code: def test_fill_mean_multichannel(self):
      samples, sample_rate = load_sound_file(
          os.path.join(DEMO_DIR, "background_noises", "hens.ogg"),
          sample_rate=None,
          mono=False,
      )
      assert samples.shape[0] == 2
      magnitude_spectrogram_chn0 = librosa.feature.melspectrogram(
          y=np.asfortranarray(samples[0, :]), sr=sample_rate
      )
      magnitude_spectrogram_chn1 = librosa.feature.melspectrogram(
          y=np.asfortranarray(samples[1, :]), sr=sample_rate
      )
      multichannel_magnitude_spectrogram = np.zeros(
          shape=(
              magnitude_spectrogram_chn0.shape[0],
              magnitude_spectrogram_chn0.shape[1],
              3,
          ),
          dtype=np.float32,
      )
      multichannel_magnitude_spectrogram[
          :, :, 0
      ] = magnitude_spectrogram_chn0
      multichannel_magnitude_spectrogram[
          :, :, 1
      ] = magnitude_spectrogram_chn1
      multichannel_magnitude_spectrogram[
          :, :, 2
      ] = magnitude_spectrogram_chn1
  
      mask_fraction = 0.05
      transform = SpecFrequencyMask(
          fill_mode="mean",
          min_mask_fraction=mask_fraction,
          max_mask_fraction=mask_fraction,
          p=1.0,
      )
      augmented_spectrogram = transform(
          multichannel_magnitude_spectrogram
      )
  
      if DEBUG:
          image = (7 + np.log10(augmented_spectrogram + 0.0000001)) / 8
          plot_matrix(image)
  
      with np.testing.assert_raises(AssertionError):
          np.testing.assert_array_equal(
              augmented_spectrogram, multichannel_magnitude_spectrogram
          )
  
      num_masked_frequencies = 0
      for i in range(augmented_spectrogram.shape[0]):
          frequency_slice = augmented_spectrogram[i]
          if (
              np.amin(frequency_slice) == np.amax(frequency_slice)
              and np.sum(frequency_slice) != 0.0
          ):
              num_masked_frequencies += 1
  
      assert num_masked_frequencies == int(
          round(
              multichannel_magnitude_spectrogram.shape[0]
              * mask_fraction
          )
      )
  
file_path: audiomentations/tests/test_spec_frequency_mask.py
imports: import os
  import librosa
  import numpy
  from audiomentations import SpecFrequencyMask
  from audiomentations.core.audio_loading_utils import load_sound_file
  from demo.demo import DEMO_DIR
  from .utils import plot_matrix
label_desc: Use image node_id 4 for calling the TestSpecFrequencyMask obj's underlying member method code with example usage: obj.test_fill_mean_multichannel() without return types