code: def select_decoder(self, mode, **kwargs):
      if mode == "unsup_speech":
          kwargs["mode"] = mode
          return self.speech_decoder, kwargs
      if mode in ("text", "bitext"):
          return self.text_decoder, kwargs
      if mode in ("speech", "sup_speech_ctc", "sup_speech_ali"):
          kwargs["mode"] = mode
          return self.speech_decoder, kwargs
      if mode in ("speech", "sup_speech_s2s"):
          if "alignment" in kwargs:
              del kwargs["alignment"]
          return self.text_decoder, kwargs
  
      raise NotImplementedError(f"{mode} is not supported")
      return None, kwargs
  
imports: import logging
  from collections import OrderedDict, namedtuple
  from typing import Dict, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.file_io import PathManager
  from fairseq.models import FairseqDecoder, FairseqEncoderDecoderModel, register_model, register_model_architecture
  from fairseq.models.speech_to_text import MultiInputDecoder, MultiModalityEncoder, SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder
  from fairseq.models.transformer import TransformerDecoder, TransformerEncoder, TransformerModel
label_desc: Use image node_id 2 for calling the SpeechTextPreTrainDecoder obj's underlying member method code with example usage: obj.select_decoder(mode) and returns: None,  kwargs, self,  kwargs, self,  kwargs, self,  kwargs, self,  kwargs