docstr: Forward computations from discriminator. Input denoised-noisy pair,
  output whether denoising was properly acheived
docstr_tok: ["Forward","computations","from","discriminator",".","Input","denoised-noisy","pair",",","output","whether","denoising","was","properly","acheived"]
code: def compute_forward_d(self, noisy_wavs, clean_wavs):
      """Forward computations from discriminator. Input denoised-noisy pair,
      output whether denoising was properly acheived"""
      noisy_wavs = noisy_wavs.to(self.device)
      clean_wavs = clean_wavs.to(self.device)
  
      inpt = torch.cat((noisy_wavs, clean_wavs), -1)
      out = self.modules["model_d"](inpt)
      return out
  
imports: import os
  import sys
  import torch
  import torchaudio
  import speechbrain
  from pesq import pesq
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 2 for calling the SEBrain obj's underlying member method code with example usage: obj.compute_forward_d(noisy_wavs, clean_wavs) and returns: out