code: def forward(self, input):
      x = self.conv1(input)
      if not TORCH_VERSION_LESS_1_12:
          assert x.is_contiguous(memory_format=torch.channels_last)
      output = torch.flatten(x, 1)
      return output
  
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 ConvModel obj's underlying member method code with example usage: obj.forward(input) and returns: output