class_docstr: Estimation of block covariance matrix.
  
  Perform a block covariance estimation for each given matrix. The
  resulting matrices are block diagonal matrices.
  
  The blocks on the diagonal are calculated as individual covariance
  matrices for a subset of channels using the given the estimator.
  Varying block sized possible by passing a list to allow incorporation
  of different modalities with different number of channels (e.g. EEG,
  ECoG, LFP, EMG) with their own respective covariance matrices.
  
  Parameters
  ----------
  block_size : int | list of int
      Sizes of individual blocks given as int for same-size block, or list
      for varying block sizes.
  estimator : string, default='scm'
      Covariance matrix estimator, see
      :func:`pyriemann.utils.covariance.covariances`.
  **kwds : dict
      Any further parameters are passed directly to the covariance estimator.
  
  Notes
  -----
  .. versionadded:: 0.3
  
  See Also
  --------
  Covariances
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 : BlockCovariances instance
      The BlockCovariances 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 : BlockCovariances instance
          The BlockCovariances 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 BlockCovariances obj's underlying member method code with example usage: obj.fit(X, y) and returns: self