code: def forward(self, x):
      x = self.model.conv1(x)
      x = self.model.bn1(x)
      x = self.model.relu(x)
      x = self.model.maxpool(x)
  
      x = self.model.layer1(x)
      x = self.model.layer2(x)
      x = self.model.layer3(x)
      x = self.model.layer4(x)
      if not TORCH_VERSION_LESS_1_12:
          assert x.is_contiguous(memory_format=torch.channels_last)
  
      x = self.model.avgpool(x)
      x = torch.flatten(x, 1)
      x = self.model.fc(x)
      x = self.head(x)
  
      return x
  
imports: import os
  import pytest
  import torch
  from unittest import TestCase
  from bigdl.nano.pytorch import Trainer
  from bigdl.nano.pytorch import TorchNano
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_12, TORCH_VERSION_LESS_2_0
  from torchvision.models.resnet import ResNet, BasicBlock
  from torchmetrics.functional import accuracy
  import pytorch_lightning
  import torch.nn.functional
  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
label_desc: Use image node_id 2 for calling the CustomResNet obj's underlying member method code with example usage: obj.forward(x) and returns: x