Save all input arguments in NodeLoader so that PyTorch Lightning ca…

…n correctly reconstruct it (pyg-team#8809)

torch_geometric/loader/node_loader.py

@@ -103,20 +103,22 @@ def __init__(
         if filter_per_worker is None:
             filter_per_worker = infer_filter_per_worker(data)
 
-        # Remove for PyTorch Lightning:
-        kwargs.pop('dataset', None)
-        kwargs.pop('collate_fn', None)
-
-        # Get node type (or `None` for homogeneous graphs):
-        input_type, input_nodes, input_id = get_input_nodes(
-            data, input_nodes, input_id)
-
         self.data = data
         self.node_sampler = node_sampler
+        self.input_nodes = input_nodes
+        self.input_time = input_time
         self.transform = transform
         self.transform_sampler_output = transform_sampler_output
         self.filter_per_worker = filter_per_worker
         self.custom_cls = custom_cls
+        self.input_id = input_id
+
+        kwargs.pop('dataset', None)
+        kwargs.pop('collate_fn', None)
+
+        # Get node type (or `None` for homogeneous graphs):
+        input_type, input_nodes, input_id = get_input_nodes(
+            data, input_nodes, input_id)
 
         self.input_data = NodeSamplerInput(
             input_id=input_id,