code: def update_moving_average(ema_updater, ma_model, current_model):
      for current_params, ma_params in zip(
          current_model.parameters(), ma_model.parameters()
      ):
          old_weight, up_weight = ma_params.data, current_params.data
          ma_params.data = ema_updater.update_average(
              old_weight, up_weight
          )
  
imports: import copy
  import random
  from functools import wraps, partial
  import torch
  from torch import nn, einsum
  import torch.nn.functional
  from torchvision import transforms
  from einops import rearrange, reduce, repeat
label_desc: Use image node_id 9 for calling a global function with example usage: update_moving_average(ema_updater, ma_model, current_model) without return types