docstr: trans dict prefix to 1-D tensor prefix
docstr_tok: ["trans","dict","prefix","to","1-D","tensor","prefix"]
code: def convert_prefix(self, prefix_dict):
      """trans dict prefix to 1-D tensor prefix"""
      trans_prefix = []
      for i, prefix in enumerate(prefix_dict):  # 12 layer
          # 'decoder_prompt', 'cross_attention_prompt', 'encoder_prompt'
          current_key_prefix = []
          for key in prefix.keys():
              # prev_key', 'prev_value', 'prev_key_padding_mask'
              current_key_prefix.append(
                  torch.stack(
                      [
                          prefix[key]["prev_key"]
                          .squeeze(0)
                          .transpose(1, 0)
                          .reshape(-1, self.n_embd)
                          .mean(0),
                          prefix[key]["prev_value"]
                          .squeeze(0)
                          .transpose(1, 0)
                          .reshape(-1, self.n_embd)
                          .mean(0),
                      ],
                      dim=0,
                  ).mean(0)
              )  #  768
          trans_prefix.append(
              torch.stack(current_key_prefix, dim=0).mean(0)
          )
      trans_prefix = torch.stack(trans_prefix, dim=0).mean(0)
      return trans_prefix
  
file_path: DeepKE/src/deepke/name_entity_re/cross/seq2seq/models.py
imports: import os
  from copy import deepcopy
  import torch
  from torch import nn
  from .modeling_t5 import T5ForConditionalGeneration
label_desc: Use image node_id 4 for calling the T5Prompt obj's underlying member method code with example usage: obj.convert_prefix(prefix_dict) and returns: trans_prefix