get_collaboration_ml_input_channel async ¶

async def get_collaboration_ml_input_channel(
    self,
    ml_input_channel_arn: "capo_cleanroomsml.types.ml_input_channel_arn.MLInputChannelArn",
    collaboration_identifier: "capo_cleanroomsml.types.uuid.UUID",
    *,
    config_overrides: Optional[AsyncCleanRoomsMLClientConfig] = None,
) -> "capo_cleanroomsml.types.get_collaboration_ml_input_channel_response.GetCollaborationMLInputChannelResponse":
    """<p>Returns information about a specific ML input channel in a collaboration.</p>

    Args:
        ml_input_channel_arn: <p>The Amazon Resource Name (ARN) of the ML input channel that you want to get.</p>
        collaboration_identifier: <p>The collaboration ID of the collaboration 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.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_cleanroomsml.types.get_collaboration_ml_input_channel_request.GetCollaborationMLInputChannelRequest]",
    ) -> AsyncOperationResponse[
        "capo_cleanroomsml.types.get_collaboration_ml_input_channel_response.GetCollaborationMLInputChannelResponse"
    ]:
        import capo_cleanroomsml._operations.aws_stark_control_service.get_collaboration_ml_input_channel

        (
            output,
            http_response,
        ) = await capo_cleanroomsml._operations.aws_stark_control_service.get_collaboration_ml_input_channel.async_get_collaboration_ml_input_channel(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("GetCollaborationMLInputChannel")
    )
    input_: capo_cleanroomsml.types.get_collaboration_ml_input_channel_request.GetCollaborationMLInputChannelRequest = {
        "ml_input_channel_arn": ml_input_channel_arn,
        "collaboration_identifier": collaboration_identifier,
    }

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output