code: def initialize(
      model: nn.Module,
      optimizer: Optimizer,
      criterion: Optional[_Loss] = None,
      train_dataloader: Optional[Iterable] = None,
      test_dataloader: Optional[Iterable] = None,
      lr_scheduler: Optional[_LRScheduler] = None,
      ophooks: Optional[List[BaseOpHook]] = None,
      verbose: bool = True,
  ) -> Tuple[Engine, DataLoader, DataLoader, _LRScheduler]:
      """Core function to wrap the essential training components with our functionality based on the config which is
      loaded into gpc.config.
  
      Args:
          model (:class:`torch.nn.Module` or Callable): Your model instance or a function to build the model.
          optimizer (:class:`torch.optim.optimizer.Optimizer` or :class:`Type[torch.optim.optimizer]`):
              Your optimizer instance.
          criterion (:class:`torch.nn.modules.loss._Loss`, optional): Your criterion instance.
          train_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for training.
          test_dataloader (:class:`torch.utils.data.DataLoader`, optional): Dataloader for testing.
          lr_scheduler (:class:`torch.nn.lr_scheduler._LRScheduler`, optional): Your lr scheduler instance, optional.
          verbose (bool, optional): Whether to print logs.
  
      Returns:
          Tuple (engine, train_dataloader, test_dataloader, lr_scheduler):
              A tuple of ``(engine, train_dataloader, test_dataloader, lr_scheduler)``
              where only ``engine`` could not be None.
      """
      # get logger
      logger = get_dist_logger()
      gpc.verbose = verbose
  
      # get config from gpc
      config = gpc.config
  
      # print config
      if verbose:
          logger.info(
              f"\n========== Your Config ========\n"
              f"{pprint.pformat(gpc.config)}\n"
              f"================================\n",
              ranks=[0],
          )
  
      # cudnn
      cudnn_benchmark = config.get("cudnn_benchmark", False)
      cudnn_deterministic = config.get("cudnn_deterministic", False)
      torch.backends.cudnn.benchmark = cudnn_benchmark
      torch.backends.cudnn.deterministic = cudnn_deterministic
      if verbose:
          logger.info(
              f"cuDNN benchmark = {cudnn_benchmark}, deterministic = {cudnn_deterministic}",
              ranks=[0],
          )
  
      # zero
      use_zero = hasattr(gpc.config, "zero")
      if use_zero:
          zero_cfg = gpc.config.get("zero", None)
          if zero_cfg is not None:
              cfg_ = zero_cfg.copy()
          else:
              cfg_ = {}
          optimizer_config = zero_cfg.get("optimizer_config", None)
          model_config = zero_cfg.get("model_config", None)
          model, optimizer = convert_to_zero_v2(
              model,
              optimizer,
              model_config=model_config,
              optimizer_config=optimizer_config,
          )
  
          logger.info(
              "Initializing ZeRO model and optimizer finished!",
              ranks=[0],
          )
      else:
          if isinstance(model, nn.Module):
              # first sync model across dp ranks
              model.to(get_current_device())
          elif isinstance(model, Callable):
              model = model().to(get_current_device())
  
          # optimizer maybe a optimizer_cls
          if isinstance(optimizer, Callable):
              optimizer = optimizer(model.parameters())
              logger.warning(
                  "Initializing an non ZeRO model with optimizer class"
              )
  
      if not use_zero:
          if is_using_sequence():
              sync_model_param(model, ParallelMode.SEQUENCE_DP)
          elif is_using_ddp():
              sync_model_param(model, ParallelMode.DATA)
      else:
          logger.warning(
              "The parameters of models is not automatically synchronized.\n"
              "Please make sure that all parameters are the same in data parallel group.",
              ranks=[0],
          )
  
      # check amp and zero
      fp16_cfg = gpc.config.get("fp16", None)
  
      if (
          fp16_cfg is not None
          and fp16_cfg.mode is not None
          and use_zero
      ):
          raise ConfigException(
              "It is not allowed to set fp16 and zero configuration in your config file at the same time"
          )
  
      # clip grad norm
      clip_grad_norm = gpc.config.get("clip_grad_norm", 0.0)
  
      # initialize amp
      amp_mode = None
      if fp16_cfg is not None and fp16_cfg.mode is not None:
          cfg_ = fp16_cfg.copy()
          amp_mode = cfg_.pop("mode")
          if is_using_pp():
              assert (
                  amp_mode == AMP_TYPE.NAIVE
              ), "Pipeline only support NaiveAMP currently"
          if amp_mode == AMP_TYPE.NAIVE:
              cfg_["clip_grad_norm"] = clip_grad_norm
          model, optimizer, criterion = convert_to_amp(
              model=model,
              optimizer=optimizer,
              criterion=criterion,
              mode=amp_mode,
              amp_config=cfg_,
          )
  
      # get torch ddp config
      torch_ddp_cfg = gpc.config.get("torch_ddp", dict())
  
      # gradient handler
      gradient_handler_cfg = gpc.config.get("gradient_handler", None)
      if gradient_handler_cfg is None:
          # if gradient handler is not specified in the configuration file,
          # check in the following order
          # 1. if optimizer is ZERO, then use zero grad handler
          # 2. if dp size is larger than 1 and pipeline is not used, use pytorch ddp
          # 3. if using pipeline and dp size larger than 1, use data parallel grad handler
          if isinstance(optimizer, ShardedOptimizerV2):
              gradient_handler_cfg = [dict(type="ZeROGradientHandler")]
              if verbose:
                  logger.info(
                      "Training with zero is detected, ZeROGradientHandler is automatically "
                      "added even though not specified in the configuration",
                      ranks=[0],
                  )
          elif is_using_sequence():
              model = DDP(
                  model,
                  process_group=gpc.get_group(ParallelMode.SEQUENCE_DP),
                  device_ids=[torch.cuda.current_device()],
                  **torch_ddp_cfg,
              )
              if verbose:
                  logger.info(
                      "Model is using torch.nn.parallel.DistributedDataParallel for Sequence Parallelism",
                      ranks=[0],
                  )
          elif (
              is_using_ddp()
              and not is_using_pp()
              and amp_mode != AMP_TYPE.NAIVE
          ):
              model = DDP(
                  model,
                  process_group=gpc.get_group(ParallelMode.DATA),
                  device_ids=[torch.cuda.current_device()],
                  **torch_ddp_cfg,
              )
              if verbose:
                  logger.info(
                      "Model is using torch.nn.parallel.DistributedDataParallel for Data Parallelism",
                      ranks=[0],
                  )
          elif is_using_ddp():
              gradient_handler_cfg = [
                  dict(type="DataParallelGradientHandler")
              ]
              if verbose:
                  logger.info(
                      "Data parallel training is detected when using pipeline parallel, "
                      "DataParallelGradientHandler is automatically "
                      "added even though not specified in the configuration",
                      ranks=[0],
                  )
          # add pipeline parallel gradient handler, if pipeline shared module is detected
          for param in model.parameters():
              if (
                  getattr(param, "pipeline_shared_module_pg", None)
                  is not None
              ):
                  if gradient_handler_cfg is None:
                      gradient_handler_cfg = [
                          dict(
                              type="PipelineSharedModuleGradientHandler"
                          )
                      ]
                  else:
                      gradient_handler_cfg.append(
                          dict(
                              type="PipelineSharedModuleGradientHandler"
                          )
                      )
                  if verbose:
                      logger.info(
                          "pipeline_shared_module is detected, PipelineSharedModuleGradientHandler is automatically "
                          "added even though not specified in the configuration",
                          ranks=[0],
                      )
                  break
      else:
          if not isinstance(gradient_handler_cfg, list):
              raise ConfigException(
                  f"expected gradient_handler in the configuration file to be a list but got {type(gradient_handler_cfg)}"
              )
  
      # turn off sync buffer for NaiveAMPModel if using torch DDP and NaiveAMPModel at the same time
      # to avoid duplicated buffer synchronization
      if isinstance(model, DDP) and isinstance(
          model.module, NaiveAMPModel
      ):
          model.module.sync_buffer = False
  
      # initialize schedule for engine
      if is_using_pp():
          tensor_shape = get_tensor_shape()
          use_interleaved = hasattr(gpc.config, "model") and hasattr(
              gpc.config.model, "num_chunks"
          )
          if gpc.is_initialized(ParallelMode.PARALLEL_1D):
              scatter_gather = True
          else:
              scatter_gather = False
          if use_interleaved:
              if isinstance(model, nn.Sequential):
                  model = nn.ModuleList([model])
              schedule = InterleavedPipelineSchedule(
                  gpc.config.NUM_MICRO_BATCHES,
                  gpc.config.model.num_chunks,
                  tensor_shape=tensor_shape,
                  scatter_gather_tensors=scatter_gather,
              )
          else:
              schedule = PipelineSchedule(
                  gpc.config.NUM_MICRO_BATCHES,
                  tensor_shape=tensor_shape,
                  scatter_gather_tensors=scatter_gather,
              )
      else:
          schedule = NonPipelineSchedule()
  
      if gradient_handler_cfg is None:
          gradient_handlers = None
          if verbose and not isinstance(model, DDP):
              logger.warning(
                  "No PyTorch DDP or gradient handler is set up, please make sure you do not need "
                  "to all-reduce the gradients after a training step.",
                  ranks=[0],
              )
      else:
          gradient_handlers = [
              build_gradient_handler(cfg, model, optimizer)
              for cfg in gradient_handler_cfg
          ]
  
      # check if optimizer is OptimizerWrapper
      if not isinstance(
          optimizer, (OptimizerWrapper, ShardedOptimizerV2)
      ):
          optimizer = OptimizerWrapper(optim=optimizer)
  
      # gradient accumulation
      grad_accum_size = gpc.config.get("gradient_accumulation", None)
      if grad_accum_size is not None:
          (
              optimizer,
              train_dataloader,
              gradient_handlers,
              lr_scheduler,
          ) = accumulate_gradient(
              model=model,
              optimizer=optimizer,
              dataloader=train_dataloader,
              accumulate_size=grad_accum_size,
              gradient_handlers=gradient_handlers,
              lr_scheduler=lr_scheduler,
          )
      engine = Engine(
          model=model,
          optimizer=optimizer,
          criterion=criterion,
          gradient_handlers=gradient_handlers,
          clip_grad_norm=clip_grad_norm,
          ophook_list=ophooks,
          schedule=schedule,
      )
  
      return engine, train_dataloader, test_dataloader, lr_scheduler
  
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 6 for calling a global function with example usage: initialize(model, optimizer, criterion, train_dataloader, test_dataloader, lr_scheduler, ophooks, verbose) and returns: engine,  train_dataloader,  test_dataloader,  lr_scheduler