code: def share_layers(
      cls, src_layers, tgt_layers
  ):  # share layer but not dropout
      # share parameters in src_layers with tgt_layers
      assert len(src_layers) == len(tgt_layers)
      for i, ly in enumerate(src_layers):
          tly = tgt_layers[i]
          tly.self_attn = ly.self_attn
          tly.self_attn_layer_norm = ly.self_attn_layer_norm
          tly.activation_fn = ly.activation_fn
          tly.normalize_before = ly.normalize_before
          tly.fc1 = ly.fc1
          tly.fc2 = ly.fc2
          tly.final_layer_norm = ly.final_layer_norm
          if hasattr(tly, "encoder_attn"):
              tly.encoder_attn = ly.encoder_attn
              tly.encoder_attn_layer_norm = ly.encoder_attn_layer_norm
      return tgt_layers
  
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 5 for calling the SpeechTextPreTrainEncoder obj's underlying member method code with example usage: obj.share_layers(cls, src_layers, tgt_layers) and returns: tgt_layers