def METHOD_NAME(
    ecr_image,
    sagemaker_session,
    instance_type,
    framework_version,
    test_data,
    record_wrapper_type=None,
):
    source_path = os.path.join(os.path.dirname(__file__), "..", "..", "resources", "pipemode")
    script = os.path.join(source_path, "pipemode.py")
    estimator = TensorFlow(
        entry_point=script,
        role="SageMakerRole",
        instance_type=instance_type,
        instance_count=1,
        sagemaker_session=sagemaker_session,
        image_uri=ecr_image,
        framework_version=framework_version,
        input_mode="Pipe",
        hyperparameters={"dimension": DIMENSION},
    )
    input = TrainingInput(
        s3_data=test_data(sagemaker_session),
        distribution="FullyReplicated",
        record_wrapping=record_wrapper_type,
        input_mode="Pipe",
    )
    with timeout(minutes=20):
        estimator.fit(
            {"elizabeth": input}, job_name=unique_name_from_base("test-sagemaker-pipemode")
        )