code: def reconstruct_results(
      X1hat,
      X2hat,
      X_stft,
      sample_rate,
      win_length,
      hop_length,
  ):
      """This function reconstructs the separated spectra into waveforms.
  
      Arguments
      ---------
      Xhat1 : torch.tensor
          The separated spectrum for source 1 of size [BS, nfft/2 + 1, T],
          where,  BS = batch size, nfft = fft size, T = length of the spectra.
      Xhat2 : torch.tensor
          The separated spectrum for source 2 of size [BS, nfft/2 + 1, T].
          The size definitions are the same as Xhat1.
      X_stft : torch.tensor
          This is the magnitude spectra for the mixtures.
          The size is [BS x nfft//2 + 1 x T x 2] where,
          BS = batch size, nfft = fft size, T = number of time steps in the spectra.
          The last dimension is to represent complex numbers.
      sample_rate : int
          The sampling rate (in Hz) in which we would like to save the results.
      win_length : int
          The length of stft windows (in ms).
      hop_length : int
          The length with which we shift the STFT windows (in ms).
  
      Returns
      -------
      x1hats : list
          List of waveforms for source 1.
      x2hats : list
          List of waveforms for source 2.
  
      Example
      -------
      >>> BS, nfft, T = 10, 512, 16000
      >>> sample_rate, win_length, hop_length = 16000, 25, 10
      >>> X1hat = torch.randn(BS, nfft//2 + 1, T)
      >>> X2hat = torch.randn(BS, nfft//2 + 1, T)
      >>> X_stft = torch.randn(BS, nfft//2 + 1, T, 2)
      >>> x1hats, x2hats = reconstruct_results(X1hat, X2hat, X_stft, sample_rate, win_length, hop_length)
      """
  
      ISTFT = spf.ISTFT(
          sample_rate=sample_rate,
          win_length=win_length,
          hop_length=hop_length,
      )
  
      phase_mix = spectral_phase(X_stft)
      mag_mix = spectral_magnitude(X_stft, power=2)
  
      x1hats, x2hats = [], []
      eps = 1e-25
      for i in range(X1hat.shape[0]):
          X1hat_stft = (
              (X1hat[i] / (eps + X1hat[i] + X2hat[i])).unsqueeze(-1)
              * mag_mix[i].unsqueeze(-1)
              * torch.cat(
                  [
                      torch.cos(phase_mix[i].unsqueeze(-1)),
                      torch.sin(phase_mix[i].unsqueeze(-1)),
                  ],
                  dim=-1,
              )
          )
  
          X2hat_stft = (
              (X2hat[i] / (eps + X1hat[i] + X2hat[i])).unsqueeze(-1)
              * mag_mix[i].unsqueeze(-1)
              * torch.cat(
                  [
                      torch.cos(phase_mix[i].unsqueeze(-1)),
                      torch.sin(phase_mix[i].unsqueeze(-1)),
                  ],
                  dim=-1,
              )
          )
          X1hat_stft = X1hat_stft.unsqueeze(0).permute(0, 2, 1, 3)
          X2hat_stft = X2hat_stft.unsqueeze(0).permute(0, 2, 1, 3)
          shat1 = ISTFT(X1hat_stft)
          shat2 = ISTFT(X2hat_stft)
  
          div_factor = 10
          x1 = shat1 / (div_factor * shat1.std())
          x2 = shat2 / (div_factor * shat2.std())
  
          x1hats.append(x1)
          x2hats.append(x2)
      return x1hats, x2hats
  
imports: import torch
  from speechbrain.processing.features import spectral_magnitude
  import speechbrain.processing.features
label_desc: Use image node_id 3 for calling a global function with example usage: reconstruct_results(X1hat, X2hat, X_stft, sample_rate, win_length, hop_length) and returns: x1hats,  x2hats