class_docstr: Regularization of SPD matrices by shrinkage.
  
  This transformer applies a shrinkage regularization to any SPD matrix.
  It directly uses the `shrunk_covariance` function from scikit-learn [1]_,
  applied on each input.
  
  Parameters
  ----------
  shrinkage : float, default=0.1
      Coefficient in the convex combination used for the computation of the
      shrunk estimate. Must be between 0 and 1.
  
  Notes
  -----
  .. versionadded:: 0.2.5
  
  References
  ----------
  .. [1] https://scikit-learn.org/stable/modules/generated/sklearn.covariance.shrunk_covariance.html
docstr: Shrink and return the SPD matrices.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of SPD matrices.
  
  Returns
  -------
  covmats : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of shrunk SPD matrices.
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code: def transform(self, X):
      """Shrink and return the SPD matrices.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_channels)
          Set of SPD matrices.
  
      Returns
      -------
      covmats : ndarray, shape (n_matrices, n_channels, n_channels)
          Set of shrunk SPD matrices.
      """
  
      covmats = np.zeros_like(X)
  
      for ii, x in enumerate(X):
          covmats[ii] = shrunk_covariance(x, self.shrinkage)
  
      return covmats
  
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 3 for calling the Shrinkage obj's underlying member method code with example usage: obj.transform(X) and returns: covmats