parameters: (self, merge_multi_context=True)
return_statement: return self._exec_group.get_input_grads(merge_multi_context=merge_multi_context)
docstring: Gets the gradients with respect to the inputs of the module.
  
          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`.
  
          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 NDArray or list of list of NDArray
                Input gradients
docstring_summary: Gets the gradients with respect to the inputs of the module.
docstring_tokens: Gets the gradients with respect to the inputs of the module .
function: def get_input_grads(self, merge_multi_context=True):
          """Gets the gradients with respect to the inputs of the module.
  
          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`.
  
          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 NDArray or list of list of NDArray
                Input gradients
          """
          assert self.binded and self.params_initialized and self.inputs_need_grad
          return self._exec_group.get_input_grads(merge_multi_context=merge_multi_context)
function_tokens: def get_input_grads ( self , merge_multi_context = True ) : assert self . binded and self . params_initialized and self . inputs_need_grad return self . _exec_group . get_input_grads ( merge_multi_context = merge_multi_context )