code: def non_tensor_tuple(pipe_class):
      class NonTensorTuple(nn.Module):
          def forward(self, x):
              return (x, "hello")
  
      model = nn.Sequential(NonTensorTuple())
      model = pipe_class(
          model, balance=[1], worker_map=get_worker_map()
      )
      x = torch.rand(1)
  
      # TypeError: CheckpointBackward.forward: expected Variable (got str) for return value 1
      with pytest.raises(TypeError):
          model(x)
  
      # TypeError: expected Tensor to scatter, but got str
      with pytest.raises(TypeError):
          model((x, "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 26 for calling a global function with example usage: non_tensor_tuple(pipe_class) and returns: x,  str