code: def stack_dicts(stats_dicts: List[Dict]) -> Dict:
      """Stack the values of a dict."""
      results = dict()
      for k in stats_dicts[0]:
          stats_list = [torch.flatten(d[k]) for d in stats_dicts]
          results[k] = pad_sequence(
              stats_list, batch_first=True, padding_value=WANDB_PADDING
          )
      return results
  
imports: import gc
  import random
  import warnings
  from contextlib import contextmanager
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_sequence
  from transformers import top_k_top_p_filtering
  from .import_utils import is_npu_available, is_xpu_available
label_desc: Use image node_id 3 for calling a global function with example usage: stack_dicts(stats_dicts) and returns: results