class_docstr: Estimation of covariance matrix with time delayed Hankel matrices.
  
  Hankel covariance matrices [1]_ are useful to catch spectral dynamics of
  the signal, similarly to the CSSP method [2]_. It is done by concatenating
  time delayed version of the signal before covariance estimation.
  
  Parameters
  ----------
  delays : int | list of int, default=4
      The delays to apply for the Hankel matrices. If `int`, it use a range
      of delays up to the given value. A list of int can be given.
  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.
  
  See Also
  --------
  Covariances
  ERPCovariances
  CospCovariances
  
  References
  ----------
  .. [1] https://en.wikipedia.org/wiki/Hankel_matrix
  .. [2] `Spatio-spectral filters for improving the classification of single
      trial EEG
      <http://doc.ml.tu-berlin.de/bbci/publications/LemBlaCurMue05.pdf>`_
      S. Lemm, B. Blankertz, B. Curio, K-R. Muller. IEEE Transactions on
      Biomedical Engineering 52(9), 1541-1548, 2005.
docstr: Init.
docstr_tok: ["Init","."]
code: def __init__(self, delays=4, estimator="scm", **kwds):
      """Init."""
      self.delays = delays
      self.estimator = estimator
      self.kwds = kwds
  
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 HankelCovariances object from inherited base classes: BaseEstimator, TransformerMixin with example: obj = HankelCovariances(delays, estimator)