get_ml_input_channel ¶

def get_ml_input_channel(
    self,
    ml_input_channel_arn: "capo_cleanroomsml.types.ml_input_channel_arn.MLInputChannelArn",
    membership_identifier: "capo_cleanroomsml.types.uuid.UUID",
    *,
    config_overrides: Optional[CleanRoomsMLClientConfig] = None,
) -> "capo_cleanroomsml.types.get_ml_input_channel_response.GetMLInputChannelResponse":
    """<p>Returns information about an ML input channel.</p>

    Args:
        ml_input_channel_arn: <p>The Amazon Resource Name (ARN) of the ML input channel that you want to get.</p>
        membership_identifier: <p>The membership ID of the membership that contains the ML input channel that you want to get.</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_ml_input_channel_request.GetMLInputChannelRequest]",
    ) -> OperationResponse[
        "capo_cleanroomsml.types.get_ml_input_channel_response.GetMLInputChannelResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.get_ml_input_channel

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

    def _redact_output(
        output: "capo_cleanroomsml.types.get_ml_input_channel_response.GetMLInputChannelResponse",
    ) -> dict[str, Any]:
        import capo_cleanroomsml.types.get_ml_input_channel_response

        return (
            capo_cleanroomsml.types.get_ml_input_channel_response.redact_sensitive(
                output
            )
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("GetMLInputChannel", redact_output=_redact_output),
    )
    input_: capo_cleanroomsml.types.get_ml_input_channel_request.GetMLInputChannelRequest = {
        "ml_input_channel_arn": ml_input_channel_arn,
        "membership_identifier": membership_identifier,
    }

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