code: def main():
      args = parse_demo_args()
  
      # Launch ColossalAI
      colossalai.launch_from_torch(config={}, seed=args.seed)
      coordinator = DistCoordinator()
      world_size = coordinator.world_size
  
      # Manage loggers
      disable_existing_loggers()
      logger = get_dist_logger()
      if coordinator.is_master():
          transformers.utils.logging.set_verbosity_info()
      else:
          transformers.utils.logging.set_verbosity_error()
  
      # Reset tp_size and pp_size to 1 if not using hybrid parallel.
      if args.plugin != "hybrid_parallel":
          args.tp_size = 1
          args.pp_size = 1
  
      # Prepare Dataset
      image_processor = ViTImageProcessor.from_pretrained(
          args.model_name_or_path
      )
      train_dataset = BeansDataset(
          image_processor, args.tp_size, split="train"
      )
      eval_dataset = BeansDataset(
          image_processor, args.tp_size, split="validation"
      )
      num_labels = train_dataset.num_labels
  
      # Load pretrained ViT model
      config = ViTConfig.from_pretrained(args.model_name_or_path)
      config.num_labels = num_labels
      config.id2label = {
          str(i): c for i, c in enumerate(train_dataset.label_names)
      }
      config.label2id = {
          c: str(i) for i, c in enumerate(train_dataset.label_names)
      }
      model = ViTForImageClassification.from_pretrained(
          args.model_name_or_path,
          config=config,
          ignore_mismatched_sizes=True,
      )
      logger.info(
          f"Finish loading model from {args.model_name_or_path}",
          ranks=[0],
      )
  
      # Enable gradient checkpointing
      if args.grad_checkpoint:
          model.gradient_checkpointing_enable()
  
      # Set plugin
      booster_kwargs = {}
      if args.plugin == "torch_ddp_fp16":
          booster_kwargs["mixed_precision"] = "fp16"
      if args.plugin.startswith("torch_ddp"):
          plugin = TorchDDPPlugin()
      elif args.plugin == "gemini":
          plugin = GeminiPlugin(
              offload_optim_frac=1.0,
              pin_memory=True,
              initial_scale=2**5,
          )
      elif args.plugin == "low_level_zero":
          plugin = LowLevelZeroPlugin(initial_scale=2**5)
      elif args.plugin == "hybrid_parallel":
          plugin = HybridParallelPlugin(
              tp_size=args.tp_size,
              pp_size=args.pp_size,
              num_microbatches=None,
              microbatch_size=1,
              enable_all_optimization=True,
              precision="fp16",
              initial_scale=1,
          )
      else:
          raise ValueError(
              f"Plugin with name {args.plugin} is not supported!"
          )
      logger.info(f"Set plugin as {args.plugin}", ranks=[0])
  
      # Prepare dataloader
      train_dataloader = plugin.prepare_dataloader(
          train_dataset,
          batch_size=args.batch_size,
          shuffle=True,
          drop_last=True,
          collate_fn=beans_collator,
      )
      eval_dataloader = plugin.prepare_dataloader(
          eval_dataset,
          batch_size=args.batch_size,
          shuffle=True,
          drop_last=True,
          collate_fn=beans_collator,
      )
  
      # Set optimizer
      optimizer = HybridAdam(
          model.parameters(),
          lr=(args.learning_rate * world_size),
          weight_decay=args.weight_decay,
      )
  
      # Set criterion (loss function)
      def criterion(outputs, inputs):
          return outputs.loss
  
      # Set lr scheduler
      total_steps = len(train_dataloader) * args.num_epoch
      num_warmup_steps = int(args.warmup_ratio * total_steps)
      lr_scheduler = CosineAnnealingWarmupLR(
          optimizer=optimizer,
          total_steps=(len(train_dataloader) * args.num_epoch),
          warmup_steps=num_warmup_steps,
      )
  
      # Set booster
      booster = Booster(plugin=plugin, **booster_kwargs)
      (
          model,
          optimizer,
          _criterion,
          train_dataloader,
          lr_scheduler,
      ) = booster.boost(
          model=model,
          optimizer=optimizer,
          criterion=criterion,
          dataloader=train_dataloader,
          lr_scheduler=lr_scheduler,
      )
  
      # Finetuning
      logger.info(f"Start finetuning", ranks=[0])
      for epoch in range(args.num_epoch):
          train_epoch(
              epoch,
              model,
              optimizer,
              criterion,
              lr_scheduler,
              train_dataloader,
              booster,
              coordinator,
          )
          evaluate_model(
              epoch,
              model,
              criterion,
              eval_dataloader,
              booster,
              coordinator,
          )
      logger.info(f"Finish finetuning", ranks=[0])
  
      # Save the finetuned model
      booster.save_model(model, args.output_path, shard=True)
      logger.info(
          f"Saving model checkpoint to {args.output_path}", ranks=[0]
      )
  
imports: from typing import Any, Callable, Iterator
  import torch
  import torch.distributed
  import torch.nn
  import transformers
  from args import parse_demo_args
  from data import BeansDataset, beans_collator
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import _LRScheduler
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin, TorchDDPPlugin
  from colossalai.cluster import DistCoordinator
  from colossalai.logging import disable_existing_loggers, get_dist_logger
  from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
  from colossalai.nn.optimizer import HybridAdam
label_desc: Use image node_id 5 for calling a global function with example usage: main() and returns: outputs