code: def default_data_collator(
      features: List[InputDataClass], return_tensors="pt"
  ) -> Dict[str, Any]:
      """
      Very simple data collator that simply collates batches of dict-like objects and performs special handling for
      potential keys named:
  
          - `label`: handles a single value (int or float) per object
          - `label_ids`: handles a list of values per object
  
      Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
      to the model. See glue and ner for example of how it's useful.
      """
  
      # In this function we'll make the assumption that all `features` in the batch
      # have the same attributes.
      # So we will look at the first element as a proxy for what attributes exist
      # on the whole batch.
  
      if return_tensors == "pt":
          return torch_default_data_collator(features)
      elif return_tensors == "tf":
          return tf_default_data_collator(features)
      elif return_tensors == "np":
          return numpy_default_data_collator(features)
  
imports: import random
  import warnings
  from collections.abc import Mapping
  from dataclasses import dataclass
  from random import randint
  from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
  import numpy
  from ..models.bert import BertTokenizer, BertTokenizerFast
  from ..tokenization_utils_base import PreTrainedTokenizerBase
  from ..utils import PaddingStrategy
label_desc: Use image node_id 2 for calling a global function with example usage: default_data_collator(features, return_tensors) and returns: torch_default_data_collator, tf_default_data_collator, numpy_default_data_collator