parameters: (self, X)
return_statement: return self._transform(X, inverse=True)
docstring: Back-projection to the original space.
  
          Parameters
          ----------
          X : ndarray or sparse matrix, shape (n_samples, n_features)
              The data used to scale along the features axis. If a sparse
              matrix is provided, it will be converted into a sparse
              ``csc_matrix``. Additionally, the sparse matrix needs to be
              nonnegative if `ignore_implicit_zeros` is False.
  
          Returns
          -------
          Xt : ndarray or sparse matrix, shape (n_samples, n_features)
              The projected data.
docstring_summary: Back-projection to the original space.
docstring_tokens: Back - projection to the original space .
function: def inverse_transform(self, X):
          """Back-projection to the original space.
  
          Parameters
          ----------
          X : ndarray or sparse matrix, shape (n_samples, n_features)
              The data used to scale along the features axis. If a sparse
              matrix is provided, it will be converted into a sparse
              ``csc_matrix``. Additionally, the sparse matrix needs to be
              nonnegative if `ignore_implicit_zeros` is False.
  
          Returns
          -------
          Xt : ndarray or sparse matrix, shape (n_samples, n_features)
              The projected data.
          """
          X = self._check_inputs(X, accept_sparse_negative=True, copy=self.copy)
          self._check_is_fitted(X)
  
          return self._transform(X, inverse=True)
function_tokens: def inverse_transform ( self , X ) : X = self . _check_inputs ( X , accept_sparse_negative = True , copy = self . copy ) self . _check_is_fitted ( X ) return self . _transform ( X , inverse = True )