code: def __init__(self, wav2vec_enc, mbart_enc, adaptor, shared_layers):
      super().__init__(None)
      self.w2v_encoder = wav2vec_enc
      self.shared_layers = self.w2v_encoder.w2v_model.encoder.layers[
          -shared_layers:
      ]
      self.w2v_encoder.w2v_model.encoder.layers = (
          self.w2v_encoder.w2v_model.encoder.layers[:-shared_layers]
      )
      self.adaptor = adaptor
      if self.shared_layers[-1].layer_norm_first:
          self.final_layer_norm = mbart_enc.layer_norm
      else:
          mbart_enc.layer_norm = None
          self.final_layer_norm = None
      shared_layer_from = len(mbart_enc.layers) - shared_layers
      if shared_layer_from < 0:
          shared_layer_from = 0
      for layer_id, layer in enumerate(self.shared_layers):
          mbart_enc.layers[
              shared_layer_from + layer_id
          ] = TransformerSentenceEncoderLayerStd(layer)
  
imports: import copy
  import torch.nn
  from fairseq import checkpoint_utils
  from fairseq import utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import register_model, register_model_architecture, FairseqEncoder
  from fairseq.models.speech_to_text import Wav2VecEncoderWithAdaptor
  from fairseq.models.speech_to_text.xm_transformer import set_default_adaptor_args, set_default_w2v_encoder_args, need_finetuning
  from fairseq.models.transformer import TransformerEncoder, TransformerDecoder
  from fairseq.models.wav2vec import TransformerSentenceEncoderLayer
  from fairseq.utils import safe_hasattr
  from .s2t_dualinputtransformer import DualInputS2TTransformerModel, TransformerMultiInputDecoder, DualInputEncoder
label_desc: Use image node_id 1 to create a new SharedEncoder object from inherited base classes: FairseqEncoder with example: obj = SharedEncoder(wav2vec_enc, mbart_enc, adaptor, shared_layers)