code: def build_adaptor(self, args):
      adaptor = None
      if args.adaptor_n_layers > 0:
          adaptor = Conv1dAdaptor(
              args.decoder_embed_dim,
              args.decoder_embed_dim,
              n_layers=args.adaptor_n_layers,
              kernel_size=args.adaptor_kernel_size,
              stride=args.adaptor_stride,
              layerdrop=args.adaptor_layerdrop,
              layernorm=args.adaptor_layernorm,
              proj=args.adaptor_proj,
          )
      return adaptor
  
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 1 for calling the Wav2VecEncoderWithAdaptor obj's underlying member method code with example usage: obj.build_adaptor(args) and returns: adaptor