def METHOD_NAME(
    is_cuda: bool,
    cosine_similarity: bool = False,
    float32: bool = False,
    disallowed_operators: Set[DisallowedOperator] = None,
) -> TorchBenchmarkRunner:
    """
    Load the benchmark runner from TorchDynamo.
    """
    @dataclass
    class RunnerArgs:
        """
        This class simulates the parsed args required by the benchmark code from TorchDynamo.
        """
        ci: bool = False  # Whether runs in CI mode. pylint: disable=invalid-name
        training: bool = False  # Whether it benchmarks training workload.
        use_eval_mode: bool = True  # Whether the model should be in eval mode.
        dynamic_shapes: bool = False  # Whether runs the model in dynamic shape mode.
        float16: bool = False  # Whether to cast model and inputs to float16
        float32: bool = False  # Whether to cast model and inputs to float32
        accuracy: bool = False  # Whether to perform a accuracy test
        performance: bool = True  # Whether to perform a performance test
        cosine: bool = False  # Whether to use consine similarity to check if output is correct.
    args = RunnerArgs(cosine=cosine_similarity, float32=float32)
    runner = TorchBenchmarkRunner()
    runner.args = args
    runner.model_iter_fn = runner.forward_pass
    if disallowed_operators:
        _disallow_operators(disallowed_operators)
    if is_cuda:
        # pylint: disable=import-outside-toplevel
        import benchmarks.common  # type: ignore
        # pylint: enable=import-outside-toplevel
        benchmarks.common.synchronize = torch.cuda.synchronize
    return runner