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resume_resource

resume_resource(
    resource_arn: Arn, *, config_overrides: Optional[forecastClientConfig] = None
) -> None

Resumes a stopped monitor resource.

Parameters:

  • resource_arn (Arn) –

    The Amazon Resource Name (ARN) of the monitor resource to resume.

Raises:

Source code in docs_src/capo_forecast/_services/forecast.py
def resume_resource(
    self,
    resource_arn: "capo_forecast.types.arn.Arn",
    *,
    config_overrides: Optional[forecastClientConfig] = None,
) -> None:
    """<p>Resumes a stopped monitor resource.</p>

    Args:
        resource_arn: <p>The Amazon Resource Name (ARN) of the monitor resource to resume.</p>

    Raises:
        capo_forecast.errors.invalid_input_exception.InvalidInputException: <p>We can't process the request because it includes an invalid value or a value that exceeds the valid range.</p>
        capo_forecast.errors.limit_exceeded_exception.LimitExceededException: <p>The limit on the number of resources per account has been exceeded.</p>
        capo_forecast.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
        capo_forecast.errors.resource_not_found_exception.ResourceNotFoundException: <p>We can't find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.</p>
        capo_forecast.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_forecast.types.resume_resource_request.ResumeResourceRequest]",
    ) -> OperationResponse[None]:
        import capo_forecast._operations.amazon_forecast.resume_resource

        output, http_response = (
            capo_forecast._operations.amazon_forecast.resume_resource.resume_resource(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ResumeResource")
    )
    input_: capo_forecast.types.resume_resource_request.ResumeResourceRequest = {
        "resource_arn": resource_arn
    }

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