code: def _sync_fp16_grads_to_fp32(self, multiply_grads=1.0):
      if self._needs_sync:
          # copy FP16 grads to FP32
          offset = 0
          for p in self.fp16_params:
              if not p.requires_grad:
                  continue
              grad_data = (
                  p.grad.data
                  if p.grad is not None
                  else p.data.new_zeros(p.data.shape)
              )
              numel = grad_data.numel()
              self.fp32_params.grad.data[offset : offset + numel].copy_(
                  grad_data.view(-1)
              )
              offset += numel
  
          # correct for dynamic loss scaler
          self.fp32_params.grad.data.mul_(
              multiply_grads / self.scaler.loss_scale
          )
  
          self._needs_sync = False
  
imports: from itertools import chain
  import torch
  from fairseq import utils
  from fairseq.optim.fp16_optimizer import DynamicLossScaler
label_desc: Use image node_id 6 for calling the Fairseq_FP16OptimizerMixin obj's underlying member method code with example usage: obj._sync_fp16_grads_to_fp32(multiply_grads) without return types