code: def test_layerchoice(predictor):
      net = LayerChoice(
          [
              nn.Sequential(
                  nn.Conv2d(32, 128, 1), nn.Conv2d(128, 32, 1)
              ),
              nn.Sequential(nn.Conv2d(32, 1, 1), nn.Conv2d(1, 32, 1)),
          ],
          label="x",
      )
      profiler = NnMeterProfiler(
          net, torch.randn(1, 32, 32, 32), predictor
      )
      assert profiler.profile({"x": 0}) > 2 * profiler.profile({"x": 1})
  
imports: import pytest
  import torch
  from torch import nn
  import nni
  from nni.mutable import Categorical
  from nni.nas.hub.pytorch import *
  from nni.nas.hub.pytorch.mobilenetv3 import SqueezeExcite
  from nni.nas.hub.pytorch.proxylessnas import ConvBNReLU, DepthwiseSeparableConv, InvertedResidual
  from nni.nas.nn.pytorch import MutableConv2d, MutableLinear, LayerChoice, Repeat
  from nni.nas.profiler.pytorch.nn_meter import NnMeterProfiler, to_onnx, combinations
  from nni.nas.profiler.pytorch.utils import MutableShape
  from nn_meter import load_latency_predictor
  from ut.nas.nn.models import MODELS
label_desc: Use image node_id 5 for calling a global function with example usage: test_layerchoice(predictor) without return types