How to use the bentoml.proto.deployment_pb2.Deployment function in bentoml

def generate_sagemaker_deployment_pb():
    test_deployment_pb = Deployment(
        name=TEST_DEPLOYMENT_NAME, namespace=TEST_DEPLOYMENT_NAMESPACE
    )
    test_deployment_pb.spec.bento_name = TEST_DEPLOYMENT_BENTO_NAME
    test_deployment_pb.spec.bento_version = TEST_DEPLOYMENT_BENTO_VERSION
    test_deployment_pb.spec.sagemaker_operator_config.api_name = TEST_BENTO_API_NAME
    test_deployment_pb.spec.sagemaker_operator_config.region = TEST_AWS_REGION
    test_deployment_pb.spec.sagemaker_operator_config.instance_count = (
        TEST_DEPLOYMENT_INSTANCE_COUNT
    )
    test_deployment_pb.spec.sagemaker_operator_config.instance_type = (
        TEST_DEPLOYMENT_INSTANCE_TYPE
    )
    test_deployment_pb.spec.operator = DeploymentSpec.AWS_SAGEMAKER

    return test_deployment_pb

def generate_lambda_deployment_pb():
    test_deployment_pb = Deployment(name='test_aws_lambda', namespace='test-namespace')
    test_deployment_pb.spec.bento_name = 'bento_name'
    test_deployment_pb.spec.bento_version = 'v1.0.0'
    # DeploymentSpec.DeploymentOperator.AWS_LAMBDA
    test_deployment_pb.spec.operator = 3
    test_deployment_pb.spec.aws_lambda_operator_config.region = 'us-west-2'
    test_deployment_pb.spec.aws_lambda_operator_config.api_name = 'predict'

    return test_deployment_pb

def generate_lambda_deployment_pb():
    test_deployment_pb = Deployment(name='test_aws_lambda', namespace='test-namespace')
    test_deployment_pb.spec.bento_name = 'bento_name'
    test_deployment_pb.spec.bento_version = 'v1.0.0'
    # DeploymentSpec.DeploymentOperator.AWS_LAMBDA
    test_deployment_pb.spec.operator = 3
    test_deployment_pb.spec.aws_lambda_operator_config.region = 'us-west-2'
    test_deployment_pb.spec.aws_lambda_operator_config.api_name = 'predict'
    test_deployment_pb.spec.aws_lambda_operator_config.memory_size = 3008
    test_deployment_pb.spec.aws_lambda_operator_config.timeout = 6

    return test_deployment_pb

def test_validate_aws_lambda_schema():
    test_pb = Deployment(name='test_deployment_name', namespace='namespace')
    test_pb.spec.bento_name = 'bento_name'
    test_pb.spec.bento_version = 'bento_version'
    test_pb.spec.operator = DeploymentSpec.DeploymentOperator.Value('AWS_LAMBDA')
    test_pb.spec.aws_lambda_operator_config.api_name = 'api_name'
    test_pb.spec.aws_lambda_operator_config.region = 'us-west-2'
    test_pb.spec.aws_lambda_operator_config.timeout = 100
    test_pb.spec.aws_lambda_operator_config.memory_size = 128

    result = validate_deployment_pb_schema(test_pb)
    assert result is None

    test_pb.spec.aws_lambda_operator_config.timeout = 1000
    test_pb.spec.aws_lambda_operator_config.memory_size = 129
    failed_memory_test = validate_deployment_pb_schema(test_pb)
    print(failed_memory_test)
    aws_spec_fail_msg = failed_memory_test['spec'][0]['aws_lambda_operator_config'][0]

serverless_project_dir,
                aws_config.region,
                # BentoML deployment namespace is mapping to serverless `stage`
                # concept
                stage=deployment_pb.namespace,
            )
            logger.info(
                'Installing additional packages: serverless-python-requirements'
            )
            install_serverless_plugin(
                "serverless-python-requirements", serverless_project_dir
            )
            logger.info('Deploying to AWS Lambda')
            call_serverless_command(["deploy"], serverless_project_dir)

        res_deployment_pb = Deployment(state=DeploymentState())
        res_deployment_pb.CopyFrom(deployment_pb)
        state = self.describe(res_deployment_pb, yatai_service).state
        res_deployment_pb.state.CopyFrom(state)
        return ApplyDeploymentResponse(status=Status.OK(), deployment=res_deployment_pb)