code: def test_iter_tensors():
      a = (
          torch.zeros(3, 1),
          {"a": torch.zeros(5, 1), "b": torch.zeros(6, 1)},
          [torch.zeros(7, 1)],
      )
      ret = []
      for x in _iter_tensors(a):
          ret.append(x.shape[0])
      assert ret == [3, 5, 6, 7]
  
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 5 for calling a global function with example usage: test_iter_tensors() without return types