code: def test_nested_input(setup_rpc):
      class NestedInput(nn.Module):
          def __init__(self):
              super().__init__()
              self.fc_a = nn.Linear(1, 1)
              self.fc_b = nn.Linear(1, 1)
  
          def forward(self, inp):
              return inp
  
      model = nn.Sequential(NestedInput())
      model = Pipe(model, chunks=2)
  
      a = torch.rand(10, 1, requires_grad=True)
      b = torch.rand(10, 1, requires_grad=True)
  
      # TypeError: expected Tensor, but got tuple
      with pytest.raises(TypeError):
          model((a, (a, b))).local_value()
  
      # TypeError: expected Tensor, but got list
      with pytest.raises(TypeError):
          model((a, [a, b])).local_value()
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import time
  import pytest
  import random
  import torch
  from torch import nn
  from torch import Tensor
  from torch.distributed.pipeline.sync import Pipe, NoChunk, WithDevice
  from torch.distributed.pipeline.sync.pipe import PipeSequential
  from torch.testing._internal.common_utils import run_tests, TEST_CUDA
  from torch.testing._internal.common_cuda import TEST_MULTIGPU
label_desc: Use image node_id 16 for calling a global function with example usage: test_nested_input(setup_rpc) and returns: inp