code: def init_optimizers(self):
      self.adam_optimizer = self.hparams.adam_opt_class(
          self.hparams.model.parameters()
      )
  
      self.optimizers_dict = {"model_optimizer": self.adam_optimizer}
  
      # Initializes the wav2vec2 optimizer if the model is not wav2vec2_frozen
      if not self.hparams.wav2vec2_frozen:
          self.wav2vec_optimizer = self.hparams.wav2vec_opt_class(
              self.modules.wav2vec2.parameters()
          )
          self.optimizers_dict[
              "wav2vec_optimizer"
          ] = self.wav2vec_optimizer
  
      # Initializes the mbart optimizer if the model is not mbart_frozen
      if not self.hparams.mbart_frozen:
          self.mbart_optimizer = self.hparams.mbart_opt_class(
              self.modules.mBART.parameters()
          )
          self.optimizers_dict["mbart_optimizer"] = self.mbart_optimizer
  
file_path: speechbrain/recipes/IWSLT22_lowresource/AST/transformer/train_with_w2v_mbart.py
imports: import sys
  import torch
  import logging
  import speechbrain
  from speechbrain.utils.distributed import run_on_main
  from hyperpyyaml import load_hyperpyyaml
  from sacremoses import MosesDetokenizer
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 4 for calling the ST obj's underlying member method code with example usage: obj.init_optimizers() without return types