if test to check for scaled_attribute

src/anemoi/training/losses/nodeweights.py

@@ -122,7 +122,12 @@ def __init__(self, target_nodes: str, node_attribute: str, scaled_attribute: str
     def weights(self, graph_data: HeteroData) -> torch.Tensor:
         attr_weight = super().weights(graph_data)
 
-        mask = graph_data[self.target][self.scaled_attribute].squeeze().bool()
+        if self.scaled_attribute in graph_data[self.target]:
+            mask = graph_data[self.target][self.scaled_attribute].squeeze().bool()
+        else:
+            error_msg = f"scaled_attribute {self.scaled_attribute} not found in graph_object"
+            raise KeyError(error_msg)
+
         unmasked_sum = torch.sum(attr_weight[~mask])
         weight_per_masked_node = self.fraction / (1 - self.fraction) * unmasked_sum / sum(mask)
         attr_weight[mask] = weight_per_masked_node