code: def removeSilentFrames(x, y, dyn_range=40, N=256, K=128):
      """Removes silent frames from the STOI computation.
  
      This function can be used as a loss function for training
      with SGD-based updates.
  
      Arguments
      ---------
      x: torch.Tensor
          The clean (reference) waveforms.
      y: torch.Tensor
          The degraded (enhanced) waveforms.
      dyn_range: int
          Dynamic range used for mask computation.
      N: int
          Window length.
      K: int
          Step size.
      """
      w = torch.unsqueeze(torch.from_numpy(np.hanning(N)), 0).to(
          torch.float
      )
  
      X1 = (
          x[0 : int(x.shape[0]) // N * N]
          .reshape(int(x.shape[0]) // N, N)
          .T
      )
      X2 = (
          x[K : (int(x.shape[0]) - K) // N * N + K]
          .reshape((int(x.shape[0]) - K) // N, N)
          .T
      )
      X = torch.zeros(N, X1.shape[1] + X2.shape[1])
      X[:, 0::2] = X1
      X[:, 1::2] = X2
  
      energy = 20 * torch.log10(
          torch.sqrt(torch.matmul(w**2, X**2)) / 16.0 + smallVal
      )
  
      Max_energy = torch.max(energy)
      msk = torch.squeeze((energy - Max_energy + dyn_range > 0))
  
      Y1 = (
          y[0 : int(y.shape[0]) // N * N]
          .reshape(int(y.shape[0]) // N, N)
          .T
      )
      Y2 = (
          y[K : (int(y.shape[0]) - K) // N * N + K]
          .reshape((int(y.shape[0]) - K) // N, N)
          .T
      )
      Y = torch.zeros(N, Y1.shape[1] + Y2.shape[1])
      Y[:, 0::2] = Y1
      Y[:, 1::2] = Y2
  
      x_sil = w.T.repeat(1, X[:, msk].shape[-1]) * X[:, msk]
      y_sil = w.T.repeat(1, X[:, msk].shape[-1]) * Y[:, msk]
  
      x_sil = torch.cat(
          (
              x_sil[0:K, 0],
              (x_sil[0:K, 1:] + x_sil[K:, 0:-1]).T.flatten(),
              x_sil[K:N, -1],
          ),
          axis=0,
      )
      y_sil = torch.cat(
          (
              y_sil[0:K, 0],
              (y_sil[0:K, 1:] + y_sil[K:, 0:-1]).T.flatten(),
              y_sil[K:N, -1],
          ),
          axis=0,
      )
  
      return [x_sil, y_sil]
  
imports: import torch
  import torchaudio
  import numpy
  from speechbrain.utils.torch_audio_backend import check_torchaudio_backend
label_desc: Use image node_id 2 for calling a global function with example usage: removeSilentFrames(x, y, dyn_range, N, K) and returns: list