code: def test_value_choice_as_two_parameters(
      space_format: Type[ExecutableModelSpace], nn: ModuleType
  ):
      class Net(ModelSpace, label_prefix="model"):
          def __init__(self):
              super().__init__()
              self.conv = MutableConv2d(
                  3,
                  Categorical([6, 8]),
                  kernel_size=Categorical([3, 5]),
              )
  
          def forward(self, x):
              return self.conv(x)
  
      model = space_format.from_model(Net())
      assert len(model.simplify()) == 2
      all_shapes = []
      for selected_model in model.grid():
          shape = selected_model.executable_model()(
              torch.randn(1, 3, 5, 5)
          ).size()
          all_shapes.append((shape[1], shape[2]))
      assert set(all_shapes) == set([(6, 3), (6, 1), (8, 3), (8, 1)])
  
imports: import copy
  import random
  from types import ModuleType
  from typing import Type
  import pytest
  import nni
  import nni.nas.nn.pytorch.layers
  import nni.nas.evaluator.pytorch.lightning
  import torch
  import torch.nn.functional
  from nni.mutable import Categorical, ensure_frozen, label_scope, SampleValidationError
  from nni.nas.evaluator import FunctionalEvaluator
  from nni.nas.space import RawFormatModelSpace, SimplifiedModelSpace, ExecutableModelSpace, GraphModelSpace, model_context
  from nni.nas.space.pytorch import PytorchGraphModelSpace
  from nni.nas.nn.pytorch import ParametrizedModule, MutableModule, ModelSpace, LayerChoice, InputChoice, Cell, ChosenInputs, MutableConv2d, MutableLinear
label_desc: Use image node_id 12 for calling a global function with example usage: test_value_choice_as_two_parameters(space_format, nn) and returns: self