fix: use raise instead of return for exceptions and avoid mutable default args

examples/pytorch/bgnn/BGNN.py

@@ -209,7 +209,7 @@ def train_model(self, model_in, target_labels, train_mask, optimizer):
             elif self.task == "classification":
                 loss = F.cross_entropy(pred, y.long())
             else:
-                raise NotImplemented(
+                raise NotImplementedError(
                     "Unknown task. Supported tasks: classification, regression."
                 )
 

examples/pytorch/evolveGCN/train.py

@@ -41,7 +41,7 @@ def train(args, device):
             in_feats=int(g.ndata["feat"].shape[1]), num_layers=args.n_layers
         )
     else:
-        return NotImplementedError("Unsupported model {}".format(args.model))
+        raise NotImplementedError("Unsupported model {}".format(args.model))
     model = model.to(device)
     optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
 

examples/pytorch/han/utils.py

@@ -263,7 +263,7 @@ def load_data(dataset, remove_self_loop=False):
     elif dataset == "ACMRaw":
         return load_acm_raw(remove_self_loop)
     else:
-        return NotImplementedError("Unsupported dataset {}".format(dataset))
+        raise NotImplementedError("Unsupported dataset {}".format(dataset))
 
 
 class EarlyStopping(object):

examples/pytorch/jtnn/jtnn/chemutils.py

@@ -314,7 +314,11 @@ def enum_attach_nx(ctr_mol, nei_node, amap, singletons):
 
 
 # Try rings first: Speed-Up
-def enum_assemble_nx(node, neighbors, prev_nodes=[], prev_amap=[]):
+def enum_assemble_nx(node, neighbors, prev_nodes=None, prev_amap=None):
+    if prev_nodes is None:
+        prev_nodes = []
+    if prev_amap is None:
+        prev_amap = []
     all_attach_confs = []
     singletons = [
         nei_node["nid"]

examples/pytorch/jtnn/jtnn/jtnn_dec.py

@@ -80,7 +80,9 @@ def can_assemble(mol_tree, u, v_node_dict):
     return len(cands) > 0
 
 
-def create_node_dict(smiles, clique=[]):
+def create_node_dict(smiles, clique=None):
+    if clique is None:
+        clique = []
     return dict(
         smiles=smiles,
         mol=get_mol(smiles),