docstr: Computes the loss of a discriminator given predicted and
  targeted outputs, with target being clean
docstr_tok: ["Computes","the","loss","of","a","discriminator","given","predicted","and","targeted","outputs",",","with","target","being","clean"]
code: def compute_objectives_d1(self, d_outs, batch):
      """Computes the loss of a discriminator given predicted and
      targeted outputs, with target being clean"""
      loss = self.hparams.compute_cost["d1"](d_outs)
      self.loss_metric_d1.append(batch.id, d_outs, reduction="batch")
      return loss
  
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 3 for calling the SEBrain obj's underlying member method code with example usage: obj.compute_objectives_d1(d_outs, batch) and returns: loss