text: """
  The MIT License
  
  Copyright (c) 2016 Thomas Kipf
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in
  all copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  THE SOFTWARE.
  
  This code was taken almost verbatim from https://github.com/tkipf/gcn/ and
  adapted to work in Spektral.
  """
  from __future__ import absolute_import
  
  import os
  
  import networkx as nx
  import numpy as np
  import requests
  import scipy.sparse as sp
  
  from spektral.utils.io import load_binary
  
  DATA_PATH = os.path.expanduser('~/.spektral/datasets/')
  AVAILABLE_DATASETS = {'cora', 'citeseer', 'pubmed'}
  RETURN_TYPES = {'numpy'}
  
  
  def _parse_index_file(filename):
      index = []
      for line in open(filename):
          index.append(int(line.strip()))
      return index
  
  
  def _sample_mask(idx, l):
      mask = np.zeros(l)
      mask[idx] = 1
      return np.array(mask, dtype=np.bool)
  
  
  def load_data(dataset_name='cora', normalize_features=True):
      """
      Loads a citation dataset using the public splits as defined in
      [Kipf & Welling (2016)](https://arxiv.org/abs/1609.02907).
      :param dataset_name: name of the dataset to load ('cora', 'citeseer', or
      'pubmed');
      :param normalize_features: if True, the node features are normalized;
      :return: the citation network in numpy format, with train, test, and
      validation splits for the targets and masks.
      """
      if dataset_name not in AVAILABLE_DATASETS:
          raise ValueError('Available datasets: {}'.format(AVAILABLE_DATASETS))
  
      if not os.path.exists(DATA_PATH + dataset_name):
          download_data(dataset_name)
  
      print('Loading {} dataset'.format(dataset_name))
  
      names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
      objects = []
      data_path = os.path.join(DATA_PATH, dataset_name)
      for n in names:
          filename = "{}/ind.{}.{}".format(data_path, dataset_name, n)
          objects.append(load_binary(filename))
  
      x, y, tx, ty, allx, ally, graph = tuple(objects)
      adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
      test_idx_reorder = _parse_index_file("{}/ind.{}.test.index".format(data_path, dataset_name))
      test_idx_range = np.sort(test_idx_reorder)
  
      if dataset_name == 'citeseer':
          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
          ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
          ty_extended[test_idx_range - min(test_idx_range), :] = ty
          ty = ty_extended
  
      features = sp.vstack((allx, tx)).tolil()
      features[test_idx_reorder, :] = features[test_idx_range, :]
  
      labels = np.vstack((ally, ty))
      labels[test_idx_reorder, :] = labels[test_idx_range, :]
  
      idx_test = test_idx_range.tolist()
      idx_train = range(len(y))
      idx_val = range(len(y), len(y) + 500)
  
      train_mask = _sample_mask(idx_train, labels.shape[0])
      val_mask = _sample_mask(idx_val, labels.shape[0])
      test_mask = _sample_mask(idx_test, labels.shape[0])
  
      # Row-normalize the features
      if normalize_features:
          print('Pre-processing node features')
          features = preprocess_features(features)
  
      y_train = np.zeros(labels.shape)
      y_val = np.zeros(labels.shape)
      y_test = np.zeros(labels.shape)
      y_train[train_mask, :] = labels[train_mask, :]
      y_val[val_mask, :] = labels[val_mask, :]
      y_test[test_mask, :] = labels[test_mask, :]
  
      return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask
  
  
  def preprocess_features(features):
      rowsum = np.array(features.sum(1))
      r_inv = np.power(rowsum, -1).flatten()
      r_inv[np.isinf(r_inv)] = 0.
      r_mat_inv = sp.diags(r_inv)
      features = r_mat_inv.dot(features)
      return features
  
  
  def download_data(dataset_name):
      names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph', 'test.index']
  
      os.makedirs(DATA_PATH + dataset_name + '/')
      data_url = 'https://github.com/tkipf/gcn/raw/master/gcn/data/'
  
      print('Downloading ' + dataset_name + 'from ' + data_url)
      for n in names:
          f_name = 'ind.' + dataset_name + '.' + n
          req = requests.get(data_url + f_name)
          with open(DATA_PATH + dataset_name + '/' + f_name, 'wb') as out_file:
              out_file.write(req.content)
  