code: def _unscale_grads(self, multiply_grads=1.0):
      if self._grads_are_scaled:
          self._grads_are_scaled = False
  
          # correct for dynamic loss scaler
          self.wrapped_optimizer.multiply_grads(
              multiply_grads / self.scaler.loss_scale
          )
      else:
          assert multiply_grads == 1.0
  
imports: from itertools import chain
  import torch
  from fairseq import utils
  from fairseq.optim.fp16_optimizer import DynamicLossScaler
label_desc: Use image node_id 5 for calling the Fairseq_MemoryEfficientFP16OptimizerMixin obj's underlying member method code with example usage: obj._unscale_grads(multiply_grads) without return types