code: def test_model_with_bn_to_pytorch(self):
      class SimpleTorchModel(nn.Module):
          def __init__(self):
              super(SimpleTorchModel, self).__init__()
              self.dense1 = nn.Linear(2, 4)
              self.bn1 = torch.nn.BatchNorm1d(4)
              self.dense2 = nn.Linear(4, 1)
  
          def forward(self, x):
              x = self.dense1(x)
              x = self.bn1(x)
              x = torch.sigmoid(self.dense2(x))
              return x
  
      torch_model = SimpleTorchModel()
      az_model = TorchModel.from_pytorch(torch_model)
      dummy_input = torch.ones(16, 2)
      zoo_result = az_model.forward(dummy_input.numpy())
  
      exported_model = az_model.to_pytorch()
      assert len((list(exported_model.named_buffers()))) != 0
  
imports: import torch
  from torch import nn
  import torchvision
  import pytest
  from unittest import TestCase
  from zoo.pipeline.api.torch import TorchModel, TorchLoss
  from zoo.common.nncontext import *
  from torch.utils.data import TensorDataset, DataLoader
  from zoo.pipeline.estimator import *
  from zoo.pipeline.api.keras.optimizers import Adam
  from bigdl.optim.optimizer import MaxEpoch, EveryEpoch
  from zoo.pipeline.api.keras.metrics import Accuracy
  from zoo.feature.common import FeatureSet
  from bigdl.nn.layer import Model
label_desc: Use image node_id 7 for calling the TestPytorch obj's underlying member method code with example usage: obj.test_model_with_bn_to_pytorch() and returns: x