code: def update_transformer_encoder_cfg(cls, args, update_dict):
      cfg = dict(args._get_kwargs())
      for fkey in update_dict.keys():
          cfg[fkey] = update_dict[fkey]
      cfg.pop("_name", None)  # remove keys start with _
      model_args = namedtuple("args", cfg.keys())(*cfg.values())
      return model_args
  
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 SpeechTextPreTrainEncoder obj's underlying member method code with example usage: obj.update_transformer_encoder_cfg(cls, args, update_dict) and returns: model_args