code: def __init__(
      self,
      wav2vec_enc,
      mbart_enc_layers,
      mbart_layer_norm,
      adaptor,
      drop_w2v_layers=0,
  ):
      super().__init__(None)
      self.w2v_encoder = wav2vec_enc
      self.adaptor = adaptor
      self.mbart_encoder_layers = mbart_enc_layers
      self.final_layer_norm = mbart_layer_norm
      if drop_w2v_layers > 0:
          self.w2v_encoder.w2v_model.encoder.layers = (
              self.w2v_encoder.w2v_model.encoder.layers[
                  :-drop_w2v_layers
              ]
          )
  
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 StackedWav2VecEncoderWithAdaptor object from inherited base classes: FairseqEncoder with example: obj = StackedWav2VecEncoderWithAdaptor(wav2vec_enc, mbart_enc_layers, mbart_layer_norm, adaptor, drop_w2v_layers)