code: def updated_latest_weight_average(
      model_parameters, model_buffers, store, last_k=10
  ):
      if len(store) > last_k:
          store.pop(0)
  
      store.append(dict(params=model_parameters, buffers=model_buffers))
      param_store = store[0]["params"]
      [
          torch._foreach_add_(param_store, storage["params"])
          for storage in store[1:]
      ]
      torch._foreach_div(param_store, float(last_k))
  
      buffer_store = store[0]["buffers"]
      [
          torch._foreach_add_(buffer_store, storage["buffers"])
          for storage in store[1:]
      ]
      torch._foreach_div(buffer_store, float(last_k))
  
      return param_store, buffer_store
  
imports: import os
  import torch
  import transformers
  from torch.utils.data import DataLoader
  from datasets.distributed import split_dataset_by_node
label_desc: Use image node_id 4 for calling a global function with example usage: updated_latest_weight_average(model_parameters, model_buffers, store, last_k) and returns: param_store,  buffer_store