code: def update(self):
          
          assert self.binded and self.params_initialized and self.optimizer_initialized
          self._params_dirty = True
          self._curr_module.update()
docs: Updates parameters according to installed optimizer and the gradient computed
          in the previous forward-backward cycle.
  
          When KVStore is used to update parameters for multi-device or multi-machine training,
          a copy of the parameters are stored in KVStore. Note that for `row_sparse` parameters,
          this function does update the copy of parameters in KVStore, but doesn't broadcast the
          updated parameters to all devices / machines. Please call `prepare` to broadcast
          `row_sparse` parameters with the next batch of data.