Skip to content

describe_snomedct_inference_job

describe_snomedct_inference_job(
    job_id: JobId, *, config_overrides: Optional[ComprehendMedicalClientConfig] = None
) -> DescribeSNOMEDCTInferenceJobResponse

Gets the properties associated with an InferSNOMEDCT job. Use this operation to get the status of an inference job.

Parameters:

  • job_id (JobId) –

    The identifier that Amazon Comprehend Medical generated for the job. The StartSNOMEDCTInferenceJob operation returns this identifier in its response.

Raises:

  • InternalServerException –

    An internal server error occurred. Retry your request.

  • InvalidRequestException –

    The request that you made is invalid. Check your request to determine why it's invalid and then retry the request.

  • ResourceNotFoundException –

    The resource identified by the specified Amazon Resource Name (ARN) was not found. Check the ARN and try your request again.

  • TooManyRequestsException –

    You have made too many requests within a short period of time. Wait for a short time and then try your request again. Contact customer support for more information about a service limit increase.

  • UnknownServiceError –

    The service returned an error code this client does not model.

Source code in docs_src/capo_comprehendmedical/_services/comprehend_medical.py
def describe_snomedct_inference_job(
    self,
    job_id: "capo_comprehendmedical.types.job_id.JobId",
    *,
    config_overrides: Optional[ComprehendMedicalClientConfig] = None,
) -> "capo_comprehendmedical.types.describe_snomedct_inference_job_response.DescribeSNOMEDCTInferenceJobResponse":
    """<p> Gets the properties associated with an InferSNOMEDCT job. Use this operation to get the status of an inference job. </p>

    Args:
        job_id: <p> The identifier that Amazon Comprehend Medical generated for the job. The StartSNOMEDCTInferenceJob operation returns this identifier in its response. </p>

    Raises:
        capo_comprehendmedical.errors.internal_server_exception.InternalServerException: <p> An internal server error occurred. Retry your request. </p>
        capo_comprehendmedical.errors.invalid_request_exception.InvalidRequestException: <p> The request that you made is invalid. Check your request to determine why it's invalid and then retry the request.</p>
        capo_comprehendmedical.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource identified by the specified Amazon Resource Name (ARN) was not found. Check the ARN and try your request again.</p>
        capo_comprehendmedical.errors.too_many_requests_exception.TooManyRequestsException: <p> You have made too many requests within a short period of time. Wait for a short time and then try your request again. Contact customer support for more information about a service limit increase. </p>
        capo_comprehendmedical.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_comprehendmedical.types.describe_snomedct_inference_job_request.DescribeSNOMEDCTInferenceJobRequest]",
    ) -> OperationResponse[
        "capo_comprehendmedical.types.describe_snomedct_inference_job_response.DescribeSNOMEDCTInferenceJobResponse"
    ]:
        import capo_comprehendmedical._operations.comprehend_medical_20181030.describe_snomedct_inference_job

        output, http_response = (
            capo_comprehendmedical._operations.comprehend_medical_20181030.describe_snomedct_inference_job.describe_snomedct_inference_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("DescribeSNOMEDCTInferenceJob")
    )
    input_: capo_comprehendmedical.types.describe_snomedct_inference_job_request.DescribeSNOMEDCTInferenceJobRequest = {
        "job_id": job_id
    }

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