code: def test_input_singleton():
      class One(nn.Module):
          def __init__(self):
              super().__init__()
              self.fc = nn.Linear(1, 1)
  
          def forward(self, only_a):
              (a,) = only_a
              return (self.fc(a),)
  
      model = nn.Sequential(One())
      model = Pipe(model, balance=[1], devices=["cpu"], chunks=2)
  
      a = torch.rand(10, 1, requires_grad=True)
  
      (a_out,) = model((a,))
      loss = a_out.mean()
      loss.backward()
  
      assert all(p.grad is not None for p in model.parameters())
      assert a.grad is not None
  
imports: from collections import OrderedDict
  from copy import deepcopy
  import time
  import pytest
  import torch
  from torch import nn
  from fairscale.nn.pipe import Pipe
label_desc: Use image node_id 19 for calling a global function with example usage: test_input_singleton() and returns: self