get_automated_reasoning_policy_next_scenario ¶

def get_automated_reasoning_policy_next_scenario(
    self,
    policy_arn: "capo_bedrock.types.automated_reasoning_policy_arn.AutomatedReasoningPolicyArn",
    build_workflow_id: "capo_bedrock.types.automated_reasoning_policy_build_workflow_id.AutomatedReasoningPolicyBuildWorkflowId",
    *,
    config_overrides: Optional[BedrockClientConfig] = None,
) -> "capo_bedrock.types.get_automated_reasoning_policy_next_scenario_response.GetAutomatedReasoningPolicyNextScenarioResponse":
    """<p>Retrieves the next test scenario for validating an Automated Reasoning policy. This is used during the interactive policy refinement process to test policy behavior.</p>

    Args:
        policy_arn: <p>The Amazon Resource Name (ARN) of the Automated Reasoning policy for which you want to get the next test scenario.</p>
        build_workflow_id: <p>The unique identifier of the build workflow associated with the test scenarios.</p>

    Raises:
        capo_bedrock.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</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.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock.types.get_automated_reasoning_policy_next_scenario_request.GetAutomatedReasoningPolicyNextScenarioRequest]",
    ) -> OperationResponse[
        "capo_bedrock.types.get_automated_reasoning_policy_next_scenario_response.GetAutomatedReasoningPolicyNextScenarioResponse"
    ]:
        import capo_bedrock._operations.amazon_bedrock_control_plane_service.get_automated_reasoning_policy_next_scenario

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

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

        return capo_bedrock.types.get_automated_reasoning_policy_next_scenario_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "GetAutomatedReasoningPolicyNextScenario", redact_output=_redact_output
        ),
    )
    input_: capo_bedrock.types.get_automated_reasoning_policy_next_scenario_request.GetAutomatedReasoningPolicyNextScenarioRequest = {
        "policy_arn": policy_arn,
        "build_workflow_id": build_workflow_id,
    }

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