code: def test_gccphat(device):
      from speechbrain.processing.features import STFT
      from speechbrain.processing.multi_mic import Covariance, GccPhat
  
      # Creating the test signal
      fs = 16000
  
      delay = 60
  
      sig = torch.randn([10, fs], device=device)
      sig_delayed = torch.cat(
          (torch.zeros([10, delay], device=device), sig[:, 0:-delay]), 1
      )
  
      xs = torch.stack((sig_delayed, sig), -1)
  
      stft = STFT(sample_rate=fs).to(device)
      Xs = stft(xs)
  
      # Computing the covariance matrix for GCC-PHAT
      cov = Covariance().to(device)
      gccphat = GccPhat().to(device)
  
      XXs = cov(Xs).to(device)
      tdoas = torch.abs(gccphat(XXs))
  
      n_valid_tdoas = torch.sum(torch.abs(tdoas[..., 1] - delay) < 1e-3)
      assert n_valid_tdoas == Xs.shape[0] * Xs.shape[1]
      assert torch.jit.trace(stft, xs)
      assert torch.jit.trace(cov, Xs)
      assert torch.jit.trace(gccphat, XXs)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_gccphat(device) without return types