columnSpecs/{column_spec_id}
  • updateMask string: The update mask applies to the resource.
  • body ColumnSpec
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ColumnSpec

automl.projects.locations.list

Lists information about the supported locations for this service.

google_automl.automl.projects.locations.list({
  "name": ""
}, context)

Input

• input object
  • name required string: The resource that owns the locations collection, if applicable.
  • filter string: The standard list filter.
  • pageSize integer: The standard list page size.
  • pageToken string: The standard list page token.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListLocationsResponse

automl.projects.locations.operations.list

Lists operations that match the specified filter in the request. If the server doesn't support this method, it returns UNIMPLEMENTED. NOTE: the name binding allows API services to override the binding to use different resource name schemes, such as users/*/operations. To override the binding, API services can add a binding such as "/v1/{name=users/*}/operations" to their service configuration. For backwards compatibility, the default name includes the operations collection id, however overriding users must ensure the name binding is the parent resource, without the operations collection id.

google_automl.automl.projects.locations.operations.list({
  "name": ""
}, context)

Input

• input object
  • name required string: The name of the operation's parent resource.
  • filter string: An expression for filtering the results of the request. * operation_id - for = or !=. * done - for = or !=. * works_on - for = or !=. Some examples of using the filter are: * done=true --> The operation has finished running. * works_on = projects/my-project/locations/us-central1/datasets/5 --> The operation works on a dataset with ID 5. * works_on = projects/my-project/locations/us-central1/models/15 --> The operation works on a model with ID 15.
  • pageSize integer: The standard list page size.
  • pageToken string: The standard list page token.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListOperationsResponse

automl.projects.locations.models.batchPredict

Perform a batch prediction. Unlike the online Predict, batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via GetOperation method. Once the operation is done, BatchPredictResult is returned in the response field. Available for following ML problems: * Image Classification * Image Object Detection * Video Classification * Video Object Tracking * Text Extraction * Tables

google_automl.automl.projects.locations.models.batchPredict({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. Name of the model requested to serve the batch prediction.
  • body BatchPredictRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.operations.cancel

Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED. Clients can use Operations.GetOperation or other methods to check whether the cancellation succeeded or whether the operation completed despite cancellation. On successful cancellation, the operation is not deleted; instead, it becomes an operation with an Operation.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED.

google_automl.automl.projects.locations.operations.cancel({
  "name": ""
}, context)

Input

• input object
  • name required string: The name of the operation resource to be cancelled.
  • body CancelOperationRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Empty

automl.projects.locations.models.deploy

Deploys a model. If a model is already deployed, deploying it with the same parameters has no effect. Deploying with different parametrs (as e.g. changing node_number) will reset the deployment state without pausing the model's availability. Only applicable for Text Classification, Image Object Detection , Tables, and Image Segmentation; all other domains manage deployment automatically. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.deploy({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. Resource name of the model to deploy.
  • body DeployModelRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.models.export

Exports a trained, "export-able", model to a user specified Google Cloud Storage location. A model is considered export-able if and only if it has an export format defined for it in ModelExportOutputConfig. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.export({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. The resource name of the model to export.
  • body ExportModelRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.datasets.exportData

Exports dataset's data to the provided output location. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.datasets.exportData({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. The resource name of the dataset.
  • body ExportDataRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.models.exportEvaluatedExamples

Exports examples on which the model was evaluated (i.e. which were in the TEST set of the dataset the model was created from), together with their ground truth annotations and the annotations created (predicted) by the model. The examples, ground truth and predictions are exported in the state they were at the moment the model was evaluated. This export is available only for 30 days since the model evaluation is created. Currently only available for Tables. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.exportEvaluatedExamples({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. The resource name of the model whose evaluated examples are to be exported.
  • body ExportEvaluatedExamplesRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.datasets.importData

Imports data into a dataset. For Tables this method can only be called on an empty Dataset. For Tables: * A schema_inference_version parameter must be explicitly set. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.datasets.importData({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. Dataset name. Dataset must already exist. All imported annotations and examples will be added.
  • body ImportDataRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.models.predict

Perform an online prediction. The prediction result will be directly returned in the response. Available for following ML problems, and their expected request payloads: * Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB. * Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded. * Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded. * Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded. * Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type. * Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

google_automl.automl.projects.locations.models.predict({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. Name of the model requested to serve the prediction.
  • body PredictRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output PredictResponse

automl.projects.locations.models.undeploy

Undeploys a model. If the model is not deployed this method has no effect. Only applicable for Text Classification, Image Object Detection and Tables; all other domains manage deployment automatically. Returns an empty response in the response field when it completes.

google_automl.automl.projects.locations.models.undeploy({
  "name": ""
}, context)

Input

• input object
  • name required string: Required. Resource name of the model to undeploy.
  • body UndeployModelRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.operations.wait

Waits for the specified long-running operation until it is done or reaches at most a specified timeout, returning the latest state. If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED. Note that this method is on a best-effort basis. It may return the latest state before the specified timeout (including immediately), meaning even an immediate response is no guarantee that the operation is done.

google_automl.automl.projects.locations.operations.wait({
  "name": ""
}, context)

Input

• input object
  • name required string: The name of the operation resource to wait on.
  • body WaitOperationRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.datasets.tableSpecs.columnSpecs.list

Lists column specs in a table spec.

google_automl.automl.projects.locations.datasets.tableSpecs.columnSpecs.list({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. The resource name of the table spec to list column specs from.
  • fieldMask string: Mask specifying which fields to read.
  • filter string: Filter expression, see go/filtering.
  • pageSize integer: Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
  • pageToken string: A token identifying a page of results for the server to return. Typically obtained from the ListColumnSpecsResponse.next_page_token field of the previous AutoMl.ListColumnSpecs call.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListColumnSpecsResponse

automl.projects.locations.datasets.list

Lists datasets in a project.

google_automl.automl.projects.locations.datasets.list({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. The resource name of the project from which to list datasets.
  • filter string: An expression for filtering the results of the request. * dataset_metadata - for existence of the case (e.g. image_classification_dataset_metadata:*). Some examples of using the filter are: * translation_dataset_metadata:* --> The dataset has translation_dataset_metadata.
  • pageSize integer: Requested page size. Server may return fewer results than requested. If unspecified, server will pick a default size.
  • pageToken string: A token identifying a page of results for the server to return Typically obtained via ListDatasetsResponse.next_page_token of the previous AutoMl.ListDatasets call.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListDatasetsResponse

automl.projects.locations.datasets.create

Creates a dataset.

google_automl.automl.projects.locations.datasets.create({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. The resource name of the project to create the dataset for.
  • body Dataset
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Dataset

automl.projects.locations.models.modelEvaluations.list

Lists model evaluations.

google_automl.automl.projects.locations.models.modelEvaluations.list({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. Resource name of the model to list the model evaluations for. If modelId is set as "-", this will list model evaluations from across all models of the parent location.
  • filter string: An expression for filtering the results of the request. * annotation_spec_id - for =, != or existence. See example below for the last. Some examples of using the filter are: * annotation_spec_id!=4 --> The model evaluation was done for annotation spec with ID different than 4. * NOT annotation_spec_id:* --> The model evaluation was done for aggregate of all annotation specs.
  • pageSize integer: Requested page size.
  • pageToken string: A token identifying a page of results for the server to return. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous AutoMl.ListModelEvaluations call.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListModelEvaluationsResponse

automl.projects.locations.models.list

Lists models.

google_automl.automl.projects.locations.models.list({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. Resource name of the project, from which to list the models.
  • filter string: An expression for filtering the results of the request. * model_metadata - for existence of the case (e.g. video_classification_model_metadata:*). * dataset_id - for = or !=. Some examples of using the filter are: * image_classification_model_metadata:* --> The model has image_classification_model_metadata. * dataset_id=5 --> The model was created from a dataset with ID 5.
  • pageSize integer: Requested page size.
  • pageToken string: A token identifying a page of results for the server to return Typically obtained via ListModelsResponse.next_page_token of the previous AutoMl.ListModels call.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListModelsResponse

automl.projects.locations.models.create

Creates a model. Returns a Model in the response field when it completes. When you create a model, several model evaluations are created for it: a global evaluation, and one evaluation for each annotation spec.

google_automl.automl.projects.locations.models.create({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. Resource name of the parent project where the model is being created.
  • body Model
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Operation

automl.projects.locations.datasets.tableSpecs.list

Lists table specs in a dataset.

google_automl.automl.projects.locations.datasets.tableSpecs.list({
  "parent": ""
}, context)

Input

• input object
  • parent required string: Required. The resource name of the dataset to list table specs from.
  • fieldMask string: Mask specifying which fields to read.
  • filter string: Filter expression, see go/filtering.
  • pageSize integer: Requested page size. The server can return fewer results than requested. If unspecified, the server will pick a default size.
  • pageToken string: A token identifying a page of results for the server to return. Typically obtained from the ListTableSpecsResponse.next_page_token field of the previous AutoMl.ListTableSpecs call.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output ListTableSpecsResponse

automl.projects.locations.models.getIamPolicy

Gets the access control policy for a resource. Returns an empty policy if the resource exists and does not have a policy set.

google_automl.automl.projects.locations.models.getIamPolicy({
  "resource": ""
}, context)

Input

• input object
  • resource required string: REQUIRED: The resource for which the policy is being requested. See the operation documentation for the appropriate value for this field.
  • options.requestedPolicyVersion integer: Optional. The policy format version to be returned. Valid values are 0, 1, and 3. Requests specifying an invalid value will be rejected. Requests for policies with any conditional bindings must specify version 3. Policies without any conditional bindings may specify any valid value or leave the field unset. To learn which resources support conditions in their IAM policies, see the IAM documentation.
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Policy

automl.projects.locations.models.setIamPolicy

Sets the access control policy on the specified resource. Replaces any existing policy. Can return NOT_FOUND, INVALID_ARGUMENT, and PERMISSION_DENIED errors.

google_automl.automl.projects.locations.models.setIamPolicy({
  "resource": ""
}, context)

Input

• input object
  • resource required string: REQUIRED: The resource for which the policy is being specified. See the operation documentation for the appropriate value for this field.
  • body SetIamPolicyRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output Policy

automl.projects.locations.testIamPermissions

Returns permissions that a caller has on the specified resource. If the resource does not exist, this will return an empty set of permissions, not a NOT_FOUND error. Note: This operation is designed to be used for building permission-aware UIs and command-line tools, not for authorization checking. This operation may "fail open" without warning.

google_automl.automl.projects.locations.testIamPermissions({
  "resource": ""
}, context)

Input

• input object
  • resource required string: REQUIRED: The resource for which the policy detail is being requested. See the operation documentation for the appropriate value for this field.
  • body TestIamPermissionsRequest
  • $.xgafv string (values: 1, 2): V1 error format.
  • access_token string: OAuth access token.
  • alt string (values: json, media, proto): Data format for response.
  • callback string: JSONP
  • fields string: Selector specifying which fields to include in a partial response.
  • key string: API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.
  • oauth_token string: OAuth 2.0 token for the current user.
  • prettyPrint boolean: Returns response with indentations and line breaks.
  • quotaUser string: Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.
  • upload_protocol string: Upload protocol for media (e.g. "raw", "multipart").
  • uploadType string: Legacy upload protocol for media (e.g. "media", "multipart").

Output

• output TestIamPermissionsResponse

Definitions

AnnotationPayload

• AnnotationPayload object: Contains annotation information that is relevant to AutoML.
  • annotationSpecId string: Output only . The resource ID of the annotation spec that this annotation pertains to. The annotation spec comes from either an ancestor dataset, or the dataset that was used to train the model in use.
  • classification ClassificationAnnotation
  • displayName string: Output only. The value of display_name when the model was trained. Because this field returns a value at model training time, for different models trained using the same dataset, the returned value could be different as model owner could update the display_name between any two model training.
  • imageObjectDetection ImageObjectDetectionAnnotation
  • tables TablesAnnotation
  • textExtraction TextExtractionAnnotation
  • textSentiment TextSentimentAnnotation
  • translation TranslationAnnotation
  • videoClassification VideoClassificationAnnotation
  • videoObjectTracking VideoObjectTrackingAnnotation

AnnotationSpec

• AnnotationSpec object: A definition of an annotation spec.
  • displayName string: Required. The name of the annotation spec to show in the interface. The name can be up to 32 characters long and must match the regexp [a-zA-Z0-9_]+.
  • exampleCount integer: Output only. The number of examples in the parent dataset labeled by the annotation spec.
  • name string: Output only. Resource name of the annotation spec. Form: 'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'

ArrayStats

• ArrayStats object: The data statistics of a series of ARRAY values.
  • memberStats DataStats

BatchPredictInputConfig

• BatchPredictInputConfig object: Input configuration for BatchPredict Action. The format of input depends on the ML problem of the model used for prediction. As input source the gcs_source is expected, unless specified otherwise. The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are: * For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png * For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png * For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf * For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf * For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf * For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt "Some text content to predict" gs://folder/text3.pdf Supported file extensions: .txt, .pdf * For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called "id", a TextSnippet proto (in json representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n): { "id": "my_first_id", "text_snippet": { "content": "dog car cat"}, "text_features": [ { "text_segment": {"start_offset": 4, "end_offset": 6}, "structural_type": PARAGRAPH, "bounding_poly": { "normalized_vertices": [ {"x": 0.1, "y": 0.1}, {"x": 0.1, "y": 0.3}, {"x": 0.3, "y": 0.3}, {"x": 0.3, "y": 0.1}, ] }, } ], }\n { "id": "2", "text_snippet": { "content": "An elaborate content", "mime_type": "text/plain" } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.pdf" ] } } } } * For Tables: Either gcs_source or bigquery_source. GCS case: CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. First three sample rows of a CSV file: "First Name","Last Name","Dob","Addresses" "John","Doe","1968-01-22","[{"status":"current","address":"123_First_Avenue","city":"Seattle","state":"WA","zip":"11111","numberOfYears":"1"},{"status":"previous","address":"456_Main_Street","city":"Portland","state":"OR","zip":"22222","numberOfYears":"5"}]" "Jane","Doe","1980-10-16","[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}]} BigQuery case: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model's input_feature_column_specs' display_name-s (order doesn't matter). The columns corresponding to the model's input feature column specs must contain values compatible with the column spec's data types. Prediction on all the rows of the table will be attempted. For FORECASTING prediction_type: all columns having TIME_SERIES_AVAILABLE_PAST_ONLY type will be ignored. Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. "gs://folder/video.avi". TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes ("") TIME_SEGMENT_START = TIME_OFFSET Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END = TIME_OFFSET Expresses an end, exclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_OFFSET = A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. "inf" is allowed and it means the end of the example. Errors: If any of the provided CSV files can't be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.
  • bigquerySource BigQuerySource
  • gcsSource GcsSource

BatchPredictOperationMetadata

• BatchPredictOperationMetadata object: Details of BatchPredict operation.
  • inputConfig BatchPredictInputConfig
  • outputInfo BatchPredictOutputInfo

BatchPredictOutputConfig

• BatchPredictOutputConfig object: Output configuration for BatchPredict Action. As destination the gcs_destination must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction--", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for. * For Image Classification: In the created directory files image_classification_1.jsonl, image_classification_2.jsonl,...,image_classification_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "" but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields. * For Image Object Detection: In the created directory files image_object_detection_1.jsonl, image_object_detection_2.jsonl,...,image_object_detection_N.jsonl will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "" but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and messagefields. * For Video Classification: In the created directory a video_classification.csv file, and a .JSON file per each video classification requested in the input (i.e. each line in given CSV(s)), will be created. The format of video_classification.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_classification.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for the video time segment the file is assigned to in the video_classification.csv. All AnnotationPayload protos will have video_classification field set, and will be sorted by video_classification.type field (note that the returned types are governed by classifaction_types parameter in PredictService.BatchPredictRequest.params). * For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set. * For Text Classification: In the created directory files text_classification_1.jsonl, text_classification_2.jsonl,...,text_classification_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input text snippet or input text file followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Text Sentiment: In the created directory files text_sentiment_1.jsonl, text_sentiment_2.jsonl,...,text_sentiment_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text snippet or input text file and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text snippet or file will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet or file failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input text snippet or input text file followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Text Extraction: In the created directory files text_extraction_1.jsonl, text_extraction_2.jsonl,...,text_extraction_N.jsonl will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additional errors_1.jsonl, errors_2.jsonl,..., errors_N.jsonl files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "" (in case of inline) or the document proto (in case of document) but here followed by exactly one [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) containing only code and message. * For Tables: Output depends on whether gcs_destination or bigquery_destination is set (either is allowed). GCS case: In the created directory files tables_1.csv, tables_2.csv,..., tables_N.csv will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additional errors_1.csv, errors_2.csv,..., errors_N.csv will be created (N depends on total number of failed rows). These files will have analogous format as tables_*.csv, but always with a single target column having [google.rpc.Status](https: //github.com/googleapis/googleapis/blob/master/google/rpc/status.proto) represented as a JSON string, and containing only code and message. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new