code: def load_hypernetworks(names, multipliers=None):
      already_loaded = {}
  
      for hypernetwork in shared.loaded_hypernetworks:
          if hypernetwork.name in names:
              already_loaded[hypernetwork.name] = hypernetwork
  
      shared.loaded_hypernetworks.clear()
  
      for i, name in enumerate(names):
          hypernetwork = already_loaded.get(name, None)
          if hypernetwork is None:
              hypernetwork = load_hypernetwork(name)
  
          if hypernetwork is None:
              continue
  
          hypernetwork.set_multiplier(
              multipliers[i] if multipliers else 1.0
          )
          shared.loaded_hypernetworks.append(hypernetwork)
  
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 4 for calling a global function with example usage: load_hypernetworks(names, multipliers) without return types