code: def transform(self, X):
          
          with Pool(self.n_jobs) as pool:
              Xs = pool.starmap(_transform_one, ((trans, weight, X[trans['col_pick']] if hasattr(trans, 'col_pick')
                      else X) for name, trans, weight in self._iter()))
          if not Xs:
              # All transformers are None
              return np.zeros((X.shape[0], 0))
          if self.concatenate:
              if any(sparse.issparse(f) for f in Xs):
                  Xs = sparse.hstack(Xs).tocsr()
              else:
                  Xs = np.hstack(Xs)
          return Xs
docs: Transform X separately by each transformer, concatenate results.
  
          Parameters
          ----------
          X : iterable or array-like, depending on transformers
              Input data to be transformed.
  
          Returns
          -------
          X_t : array-like or sparse matrix, shape (n_samples, sum_n_components)
              hstack of results of transformers. sum_n_components is the
              sum of n_components (output dimension) over transformers.