code: def train_epoch(
      epoch: int,
      model: nn.Module,
      optimizer: Optimizer,
      criterion: nn.Module,
      train_dataloader: DataLoader,
      booster: Booster,
      coordinator: DistCoordinator,
  ):
      model.train()
      with tqdm(
          train_dataloader,
          desc=f"Epoch [{epoch + 1}/{NUM_EPOCHS}]",
          disable=not coordinator.is_master(),
      ) as pbar:
          for images, labels in pbar:
              images = images.cuda()
              labels = labels.cuda()
              # Forward pass
              outputs = model(images)
              loss = criterion(outputs, labels)
  
              # Backward and optimize
              booster.backward(loss, optimizer)
              optimizer.step()
              optimizer.zero_grad()
  
              # Print log info
              pbar.set_postfix({"loss": loss.item()})
  
imports: import argparse
  import os
  from pathlib import Path
  import torch
  import torch.distributed
  import torch.nn
  import torchvision
  import torchvision.transforms
  from timm.models.vision_transformer import _cfg, _create_vision_transformer
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  import colossalai
  from colossalai.booster import Booster
  from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
  from colossalai.booster.plugin.dp_plugin_base import DPPluginBase
  from colossalai.cluster import DistCoordinator
  from colossalai.nn.lr_scheduler import LinearWarmupLR
  from colossalai.nn.optimizer import HybridAdam
  from colossalai.utils import get_current_device
label_desc: Use image node_id 4 for calling a global function with example usage: train_epoch(epoch, model, optimizer, criterion, train_dataloader, booster, coordinator) without return types