torchdrug: [Bug] AttributeError: can't set attribute

In the retrosynthesis tutorial

this code of synthon completion

synthon_optimizer = torch.optim.Adam(synthon_task.parameters(), lr=1e-3)
synthon_solver = core.Engine(synthon_task, synthon_train, synthon_valid,
                             synthon_test, synthon_optimizer,
                             gpus=[0], batch_size=128)
synthon_solver.train(num_epoch=1)
synthon_solver.evaluate("valid")
synthon_solver.save("g2gs_synthon_model.pth")

gives below error

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Input In [13], in <cell line: 11>()
      7 synthon_optimizer = torch.optim.Adam(synthon_task.parameters(), lr=1e-3)
      8 synthon_solver = core.Engine(synthon_task, synthon_train, synthon_valid,
      9                              synthon_test, synthon_optimizer,
     10                              gpus=[0], batch_size=128)
---> 11 synthon_solver.train(num_epoch=1)
     12 synthon_solver.evaluate("valid")
     13 synthon_solver.save("g2gs_synthon_model.pth")

File /usr/local/lib/python3.9/dist-packages/torchdrug/core/engine.py:155, in Engine.train(self, num_epoch, batch_per_epoch)
    152 if self.device.type == "cuda":
    153     batch = utils.cuda(batch, device=self.device)
--> 155 loss, metric = model(batch)
    156 if not loss.requires_grad:
    157     raise RuntimeError("Loss doesn't require grad. Did you define any loss in the task?")

File /usr/local/lib/python3.9/dist-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
   1126 # If we don't have any hooks, we want to skip the rest of the logic in
   1127 # this function, and just call forward.
   1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
   1129         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130     return forward_call(*input, **kwargs)
   1131 # Do not call functions when jit is used
   1132 full_backward_hooks, non_full_backward_hooks = [], []

File /usr/local/lib/python3.9/dist-packages/torchdrug/tasks/retrosynthesis.py:592, in SynthonCompletion.forward(self, batch)
    589 all_loss = torch.tensor(0, dtype=torch.float32, device=self.device)
    590 metric = {}
--> 592 pred, target = self.predict_and_target(batch, all_loss, metric)
    593 node_in_pred, node_out_pred, bond_pred, stop_pred = pred
    594 node_in_target, node_out_target, bond_target, stop_target, size = target

File /usr/local/lib/python3.9/dist-packages/torchdrug/tasks/retrosynthesis.py:984, in SynthonCompletion.predict_and_target(self, batch, all_loss, metric)
    981 with reactant.graph():
    982     reactant.reaction = batch["reaction"]
--> 984 graph1, node_in_target1, node_out_target1, bond_target1, stop_target1 = self.all_edge(reactant, synthon)
    985 graph2, node_in_target2, node_out_target2, bond_target2, stop_target2 = self.all_stop(reactant, synthon)
    987 graph = self._cat([graph1, graph2])

File /usr/local/lib/python3.9/dist-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.__call__.<locals>.decorate_context(*args, **kwargs)
     24 @functools.wraps(func)
     25 def decorate_context(*args, **kwargs):
     26     with self.clone():
---> 27         return func(*args, **kwargs)

File /usr/local/lib/python3.9/dist-packages/torchdrug/tasks/retrosynthesis.py:557, in SynthonCompletion.all_edge(self, reactant, synthon)
    555 assert (graph.num_edges % 2 == 0).all()
    556 # node / edge features may change because we mask some nodes / edges
--> 557 graph, feature_valid = self._update_molecule_feature(graph)
    559 return graph[feature_valid], node_in_target[feature_valid], node_out_target[feature_valid], \
    560        bond_target[feature_valid], stop_target[feature_valid]

File /usr/local/lib/python3.9/dist-packages/torchdrug/tasks/retrosynthesis.py:398, in SynthonCompletion._update_molecule_feature(self, graphs)
    395 bond_type[edge_mask] = new_graphs.bond_type.to(device=graphs.device)
    397 with graphs.node():
--> 398     graphs.node_feature = node_feature
    399 with graphs.edge():
    400     graphs.edge_feature = edge_feature

File /usr/local/lib/python3.9/dist-packages/torchdrug/data/graph.py:160, in Graph.__setattr__(self, key, value)
    158 if hasattr(self, "meta_dict"):
    159     self._check_attribute(key, value)
--> 160 super(Graph, self).__setattr__(key, value)

File /usr/local/lib/python3.9/dist-packages/torchdrug/core/core.py:84, in _MetaContainer.__setattr__(self, key, value)
     82     if types:
     83         self.meta_dict[key] = types.copy()
---> 84 self._setattr(key, value)

File /usr/local/lib/python3.9/dist-packages/torchdrug/core/core.py:93, in _MetaContainer._setattr(self, key, value)
     92 def _setattr(self, key, value):
---> 93     return super(_MetaContainer, self).__setattr__(key, value)

AttributeError: can't set attribute