def METHOD_NAME(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)