code: def upgrade_fairseq_state_dict_named(self, state_dict):
      for lid in range(self.args.encoder_layers):
          name = f"encoder.layers.{lid}.self_attn"
          self.upgrade_attn_state_dict_named(state_dict, name)
          name = f"encoder.layers.{lid}"
          layer_norm_map = {
              "0": "self_attn_layer_norm",
              "1": "final_layer_norm",
          }
          for old, new in layer_norm_map.items():
              for m in ("weight", "bias"):
                  k = "{}.layer_norms.{}.{}".format(name, old, m)
                  if k in state_dict:
                      state_dict[
                          "{}.{}.{}".format(name, new, m)
                      ] = state_dict[k]
                      del state_dict[k]
  
      for i in range(self.args.decoder_layers):
          name = f"decoder.layers.{i}.self_attn"
          self.upgrade_attn_state_dict_named(state_dict, name)
          name = f"decoder.layers.{i}.encoder_attn"
          self.upgrade_attn_state_dict_named(state_dict, name)
          name = f"decoder.layers.{i}"
          # update layer norms
          layer_norm_map = {
              "0": "self_attn_layer_norm",
              "1": "encoder_attn_layer_norm",
              "2": "final_layer_norm",
          }
          for old, new in layer_norm_map.items():
              for m in ("weight", "bias"):
                  k = "{}.layers.{}.layer_norms.{}.{}".format(
                      name, i, old, m
                  )
                  if k in state_dict:
                      state_dict[
                          "{}.layers.{}.{}.{}".format(name, i, new, m)
                      ] = state_dict[k]
                      del state_dict[k]
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import TransformerModel
  from fairseq.models.bart import BARTHubInterface, BARTModel
  from .ls_transformer import LSTransformerModel
label_desc: Use image node_id 8 for calling the LSBARTModel obj's underlying member method code with example usage: obj.upgrade_fairseq_state_dict_named(state_dict) without return types