describe_pipeline_execution ¶

def describe_pipeline_execution(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    pipeline_execution_arn: Optional[
        "capo_sagemaker.types.pipeline_execution_arn.PipelineExecutionArn"
    ] = None,
) -> "capo_sagemaker.types.describe_pipeline_execution_response.DescribePipelineExecutionResponse":
    """<p>Describes the details of a pipeline execution.</p>

    Args:
        pipeline_execution_arn: <p>The Amazon Resource Name (ARN) of the pipeline execution.</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_pipeline_execution_request.DescribePipelineExecutionRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.describe_pipeline_execution_response.DescribePipelineExecutionResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_pipeline_execution

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribePipelineExecution")
    )
    input_: capo_sagemaker.types.describe_pipeline_execution_request.DescribePipelineExecutionRequest = {}
    if pipeline_execution_arn is not None:
        input_["pipeline_execution_arn"] = pipeline_execution_arn

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