code: def train(self, lr):
      dataset = TensorDataset(
          torch.tensor([[0.0], [0.0], [1.0], [1.0]]),
          torch.tensor([[0.0], [0.0], [0.0], [0.0]]),
      )
      train_loader = DataLoader(
          dataset=dataset, batch_size=2, shuffle=False
      )
      origin_model = LinearModel()
      loss_func = nn.MSELoss()
      optimizer = torch.optim.SGD(origin_model.parameters(), lr=lr)
  
      model, optimizer, train_loader = self.setup(
          origin_model, optimizer, train_loader
      )
  
      model.train()
  
      expect_weight = torch.tensor([[1.0]])
      cur_lr_ratio = 1.0
      max_lr_ratio = self.num_processes
      cur_step = 0
      max_step = optimizer.max_step
      num_epochs = max_step + 10
      for _i in range(num_epochs):
          for X, y in train_loader:
              optimizer.zero_grad()
              loss = loss_func(model(X), y)
              self.backward(loss)
              optimizer.step()
  
              expect_weight = (
                  expect_weight - expect_weight * lr * cur_lr_ratio
              )
              expect = expect_weight.item()
              real = model.fc1.weight.data.item()
              assert math.isclose(
                  expect, real, rel_tol=1e-5
              ), f"step: {_i}, expect: {expect}, real: {real}"
              if cur_step < max_step:
                  cur_step += 1
                  cur_lr_ratio = (
                      max_lr_ratio - 1.0
                  ) * cur_step / max_step + 1
  
imports: import os
  import math
  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 MyNanoAutoLRCorrectness obj's underlying member method code with example usage: obj.train(lr) without return types