code: def test_model_save_and_load(self):
      class SimpleTorchModel(nn.Module):
          def __init__(self):
              super(SimpleTorchModel, self).__init__()
              self.dense1 = nn.Linear(2, 4)
              self.dense2 = nn.Linear(4, 1)
  
          def forward(self, x):
              x = self.dense1(x)
              x = torch.sigmoid(self.dense2(x))
              return x
  
      torch_model = SimpleTorchModel()
      az_model = TorchModel.from_pytorch(torch_model)
  
      with tempfile.TemporaryDirectory() as tmp_dir_name:
          path = tmp_dir_name + "/model.obj"
          az_model.saveModel(path, over_write=True)
          loaded_model = TorchModel.loadModel(path)
          loaded_torchModel = TorchModel.from_value(loaded_model.value)
          dummy_input = torch.ones(16, 2)
          loaded_torchModel.forward(dummy_input.numpy())
          loaded_torchModel.to_pytorch()
  
imports: import torch
  from torch import nn
  import torchvision
  import pytest
  from unittest import TestCase
  from bigdl.orca.torch import TorchModel, TorchLoss
  from bigdl.dllib.nncontext import *
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.dllib.estimator import *
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.dllib.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.dllib.keras.metrics import Accuracy
  from bigdl.dllib.feature.common import FeatureSet
label_desc: Use image node_id 11 for calling the TestPytorch obj's underlying member method code with example usage: obj.test_model_save_and_load() and returns: x