code: def test_label():
      from nni.nas.nn.pytorch import LayerChoice
  
      class Model(ModelSpace, label_prefix="model"):
          def __init__(self, in_channels):
              super().__init__()
              self.conv1 = nn.Conv2d(in_channels, 10, 3)
              self.conv2 = LayerChoice(
                  [nn.Conv2d(10, 10, 3), nn.MaxPool2d(3)]
              )
              self.conv3 = LayerChoice(
                  [nn.Identity(), nn.Conv2d(10, 10, 1)]
              )
              self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
              self.fc = nn.Linear(10, 1)
  
          def forward(self, x):
              x = self.conv1(x)
              x = self.conv2(x)
              x = self.conv3(x)
              x = self.avgpool(x).view(x.size(0), -1)
              x = self.fc(x)
              return x
  
      model = Model(3)
      assert model.trace_symbol == Model
      assert model.trace_kwargs == {"in_channels": 3}
      assert model.conv2.label == "model/1"
      assert model.conv3.label == "model/2"
      assert model(torch.randn(1, 3, 5, 5)).size() == torch.Size([1, 1])
  
      model = Model(4)
      assert model.trace_symbol == Model
      assert model.conv2.label == "model/1"
  
imports: import copy
  import pytest
  import torch
  import nni
  from torch import nn
  from nni.common.serializer import is_traceable
  from nni.mutable import Categorical, ExpressionConstraint, ensure_frozen, frozen_context, label_scope, auto_label, SampleMissingError
  from nni.mutable.mutable import _mutable_equal
  from nni.nas.nn.pytorch import ModelSpace, MutableModule, ParametrizedModule
  from nni.nas.space import model_context
label_desc: Use image node_id 16 for calling a global function with example usage: test_label() and returns: x