code: def multi_node_mean_grad(self, model, zero_fill=False):
      params = _memory_utility.extract_params_set_grad(model, zero_fill)
      for param in params:
          if zero_fill and param.grad is None:
              if param.data is None:
                  continue
              param.grad = param.xp.zeros_like(param.data)
          self._multi_node_mean(None, param.grad)
  
imports: from chainermn.communicators import _memory_utility
  from chainermn.communicators import mpi_communicator_base
label_desc: Use image node_id 2 for calling the NaiveCommunicator obj's underlying member method code with example usage: obj.multi_node_mean_grad(model, zero_fill) without return types