docstr: Clears the gradients of all optimized parameters.
docstr_tok: ["Clears","the","gradients","of","all","optimized","parameters","."]
code: def zero_grad(self):
      """Clears the gradients of all optimized parameters."""
      self.wrapped_optimizer.zero_grad()
      if self.scaler is not None:
          self._multiply_factor = 1.0 / float(self.scaler.loss_scale)
      else:
          self._multiply_factor = 1.0
  
imports: from collections import defaultdict
  from itertools import chain
  import torch
  from omegaconf import DictConfig
  from fairseq import optim
  from .dynamic_loss_scaler import DynamicLossScaler
label_desc: Use image node_id 10 for calling the _MemoryEfficientFP16OptimizerMixin obj's underlying member method code with example usage: obj.zero_grad() without return types