class_docstr: Estimation of covariance matrix.
  
  Perform a simple covariance matrix estimation for each given input.
  
  Parameters
  ----------
  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
  --------
  ERPCovariances
  XdawnCovariances
  CospCovariances
  HankelCovariances
docstr: Estimate covariance matrices.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_times)
      Multi-channel time-series.
  
  Returns
  -------
  covmats : ndarray, shape (n_matrices, n_channels, n_channels)
      Covariance matrices.
docstr_tok: ["Estimate","covariance","matrices",".","Parameters","--","--","--","--","--","X",":","ndarray",",","shape","(","n_matrices",",","n_channels",",","n_times",")","Multi-channel","time-series",".","Returns","--","--","--","-","covmats",":","ndarray",",","shape","(","n_matrices",",","n_channels",",","n_channels",")","Covariance","matrices","."]
code: def transform(self, X):
      """Estimate covariance matrices.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_times)
          Multi-channel time-series.
  
      Returns
      -------
      covmats : ndarray, shape (n_matrices, n_channels, n_channels)
          Covariance matrices.
      """
      covmats = covariances(X, estimator=self.estimator, **self.kwds)
      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 Covariances obj's underlying member method code with example usage: obj.transform(X) and returns: covmats