def METHOD_NAME(
    mxnet_training_job,
    sagemaker_session,
    cpu_instance_type,
    cpu_instance_family,
    neo_mxnet_latest_version,
    neo_mxnet_latest_py_version,
):
    endpoint_name = unique_name_from_base("test-neo-deploy-model")
    with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
        desc = sagemaker_session.sagemaker_client.describe_training_job(
            TrainingJobName=mxnet_training_job
        )
        model_data = desc["ModelArtifacts"]["S3ModelArtifacts"]
        script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_neo.py")
        role = "SageMakerRole"
        model = MXNetModel(
            model_data,
            role,
            entry_point=script_path,
            py_version=neo_mxnet_latest_py_version,
            framework_version=neo_mxnet_latest_version,
            sagemaker_session=sagemaker_session,
        )
        serializer = JSONSerializer(content_type="application/vnd+python.numpy+binary")
        model.compile(
            target_instance_family=cpu_instance_family,
            input_shape={"data": [1, 1, 28, 28]},
            role=role,
            job_name=unique_name_from_base("test-deploy-model-compilation-job"),
            output_path="/".join(model_data.split("/")[:-1]),
        )
        predictor = model.deploy(
            1, cpu_instance_type, serializer=serializer, endpoint_name=endpoint_name
        )
        data = numpy.zeros(shape=(1, 1, 28, 28))
        predictor.predict(data)