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
      )
      loss_func = nn.MSELoss()
      origin_model = LinearModel()
      origin_optimizer = torch.optim.SGD(
          origin_model.parameters(), lr=lr
      )
  
      def train_one_epoch(model, optimizer, loss_func, data_loader):
          for X, y in data_loader:
              optimizer.zero_grad()
              loss = loss_func(model(X), y)
              self.backward(loss)
              optimizer.step()
  
      model, optimizer, train_loader = self.setup(
          origin_model, origin_optimizer, train_loader
      )
      model.train()
      train_one_epoch(model, optimizer, loss_func, train_loader)
  
      # load state dict using original pytorch model
      origin_model.load_state_dict(model.state_dict())
      origin_optimizer.load_state_dict(optimizer.state_dict())
  
      model, optimizer = self.setup(origin_model, origin_optimizer)
      model.train()
      train_one_epoch(model, optimizer, loss_func, train_loader)
  
      assert (
          model.fc1.weight.data == 0.25
      ), f"wrong weights: {model.fc1.weight.data}"
  
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 MyNanoLoadStateDict obj's underlying member method code with example usage: obj.train(lr) without return types