list_inference_executions ¶

def list_inference_executions(
    self,
    inference_scheduler_name: "capo_lookoutequipment.types.inference_scheduler_identifier.InferenceSchedulerIdentifier",
    *,
    config_overrides: Optional[LookoutEquipmentClientConfig] = None,
    next_token: Optional["capo_lookoutequipment.types.next_token.NextToken"] = None,
    max_results: Optional[
        "capo_lookoutequipment.types.max_results.MaxResults"
    ] = None,
    data_start_time_after: Optional[
        "capo_lookoutequipment.types.timestamp.Timestamp"
    ] = None,
    data_end_time_before: Optional[
        "capo_lookoutequipment.types.timestamp.Timestamp"
    ] = None,
    status: Optional[
        "capo_lookoutequipment.types.inference_execution_status.InferenceExecutionStatus"
    ] = None,
) -> "capo_lookoutequipment.types.list_inference_executions_response.ListInferenceExecutionsResponse":
    """<p> Lists all inference executions that have been performed by the specified inference scheduler. </p>

    Args:
        next_token: <p>An opaque pagination token indicating where to continue the listing of inference executions.</p>
        max_results: <p>Specifies the maximum number of inference executions to list. </p>
        inference_scheduler_name: <p>The name of the inference scheduler for the inference execution listed. </p>
        data_start_time_after: <p>The time reference in the inferenced dataset after which Amazon Lookout for Equipment started the inference execution. </p>
        data_end_time_before: <p>The time reference in the inferenced dataset before which Amazon Lookout for Equipment stopped the inference execution. </p>
        status: <p>The status of the inference execution. </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.resource_not_found_exception.ResourceNotFoundException: <p> The resource requested could not be found. Verify the resource ID and retry your request. </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.
    """

    def _handler(
        req: "OperationRequest[capo_lookoutequipment.types.list_inference_executions_request.ListInferenceExecutionsRequest]",
    ) -> OperationResponse[
        "capo_lookoutequipment.types.list_inference_executions_response.ListInferenceExecutionsResponse"
    ]:
        import capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.list_inference_executions

        output, http_response = (
            capo_lookoutequipment._operations.aws_lookout_equipment_frontend_service.list_inference_executions.list_inference_executions(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListInferenceExecutions")
    )
    input_: capo_lookoutequipment.types.list_inference_executions_request.ListInferenceExecutionsRequest = {
        "inference_scheduler_name": inference_scheduler_name
    }
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if data_start_time_after is not None:
        input_["data_start_time_after"] = data_start_time_after
    if data_end_time_before is not None:
        input_["data_end_time_before"] = data_end_time_before
    if status is not None:
        input_["status"] = status

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