🚨 fix lint error

      dest += [end, start]

      bond_features += 2 * [_construct_bond_feature(bond)]

    if self.add_self_loop:

    if self.add_self_edges:

      num_atoms = mol.GetNumAtoms()

      src += [i for i in range(num_atoms)]

      dest += [i for i in range(num_atoms)]

            dropout=dropout) for _ in range(num_conv)

    ])

    self.pooling = global_mean_pool

    self.fc = nn.Linear(hidden_node_dim, predicator_hidden_feats)

    self.out = nn.Linear(predicator_hidden_feats, n_tasks)

    self.fc = nn.Linear(hidden_node_dim, predictor_hidden_feats)

    self.out = nn.Linear(predictor_hidden_feats, n_tasks)

  def forward(self, data):

    """Predict labels

               heads: int = 4,

               dropout: float = 0.0,

               num_conv: int = 3,

               predicator_hidden_feats: int = 32,

               predictor_hidden_feats: int = 32,

               n_tasks: int = 1,

               **kwargs):

    """

      The dropout probability for each convolutional layer.

    num_conv: int, default 3

      The number of convolutional layers.

    predicator_hidden_feats: int, default 32

    predictor_hidden_feats: int, default 32

      The size for hidden representations in the output MLP predictor, default to 32.

    n_tasks: int, default 1

      The number of the output size, default to 1.

        heads,

        dropout,

        num_conv,

        predicator_hidden_feats,

        predictor_hidden_feats,

        n_tasks,

    )

    super(GATModel, self).__init__(model, **kwargs)