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: Fit.
  
  Do nothing. For compatibility purpose.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of SPD matrices.
  y : None
      Not used, here for compatibility with sklearn API.
  
  Returns
  -------
  self : Shrinkage instance
      The Shrinkage 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_channels)
          Set of SPD matrices.
      y : None
          Not used, here for compatibility with sklearn API.
  
      Returns
      -------
      self : Shrinkage instance
          The Shrinkage 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 Shrinkage obj's underlying member method code with example usage: obj.fit(X, y) and returns: self