describe_studio ¶

def describe_studio(
    self,
    *,
    config_overrides: Optional[EMRClientConfig] = None,
    studio_id: Optional[
        "capo_emr.types.xml_string_max_len256.XmlStringMaxLen256"
    ] = None,
) -> "capo_emr.types.describe_studio_output.DescribeStudioOutput":
    """<p>Returns details for the specified Amazon EMR Studio including ID, Name, VPC, Studio access URL, and so on.</p>

    Args:
        studio_id: <p>The Amazon EMR Studio ID.</p>

    Raises:
        capo_emr.errors.internal_server_exception.InternalServerException: <p>This exception occurs when there is an internal failure in the Amazon EMR service.</p>
        capo_emr.errors.invalid_request_exception.InvalidRequestException: <p>This exception occurs when there is something wrong with user input.</p>
        capo_emr.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_emr.types.describe_studio_input.DescribeStudioInput]",
    ) -> OperationResponse[
        "capo_emr.types.describe_studio_output.DescribeStudioOutput"
    ]:
        import capo_emr._operations.elastic_map_reduce.describe_studio

        output, http_response = (
            capo_emr._operations.elastic_map_reduce.describe_studio.describe_studio(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeStudio")
    )
    input_: capo_emr.types.describe_studio_input.DescribeStudioInput = {}
    if studio_id is not None:
        input_["studio_id"] = studio_id

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output