class_docstr: Strategy for distributed training using torch.distributed.
class_docstr_tok: ["Strategy","for","distributed","training","using","torch.distributed","."]
code: def _try_init_dist(self, force: bool = False) -> None:
      try:
          rank = int(os.environ["RANK"])
          local_rank = int(os.environ["LOCAL_RANK"])
          world_size = int(os.environ["WORLD_SIZE"])
          host = os.environ["MASTER_ADDR"]
          port = int(os.environ["MASTER_PORT"])
          dist.init_process_group(
              "nccl",
              init_method=f"tcp://[{host}]:{port}",
              world_size=world_size,
              rank=rank,
          )
          torch.cuda.set_device(local_rank)
      except KeyError as e:
          if force:
              raise RuntimeError(
                  f"Could not find {e} in the torch environment, visit https://www.colossalai.org/ for more information on launching with torch"
              )
      except Exception as e:
          if force:
              raise e
  
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 2 for calling the DDPStrategy obj's underlying member method code with example usage: obj._try_init_dist(force) without return types