code: def apply_single_hypernetwork(
      hypernetwork, context_k, context_v, layer=None
  ):
      hypernetwork_layers = (
          hypernetwork.layers if hypernetwork is not None else {}
      ).get(context_k.shape[2], None)
  
      if hypernetwork_layers is None:
          return context_k, context_v
  
      if layer is not None:
          layer.hyper_k = hypernetwork_layers[0]
          layer.hyper_v = hypernetwork_layers[1]
  
      context_k = devices.cond_cast_unet(
          hypernetwork_layers[0](devices.cond_cast_float(context_k))
      )
      context_v = devices.cond_cast_unet(
          hypernetwork_layers[1](devices.cond_cast_float(context_v))
      )
      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 5 for calling a global function with example usage: apply_single_hypernetwork(hypernetwork, context_k, context_v, layer) and returns: context_k,  context_v, context_k,  context_v