parameters: (self, X, copy=True)
return_statement: return X
docstring: Transform the sources back to the mixed data (apply mixing matrix).
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_components)
              Sources, where n_samples is the number of samples
              and n_components is the number of components.
          copy : bool (optional)
              If False, data passed to fit are overwritten. Defaults to True.
  
          Returns
          -------
          X_new : array-like, shape (n_samples, n_features)
docstring_summary: Transform the sources back to the mixed data (apply mixing matrix).
docstring_tokens: Transform the sources back to the mixed data ( apply mixing matrix ) .
function: def inverse_transform(self, X, copy=True):
          """Transform the sources back to the mixed data (apply mixing matrix).
  
          Parameters
          ----------
          X : array-like, shape (n_samples, n_components)
              Sources, where n_samples is the number of samples
              and n_components is the number of components.
          copy : bool (optional)
              If False, data passed to fit are overwritten. Defaults to True.
  
          Returns
          -------
          X_new : array-like, shape (n_samples, n_features)
          """
          check_is_fitted(self)
  
          X = check_array(X, copy=(copy and self.whiten), dtype=FLOAT_DTYPES)
          X = np.dot(X, self.mixing_.T)
          if self.whiten:
              X += self.mean_
  
          return X
function_tokens: def inverse_transform ( self , X , copy = True ) : check_is_fitted ( self ) X = check_array ( X , copy = ( copy and self . whiten ) , dtype = FLOAT_DTYPES ) X = np . dot ( X , self . mixing_ . T ) if self . whiten : X += self . mean_ return X