Change mutable default arguments to None (#6376)

Reading PyG code I accidently saw that class and noticed that it uses
mutable default arguments what is generally considered a bad practice
and can lead to a lot of problems. I believe it's not a desired
behaviour (am I wrong?).

Problems can occur when the class is extended in the future such that it
changes the value of any of those lists, the default value for new
instances of this class would also be affected. The other possibility is
that user changes any of the `follow_batch`, `exclude_keys` fields
(those doesn't start with an underscore, so it's kinda possible) after
the object is initialized, what's also going to change the default value
for new instances.

Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>

CHANGELOG.md

@@ -45,6 +45,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for dropping nodes in `utils.to_dense_batch` in case `max_num_nodes` is smaller than the number of nodes  ([#6124](https://github.com/pyg-team/pytorch_geometric/pull/6124))
 - Added the RandLA-Net architecture as an example ([#5117](https://github.com/pyg-team/pytorch_geometric/pull/5117))
 ### Changed
+- Fix the default arguments of `DataParallel` class ([#6376](https://github.com/pyg-team/pytorch_geometric/pull/6376))
 - Fix `ImbalancedSampler` on sliced `InMemoryDataset` ([#6374](https://github.com/pyg-team/pytorch_geometric/pull/6374))
 - Breaking Change: Changed the interface and implementation of `GraphMultisetTransformer` ([#6343](https://github.com/pyg-team/pytorch_geometric/pull/6343))
 - Fixed the approximate PPR variant in `transforms.GDC` to not crash on graphs with isolated nodes ([#6242](https://github.com/pyg-team/pytorch_geometric/pull/6242))

torch_geometric/nn/data_parallel.py

@@ -34,16 +34,16 @@ class DataParallel(torch.nn.DataParallel):
         output_device (int or torch.device): Device location of output.
             (default: :obj:`device_ids[0]`)
         follow_batch (list or tuple, optional): Creates assignment batch
-            vectors for each key in the list. (default: :obj:`[]`)
+            vectors for each key in the list. (default: :obj:`None`)
         exclude_keys (list or tuple, optional): Will exclude each key in the
-            list. (default: :obj:`[]`)
+            list. (default: :obj:`None`)
     """
     def __init__(self, module, device_ids=None, output_device=None,
-                 follow_batch=[], exclude_keys=[]):
+                 follow_batch=None, exclude_keys=None):
         super().__init__(module, device_ids, output_device)
         self.src_device = torch.device(f'cuda:{self.device_ids[0]}')
-        self.follow_batch = follow_batch
-        self.exclude_keys = exclude_keys
+        self.follow_batch = follow_batch or []
+        self.exclude_keys = exclude_keys or []
 
     def forward(self, data_list):
         """"""