code: def test_autoformer_supernet(name):
      # check subnet & supernet weights load
      model = ss.AutoformerSpace.load_searched_model(
          f"autoformer-{name}", pretrained=True, download=True
      )
      model(torch.rand(1, 3, 224, 224))
      model_space = ss.AutoformerSpace.load_pretrained_supernet(
          f"random-one-shot-{name}"
      )
      model_space.random()(torch.rand(1, 3, 224, 224))
  
imports: import logging
  import sys
  import pytest
  import numpy
  import torch
  from torch.utils.data import Subset
  from torchvision import transforms
  from torchvision.datasets import CIFAR10, ImageNet
  import nni
  import nni.nas.hub.pytorch
  import nni.nas.evaluator.pytorch
  import nni.nas.strategy
  from nni.nas.space import RawFormatModelSpace
  from nni.nas.execution import SequentialExecutionEngine
  from nni.nas.oneshot.pytorch.profiler import ExpectationProfilerPenalty
  from nni.nas.profiler.pytorch.flops import FlopsProfiler
label_desc: Use image node_id 6 for calling a global function with example usage: test_autoformer_supernet(name) without return types