describe_feature_group ¶

def describe_feature_group(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    feature_group_name: Optional[
        "capo_sagemaker.types.feature_group_name_or_arn.FeatureGroupNameOrArn"
    ] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
) -> "capo_sagemaker.types.describe_feature_group_response.DescribeFeatureGroupResponse":
    """<p>Use this operation to describe a <code>FeatureGroup</code>. The response includes information on the creation time, <code>FeatureGroup</code> name, the unique identifier for each <code>FeatureGroup</code>, and more.</p>

    Args:
        feature_group_name: <p>The name or Amazon Resource Name (ARN) of the <code>FeatureGroup</code> you want described. </p>
        next_token: <p>A token to resume pagination of the list of <code>Features</code> (<code>FeatureDefinitions</code>). 2,500 <code>Features</code> are returned by default.</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_feature_group_request.DescribeFeatureGroupRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.describe_feature_group_response.DescribeFeatureGroupResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.describe_feature_group

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeFeatureGroup")
    )
    input_: capo_sagemaker.types.describe_feature_group_request.DescribeFeatureGroupRequest = {}
    if feature_group_name is not None:
        input_["feature_group_name"] = feature_group_name
    if next_token is not None:
        input_["next_token"] = next_token

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