class_docstr: Implements test for Transforms that are performed for different devices
class_docstr_tok: ["Implements","test","for","Transforms","that","are","performed","for","different","devices"]
code: def test_rtf_mvdr(self):
      tensor = common_utils.get_whitenoise(
          sample_rate=8000, n_channels=4
      )
      specgram = common_utils.get_spectrogram(
          tensor, n_fft=400, hop_length=100
      )
      channel, freq, _ = specgram.shape
      rtf = torch.rand(
          freq, channel, dtype=self.complex_dtype, device=self.device
      )
      psd_n = torch.rand(
          freq,
          channel,
          channel,
          dtype=self.complex_dtype,
          device=self.device,
      )
      reference_channel = 0
      self._assert_consistency_complex(
          T.RTFMVDR(), specgram, rtf, psd_n, reference_channel
      )
  
imports: import torch
  import torchaudio.transforms
  from parameterized import parameterized
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import skipIfRocm, TestBaseMixin, torch_script
label_desc: Use image node_id 28 for calling the Transforms obj's underlying member method code with example usage: obj.test_rtf_mvdr() without return types