code: def test_mobilenetv3(predictor):
      custom_leaf_types = (
          ConvBNReLU,
          DepthwiseSeparableConv,
          InvertedResidual,
          SqueezeExcite,
      )
  
      model_space = MobileNetV3Space(
          width_multipliers=(0.5, 1.0), expand_ratios=(3.0, 6.0)
      )
      sample = {}
      model = model_space.random(sample)
  
      profiler = NnMeterProfiler(
          model_space,
          torch.randn(1, 3, 224, 224),
          predictor,
          custom_leaf_types=custom_leaf_types,
          simplify_shapes=True,
      )
      result1 = profiler.profile(sample)
  
      profiler = NnMeterProfiler(
          model,
          torch.randn(1, 3, 224, 224),
          predictor,
          custom_leaf_types=custom_leaf_types,
          simplify_shapes=True,
      )
      result2 = profiler.profile({})
  
      assert result1 > 0
      assert abs(result1 - result2) < 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 10 for calling a global function with example usage: test_mobilenetv3(predictor) without return types