code: def _sync_fp16_grads_to_fp32(self, multiply_grads=1.0):
      if self._needs_sync:
          if self.scaler is not None:
              # correct for dynamic loss scaler
              multiply_grads /= self.scaler.loss_scale
  
          # copy FP16 grads to FP32
          for p, p32 in zip(self.fp16_params, self.fp32_params):
              if not p.requires_grad:
                  continue
              if p.grad is not None:
                  p32.grad.data.copy_(p.grad.data)
                  p32.grad.data.mul_(multiply_grads)
              else:
                  p32.grad = torch.zeros_like(p.data, dtype=torch.float)
  
          self._needs_sync = False
  
imports: import math
  from itertools import chain
  from typing import Optional
  import parlai.utils.logging
label_desc: Use image node_id 7 for calling the SafeFP16Optimizer obj's underlying member method code with example usage: obj._sync_fp16_grads_to_fp32(multiply_grads) without return types