code: def __init__(self, pretrained=True, include_top=False, freeze=True):
      super().__init__()
      backbone = vision.resnet18(
          pretrained=pretrained, include_top=include_top, freeze=freeze
      )
      output_size = backbone.get_output_size()
      head = nn.Linear(output_size, num_classes)
      self.model = torch.nn.Sequential(backbone, head)
  
file_path: BigDL/python/nano/test/pytorch/tests/train/trainer/test_lightning.py
imports: import os
  from unittest import TestCase
  import torch
  import torchmetrics
  from torch import nn
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
label_desc: Use image node_id 1 to create a new ResNet18 object from inherited base classes: nn with example: obj = ResNet18(pretrained, include_top, freeze)