code: def test_proxyless_input():
      inp = ProxylessMixedInput(
          6,
          2,
          nn.Parameter(torch.zeros(6)),
          nn.Softmax(-1),
          "proxyless",
      )
  
      optimizer = torch.optim.SGD(inp.arch_parameters(), 0.1)
      for _ in range(10):
          x = [
              torch.randn(1, 3, 9, 9).requires_grad_() for _ in range(6)
          ]
          assert len(inp.resample({})["proxyless"]) == 2
          y = inp(x).sum()
          optimizer.zero_grad()
          y.backward()
  
      assert all(0 <= x < 6 for x in inp.export({})["proxyless"])
  
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 4 for calling a global function with example usage: test_proxyless_input() without return types