code: def upgrade_state_dict_named(self, state_dict, name):
      prefix = name + "." if name != "" else ""
  
      # rename decoder -> encoder before upgrading children modules
      for k in list(state_dict.keys()):
          if k.startswith(prefix + "decoder"):
              new_k = prefix + "encoder" + k[len(prefix + "decoder") :]
              state_dict[new_k] = state_dict[k]
              del state_dict[k]
  
      # rename emb_layer_norm -> layernorm_embedding
      for k in list(state_dict.keys()):
          if ".emb_layer_norm." in k:
              new_k = k.replace(
                  ".emb_layer_norm.", ".layernorm_embedding."
              )
              state_dict[new_k] = state_dict[k]
              del state_dict[k]
  
          if self.encoder.regression_head is not None:
              if ".lm_head." in k:
                  new_k = k.replace(".lm_head.", ".regression_head.")
                  state_dict[new_k] = state_dict[k]
                  del state_dict[k]
          else:
              if ".regression_head." in k:
                  del state_dict[k]
  
      # upgrade children modules
      super().upgrade_state_dict_named(state_dict, name)
  
      # Handle new classification heads present in the state dict.
      current_head_names = (
          []
          if not hasattr(self, "classification_heads")
          or self.classification_heads is None
          else self.classification_heads.keys()
      )
      keys_to_delete = []
      for k in state_dict.keys():
          if not k.startswith(prefix + "classification_heads."):
              continue
  
          head_name = k[len(prefix + "classification_heads.") :].split(
              "."
          )[0]
          num_classes = state_dict[
              prefix
              + "classification_heads."
              + head_name
              + ".out_proj.weight"
          ].size(0)
          inner_dim = state_dict[
              prefix
              + "classification_heads."
              + head_name
              + ".dense.weight"
          ].size(0)
  
          if self.cfg.load_checkpoint_heads:
              if head_name not in current_head_names:
                  self.register_classification_head(
                      head_name, num_classes, inner_dim
                  )
          else:
              if head_name not in current_head_names:
                  logger.warning(
                      "deleting classification head ({}) from checkpoint "
                      "not present in current model: {}".format(
                          head_name, k
                      )
                  )
                  keys_to_delete.append(k)
              elif (
                  num_classes
                  != self.classification_heads[
                      head_name
                  ].out_proj.out_features
                  or inner_dim
                  != self.classification_heads[
                      head_name
                  ].dense.out_features
              ):
                  logger.warning(
                      "deleting classification head ({}) from checkpoint "
                      "with different dimensions than current model: {}".format(
                          head_name, k
                      )
                  )
                  keys_to_delete.append(k)
      for k in keys_to_delete:
          del state_dict[k]
  
      # Copy any newly-added classification heads into the state dict
      # with their current weights.
      if (
          hasattr(self, "classification_heads")
          and self.classification_heads is not None
          and len(self.classification_heads) > 0
      ):
          cur_state = self.classification_heads.state_dict()
          for k, v in cur_state.items():
              if prefix + "classification_heads." + k not in state_dict:
                  logger.info(
                      "Overwriting "
                      + prefix
                      + "classification_heads."
                      + k
                  )
                  state_dict[prefix + "classification_heads." + k] = v
  
          for k in list(state_dict.keys()):
              if k.startswith(
                  prefix + "encoder.lm_head."
              ) or k.startswith(prefix + "encoder.emb_head."):
                  del state_dict[k]
  
          self.encoder.lm_head = None
  
      if self.encoder.target_model is None:
          for k in list(state_dict.keys()):
              if k.startswith(prefix + "encoder.target_model."):
                  del state_dict[k]
  
      if (self.encoder.ema is None) and (
          prefix + "encoder._ema" in state_dict
      ):
          del state_dict[prefix + "encoder._ema"]
  
imports: from dataclasses import dataclass, field
  from typing import Optional
  import logging
  import math
  import torch
  import torch.nn
  import torch.nn.functional
  from omegaconf import II
  from fairseq.dataclass import FairseqDataclass
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model
  from fairseq.models.roberta.model import RobertaLMHead, RobertaClassificationHead
  from fairseq.models.transformer import TransformerEncoder, TransformerConfig
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
label_desc: Use image node_id 7 for calling the Data2VecTextModel obj's underlying member method code with example usage: obj.upgrade_state_dict_named(state_dict, name) without return types