code: def predict_features(self, df_features, df_target, nh=20, idx=0, dropout=0.,
                           activation_function=th.nn.ReLU, lr=0.01, l1=0.1,  batch_size=-1,
                           train_epochs=1000, test_epochs=1000, device=None,
                           verbose=None, nb_runs=3):
          
          device, verbose = SETTINGS.get_default(('device', device), ('verbose', verbose))
          x = th.FloatTensor(scale(df_features.values)).to(device)
          y = th.FloatTensor(scale(df_target.values)).to(device)
          out = []
          for i in range(nb_runs):
              model = FSGNN_model([x.size()[1] + 1, nh, 1],
                                  dropout=dropout,
                                  activation_function=activation_function).to(device)
  
              out.append(model.train(x, y, lr=0.01, l1=0.1, batch_size=-1,
                                     train_epochs=train_epochs, test_epochs=test_epochs,
                                     device=device, verbose=verbose))
          return list(np.mean(np.array(out), axis=0))
docs: For one variable, predict its neighbours.
  
          Args:
              df_features (pandas.DataFrame):
              df_target (pandas.Series):
              nh (int): number of hidden units
              idx (int): (optional) for printing purposes
              dropout (float): probability of dropout (between 0 and 1)
              activation_function (torch.nn.Module): activation function of the NN
              lr (float): learning rate of Adam
              l1 (float): L1 penalization coefficient
              batch_size (int): batch size, defaults to full-batch
              train_epochs (int): number of train epochs
              test_epochs (int): number of test epochs
              device (str): cuda or cpu device (defaults to ``cdt.SETTINGS.default_device``)
              verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)
              nb_runs (int): number of bootstrap runs
  
          Returns:
              list: scores of each feature relatively to the target