Review comments 2

graphstorm-processing/graphstorm_processing/distributed_executor.py

@@ -452,26 +452,33 @@ def append_transformations(
             """Appends the pre-computed transformations to the input dicts."""
             assert structure_type in ["edge", "node"]
             for input_dict in structure_input_dicts:
+                # type_name is the name of either a node type or edge type
                 type_name = get_structure_type(input_dict, structure_type)
                 # If we have pre-computed transformations for this type
                 if type_name in structure_transforms:
+                    # type_transforms holds the transformation representations for
+                    # every feature that has one for type_name, from feature name to
+                    # feature representation dict.
                     type_transforms: Mapping[str, Mapping] = structure_transforms[type_name]
                     assert (
                         "features" in input_dict
                     ), f"Expected type {type_name} to have have features in the input config"
 
-                    # Iterate over every feature of the type,
-                    # and append representation if one exists
+                    # Iterate over every feature for the node/edge type,
+                    # and append representation to its input dict, if one exists
                     for type_feat_dict in input_dict["features"]:
+                        # We take a feature's name either explicitly if it exists,
+                        # or from the column name otherwise.
                         feat_name = (
                             type_feat_dict["name"]
                             if "name" in type_feat_dict
                             else type_feat_dict["column"]
                         )
                         if feat_name in type_transforms:
-                            type_feat_dict["precomputed_transformation"] = type_transforms[
-                                feat_name
-                            ]
+                            # Feature representation needs to contain all the
+                            # necessary information to re-apply the feature transformation
+                            feature_representation = type_transforms[feat_name]
+                            type_feat_dict["precomputed_transformation"] = feature_representation
 
         if edge_transformations:
             append_transformations(edge_input_dicts, edge_transformations, "edge")