code: def test_input_normalization(device):
      from speechbrain.processing.features import InputNormalization
  
      norm = InputNormalization().to(device)
      inputs = torch.randn([10, 101, 20], device=device)
      inp_len = torch.ones([10], device=device)
      assert torch.jit.trace(norm, (inputs, inp_len))
  
      norm = InputNormalization().to(device)
      inputs = (
          torch.FloatTensor([1, 2, 3, 0, 0, 0])
          .to(device)
          .unsqueeze(0)
          .unsqueeze(2)
      )
      inp_len = torch.FloatTensor([0.5]).to(device)
      out_norm = norm(inputs, inp_len).squeeze()
      target = torch.FloatTensor([-1, 0, 1, -2, -2, -2]).to(device)
      assert torch.equal(out_norm, target)
  
imports: import torch
label_desc: Use image node_id 6 for calling a global function with example usage: test_input_normalization(device) without return types