Create processor.py

data/processor.py

@@ -0,0 +1,30 @@
+import torch
+import torch.nn.functional as F
+from torch_geometric.data import Data
+
+class GraphProcessor:
+    """Data preprocessing utilities"""
+    
+    @staticmethod
+    def normalize_features(x):
+        """Normalize node features"""
+        return F.normalize(x, p=2, dim=1)
+    
+    @staticmethod
+    def add_self_loops(edge_index, num_nodes):
+        """Add self loops to graph"""
+        self_loops = torch.arange(num_nodes).unsqueeze(0).repeat(2, 1)
+        edge_index = torch.cat([edge_index, self_loops], dim=1)
+        return edge_index
+    
+    @staticmethod
+    def to_device(data, device):
+        """Move data to device safely"""
+        if hasattr(data, 'to'):
+            return data.to(device)
+        elif isinstance(data, (list, tuple)):
+            return [GraphProcessor.to_device(item, device) for item in data]
+        elif isinstance(data, dict):
+            return {k: GraphProcessor.to_device(v, device) for k, v in data.items()}
+        else:
+            return data