code: def prepare_model_optimizer(self, model):
      # Initialize torch distributed
      deepspeed.init_distributed(dist_backend="nccl")
  
      # FIXME
      from dataclasses import dataclass
  
      @dataclass
      class TmpClass:
          local_rank: int
  
      fake_arg = TmpClass(self.fs_args.device_id)
      # DeepSpeed initializer handles FP16, distributed, optimizer automatically.
      self.model, self.optimizer, _, _ = deepspeed.initialize(
          args=fake_arg,
          model=model,
          model_parameters=model.parameters(),
          config_params=self.ds_config,
      )
  
imports: import math
  import torch
  from torch import nn
  import torch.distributed
  import deepspeed
  from deepspeed.utils import log_dist
  from fairseq import tasks, distributed_utils
  from fairseq.logging import metrics
  from ds_fairseq_data import BatchIterator
  from ds_fairseq_argument import gen_ds_fairseq_arg
label_desc: Use image node_id 2 for calling the DsFairseqTrainer obj's underlying member method code with example usage: obj.prepare_model_optimizer(model) without return types