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)
  
imports: import os
  import torch
  import torchmetrics
  from torch import nn
  from torchvision.datasets import CIFAR10
  from torch.utils.data import DataLoader
  import torchvision.transforms
  from torch.utils.data.sampler import SequentialSampler
  import pytorch_lightning
  from pytorch_lightning.callbacks import Callback
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from typing import Any
  from bigdl.nano.pytorch.lightning import LightningModule
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch.vision.models import vision
  from bigdl.nano.pytorch.algorithms.selective_backprop import SelectiveBackprop
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)