class_docstr: A version of the LARC optimizer which works with FSDP:
  https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
  
  Args:
      optimizer: Pytorch optimizer to wrap and modify learning rate for.
      distributed_norm:
          if True compute the norm in a distributed fashion
          if False, revert to the same computations as APEX LARC
      trust_coefficient:
          Trust coefficient for calculating the lr.
          See https://arxiv.org/abs/1708.03888
      clip: Decides between clipping or scaling mode of LARC.
          If `clip=True` the learning rate is set to `min(optimizer_lr, local_lr)` for each parameter.
          If `clip=False` the learning rate is set to `local_lr*optimizer_lr`.
      eps: epsilon kludge to help with numerical stability while calculating adaptive_lr
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code: def __getstate__(self):
      return self.optim.__getstate__()
  
imports: from typing import Any, Dict
  import torch
  import torch.distributed
  from classy_vision.optim import register_optimizer, SGD
  from vissl.utils.fsdp_utils import get_global_group
label_desc: Use image node_id 2 for calling the LARC_FSDP obj's underlying member method code with example usage: obj.__getstate__() and returns: self