code: def stoi_loss(y_pred_batch, y_true_batch, lens, reduction="mean"):
      """Compute the STOI 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
      -------
      >>> a = torch.sin(torch.arange(16000, dtype=torch.float32)).unsqueeze(0)
      >>> b = a + 0.001
      >>> -stoi_loss(b, a, torch.ones(1))
      tensor(0.7...)
      """
  
      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]
  
      fs = 16000  # Sampling rate
      N = 30  # length of temporal envelope vectors
      J = 15.0  # Number of one-third octave bands
  
      octave_band = thirdoct(
          fs=10000, nfft=512, num_bands=15, min_freq=150
      )
      c = 5.62341325  # 10^(-Beta/20) with Beta = -15
      D = torch.zeros(batch_size)
      resampler = torchaudio.transforms.Resample(fs, 10000).to(
          y_pred_batch.device
      )
  
      for i in range(0, batch_size):  # Run over mini-batches
          y_true = y_true_batch[
              i, 0 : int(lens[i] * y_pred_batch.shape[1])
          ]
          y_pred = y_pred_batch[
              i, 0 : int(lens[i] * y_pred_batch.shape[1])
          ]
  
          y_true, y_pred = resampler(y_true), resampler(y_pred)
  
          [y_sil_true, y_sil_pred] = removeSilentFrames(y_true, y_pred)
  
          stft_true = torchaudio.transforms.Spectrogram(
              n_fft=512, win_length=256, hop_length=128, power=2
          )(y_sil_true)
          stft_pred = torchaudio.transforms.Spectrogram(
              n_fft=512, win_length=256, hop_length=128, power=2
          )(y_sil_pred)
  
          OCT_true = torch.sqrt(
              torch.matmul(octave_band, stft_true) + 1e-14
          )
          OCT_pred = torch.sqrt(
              torch.matmul(octave_band, stft_pred) + 1e-14
          )
  
          M = int(
              stft_pred.shape[-1] - (N - 1)
          )  # number of temporal envelope vectors
  
          X = torch.zeros(15 * M, 30)
          Y = torch.zeros(15 * M, 30)
          for m in range(0, M):  # Run over temporal envelope vectors
              X[m * 15 : (m + 1) * 15, :] = OCT_true[:, m : m + N]
              Y[m * 15 : (m + 1) * 15, :] = OCT_pred[:, m : m + N]
  
          alpha = torch.norm(X, dim=-1, keepdim=True) / (
              torch.norm(Y, dim=-1, keepdim=True) + smallVal
          )
  
          ay = Y * alpha
          y = torch.min(ay, X + X * c)
  
          xn = X - torch.mean(X, dim=-1, keepdim=True)
          xn = xn / (torch.norm(xn, dim=-1, keepdim=True) + smallVal)
  
          yn = y - torch.mean(y, dim=-1, keepdim=True)
          yn = yn / (torch.norm(yn, dim=-1, keepdim=True) + smallVal)
          d = torch.sum(xn * yn)
          D[i] = d / (J * M)
  
      if reduction == "mean":
          return -D.mean()
  
      return -D
  
imports: import torch
  import torchaudio
  import numpy
  from speechbrain.utils.torch_audio_backend import check_torchaudio_backend
label_desc: Use image node_id 3 for calling a global function with example usage: stoi_loss(y_pred_batch, y_true_batch, lens, reduction) and returns: unknown, unknown