code: def share_speech_text_encoder(
      cls, speech_encoder, text_encoder, shared_layers_from_begin
  ):
      if shared_layers_from_begin > 0:
          num_text_encoder_layers = len(text_encoder.layers)
          assert len(speech_encoder.layers) >= shared_layers_from_begin
          assert num_text_encoder_layers >= shared_layers_from_begin
          assert len(speech_encoder.layers) >= num_text_encoder_layers
          for i, ly in enumerate(
              speech_encoder.layers[
                  -num_text_encoder_layers : -num_text_encoder_layers
                  + shared_layers_from_begin
              ]
          ):
              assert isinstance(text_encoder.layers[i], type(ly))
              text_encoder.layers[i] = ly
  
imports: import logging
  from collections import OrderedDict, namedtuple
  import torch.nn
  from fairseq import checkpoint_utils, utils
  from fairseq.checkpoint_utils import load_checkpoint_to_cpu
  from fairseq.file_io import PathManager
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.speech_to_text import SpeechWavTransformerEncoder, StackedSpeechWavTransformerEncoder, TransformerDecoder
  from fairseq.models.transformer import TransformerEncoder
  from .s2t_dualinputtransformer import DualInputEncoder, DualInputS2TTransformerModel, TransformerMultiInputDecoder
label_desc: Use image node_id 11 for calling the DualInputWavTransformerModel obj's underlying member method code with example usage: obj.share_speech_text_encoder(cls, speech_encoder, text_encoder, shared_layers_from_begin) without return types