code: def test_pooling1d(device):
      from speechbrain.nnet.pooling import Pooling1d
  
      input = (
          torch.tensor([1, 3, 2], device=device)
          .unsqueeze(0)
          .unsqueeze(-1)
          .float()
      )
      pool = Pooling1d("max", 3).to(device)
      output = pool(input)
      assert output == 3
  
      pool = Pooling1d("avg", 3).to(device)
      output = pool(input)
      assert output == 2
  
      assert torch.jit.trace(pool, input)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 for calling a global function with example usage: test_pooling1d(device) without return types