code: def _vectorize_one_dataitem(
      self, data_item, vocab_model, use_ie=None
  ):
      item = deepcopy(data_item)
      graph: GraphData = item.graph
      token_matrix = []
      for node_idx in range(graph.get_node_num()):
          node_token = graph.node_attributes[node_idx]["token"]
          node_token_id = vocab_model.in_word_vocab.getIndex(node_token)
          graph.node_attributes[node_idx]["token_id"] = node_token_id
          token_matrix.append([node_token_id])
      token_matrix = torch.tensor(token_matrix, dtype=torch.long)
      graph.node_features["token_id"] = token_matrix
  
      item.output_id = None
      return item
  
imports: from copy import deepcopy
  import stanfordcorenlp
  import torch
  from graph4nlp.pytorch.data.data import GraphData
  from graph4nlp.pytorch.data.dataset import SequenceLabelingDataset
  from dependency_graph_construction_without_tokenize import DependencyBasedGraphConstruction_without_tokenizer
  from line_graph_construction import LineBasedGraphConstruction
label_desc: Use image node_id 6 for calling the ConllDataset_inference obj's underlying member method code with example usage: obj._vectorize_one_dataitem(data_item, vocab_model, use_ie) and returns: item