start_vector_enrichment_job async ¶

async def start_vector_enrichment_job(
    self,
    name: str,
    input_config: "capo_sagemaker_geospatial.types.vector_enrichment_job_input_config.VectorEnrichmentJobInputConfig",
    job_config: "capo_sagemaker_geospatial.types.vector_enrichment_job_config.VectorEnrichmentJobConfig",
    execution_role_arn: "capo_sagemaker_geospatial.types.execution_role_arn.ExecutionRoleArn",
    *,
    config_overrides: Optional[AsyncSageMakerGeospatialClientConfig] = None,
    client_token: Optional[str] = None,
    kms_key_id: Optional["capo_sagemaker_geospatial.types.kms_key.KmsKey"] = None,
    tags: Optional["capo_sagemaker_geospatial.types.tags.Tags"] = None,
) -> "capo_sagemaker_geospatial.types.start_vector_enrichment_job_output.StartVectorEnrichmentJobOutput":
    """<p>Creates a Vector Enrichment job for the supplied job type. Currently, there are two supported job types: reverse geocoding and map matching.</p>

    Args:
        name: <p>The name of the Vector Enrichment job.</p>
        client_token: <p>A unique token that guarantees that the call to this API is idempotent.</p>
        kms_key_id: <p>The Key Management Service key ID for server-side encryption.</p>
        input_config: <p>Input configuration information for the Vector Enrichment job.</p>
        job_config: <p>An object containing information about the job configuration.</p>
        execution_role_arn: <p>The Amazon Resource Name (ARN) of the IAM role that you specified for the job.</p>
        tags: <p>Each tag consists of a key and a value.</p>

    Raises:
        capo_sagemaker_geospatial.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
        capo_sagemaker_geospatial.errors.conflict_exception.ConflictException: <p>Updating or deleting a resource can cause an inconsistent state.</p>
        capo_sagemaker_geospatial.errors.internal_server_exception.InternalServerException: <p>The request processing has failed because of an unknown error, exception, or failure.</p>
        capo_sagemaker_geospatial.errors.resource_not_found_exception.ResourceNotFoundException: <p>The request references a resource which does not exist.</p>
        capo_sagemaker_geospatial.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>You have exceeded the service quota.</p>
        capo_sagemaker_geospatial.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
        capo_sagemaker_geospatial.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints specified by an Amazon Web Services service.</p>
        capo_sagemaker_geospatial.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_sagemaker_geospatial.types.start_vector_enrichment_job_input.StartVectorEnrichmentJobInput]",
    ) -> AsyncOperationResponse[
        "capo_sagemaker_geospatial.types.start_vector_enrichment_job_output.StartVectorEnrichmentJobOutput"
    ]:
        import capo_sagemaker_geospatial._operations.sage_maker_geospatial.start_vector_enrichment_job

        (
            output,
            http_response,
        ) = await capo_sagemaker_geospatial._operations.sage_maker_geospatial.start_vector_enrichment_job.async_start_vector_enrichment_job(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("StartVectorEnrichmentJob")
    )
    input_: capo_sagemaker_geospatial.types.start_vector_enrichment_job_input.StartVectorEnrichmentJobInput = {
        "name": name,
        "input_config": input_config,
        "job_config": job_config,
        "execution_role_arn": execution_role_arn,
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if kms_key_id is not None:
        input_["kms_key_id"] = kms_key_id
    if tags is not None:
        input_["tags"] = tags

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