code: def test_mfcc(self, n_fft, hop_length, n_mels, n_mfcc):
      sample_rate = 16000
      waveform = get_whitenoise(
          sample_rate=sample_rate, n_channels=1
      ).to(self.device, self.dtype)
      result = T.MFCC(
          sample_rate=sample_rate,
          n_mfcc=n_mfcc,
          norm="ortho",
          melkwargs={
              "hop_length": hop_length,
              "n_fft": n_fft,
              "n_mels": n_mels,
          },
      ).to(self.device, self.dtype)(waveform)[0]
  
      melspec = librosa.feature.melspectrogram(
          y=waveform[0].cpu().numpy(),
          sr=sample_rate,
          n_fft=n_fft,
          win_length=n_fft,
          hop_length=hop_length,
          n_mels=n_mels,
          htk=True,
          norm=None,
          pad_mode="reflect",
      )
      expected = librosa.feature.mfcc(
          S=librosa.core.spectrum.power_to_db(melspec),
          n_mfcc=n_mfcc,
          dct_type=2,
          norm="ortho",
      )
      self.assertEqual(
          result, torch.from_numpy(expected), atol=5e-4, rtol=1e-5
      )
  
file_path: audio/test/torchaudio_unittest/transforms/librosa_compatibility_test_impl.py
imports: import unittest
  import torch
  import torchaudio.transforms
  from parameterized import param, parameterized
  from torchaudio._internal.module_utils import is_module_available
  from torchaudio_unittest.common_utils import get_sinusoid, get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
label_desc: Use image node_id 6 for calling the TransformsTestBase obj's underlying member method code with example usage: obj.test_mfcc(n_fft, hop_length, n_mels, n_mfcc) without return types