tag_resource ¶

def tag_resource(
    self,
    resource_arn: "capo_docdb_elastic.types.arn.Arn",
    tags: "capo_docdb_elastic.types.tag_map.TagMap",
    *,
    config_overrides: Optional[DocDBElasticClientConfig] = None,
) -> "capo_docdb_elastic.types.tag_resource_response.TagResourceResponse":
    """<p>Adds metadata tags to an elastic cluster resource</p>

    Args:
        resource_arn: <p>The ARN identifier of the elastic cluster resource.</p>
        tags: <p>The tags that are assigned to the elastic cluster resource.</p>

    Raises:
        capo_docdb_elastic.errors.internal_server_exception.InternalServerException: <p>There was an internal server error.</p>
        capo_docdb_elastic.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource could not be located.</p>
        capo_docdb_elastic.errors.throttling_exception.ThrottlingException: <p>ThrottlingException will be thrown when request was denied due to request throttling.</p>
        capo_docdb_elastic.errors.validation_exception.ValidationException: <p>A structure defining a validation exception.</p>
        capo_docdb_elastic.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_docdb_elastic.types.tag_resource_request.TagResourceRequest]",
    ) -> OperationResponse[
        "capo_docdb_elastic.types.tag_resource_response.TagResourceResponse"
    ]:
        import capo_docdb_elastic._operations.chimera_db_lionfish_service_lambda.tag_resource

        output, http_response = (
            capo_docdb_elastic._operations.chimera_db_lionfish_service_lambda.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_docdb_elastic.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