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]
  
      # 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")
          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 getattr(self.args, "load_checkpoint_heads", False):
              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"):
          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
  
          # adapt data2vec models
          if (
              "encoder._ema" in state_dict
              and "encoder.lm_head.weight" not in state_dict
          ):
              lm_state = self.encoder.lm_head.state_dict()
              for k, v in lm_state.items():
                  state_dict["encoder.lm_head." + k] = v
  
          for k in list(state_dict.keys()):
              if (
                  k.startswith("encoder.regression_head")
                  or k == "encoder._ema"
              ):
                  del state_dict[k]
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 12 for calling the RobertaModel obj's underlying member method code with example usage: obj.upgrade_state_dict_named(state_dict, name) without return types