How to use the torchaudio.transforms.MFCC function in torchaudio

def test_scriptmodule_MFCC(self):
        tensor = torch.rand((1, 1000), device="cuda")

        self._test_script_module(tensor, transforms.MFCC)

# check defaults
        torch_mfcc = mfcc_transform(audio_scaled)  # (1, 40, 321)
        self.assertTrue(torch_mfcc.dim() == 3)
        self.assertTrue(torch_mfcc.shape[1] == n_mfcc)
        self.assertTrue(torch_mfcc.shape[2] == 321)
        # check melkwargs are passed through
        melkwargs = {'win_length': 200}
        mfcc_transform2 = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                     n_mfcc=n_mfcc,
                                                     norm='ortho',
                                                     melkwargs=melkwargs)
        torch_mfcc2 = mfcc_transform2(audio_scaled)  # (1, 40, 641)
        self.assertTrue(torch_mfcc2.shape[2] == 641)

        # check norms work correctly
        mfcc_transform_norm_none = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                              n_mfcc=n_mfcc,
                                                              norm=None)
        torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled)  # (1, 40, 321)

        norm_check = torch_mfcc.clone()
        norm_check[:, 0, :] *= math.sqrt(n_mels) * 2
        norm_check[:, 1:, :] *= math.sqrt(n_mels / 2) * 2

        self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))

# test s2db
            db_transform = torchaudio.transforms.AmplitudeToDB('power', 80.)
            db_torch = db_transform(spect_transform(sound)).squeeze().cpu()
            db_librosa = librosa.core.spectrum.power_to_db(out_librosa)
            self.assertTrue(torch.allclose(db_torch, torch.from_numpy(db_librosa), atol=5e-3))

            db_torch = db_transform(melspect_transform(sound)).squeeze().cpu()
            db_librosa = librosa.core.spectrum.power_to_db(librosa_mel)
            db_librosa_tensor = torch.from_numpy(db_librosa)

            self.assertTrue(torch.allclose(db_torch.type(db_librosa_tensor.dtype), db_librosa_tensor, atol=5e-3))

            # test MFCC
            melkwargs = {'hop_length': hop_length, 'n_fft': n_fft}
            mfcc_transform = torchaudio.transforms.MFCC(sample_rate=sample_rate,
                                                        n_mfcc=n_mfcc,
                                                        norm='ortho',
                                                        melkwargs=melkwargs)

            # librosa.feature.mfcc doesn't pass kwargs properly since some of the
            # kwargs for melspectrogram and mfcc are the same. We just follow the
            # function body in https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram
            # to mirror this function call with correct args:

    #         librosa_mfcc = librosa.feature.mfcc(y=sound_librosa,
    #                                             sr=sample_rate,
    #                                             n_mfcc = n_mfcc,
    #                                             hop_length=hop_length,
    #                                             n_fft=n_fft,
    #                                             htk=True,
    #                                             norm=None,

mel_kwargs['n_fft'] = n_fft or 2 ** math.ceil(math.log2(n_window_size))

        mel_kwargs['win_length'] = n_window_size
        mel_kwargs['hop_length'] = n_window_stride

        # Set window_fn. None defaults to torch.ones.
        window_fn = self.torch_windows.get(window, None)
        if window_fn is None:
            raise ValueError(
                f"Window argument for AudioProcessor is invalid: {window}."
                f"For no window function, use 'ones' or None.")
        mel_kwargs['window_fn'] = window_fn

        # Use torchaudio's implementation of MFCCs as featurizer
        self.featurizer = torchaudio.transforms.MFCC(
            sample_rate=sample_rate,
            n_mfcc=n_mfcc,
            dct_type=dct_type,
            norm=norm,
            log_mels=log,
            melkwargs=mel_kwargs
        )
        self.featurizer.to(self._device)

mel_kwargs['n_fft'] = n_fft or 2 ** math.ceil(math.log2(n_window_size))

        mel_kwargs['win_length'] = n_window_size
        mel_kwargs['hop_length'] = n_window_stride

        # Set window_fn. None defaults to torch.ones.
        window_fn = self.torch_windows.get(window, None)
        if window_fn is None:
            raise ValueError(
                f"Window argument for AudioProcessor is invalid: {window}."
                f"For no window function, use 'ones' or None.")
        mel_kwargs['window_fn'] = window_fn

        # Use torchaudio's implementation of MFCCs as featurizer
        self.featurizer = torchaudio.transforms.MFCC(
            sample_rate=sample_rate,
            n_mfcc=n_mfcc,
            dct_type=dct_type,
            norm=norm,
            log_mels=log,
            melkwargs=mel_kwargs
        )
        self.featurizer.to(self._device)

class AmplitudeToDB:
    forward = torchaudio.transforms.AmplitudeToDB().forward


class MelScale:
    forward = torchaudio.transforms.MelScale().forward


class MelSpectrogram:
    forward = torchaudio.transforms.MelSpectrogram().forward


class MFCC:
    forward = torchaudio.transforms.MFCC().forward


class MuLawEncoding:
    forward = torchaudio.transforms.MuLawEncoding().forward


class MuLawDecoding:
    forward = torchaudio.transforms.MuLawDecoding().forward


class Resample:
    # Resample isn't a script_method
    forward = torchaudio.transforms.Resample.forward

def __init__(self, sample_rate=16000, n_mfcc=40, dct_type=2, norm='ortho', log_mels=False,
                 melkwargs=None):
        super(MFCC, self).__init__()
        supported_dct_types = [2]
        if dct_type not in supported_dct_types:
            raise ValueError('DCT type not supported'.format(dct_type))
        self.sample_rate = sample_rate
        self.n_mfcc = n_mfcc
        self.dct_type = dct_type
        self.norm = norm
        self.top_db = 80.0
        self.amplitude_to_DB = AmplitudeToDB('power', self.top_db)

        if melkwargs is not None:
            self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate, **melkwargs)
        else:
            self.MelSpectrogram = MelSpectrogram(sample_rate=self.sample_rate)

        if self.n_mfcc > self.MelSpectrogram.n_mels: