code: def load_data(dataset):
      # load the data: x, tx, allx, graph
      names = ["x", "tx", "allx", "graph"]
      objects = []
      for i in range(len(names)):
          with open(
              "data/ind.{}.{}".format(dataset, names[i]), "rb"
          ) as f:
              if sys.version_info > (3, 0):
                  objects.append(pkl.load(f, encoding="latin1"))
              else:
                  objects.append(pkl.load(f))
      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, :]
      adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
  
      return adj, features
  
imports: import pickle
  import sys
  import networkx
  import numpy
  import scipy.sparse
label_desc: Use image node_id 2 for calling a global function with example usage: load_data(dataset) and returns: adj,  features