code: def upgrade_state_dict(self, state_dict):
      for k, _ in state_dict.items():
          if "adaptor.layers" in state_dict:
              new = k.replace("adaptor.layers", "adaptor_layers")
              state_dict[new] = state_dict[k]
              del state_dict[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 14 for calling the XMTransformerModel obj's underlying member method code with example usage: obj.upgrade_state_dict(state_dict) without return types