code: def si_snr_loss(y_pred_batch, y_true_batch, lens, reduction="mean"):
      """Compute the si_snr score and return -1 * that score.
  
      This function can be used as a loss function for training
      with SGD-based updates.
  
      Arguments
      ---------
      y_pred_batch : torch.Tensor
          The degraded (enhanced) waveforms.
      y_true_batch : torch.Tensor
          The clean (reference) waveforms.
      lens : torch.Tensor
          The relative lengths of the waveforms within the batch.
      reduction : str
          The type of reduction ("mean" or "batch") to use.
  
      Example
      -------
      """
  
      y_pred_batch = torch.squeeze(y_pred_batch, dim=-1)
      y_true_batch = torch.squeeze(y_true_batch, dim=-1)
  
      batch_size = y_pred_batch.shape[0]
      SI_SNR = torch.zeros(batch_size)
  
      for i in range(0, batch_size):  # Run over mini-batches
          s_target = y_true_batch[
              i, 0 : int(lens[i] * y_pred_batch.shape[1])
          ]
          s_estimate = y_pred_batch[
              i, 0 : int(lens[i] * y_pred_batch.shape[1])
          ]
  
          # s_target = <s', s>s / ||s||^2
          dot = torch.sum(s_estimate * s_target, dim=0, keepdim=True)
          s_target_energy = (
              torch.sum(s_target**2, dim=0, keepdim=True) + smallVal
          )
          proj = dot * s_target / s_target_energy
  
          # e_noise = s' - s_target
          e_noise = s_estimate - proj
  
          # SI-SNR = 10 * log_10(||s_target||^2 / ||e_noise||^2)
          si_snr_beforelog = torch.sum(proj**2, dim=0) / (
              torch.sum(e_noise**2, dim=0) + smallVal
          )
          SI_SNR[i] = 10 * torch.log10(si_snr_beforelog + smallVal)
  
      if reduction == "mean":
          return -SI_SNR.mean()
  
      return -SI_SNR
  
label_desc: Use image node_id 1 for calling a global function with example usage: si_snr_loss(y_pred_batch, y_true_batch, lens, reduction) and returns: unknown, unknown