code: def __init__(self, args):
      super().__init__(None)
      self.w2v_encoder = Wav2VecEncoder(args)
      self.is_v0_arch = not args.adaptor_proj
      self.w2v_proj_ln = None
      if not self.is_v0_arch and self.w2v_encoder.proj is not None:
          self.w2v_proj_ln = LayerNorm(args.decoder_embed_dim)
      self.adaptor = self.build_adaptor(args)
  
      self.num_updates = 0
      self.freezing_updates = args.w2v_freezing_updates
      self.finetuning_params = args.finetune_w2v_params
      for k, p in self.w2v_encoder.w2v_model.named_parameters():
          p.requires_grad = need_finetuning(self.finetuning_params, k)
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.transformer import Embedding, TransformerDecoder, TransformerModelBase
  from fairseq.models.wav2vec import Wav2VecEncoder
  from fairseq.modules.layer_norm import LayerNorm
label_desc: Use image node_id 2 to create a new Wav2VecEncoderWithAdaptor object from inherited base classes: FairseqEncoder with example: obj = Wav2VecEncoderWithAdaptor(args)