class_docstr: Transform n-dimensional array into 2D array of n_samples by n_features.
  
  This class reshapes an n-dimensional array into an n_samples * n_features
  array, usable by the estimators and transformers of scikit-learn.
  
  Attributes
  ----------
  features_shape_ : tuple
       Stores the original shape of data.
  
  Examples
  --------
  clf = make_pipeline(SpatialFilter(), _XdawnTransformer(), Vectorizer(),
                      LogisticRegression())
docstr: Transform 2D data back to its original feature shape.
  
  Parameters
  ----------
  X : array-like, shape (n_samples,  n_features)
      Data to be transformed back to original shape.
  
  Returns
  -------
  X : array
      The data transformed into shape as used in fit. The first
      dimension is of length n_samples.
docstr_tok: ["Transform","2D","data","back","to","its","original","feature","shape",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_samples",",","n_features",")","Data","to","be","transformed","back","to","original","shape",".","Returns","--","--","--","-","X",":","array","The","data","transformed","into","shape","as","used","in","fit",".","The","first","dimension","is","of","length","n_samples","."]
code: def inverse_transform(self, X):
      """Transform 2D data back to its original feature shape.
  
      Parameters
      ----------
      X : array-like, shape (n_samples,  n_features)
          Data to be transformed back to original shape.
  
      Returns
      -------
      X : array
          The data transformed into shape as used in fit. The first
          dimension is of length n_samples.
      """
      X = np.asarray(X)
      if X.ndim not in (2, 3):
          raise ValueError(
              "X should be of 2 or 3 dimensions but has shape "
              f"{X.shape}"
          )
      return X.reshape(X.shape[:-1] + self.features_shape_)
  
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 4 for calling the Vectorizer obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X