code: def input_varargs(pipe_class):
      model = nn.Sequential(nn.Linear(1, 1))
      model = pipe_class(
          model, balance=[1], worker_map=get_worker_map()
      )
  
      a = torch.rand(1)
      b = torch.rand(1)
  
      # TypeError: forward() takes 2 positional arguments but 3 were given
      with pytest.raises(TypeError):
          model(a, b)
  
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 24 for calling a global function with example usage: input_varargs(pipe_class) without return types