docstr: Called during ``on_fit_start()``, initialize optimizers
  after parameters are fully configured (e.g. DDP, jit).
  
  The default implementation of this method depends on an optimizer
  class being passed at initialization that takes only a list
  of parameters (e.g., a lambda or a partial function definition).
  This creates a single optimizer that optimizes all trainable params.
  
  Override this class if there are multiple optimizers.
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code: def init_optimizers(self):
      """Called during ``on_fit_start()``, initialize optimizers
      after parameters are fully configured (e.g. DDP, jit).
  
      The default implementation of this method depends on an optimizer
      class being passed at initialization that takes only a list
      of parameters (e.g., a lambda or a partial function definition).
      This creates a single optimizer that optimizes all trainable params.
  
      Override this class if there are multiple optimizers.
      """
      if self.opt_class is not None:
          self.optimizer_d = self.opt_class(
              self.modules["model_d"].parameters()
          )
          self.optimizer_g = self.opt_class(
              self.modules["model_g"].parameters()
          )
  
          if self.checkpointer is not None:
              self.checkpointer.add_recoverable(
                  "optimizer_g", self.optimizer_g
              )
              self.checkpointer.add_recoverable(
                  "optimizer_d", self.optimizer_d
              )
  
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 8 for calling the SEBrain obj's underlying member method code with example usage: obj.init_optimizers() without return types