code: def select_encoder(self, mode, **kwargs):
      if mode in (
          "speech",
          "sup_speech_ctc",
          "sup_speech_ali",
          "sup_speech_s2s",
      ):
          kwargs["features_only"] = True
          if mode == "sup_speech_s2s":
              return self.sup_s2s_speech_encoder, kwargs
          return self.sup_speech_encoder, kwargs
      elif mode == "unsup_speech":
          kwargs["features_only"] = False
          return self.unsup_speech_encoder, kwargs
      elif mode in ("text", "bitext"):
          return self.text_encoder, kwargs
      else:
          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 9 for calling the SpeechTextPreTrainEncoder obj's underlying member method code with example usage: obj.select_encoder(mode) and returns: None,  kwargs, self,  kwargs, self,  kwargs, self,  kwargs, self,  kwargs