code: def prepare(self, param_groups) -> None:
      assert (
          dist.is_initialized()
      ), "torch.distributed needs to be initialized to prepare this rank"
  
      def optimizer_constructor(param_groups: Any, *args, **kwargs):
          # ClassyOptimizer have deferred initialization, while OSS needs access to the
          # raw optimizer instance, hence the trampoline
          logging.debug("Building a ZeRO enabled optimizer")
          self.base_optimizer.prepare(param_groups)
          return self.base_optimizer.optimizer
  
      self.optimizer = OSS(
          params=param_groups, optim=optimizer_constructor
      )
  
file_path: ClassyVision/classy_vision/optim/zero.py
imports: import logging
  from typing import Any
  import torch.distributed
  from classy_vision.generic.distributed_util import get_primary_rank
  from classy_vision.optim import build_optimizer, ClassyOptimizer, register_optimizer
label_desc: Use image node_id 2 for calling the ZeRO obj's underlying member method code with example usage: obj.prepare(param_groups) and returns: self