def METHOD_NAME(
    image_uri, sagemaker_session, framework_version, instance_type, suites
):
    output_path = f"s3://sm-inductor-test/{suites}"
    pytorch = PyTorch(
        entry_point=f"run_inductor_{suites}.sh",
        source_dir=inductor_path,
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=framework_version,
        output_path=output_path,
        image_uri=image_uri,
        debugger_hook_config=None,
        disable_profiler=True,
    )
    pytorch.fit(job_name=utils.unique_name_from_base(f"test-pt-performance-inductor-{suites}"))
    job_name = pytorch.latest_training_job.name
    pytorch = PyTorch(
        entry_point="test_inductor_helper.py",
        source_dir=inductor_path,
        role="SageMakerRole",
        instance_count=1,
        instance_type=instance_type,
        sagemaker_session=sagemaker_session,
        framework_version=framework_version,
        image_uri=image_uri,
        hyperparameters={
            "output_path": output_path,
            "job_name": job_name,
            "instance_type": instance_type,
            "suites": suites,
        },
        debugger_hook_config=None,
        disable_profiler=True,
    )
    pytorch.fit(job_name=utils.unique_name_from_base(f"upload-pt-performance-inductor-{suites}"))