docstr: 3 reserved keys: 'train', 'val' (optional), 'test'.
  Represent the split of dataset.
docstr_tok: ["3","reserved","keys",":","'train","'",",","'val","'","(","optional",")",",","'test","'",".","Represent","the","split","of","dataset","."]
code: def raw_file_names(self):
      """
      3 reserved keys: 'train', 'val' (optional), 'test'.
      Represent the split of dataset.
      """
      return {
          "train": "train_3w.json",
          "val": "val.json",
          "test": "test.json",
      }
  
file_path: graph4nlp/graph4nlp/pytorch/datasets/cnn.py
imports: import json
  import torch
  from nltk.tokenize import word_tokenize
  from graph4nlp.pytorch.data.dataset import Text2TextDataItem, Text2TextDataset
  from graph4nlp.pytorch.modules.utils.padding_utils import pad_2d_vals, pad_2d_vals_no_size
label_desc: Use image node_id 2 for calling the CNNDataset obj's underlying member method code with example usage: obj.raw_file_names() and returns: dict