code: def train_with_linear_top_layer(
      model_without_top,
      batch_size,
      num_workers,
      data_dir,
      use_ipex=False,
  ):
      model = Net(model_without_top)
      train_torch_lightning(
          model, batch_size, num_workers, data_dir, use_ipex=use_ipex
      )
  
file_path: BigDL/python/nano/test/pytorch/utils/_train_torch_lightning.py
imports: import torch
  from copy import deepcopy
  import pytorch_lightning
  import torchvision.transforms
  from torchvision.datasets import CIFAR10
  from torch.utils.data import DataLoader
  from torch.utils.data.sampler import SequentialSampler
  from bigdl.nano.pytorch.vision.models import ImageClassifier
label_desc: Use image node_id 3 for calling a global function with example usage: train_with_linear_top_layer(model_without_top, batch_size, num_workers, data_dir, use_ipex) without return types