code: def collate_fn(data_list: [Text2TextDataItem]):
      graph_data = [item.graph for item in data_list]
      max_node_len = 0
      for graph_item in graph_data:
          max_node_len = max(
              max_node_len,
              graph_item.node_features["token_id"].size()[1],
          )
      for graph_item in graph_data:
          token_id_numpy = graph_item.node_features["token_id"].numpy()
          token_id_pad = pad_2d_vals(
              token_id_numpy, token_id_numpy.shape[0], max_node_len
          )
          graph_item.node_features["token_id"] = torch.from_numpy(
              token_id_pad
          ).long()
  
      from graph4nlp.pytorch.data.data import to_batch
  
      big_graph = to_batch(graph_data)
  
      output_numpy = [item.output_np for item in data_list]
      output_str = [
          item.output_text.lower().strip() for item in data_list
      ]
      output_pad = pad_2d_vals_no_size(output_numpy)
      tgt_seq = torch.from_numpy(output_pad).long()
  
      return {
          "graph_data": big_graph,
          "tgt_seq": tgt_seq,
          "output_str": output_str,
      }
  
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 6 for calling the CNNDataset obj's underlying member method code with example usage: obj.collate_fn(data_list) and returns: dict