code: def test_proxyless_repeat():
      repeat = Repeat(nn.Linear(3, 3), (2, 4), label="rep")
      repeat = ProxylessMixedRepeat.mutate(repeat, "", {}, {})
      assert isinstance(repeat.blocks[1], nn.Linear)
      assert isinstance(repeat.blocks[2], ProxylessMixedLayer)
      memo = {}
      for module in repeat.modules():
          if isinstance(module, ProxylessMixedLayer):
              memo.update(module.resample(memo))
      assert len(memo) == 2
      assert memo["rep/in_repeat_2"] in ["0", "1"]
      assert memo["rep/in_repeat_3"] in ["0", "1"]
      assert repeat(torch.randn(2, 3)).size(1) == 3
      assert len(repeat.export_probs({})["rep"]) == 3
      assert repeat.export({})["rep"] in [2, 3, 4]
  
      assert repeat.contains({"rep": 3})
      assert not repeat.contains({"rep": 1})
      repeat = repeat.freeze({"rep": 3})
      assert isinstance(repeat, nn.Sequential) and len(repeat) == 3
      assert isinstance(repeat[1], nn.Linear)
      assert isinstance(repeat[2], nn.Linear)
      assert repeat(torch.randn(3, 3)).size(1) == 3
  
imports: import random
  import pytest
  import torch
  import torch.nn
  from torch.utils.data import TensorDataset, DataLoader
  import nni
  from nni.mutable import frozen
  from nni.nas.hub.pytorch.nasbench201 import OPS_WITH_STRIDE
  from nni.nas.nn.pytorch import Repeat
  from nni.nas.oneshot.pytorch.supermodule.base import BaseSuperNetModule
  from nni.nas.oneshot.pytorch.supermodule.proxyless import ProxylessMixedLayer, ProxylessMixedInput, ProxylessMixedRepeat, _iter_tensors
  from pytorch_lightning import LightningModule, Trainer
label_desc: Use image node_id 7 for calling a global function with example usage: test_proxyless_repeat() without return types