code: def test_WarmAndExpDecayLRSchedule():
      from speechbrain.nnet.schedulers import WarmAndExpDecayLRSchedule
      from speechbrain.nnet.linear import Linear
      import torch
  
      model = Linear(input_size=3, n_neurons=4)
      optim = torch.optim.Adam(model.parameters(), lr=1)
      scheduler = WarmAndExpDecayLRSchedule(
          lr=1, n_warmup_steps=2, decay_factor=0.01, total_steps=6
      )
  
      scheduler(optim)
      assert optim.param_groups[0]["lr"] == 0.0
  
      scheduler(optim)
      assert optim.param_groups[0]["lr"] == 0.5
  
      scheduler(optim)
      assert optim.param_groups[0]["lr"] == 1
  
      scheduler(optim)
      assert optim.param_groups[0]["lr"] == 0.31622776601683794
  
label_desc: Use image node_id 2 for calling a global function with example usage: test_WarmAndExpDecayLRSchedule() without return types