code: def non_tensor(pipe_class):
      class NonTensor(nn.Module):
          def forward(self, _):
              return "hello"
  
      model = nn.Sequential(NonTensor())
      model = pipe_class(
          model, balance=[1], worker_map=get_worker_map()
      )
      x = torch.rand(1)
  
      # TypeError: expected Tensor as element 0 in argument 0, but got str
      with pytest.raises(TypeError):
          model(x)
  
      # TypeError: expected Tensor to scatter, but got str
      with pytest.raises(TypeError):
          model("hello")
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import os
  import time
  import pytest
  import torch
  from torch import nn
  from fairscale.fair_dev.testing.testing import get_worker_map, torch_spawn
  from fairscale.internal import torch_version
  from fairscale.nn.model_parallel.initialize import get_pipeline_parallel_group
  from fairscale.nn.pipe import AsyncPipe
  from fairscale.nn.pipe.types import LazyModule
label_desc: Use image node_id 25 for calling a global function with example usage: non_tensor(pipe_class) and returns: str