docstr: A total of 3 data passes to update discriminator.
docstr_tok: ["A","total","of","3","data","passes","to","update","discriminator","."]
code: def train_discriminator(self):
      """A total of 3 data passes to update discriminator."""
      # First, iterate train subset w/ updates for enh, noisy
      print("Discriminator training by current data...")
      self.sub_stage = SubStage.CURRENT
      self.fit(
          range(1),
          self.train_set,
          train_loader_kwargs=self.hparams.dataloader_options,
      )
  
      # Next, iterate historical subset w/ updates for enh
      if self.historical_set:
          print("Discriminator training by historical data...")
          self.sub_stage = SubStage.HISTORICAL
          self.fit(
              range(1),
              self.historical_set,
              train_loader_kwargs=self.hparams.dataloader_options,
          )
  
      # Finally, iterate train set again. Should iterate same
      # samples as before, due to ReproducibleRandomSampler
      print("Discriminator training by current data again...")
      self.sub_stage = SubStage.CURRENT
      self.fit(
          range(1),
          self.train_set,
          train_loader_kwargs=self.hparams.dataloader_options,
      )
  
imports: import os
  import sys
  import shutil
  import torch
  import torchaudio
  import speechbrain
  import numpy
  import json
  import pickle
  import requests
  import time
  from urllib.parse import urlparse, urljoin
  from srmrpy import srmr
  from pesq import pesq
  from enum import Enum, auto
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.utils.metric_stats import MetricStats
  from speechbrain.processing.features import spectral_magnitude
  from speechbrain.nnet.loss.stoi_loss import stoi_loss
  from speechbrain.utils.distributed import run_on_main
  from speechbrain.dataio.sampler import ReproducibleWeightedRandomSampler
label_desc: Use image node_id 12 for calling the MetricGanBrain obj's underlying member method code with example usage: obj.train_discriminator() without return types