code: def fix_old_state_dict(self, state_dict):
      changes = {
          "linear1.bias": "linear.0.bias",
          "linear1.weight": "linear.0.weight",
          "linear2.bias": "linear.1.bias",
          "linear2.weight": "linear.1.weight",
      }
  
      for fr, to in changes.items():
          x = state_dict.get(fr, None)
          if x is None:
              continue
  
          del state_dict[fr]
          state_dict[to] = x
  
imports: import datetime
  import glob
  import html
  import os
  import inspect
  from contextlib import closing
  import modules.textual_inversion.dataset
  import torch
  import tqdm
  from einops import rearrange, repeat
  from ldm.util import default
  from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
  from modules.textual_inversion import textual_inversion, logging
  from modules.textual_inversion.learn_schedule import LearnRateScheduler
  from torch import einsum
  from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
  from collections import deque
  from statistics import stdev, mean
label_desc: Use image node_id 2 for calling the HypernetworkModule obj's underlying member method code with example usage: obj.fix_old_state_dict(state_dict) without return types