code: def pytorch_simple_model(
      device: Any = None, preprocessing: fbn.types.Preprocessing = None
  ) -> ModelAndData:
      import torch
  
      class Model(torch.nn.Module):
          def forward(self, x: torch.Tensor) -> torch.Tensor:
              x = torch.mean(x, 3)
              x = torch.mean(x, 2)
              return x
  
      model = Model().eval()
      bounds = (0, 1)
      fmodel = fbn.PyTorchModel(
          model,
          bounds=bounds,
          device=device,
          preprocessing=preprocessing,
      )
  
      x, _ = fbn.samples(fmodel, dataset="imagenet", batchsize=8)
      x = ep.astensor(x)
      y = fmodel(x).argmax(axis=-1)
      return fmodel, x, y
  
imports: from typing import Optional, Callable, Tuple, Dict, Any, List, NamedTuple
  import functools
  import pytest
  import eagerpy
  import foolbox
  import foolbox
label_desc: Use image node_id 4 for calling a global function with example usage: pytorch_simple_model(device, preprocessing) and returns: fmodel,  x,  y, x