code: def test_filterbank(device):
      from speechbrain.processing.features import Filterbank
  
      compute_fbanks = Filterbank().to(device)
      inputs = torch.ones([10, 101, 201], device=device)
      assert torch.jit.trace(compute_fbanks, inputs)
  
      # Check amin (-100 dB)
      inputs = torch.zeros([10, 101, 201], device=device)
      fbanks = compute_fbanks(inputs)
      assert torch.equal(fbanks, torch.ones_like(fbanks) * -100)
  
      # Check top_db
      fbanks = torch.zeros([1, 1, 1], device=device)
      expected = torch.Tensor([[[-100]]]).to(device)
      fbanks_db = compute_fbanks._amplitude_to_DB(fbanks)
      assert torch.equal(fbanks_db, expected)
  
      # Making sure independent computation gives same results
      # as the batch computation
      input1 = torch.rand([1, 101, 201], device=device) * 10
      input2 = torch.rand([1, 101, 201], device=device)
      input3 = torch.cat([input1, input2], dim=0)
      fbank1 = compute_fbanks(input1)
      fbank2 = compute_fbanks(input2)
      fbank3 = compute_fbanks(input3)
      assert torch.sum(torch.abs(fbank1[0] - fbank3[0])) < 8e-05
      assert torch.sum(torch.abs(fbank2[0] - fbank3[1])) < 8e-05
  
imports: import torch
label_desc: Use image node_id 4 for calling a global function with example usage: test_filterbank(device) without return types