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: Init.
docstr_tok: ["Init","."]
code: def __init__(self, shrinkage=0.1):
      """Init."""
      self.shrinkage = shrinkage
  
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 1 to create a new Shrinkage object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = Shrinkage(shrinkage)