code: def apply_hypernetworks(hypernetworks, context, layer=None):
      context_k = context
      context_v = context
      for hypernetwork in hypernetworks:
          context_k, context_v = apply_single_hypernetwork(
              hypernetwork, context_k, context_v, layer
          )
  
      return context_k, context_v
  
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 6 for calling a global function with example usage: apply_hypernetworks(hypernetworks, context, layer) and returns: context_k,  context_v