code: def test_nasbench201_cell(
      space_format: Type[ExecutableModelSpace], nn: ModuleType
  ):
      class Net(ModelSpace):
          def __init__(self):
              super().__init__()
              self.cell = NasBench201Cell(
                  [
                      lambda x, y: nn.Linear(x, y),
                      lambda x, y: nn.Linear(x, y, bias=False),
                  ],
                  10,
                  16,
                  label="cell1",
              )
  
          def forward(self, x):
              return self.cell(x)
  
      net = Net()
      assert net.cell.label == "cell1"
      model = space_format.from_model(net)
      assert len(model.simplify()) == 6
      for _ in range(10):
          selected_model = model.random().executable_model()
          assert selected_model(
              torch.randn(2, 10)
          ).size() == torch.Size([2, 16])
  
file_path: nni/test/ut/nas/nn/test_hub_modules.py
imports: from types import ModuleType
  from typing import Type
  import pytest
  import torch
  from torch import nn
  from nni.mutable import ConstraintViolation
  from nni.nas.hub.pytorch.modules import AutoActivation, NasBench101Cell, NasBench201Cell
  from nni.nas.nn.pytorch import ModelSpace
  from nni.nas.space import ExecutableModelSpace, GraphModelSpace
  from .test_choice import space_format, nn
label_desc: Use image node_id 1 for calling a global function with example usage: test_nasbench201_cell(space_format, nn) and returns: self