code: def train(self):
      x = torch.Tensor(
          [
              [[[1, 0]], [[1, 0]]],
              [[[1, 0]], [[2, 0]]],
              [[[0, 3]], [[1, 0]]],
              [[[1, 1]], [[2, 1]]],
          ]
      )
      y = torch.Tensor([[0.0], [1.0], [0.0], [1.0]])
      train_dataset = torch.utils.data.TensorDataset(x, y)
      train_loader = torch.utils.data.DataLoader(
          train_dataset, batch_size=4, shuffle=False
      )
      origin_model = ConvModel()
      loss_fuc = torch.nn.MSELoss()
      optimizer = torch.optim.SGD(origin_model.parameters(), lr=0.25)
  
      model, optimizer, train_loader = self.setup(
          origin_model, optimizer, train_loader
      )
  
      model.train()
  
      for X, y in train_loader:
          optimizer.zero_grad()
          loss = loss_fuc(model(X), y)
          self.backward(loss)
          optimizer.step()
  
      result = torch.tensor([[[[0.0, -1.0]], [[-1.25, 0.5]]]])
      assert origin_model.conv1.weight.equal(result)
  
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 1 for calling the MyNanoChannelsLastCorrectness obj's underlying member method code with example usage: obj.train() without return types