describe_studio_lifecycle_config `async` ¶

describe_studio_lifecycle_config(
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = None,
    studio_lifecycle_config_name: Optional[StudioLifecycleConfigName] = None,
) -> DescribeStudioLifecycleConfigResponse

Describes the Amazon SageMaker AI Studio Lifecycle Configuration.

Parameters:

studio_lifecycle_config_name (Optional[StudioLifecycleConfigName], default: None ) –

The name of the Amazon SageMaker AI Studio Lifecycle Configuration to describe.

Raises:

ResourceNotFound –

Resource being access is not found.
UnknownServiceError –

The service returned an error code this client does not model.

Source code in docs_src/capo_sagemaker/_services/async_sage_maker.py

async def describe_studio_lifecycle_config(
    self,
    *,
    config_overrides: Optional[AsyncSageMakerClientConfig] = 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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker.types.describe_studio_lifecycle_config_request.DescribeStudioLifecycleConfigRequest]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker.types.describe_studio_lifecycle_config_response.DescribeStudioLifecycleConfigResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_studio_lifecycle_config

        (
            output,
            http_response,
        ) = await capo_sagemaker._operations.sage_maker.describe_studio_lifecycle_config.async_describe_studio_lifecycle_config(
            req.options, req.input
        )
        return AsyncOperationResponse(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 = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output

describe_studio_lifecycle_config async ¶

describe_studio_lifecycle_config `async` ¶