code: def vit_cifar(**kwargs):
      pretrained_cfg = _cfg(
          num_classes=10, input_size=(3, 32, 32), crop_pct=1.0
      )
      model_kwargs = dict(
          patch_size=4,
          embed_dim=512,
          depth=6,
          num_heads=8,
          drop_rate=0.1,
          mlp_ratio=1.0,
          **kwargs,
      )
      model = _create_vision_transformer(
          "vit_cifar", pretrained_cfg=pretrained_cfg, **model_kwargs
      )
      return model
  
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 1 for calling a global function with example usage: vit_cifar() and returns: model