code: def __init__(self, optimizer, aggregate_gnorms=False):
      self.fp16_params = self._get_parameters(optimizer)
      self.fp32_params = self._build_fp32_params(
          self.fp16_params, flatten=False
      )
      self.optimizer = optimizer
  
      # we want the optimizer to be tracking the fp32 parameters
      if len(optimizer.param_groups) != 1:
          # future implementers: this should hopefully be a matter of just
          # iterating through the param groups and keeping track of the pointer
          # through the fp32_params
          raise NotImplementedError(
              "Need to implement the parameter group transfer."
          )
      optimizer.param_groups[0]["params"] = self.fp32_params
  
      self.scaler = DynamicLossScaler(2.0**15)
      self.min_loss_scale = 2**-5
      self._aggregate_gnorms = aggregate_gnorms
  
imports: import math
  from itertools import chain
  from typing import Optional
  import parlai.utils.logging
label_desc: Use image node_id 1 to create a new SafeFP16Optimizer object from inherited base classes: torch.optim with example: obj = SafeFP16Optimizer(optimizer, aggregate_gnorms)