code: def init_ddp_connection(
      self, global_rank=None, world_size=None
  ) -> None:
      module = self.model
      global_rank = (
          global_rank
          if global_rank is not None
          else self.cluster_environment.global_rank()
      )
      world_size = (
          world_size
          if world_size is not None
          else self.cluster_environment.world_size()
      )
      os.environ[
          "MASTER_ADDR"
      ] = self.cluster_environment.master_address()
      os.environ["MASTER_PORT"] = str(
          self.cluster_environment.master_port()
      )
      if not torch.distributed.is_initialized():
          logger.info(
              f"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank + 1}/{world_size}"
          )
          torch_distrib.init_process_group(
              self.torch_distributed_backend,
              rank=global_rank,
              world_size=world_size,
          )
  
      if module.is_rag_model:
          self.distributed_port = module.hparams.distributed_port
          if module.distributed_retriever == "pytorch":
              module.model.rag.retriever.init_retrieval(
                  self.distributed_port
              )
          elif (
              module.distributed_retriever == "ray" and global_rank == 0
          ):
              # For the Ray retriever, only initialize it once when global
              # rank is 0.
              module.model.rag.retriever.init_retrieval()
  
imports: import argparse
  import logging
  import os
  import sys
  import time
  from collections import defaultdict
  from pathlib import Path
  from typing import Any, Dict, List, Tuple
  import numpy
  import pytorch_lightning
  import torch
  import torch.distributed
  import torch.distributed
  from pytorch_lightning.plugins.training_type import DDPPlugin
  from torch.utils.data import DataLoader
  from transformers import AutoConfig, AutoTokenizer, BartForConditionalGeneration, BatchEncoding, RagConfig, RagSequenceForGeneration, RagTokenForGeneration, RagTokenizer, T5ForConditionalGeneration
  from transformers import logging
  from transformers.integrations import is_ray_available
  from callbacks_rag import get_checkpoint_callback, get_early_stopping_callback, Seq2SeqLoggingCallback
  from distributed_pytorch_retriever import RagPyTorchDistributedRetriever
  from utils_rag import calculate_exact_match, flatten_list, get_git_info, is_rag_model, lmap, pickle_save, save_git_info, save_json, set_extra_model_params, Seq2SeqDataset
  from lightning_base import BaseTransformer, add_generic_args, generic_train
label_desc: Use image node_id 1 for calling the CustomDDP obj's underlying member method code with example usage: obj.init_ddp_connection(global_rank, world_size) without return types