code: def get_input_grads(self, merge_multi_context=True):
          
          assert self.binded and self.params_initialized and self.inputs_need_grad
          return self._modules[0].get_input_grads(merge_multi_context=merge_multi_context)
docs: Gets the gradients with respect to the inputs of the module.
  
          Parameters
          ----------
          merge_multi_context : bool
              Default is ``True``. In the case when data-parallelism is used, the outputs
              will be collected from multiple devices. A ``True`` value indicate that we
              should merge the collected results so that they look like from a single
              executor.
  
          Returns
          -------
          list of NDArrays or list of list of NDArrays
              If `merge_multi_context` is ``True``, it is like ``[grad1, grad2]``. Otherwise, it
              is like ``[[grad1_dev1, grad1_dev2], [grad2_dev1, grad2_dev2]]``. All the output
              elements are `NDArray`.