class_docstr: Performs loss scaling during backward pass to prevent underflow of half
  precision gradients.
class_docstr_tok: ["Performs","loss","scaling","during","backward","pass","to","prevent","underflow","of","half","precision","gradients","."]
docstr: Unscale the associated optimizer's gradients by the current scale.
docstr_tok: ["Unscale","the","associated","optimizer","'s","gradients","by","the","current","scale","."]
code: def unscale_optimizer_grads_(self) -> None:
      """Unscale the associated optimizer's gradients by the current scale."""
      self._grad_scaler.unscale_(self.optimizer)
  
imports: from __future__ import annotations
  import logging
  import math
  from dataclasses import dataclass
  from logging import Logger
  from typing import Any, Callable, Dict, Mapping, Optional, Tuple, cast
  import torch
  from torch import Tensor
  from torch.cuda.amp.grad_scaler import GradScaler
  from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
  from torch.optim import Optimizer
  from fairseq2.gang import Gang
label_desc: Use image node_id 7 for calling the DynamicLossScaler obj's underlying member method code with example usage: obj.unscale_optimizer_grads_() without return types