batch_delete_evaluation_job ¶

def batch_delete_evaluation_job(
    self,
    job_identifiers: "capo_bedrock.types.evaluation_job_identifiers.EvaluationJobIdentifiers",
    *,
    config_overrides: Optional[BedrockClientConfig] = None,
) -> "capo_bedrock.types.batch_delete_evaluation_job_response.BatchDeleteEvaluationJobResponse":
    """<p>Deletes a batch of evaluation jobs. An evaluation job can only be deleted if it has following status <code>FAILED</code>, <code>COMPLETED</code>, and <code>STOPPED</code>. You can request up to 25 model evaluation jobs be deleted in a single request.</p>

    Args:
        job_identifiers: <p>A list of one or more evaluation job Amazon Resource Names (ARNs) you want to delete.</p>

    Raises:
        capo_bedrock.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</p>
        capo_bedrock.errors.conflict_exception.ConflictException: <p>Error occurred because of a conflict while performing an operation.</p>
        capo_bedrock.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.</p>
        capo_bedrock.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock.errors.UnknownServiceError: The service returned an error code this client does not model.

    Examples:
        Delete evaluation jobs
        The following example shows a request to delete two model evaluation jobs, where one of the jobs is not found.

        >>> client.batch_delete_evaluation_job(job_identifiers=['arn:aws:bedrock:us-east-2:123456789012:evaluation-job/12rnxmplqv0v', 'arn:aws:bedrock:us-east-2:123456789012:evaluation-job/rispxmpl12rn'])
    """

    def _handler(
        req: "OperationRequest[capo_bedrock.types.batch_delete_evaluation_job_request.BatchDeleteEvaluationJobRequest]",
    ) -> OperationResponse[
        "capo_bedrock.types.batch_delete_evaluation_job_response.BatchDeleteEvaluationJobResponse"
    ]:
        import capo_bedrock._operations.amazon_bedrock_control_plane_service.batch_delete_evaluation_job

        output, http_response = (
            capo_bedrock._operations.amazon_bedrock_control_plane_service.batch_delete_evaluation_job.batch_delete_evaluation_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock.types.batch_delete_evaluation_job_request.BatchDeleteEvaluationJobRequest",
    ) -> dict[str, Any]:
        import capo_bedrock.types.batch_delete_evaluation_job_request

        return (
            capo_bedrock.types.batch_delete_evaluation_job_request.redact_sensitive(
                input_
            )
        )

    def _redact_output(
        output: "capo_bedrock.types.batch_delete_evaluation_job_response.BatchDeleteEvaluationJobResponse",
    ) -> dict[str, Any]:
        import capo_bedrock.types.batch_delete_evaluation_job_response

        return capo_bedrock.types.batch_delete_evaluation_job_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "BatchDeleteEvaluationJob",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_bedrock.types.batch_delete_evaluation_job_request.BatchDeleteEvaluationJobRequest = {
        "job_identifiers": job_identifiers
    }

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