class_docstr: Computes the covariance matrices of the signals.
  
  Arguments:
  ----------
  average : bool
      Informs the module if it should return an average
      (computed on the time dimension) of the covariance
      matrices. The Default value is True.
  
  Example
  -------
  >>> import torch
  >>> from speechbrain.dataio.dataio import read_audio
  >>> from speechbrain.processing.features import STFT
  >>> from speechbrain.processing.multi_mic import Covariance
  >>>
  >>> xs_speech = read_audio(
  ...    'tests/samples/multi-mic/speech_-0.82918_0.55279_-0.082918.flac'
  ... )
  >>> xs_speech = xs_speech.unsqueeze(0) # [batch, time, channels]
  >>> xs_noise = read_audio('tests/samples/multi-mic/noise_diffuse.flac')
  >>> xs_noise = xs_noise.unsqueeze(0)
  >>> xs = xs_speech + 0.05 * xs_noise
  >>> fs = 16000
  
  >>> stft = STFT(sample_rate=fs)
  >>> cov = Covariance()
  >>>
  >>> Xs = stft(xs)
  >>> XXs = cov(Xs)
  >>> XXs.shape
  torch.Size([1, 1001, 201, 2, 10])
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code: def __init__(self, average=True):
      super().__init__()
      self.average = average
  
imports: import torch
  from packaging import version
  import speechbrain.processing.decomposition
label_desc: Use image node_id 1 to create a new Covariance object from inherited base classes: torch.nn with example: obj = Covariance(average)