code: def resynthesize(
      enhanced_mag, noisy_inputs, stft, istft, normalize_wavs=True
  ):
      """Function for resynthesizing waveforms from enhanced mags.
  
      Arguments
      ---------
      enhanced_mag : torch.Tensor
          Predicted spectral magnitude, should be three dimensional.
      noisy_inputs : torch.Tensor
          The noisy waveforms before any processing, to extract phase.
      lengths : torch.Tensor
          The length of each waveform for normalization.
      stft : torch.nn.Module
          Module for computing the STFT for extracting phase.
      istft : torch.nn.Module
          Module for computing the iSTFT for resynthesis.
      normalize_wavs : bool
          Whether to normalize the output wavs before returning them.
  
      Returns
      -------
      enhanced_wav : torch.Tensor
          The resynthesized waveforms of the enhanced magnitudes with noisy phase.
      """
  
      # Extract noisy phase from inputs
      noisy_feats = stft(noisy_inputs)
      noisy_phase = torch.atan2(
          noisy_feats[:, :, :, 1], noisy_feats[:, :, :, 0]
      )
  
      # Combine with enhanced magnitude
      complex_predictions = torch.mul(
          torch.unsqueeze(enhanced_mag, -1),
          torch.cat(
              (
                  torch.unsqueeze(torch.cos(noisy_phase), -1),
                  torch.unsqueeze(torch.sin(noisy_phase), -1),
              ),
              -1,
          ),
      )
      pred_wavs = istft(
          complex_predictions, sig_length=noisy_inputs.shape[1]
      )
  
      # Normalize. Since we're using peak amplitudes, ignore lengths
      if normalize_wavs:
          pred_wavs = normalize(pred_wavs, amp_type="peak")
  
      return pred_wavs
  
file_path: speechbrain/speechbrain/processing/signal_processing.py
imports: import torch
  import math
  from packaging import version
label_desc: Use image node_id 10 for calling a global function with example usage: resynthesize(enhanced_mag, noisy_inputs, stft, istft, normalize_wavs) and returns: pred_wavs