code: def test_layer_choice_multiple(
      space_format: Type[ExecutableModelSpace], nn: ModuleType
  ):
      class Net(ModelSpace, label_prefix="model"):
          def __init__(self):
              super().__init__()
              self.module = LayerChoice(
                  [nn.Conv2d(3, i, kernel_size=1) for i in range(1, 11)]
              )
  
          def forward(self, x):
              return self.module(x)
  
      model = Net()
      model = space_format.from_model(model)
      space = model.simplify()
      assert len(space) == 1
      layer_label = next(iter(space.keys()))
      choices = space[layer_label].values
  
      for i in range(1, 11):
          model_new = model.freeze(
              {layer_label: choices[i - 1]}
          ).executable_model()
          assert model_new(
              torch.randn(1, 3, 3, 3)
          ).size() == torch.Size([1, i, 3, 3])
  
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 6 for calling a global function with example usage: test_layer_choice_multiple(space_format, nn) and returns: self