def tag_resource(
self,
resource_arn: "capo_databrew.types.arn.Arn",
tags: "capo_databrew.types.tag_map.TagMap",
*,
config_overrides: Optional[DataBrewClientConfig] = None,
) -> "capo_databrew.types.tag_resource_response.TagResourceResponse":
"""<p>Adds metadata tags to a DataBrew resource, such as a dataset, project, recipe, job, or schedule.</p>
Args:
resource_arn: <p>The DataBrew resource to which tags should be added. The value for this parameter is an Amazon Resource Name (ARN). For DataBrew, you can tag a dataset, a job, a project, or a recipe.</p>
tags: <p>One or more tags to be assigned to the resource.</p>
Raises:
capo_databrew.errors.internal_server_exception.InternalServerException: <p>An internal service failure occurred.</p>
capo_databrew.errors.resource_not_found_exception.ResourceNotFoundException: <p>One or more resources can't be found.</p>
capo_databrew.errors.validation_exception.ValidationException: <p>The input parameters for this request failed validation.</p>
capo_databrew.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_databrew.types.tag_resource_request.TagResourceRequest]",
) -> OperationResponse[
"capo_databrew.types.tag_resource_response.TagResourceResponse"
]:
import capo_databrew._operations.aws_glue_data_brew.tag_resource
output, http_response = (
capo_databrew._operations.aws_glue_data_brew.tag_resource.tag_resource(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("TagResource")
)
input_: capo_databrew.types.tag_resource_request.TagResourceRequest = {
"resource_arn": resource_arn,
"tags": tags,
}
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output