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 labse optimizer if the model is not labse_frozen
      if not self.hparams.labse_frozen:
          self.labse_optimizer = self.hparams.labse_opt_class(
              self.modules.LaBSE.parameters()
          )
          self.optimizers_dict["labse_optimizer"] = self.labse_optimizer
  
file_path: speechbrain/recipes/IWSLT22_lowresource/AST/transformer/train_samu.py
imports: import sys
  import torch
  import logging
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  import torch.nn.functional
  from speechbrain.utils.distributed import run_on_main
label_desc: Use image node_id 3 for calling the ST obj's underlying member method code with example usage: obj.init_optimizers() without return types