describe_studio_lifecycle_config ¶

def describe_studio_lifecycle_config(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    studio_lifecycle_config_name: Optional[
        "capo_sagemaker.types.studio_lifecycle_config_name.StudioLifecycleConfigName"
    ] = None,
) -> "capo_sagemaker.types.describe_studio_lifecycle_config_response.DescribeStudioLifecycleConfigResponse":
    """<p>Describes the Amazon SageMaker AI Studio Lifecycle Configuration.</p>

    Args:
        studio_lifecycle_config_name: <p>The name of the Amazon SageMaker AI Studio Lifecycle Configuration to describe.</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_studio_lifecycle_config_request.DescribeStudioLifecycleConfigRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.describe_studio_lifecycle_config_response.DescribeStudioLifecycleConfigResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_studio_lifecycle_config

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeStudioLifecycleConfig")
    )
    input_: capo_sagemaker.types.describe_studio_lifecycle_config_request.DescribeStudioLifecycleConfigRequest = {}
    if studio_lifecycle_config_name is not None:
        input_["studio_lifecycle_config_name"] = studio_lifecycle_config_name

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