code: def _optimize_fp16(self, opt: th.optim.Optimizer):
      logger.logkv_mean("lg_loss_scale", self.lg_loss_scale)
      model_grads_to_master_grads(
          self.param_groups_and_shapes, self.master_params
      )
      grad_norm, param_norm = self._compute_norms(
          grad_scale=2**self.lg_loss_scale
      )
      if check_overflow(grad_norm):
          self.lg_loss_scale -= 1
          logger.log(
              f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}"
          )
          zero_master_grads(self.master_params)
          return False
  
      logger.logkv_mean("grad_norm", grad_norm)
      logger.logkv_mean("param_norm", param_norm)
  
      for p in self.master_params:
          p.grad.mul_(1.0 / (2**self.lg_loss_scale))
      opt.step()
      zero_master_grads(self.master_params)
      master_params_to_model_params(
          self.param_groups_and_shapes, self.master_params
      )
      self.lg_loss_scale += self.fp16_scale_growth
      return True
  
imports: import numpy
  import torch
  import torch.nn
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from .None import logger
label_desc: Use image node_id 5 for calling the MixedPrecisionTrainer obj's underlying member method code with example usage: obj._optimize_fp16(opt) and returns: True, False