class_docstr: Test suite for classes defined in `transforms` module
class_docstr_tok: ["Test","suite","for","classes","defined","in","`","transforms","`","module"]
code: def test_MVDR(self, multi_mask):
      waveform = common_utils.get_whitenoise(
          sample_rate=8000, duration=1, n_channels=6
      )
      specgram = common_utils.get_spectrogram(waveform, n_fft=400)
      specgram = specgram.reshape(
          3, 2, specgram.shape[-2], specgram.shape[-1]
      )
      if multi_mask:
          mask_s = torch.rand(
              (3, 2, specgram.shape[-2], specgram.shape[-1])
          )
          mask_n = torch.rand(
              (3, 2, specgram.shape[-2], specgram.shape[-1])
          )
      else:
          mask_s = torch.rand(
              (3, specgram.shape[-2], specgram.shape[-1])
          )
          mask_n = torch.rand(
              (3, specgram.shape[-2], specgram.shape[-1])
          )
      transform = T.MVDR(multi_mask=multi_mask)
  
      # Single then transform then batch
      expected = [
          transform(specgram[i], mask_s[i], mask_n[i]) for i in range(3)
      ]
      expected = torch.stack(expected)
  
      # Batch then transform
      computed = transform(specgram, mask_s, mask_n)
  
      self.assertEqual(computed, expected)
  
imports: import os
  import torch
  from parameterized import parameterized
  from torchaudio import transforms
  from torchaudio_unittest import common_utils
label_desc: Use image node_id 20 for calling the TestTransforms obj's underlying member method code with example usage: obj.test_MVDR(multi_mask) without return types