describe_hyper_parameter_tuning_job ¶

def describe_hyper_parameter_tuning_job(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    hyper_parameter_tuning_job_name: Optional[
        "capo_sagemaker.types.hyper_parameter_tuning_job_name.HyperParameterTuningJobName"
    ] = None,
) -> "capo_sagemaker.types.describe_hyper_parameter_tuning_job_response.DescribeHyperParameterTuningJobResponse":
    """<p>Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.</p>

    Args:
        hyper_parameter_tuning_job_name: <p>The name of the tuning job.</p>

    Raises:
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.describe_hyper_parameter_tuning_job_request.DescribeHyperParameterTuningJobRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.describe_hyper_parameter_tuning_job_response.DescribeHyperParameterTuningJobResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_hyper_parameter_tuning_job

        output, http_response = (
            capo_sagemaker._operations.sage_maker.describe_hyper_parameter_tuning_job.describe_hyper_parameter_tuning_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeHyperParameterTuningJob")
    )
    input_: capo_sagemaker.types.describe_hyper_parameter_tuning_job_request.DescribeHyperParameterTuningJobRequest = {}
    if hyper_parameter_tuning_job_name is not None:
        input_["hyper_parameter_tuning_job_name"] = hyper_parameter_tuning_job_name

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