deploy method

Deploys a model.

If a model is already deployed, deploying it has no effect. A model can only serve prediction requests after it gets deployed. For article suggestion, custom model will not be used unless it is deployed. This method is a [long-running operation](https://cloud.google.com/dialogflow/es/docs/how/long-running-operations). The returned Operation type has the following method-specific fields: - metadata: DeployConversationModelOperationMetadata - response: An Empty message

request - The metadata request object.

Request parameters:

name - Required. The conversation model to deploy. Format: projects//conversationModels/ Value must have pattern ^projects/\[^/\]+/locations/\[^/\]+/conversationModels/\[^/\]+$.

$fields - Selector specifying which fields to include in a partial response.

Completes with a GoogleLongrunningOperation.

Completes with a commons.ApiRequestError if the API endpoint returned an error.

If the used http.Client completes with an error when making a REST call, this method will complete with the same error.

Implementation

async.Future<GoogleLongrunningOperation> deploy(
  GoogleCloudDialogflowV2DeployConversationModelRequest request,
  core.String name, {
  core.String? $fields,
}) async {
  final body_ = convert.json.encode(request);
  final queryParams_ = <core.String, core.List<core.String>>{
    if ($fields != null) 'fields': [$fields],
  };

  final url_ = 'v2/' + core.Uri.encodeFull('$name') + ':deploy';

  final response_ = await _requester.request(
    url_,
    'POST',
    body: body_,
    queryParams: queryParams_,
  );
  return GoogleLongrunningOperation.fromJson(
      response_ as core.Map<core.String, core.dynamic>);
}