code: def _clip_by_global_norm(self, max_norm):
          
          assert self._module.binded and self._module.params_initialized \
                 and self._module.optimizer_initialized
          grad_array = []
          for grad in self._module._exec_group.grad_arrays:
              grad_array += grad
          return mx.gluon.utils.clip_global_norm(grad_array, max_norm)
docs: Clips gradient norm.
  
          The norm is computed over all gradients together, as if they were
          concatenated into a single vector. Gradients are modified in-place.
          The method is first used in
           `[ICML2013] On the difficulty of training recurrent neural networks`
  
          Parameters
          ----------
          max_norm : float or int
              The maximum clipping threshold of the gradient norm.
  
          Returns
          -------
          norm_val : float
              The computed norm of the gradients.