class_docstr: Estimation of kernel matrix between channels of time series.
  
  Perform a kernel matrix estimation for each given time series, evaluating a
  kernel function between each pair of channels (rather than between pairs of
  time samples) and allowing to extract nonlinear channel relationship [1]_.
  
  For an input time series :math:`X \in \mathbb{R}^{c \times t}`, composed of
  :math:`c` channels and :math:`t` time samples, kernel function
  :math:`\kappa()` is computed between channels :math:`i` and :math:`j`:
  
  .. math::
      K_{i,j} = \kappa \left( X[i], X[j] \right)
  
  Linear kernel is related to :class:`pyriemann.estimation.Covariances` [1]_,
  but this class allows to generalize to nonlinear relationships.
  
  Parameters
  ----------
  metric : string, default='linear'
      The metric to use when computing kernel function between channels [2]_:
      'linear', 'poly', 'polynomial', 'rbf', 'laplacian', 'cosine'.
  n_jobs : int, default=None
      The number of jobs to use for the computation [2]_. This works by
      breaking down the pairwise matrix into n_jobs even slices and computing
      them in parallel.
  **kwds : dict
      Any further parameters are passed directly to the kernel function [2]_.
  
  See Also
  --------
  Covariances
  
  Notes
  -----
  .. versionadded:: 0.4
  
  References
  ----------
  .. [1] `Beyond Covariance: Feature Representation with Nonlinear Kernel
      Matrices
      <https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Beyond_Covariance_Feature_ICCV_2015_paper.pdf>`_
      L. Wang, J. Zhang, L. Zhou, C. Tang, W Li. ICCV, 2015.
  .. [2]
      https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.pairwise_kernels.html
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 : Kernels instance
      The Kernels 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 : Kernels instance
          The Kernels 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 Kernels obj's underlying member method code with example usage: obj.fit(X, y) and returns: self