list_inference_schedulers async ¶

async def list_inference_schedulers(
    self,
    *,
    config_overrides: Optional[AsyncLookoutEquipmentClientConfig] = None,
    next_token: Optional["capo_lookoutequipment.types.next_token.NextToken"] = None,
    max_results: Optional[
        "capo_lookoutequipment.types.max_results.MaxResults"
    ] = None,
    inference_scheduler_name_begins_with: Optional[
        "capo_lookoutequipment.types.inference_scheduler_identifier.InferenceSchedulerIdentifier"
    ] = None,
    model_name: Optional["capo_lookoutequipment.types.model_name.ModelName"] = None,
    status: Optional[
        "capo_lookoutequipment.types.inference_scheduler_status.InferenceSchedulerStatus"
    ] = None,
) -> "capo_lookoutequipment.types.list_inference_schedulers_response.ListInferenceSchedulersResponse":
    """<p>Retrieves a list of all inference schedulers currently available for your account. </p>

    Args:
        next_token: <p> An opaque pagination token indicating where to continue the listing of inference schedulers. </p>
        max_results: <p> Specifies the maximum number of inference schedulers to list. </p>
        inference_scheduler_name_begins_with: <p>The beginning of the name of the inference schedulers to be listed. </p>
        model_name: <p>The name of the machine learning model used by the inference scheduler to be listed. </p>
        status: <p>Specifies the current status of the inference schedulers.</p>

    Raises:
        capo_lookoutequipment.errors.access_denied_exception.AccessDeniedException: <p>The request could not be completed because you do not have access to the resource. </p>
        capo_lookoutequipment.errors.internal_server_exception.InternalServerException: <p> Processing of the request has failed because of an unknown error, exception or failure. </p>
        capo_lookoutequipment.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_lookoutequipment.errors.validation_exception.ValidationException: <p> The input fails to satisfy constraints specified by Amazon Lookout for Equipment or a related Amazon Web Services service that's being utilized. </p>
        capo_lookoutequipment.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_lookoutequipment.types.list_inference_schedulers_request.ListInferenceSchedulersRequest]",
    ) -> AsyncOperationResponse[
        "capo_lookoutequipment.types.list_inference_schedulers_response.ListInferenceSchedulersResponse"
    ]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.list_inference_schedulers

        (
            output,
            http_response,
        ) = await capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.list_inference_schedulers.async_list_inference_schedulers(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListInferenceSchedulers")
    )
    input_: capo_lookoutequipment.types.list_inference_schedulers_request.ListInferenceSchedulersRequest = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if inference_scheduler_name_begins_with is not None:
        input_["inference_scheduler_name_begins_with"] = (
            inference_scheduler_name_begins_with
        )
    if model_name is not None:
        input_["model_name"] = model_name
    if status is not None:
        input_["status"] = status

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