code: def load_data(dataset):
      # load the data: x, tx, allx, graph
      names = ["x", "tx", "allx", "graph"]
      objects = []
      for i in range(len(names)):
          """
          fix Pickle incompatibility of numpy arrays between Python 2 and 3
          https://stackoverflow.com/questions/11305790/pickle-incompatibility-of-numpy-arrays-between-python-2-and-3  # noqa
          """
          with open(
              "data/ind.{}.{}".format(dataset, names[i]), "rb"
          ) as rf:
              u = pkl._Unpickler(rf)
              u.encoding = "latin1"
              cur_data = u.load()
              objects.append(cur_data)
          # objects.append(
          #     pkl.load(open("data/ind.{}.{}".format(dataset, names[i]), 'rb')))
      x, tx, allx, graph = tuple(objects)
      test_idx_reorder = parse_index_file(
          "data/ind.{}.test.index".format(dataset)
      )
      test_idx_range = np.sort(test_idx_reorder)
  
      if dataset == "citeseer":
          # Fix citeseer dataset (there are some isolated nodes in the graph)
          # Find isolated nodes, add them as zero-vecs into the right position
          test_idx_range_full = range(
              min(test_idx_reorder), max(test_idx_reorder) + 1
          )
          tx_extended = sp.lil_matrix(
              (len(test_idx_range_full), x.shape[1])
          )
          tx_extended[test_idx_range - min(test_idx_range), :] = tx
          tx = tx_extended
  
      features = sp.vstack((allx, tx)).tolil()
      features[test_idx_reorder, :] = features[test_idx_range, :]
      features = torch.FloatTensor(np.array(features.todense()))
      adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
  
      return adj, features
  
file_path: graph4nlp/graph4nlp/pytorch/test/link_prediction/utils.py
imports: import pickle
  import networkx
  import numpy
  import scipy.sparse
  import torch
  from sklearn.metrics import average_precision_score, roc_auc_score
label_desc: Use image node_id 1 for calling a global function with example usage: load_data(dataset) and returns: adj,  features