get_training_dataset ¶

def get_training_dataset(
    self,
    training_dataset_arn: "capo_cleanroomsml.types.training_dataset_arn.TrainingDatasetArn",
    *,
    config_overrides: Optional[CleanRoomsMLClientConfig] = None,
) -> "capo_cleanroomsml.types.get_training_dataset_response.GetTrainingDatasetResponse":
    """<p>Returns information about a training dataset.</p>

    Args:
        training_dataset_arn: <p>The Amazon Resource Name (ARN) of the training dataset that you are interested in.</p>

    Raises:
        capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_cleanroomsml.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you are requesting does not exist.</p>
        capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
        capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_cleanroomsml.types.get_training_dataset_request.GetTrainingDatasetRequest]",
    ) -> OperationResponse[
        "capo_cleanroomsml.types.get_training_dataset_response.GetTrainingDatasetResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.get_training_dataset

        output, http_response = (
            capo_cleanroomsml._operations.aws_stark_control_service.get_training_dataset.get_training_dataset(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetTrainingDataset")
    )
    input_: capo_cleanroomsml.types.get_training_dataset_request.GetTrainingDatasetRequest = {
        "training_dataset_arn": training_dataset_arn
    }

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