Add weights_only=True to all torch.load calls (#86)

## Describe your changes

Currently running neural-lam with the latest version of pytorch gives a
warning:

```
FutureWarning: You are using torch.load with weights_only=False (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models  for more details). In a future release, the default value for weights_only will be flipped to True. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via torch.serialization.add_safe_globals. We recommend you start setting weights_only=True for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
```

As we only use `torch.load` to load tensors and lists, we can just set
`weights_only=True` and get rid of this warning (and increase security I
suppose).

## Issue Link
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## Type of change

- [x] 🐛 Bug fix (non-breaking change that fixes an issue)
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functionality to not work as expected)
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neural_lam/utils.py

@@ -17,7 +17,9 @@ def load_dataset_stats(dataset_name, device="cpu"):
 
     def loads_file(fn):
         return torch.load(
-            os.path.join(static_dir_path, fn), map_location=device
+            os.path.join(static_dir_path, fn),
+            map_location=device,
+            weights_only=True,
         )
 
     data_mean = loads_file("parameter_mean.pt")  # (d_features,)
@@ -42,7 +44,9 @@ def load_static_data(dataset_name, device="cpu"):
 
     def loads_file(fn):
         return torch.load(
-            os.path.join(static_dir_path, fn), map_location=device
+            os.path.join(static_dir_path, fn),
+            map_location=device,
+            weights_only=True,
         )
 
     # Load border mask, 1. if node is part of border, else 0.
@@ -116,7 +120,11 @@ def load_graph(graph_name, device="cpu"):
     graph_dir_path = os.path.join("graphs", graph_name)
 
     def loads_file(fn):
-        return torch.load(os.path.join(graph_dir_path, fn), map_location=device)
+        return torch.load(
+            os.path.join(graph_dir_path, fn),
+            map_location=device,
+            weights_only=True,
+        )
 
     # Load edges (edge_index)
     m2m_edge_index = BufferList(