code: def launch(
      config: Union[str, Path, Config, Dict],
      rank: int,
      world_size: int,
      host: str,
      port: int,
      backend: str = "nccl",
      local_rank: int = None,
      seed: int = 1024,
      verbose: bool = True,
  ):
      """This function first parses the configuration arguments, using :func:`parse_args()` in case one of the input
      arguments are not given. Then initialize and set distributed environment by calling global_context's functions.
  
      Args:
          config (Union[str, dict, Config]): Config file or config file path are both acceptable
          rank (int): Rank for the default process group
          world_size (int): World size of the default process group
          host (str): The master address for distributed training
          port (str): The master port for distributed training
          backend (str, optional): Backend for ``torch.distributed``, defaults to ``nccl``
          local_rank (int, optional):
              Rank for the process on the node and is used to set the default CUDA device,
              defaults to None. If local_rank = None, the default device ordinal will be calculated automatically.
          seed (int, optional): Specified random seed for every process. Defaults to 1024.
          verbose (bool, optional): Whether to print logs. Defaults to True.
  
      Raises:
          Exception: Raise exception when config type is wrong
      """
      gpc.verbose = verbose
  
      # set config
      assert isinstance(
          config, (Config, str, Path, dict)
      ), f"expected argument config to be Config, str or Path, but got {type(config)}"
      if not isinstance(config, Config) and isinstance(config, dict):
          config = Config(config)
      if isinstance(config, (str, Path)):
          config = Config.from_file(config)
      gpc.load_config(config)
  
      # init default process group
      gpc.init_global_dist(rank, world_size, backend, host, port)
  
      # init process groups for different parallel modes from config
      gpc.init_parallel_groups()
  
      # set cuda device
      if torch.cuda.is_available():
          # if local rank is not given, calculate automatically
          gpc.set_device(local_rank)
  
      # set the number of processes running on the same node
      gpc.detect_num_processes_on_current_node()
  
      gpc.set_seed(seed)
  
      if verbose:
          logger = get_dist_logger()
          logger.info(
              f"Distributed environment is initialized, "
              f"data parallel size: {gpc.data_parallel_size}, pipeline parallel size: {gpc.pipeline_parallel_size}, "
              f"tensor parallel size: {gpc.tensor_parallel_size}",
              ranks=[0],
          )
  
imports: import argparse
  import os
  import pprint
  from pathlib import Path
  from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from torch.nn.modules.loss import _Loss
  from torch.nn.parallel import DistributedDataParallel
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from colossalai.context import Config, ConfigException
  from colossalai.interface import OptimizerWrapper
  from colossalai.legacy.amp import AMP_TYPE, convert_to_amp
  from colossalai.legacy.amp.naive_amp import NaiveAMPModel
  from colossalai.legacy.builder.builder import build_gradient_handler
  from colossalai.legacy.context import ParallelMode
  from colossalai.legacy.core import global_context
  from colossalai.legacy.engine import Engine
  from colossalai.legacy.engine.gradient_accumulation import accumulate_gradient
  from colossalai.legacy.engine.schedule import InterleavedPipelineSchedule, NonPipelineSchedule, PipelineSchedule, get_tensor_shape
  from colossalai.legacy.utils import is_using_ddp, is_using_pp, is_using_sequence, sync_model_param
  from colossalai.legacy.zero import ShardedOptimizerV2, convert_to_zero_v2
  from colossalai.legacy.zero.gemini.ophooks import BaseOpHook
  from colossalai.logging import get_dist_logger
  from colossalai.utils import get_current_device
label_desc: Use image node_id 2 for calling a global function with example usage: launch(config, rank, world_size, host, port, backend, local_rank, seed, verbose) without return types