class_docstr: Use unsupervised spatial filtering across time and samples.
  
  Parameters
  ----------
  estimator : instance of sklearn.base.BaseEstimator
      Estimator using some decomposition algorithm.
  average : bool, default False
      If True, the estimator is fitted on the average across samples
      (e.g. epochs).
docstr: Inverse transform the data to its original space.
  
  Parameters
  ----------
  X : array, shape (n_epochs, n_components, n_times)
      The data to be inverted.
  
  Returns
  -------
  X : array, shape (n_epochs, n_channels, n_times)
      The transformed data.
docstr_tok: ["Inverse","transform","the","data","to","its","original","space",".","Parameters","--","--","--","--","--","X",":","array",",","shape","(","n_epochs",",","n_components",",","n_times",")","The","data","to","be","inverted",".","Returns","--","--","--","-","X",":","array",",","shape","(","n_epochs",",","n_channels",",","n_times",")","The","transformed","data","."]
code: def inverse_transform(self, X):
      """Inverse transform the data to its original space.
  
      Parameters
      ----------
      X : array, shape (n_epochs, n_components, n_times)
          The data to be inverted.
  
      Returns
      -------
      X : array, shape (n_epochs, n_channels, n_times)
          The transformed data.
      """
      return self._apply_method(X, "inverse_transform")
  
imports: import numpy
  from .._fiff.pick import _pick_data_channels, _picks_by_type, _picks_to_idx, pick_info, pick_types
  from ..cov import _check_scalings_user
  from ..filter import filter_data
  from ..time_frequency import psd_array_multitaper
  from ..utils import _check_option, _validate_type, fill_doc, verbose
  from .base import BaseEstimator
  from .mixin import TransformerMixin
label_desc: Use image node_id 5 for calling the UnsupervisedSpatialFilter obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: self