class_docstr: Strategy for distributed training using torch.distributed.
class_docstr_tok: ["Strategy","for","distributed","training","using","torch.distributed","."]
code: def unwrap_model(self, model: nn.Module) -> nn.Module:
      assert isinstance(
          model, TorchDDPModel
      ), "model is not wrapped by TorchDDPModel."
      return model.unwrap()
  
imports: import os
  import random
  from collections import OrderedDict
  from typing import Callable, Optional
  import numpy
  import torch
  import torch.distributed
  import torch.nn
  from coati.experience_buffer import ExperienceBuffer
  from coati.models import Actor, Critic, RewardModel
  from torch.utils.data import DataLoader
  from transformers.modeling_utils import PreTrainedModel
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  from colossalai.booster.plugin import TorchDDPPlugin
  from colossalai.booster.plugin.torch_ddp_plugin import TorchDDPModel
  from .base import Strategy
  from .sampler import DistributedSampler
label_desc: Use image node_id 8 for calling the DDPStrategy obj's underlying member method code with example usage: obj.unwrap_model(model) and returns: model