code: def __init__(self) -> None:
      super().__init__()
  
      self.backbone = vision.resnet18(
          pretrained=False, include_top=False, freeze=False
      )
      output_size = self.backbone.get_output_size()
      self.head = nn.Linear(output_size, num_classes)
  
imports: import os
  import pytest
  from unittest import TestCase
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from test.pytorch.utils._train_torch_lightning import create_test_data_loader
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10, TORCH_VERSION_LESS_2_0
  import pytorch_lightning
  import torch
  from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new ResNetBase object from inherited base classes: pl with example: obj = ResNetBase()