code: def named_model_space(request):
      if request.param == "base":
          model = MyModelSpace(
              [
                  nni.choice("a", [1, 2, 3]),
                  nni.choice("b", [4, 5, 6, 7]),
              ]
          )
          evaluator = FunctionalEvaluator(
              foo, a=nni.choice("c", [0, 1]), optimal=11.0, minimal=5.0
          )
      if request.param == "constraint":
          a, b = nni.choice("a", [1, 2, 3]), nni.choice(
              "b", [4, 5, 6, 7]
          )
          model = MyModelSpace(
              [
                  a,
                  b,
                  ExpressionConstraint((a + b) % 2 == 1, label="odd"),
              ]
          )
          evaluator = FunctionalEvaluator(
              foo, a=nni.choice("c", [0, 1]), optimal=10.0, minimal=5.0
          )
      if request.param == "numerical":
          model = MyModelSpace(
              [
                  nni.quniform("a", 1, 3, 0.75),
                  nni.quniform("b", 4, 7, 1),
              ]
          )
          evaluator = FunctionalEvaluator(
              foo, a=1, optimal=11.0, minimal=6.0
          )
      return request.param, SimplifiedModelSpace.from_model(
          model, evaluator
      )
  
imports: from __future__ import annotations
  import pytest
  import nni
  from nni.mutable import *
  from nni.nas.evaluator import FunctionalEvaluator
  from nni.nas.execution import SequentialExecutionEngine
  from nni.nas.space import *
  from nni.nas.strategy import GridSearch, Random, RegularizedEvolution, PolicyBasedRL
  import torch
  from torch import nn
  from tianshou.policy import A2CPolicy
  from tianshou.data import to_torch
label_desc: Use image node_id 2 for calling a global function with example usage: named_model_space(request) and returns: request,  SimplifiedModelSpace