def METHOD_NAME(batch, LFR_m=1, LFR_n=1):
    # From: espnet/src/asr/asr_utils.py: load_inputs_and_targets
    # load acoustic features and target sequence of token ids
    # for b in batch:
    #     print(b[1]['input'][0]['feat'])
    # TorchBench: Patch the input data with current file directory
    # Input data path: TORCHBENCH_DATA_ROOT/speech_transformer_inputs/
    from torchbenchmark import DATA_PATH
    TORCHBENCH_DATA_ROOT = Path(DATA_PATH).joinpath("speech_transformer_inputs")
    xs = [kaldi_io.read_mat(str(TORCHBENCH_DATA_ROOT.joinpath(b[1]['input'][0]['feat']).resolve())) for b in batch]
    ys = [b[1]['output'][0]['tokenid'].split() for b in batch]
    if LFR_m != 1 or LFR_n != 1:
        # xs = build_LFR_features(xs, LFR_m, LFR_n)
        xs = [build_LFR_features(x, LFR_m, LFR_n) for x in xs]
    # get index of non-zero length samples
    nonzero_idx = filter(lambda i: len(ys[i]) > 0, range(len(xs)))
    # sort in input lengths
    nonzero_sorted_idx = sorted(nonzero_idx, key=lambda i: -len(xs[i]))
    if len(nonzero_sorted_idx) != len(xs):
        print("warning: Target sequences include empty tokenid")
    # remove zero-lenght samples
    xs = [xs[i] for i in nonzero_sorted_idx]
    ys = [np.fromiter(map(int, ys[i]), dtype=np.int64)
          for i in nonzero_sorted_idx]
    return xs, ys