code: def stack_dicts(stats_dicts):
      """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=-1
          )
      return results
  
imports: import math
  import time
  import warnings
  from collections.abc import Mapping
  from typing import Any, Dict, List, Optional, Union
  import numpy
  import torch
  import torch.nn.functional
  from datasets import Dataset
  from huggingface_hub import PyTorchModelHubMixin
  from torch.cuda.amp import autocast
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from transformers import DataCollatorForLanguageModeling, PreTrainedTokenizer, PreTrainedTokenizerFast
  from llm_studio.src.utils.modeling_utils import unwrap_model
label_desc: Use image node_id 2 for calling a global function with example usage: stack_dicts(stats_dicts) and returns: results