class_docstr: Estimation of cospectral covariance matrix.
  
  Co-spectral matrices are the real part of complex cross-spectral matrices
  (see :func:`pyriemann.utils.covariance.cross_spectrum`), estimated as the
  spectrum covariance in the frequency domain. This method returns a 4-d
  array with a cospectral covariance matrix for each input and in each
  frequency bin of the FFT.
  
  Parameters
  ----------
  window : int, default=128
      The length of the FFT window used for spectral estimation.
  overlap : float, default=0.75
      The percentage of overlap between window.
  fmin : float | None, default=None
      The minimal frequency to be returned.
  fmax : float | None, default=None
      The maximal frequency to be returned.
  fs : float | None, default=None
      The sampling frequency of the signal.
  
  Attributes
  ----------
  freqs_ : ndarray, shape (n_freqs,)
      If transformed, the frequencies associated to cospectra.
      None if ``fs`` is None.
  
  See Also
  --------
  Covariances
  HankelCovariances
  Coherences
docstr: Fit.
  
  Do nothing. For compatibility purpose.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_times)
      Multi-channel time-series.
  y : None
      Not used, here for compatibility with sklearn API.
  
  Returns
  -------
  self : CospCovariances instance
      The CospCovariances instance.
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code: def fit(self, X, y=None):
      """Fit.
  
      Do nothing. For compatibility purpose.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_times)
          Multi-channel time-series.
      y : None
          Not used, here for compatibility with sklearn API.
  
      Returns
      -------
      self : CospCovariances instance
          The CospCovariances instance.
      """
      return self
  
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.covariance import shrunk_covariance
  from sklearn.metrics.pairwise import pairwise_kernels
  from .spatialfilters import Xdawn
  from .utils.covariance import covariances, covariances_EP, cospectrum, coherence, block_covariances
label_desc: Use image node_id 2 for calling the CospCovariances obj's underlying member method code with example usage: obj.fit(X, y) and returns: self