class_docstr: Standardize channel data.
  
  This class scales data for each channel. It differs from scikit-learn
  classes (e.g., :class:`sklearn.preprocessing.StandardScaler`) in that
  it scales each *channel* by estimating μ and σ using data from all
  time points and epochs, as opposed to standardizing each *feature*
  (i.e., each time point for each channel) by estimating using μ and σ
  using data from all epochs.
  
  Parameters
  ----------
  %(info)s Only necessary if ``scalings`` is a dict or None.
  scalings : dict, str, default None
      Scaling method to be applied to data channel wise.
  
      * if scalings is None (default), scales mag by 1e15, grad by 1e13,
        and eeg by 1e6.
      * if scalings is :class:`dict`, keys are channel types and values
        are scale factors.
      * if ``scalings=='median'``,
        :class:`sklearn.preprocessing.RobustScaler`
        is used (requires sklearn version 0.17+).
      * if ``scalings=='mean'``,
        :class:`sklearn.preprocessing.StandardScaler`
        is used.
  
  with_mean : bool, default True
      If True, center the data using mean (or median) before scaling.
      Ignored for channel-type scaling.
  with_std : bool, default True
      If True, scale the data to unit variance (``scalings='mean'``),
      quantile range (``scalings='median``), or using channel type
      if ``scalings`` is a dict or None).
docstr: Invert standardization of data across channels.
  
  Parameters
  ----------
  epochs_data : array, shape (n_epochs, n_channels, n_times)
      The data.
  
  Returns
  -------
  X : array, shape (n_epochs, n_channels, n_times)
      The data concatenated over channels.
  
  Notes
  -----
  This function makes a copy of the data before the operations and the
  memory usage may be large with big data.
docstr_tok: ["Invert","standardization","of","data","across","channels",".","Parameters","--","--","--","--","--","epochs_data",":","array",",","shape","(","n_epochs",",","n_channels",",","n_times",")","The","data",".","Returns","--","--","--","-","X",":","array",",","shape","(","n_epochs",",","n_channels",",","n_times",")","The","data","concatenated","over","channels",".","Notes","--","--","-","This","function","makes","a","copy","of","the","data","before","the","operations","and","the","memory","usage","may","be","large","with","big","data","."]
code: def inverse_transform(self, epochs_data):
      """Invert standardization of data across channels.
  
      Parameters
      ----------
      epochs_data : array, shape (n_epochs, n_channels, n_times)
          The data.
  
      Returns
      -------
      X : array, shape (n_epochs, n_channels, n_times)
          The data concatenated over channels.
  
      Notes
      -----
      This function makes a copy of the data before the operations and the
      memory usage may be large with big data.
      """
      assert epochs_data.ndim == 3, epochs_data.shape
      return _sklearn_reshape_apply(
          self._scaler.inverse_transform, True, epochs_data
      )
  
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 Scaler obj's underlying member method code with example usage: obj.inverse_transform(epochs_data) and returns: _sklearn_reshape_apply