code: def train(self):
      model = ResNet18(
          10, pretrained=False, include_top=False, freeze=True
      )
      loss_func = nn.CrossEntropyLoss()
      optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
      train_loader = create_data_loader(
          data_dir, batch_size, num_workers, data_transform
      )
  
      model, optimizer, train_loader = self.setup(
          model, optimizer, train_loader
      )
  
      model.train()
  
      num_epochs = 1
      for _i in range(num_epochs):
          total_loss, num = 0, 0
          for X, y in train_loader:
              optimizer.zero_grad()
              loss = loss_func(model(X), y)
              self.backward(loss)
              optimizer.step()
  
              total_loss += loss.sum()
              num += 1
          print(f"avg_loss: {total_loss / num}")
  
imports: import os
  import platform
  import pytest
  from unittest import TestCase
  import torch
  from torch import nn
  from torch.utils.data import DataLoader, TensorDataset
  from test.pytorch.utils._train_torch_lightning import create_data_loader, data_transform
  from bigdl.nano.pytorch import TorchNano
  from bigdl.nano.pytorch.vision.models import vision
label_desc: Use image node_id 1 for calling the MyNano obj's underlying member method code with example usage: obj.train() without return types