linting

python/dgl/distributed/partition.py

@@ -3,11 +3,11 @@
 import concurrent
 import concurrent.futures
 import copy
-from functools import partial
 import json
 import logging
 import os
 import time
+from functools import partial
 
 import numpy as np
 
@@ -1257,9 +1257,9 @@ def get_homogeneous(g, balance_ntypes):
                 for name in g.edges[etype].data:
                     if name in [EID, "inner_edge"]:
                         continue
-                    edge_feats[
-                        _etype_tuple_to_str(etype) + "/" + name
-                    ] = F.gather_row(g.edges[etype].data[name], local_edges)
+                    edge_feats[_etype_tuple_to_str(etype) + "/" + name] = (
+                        F.gather_row(g.edges[etype].data[name], local_edges)
+                    )
         else:
             for ntype in g.ntypes:
                 if len(g.ntypes) > 1:
@@ -1294,9 +1294,9 @@ def get_homogeneous(g, balance_ntypes):
                 for name in g.edges[etype].data:
                     if name in [EID, "inner_edge"]:
                         continue
-                    edge_feats[
-                        _etype_tuple_to_str(etype) + "/" + name
-                    ] = F.gather_row(g.edges[etype].data[name], local_edges)
+                    edge_feats[_etype_tuple_to_str(etype) + "/" + name] = (
+                        F.gather_row(g.edges[etype].data[name], local_edges)
+                    )
         # delete `orig_id` from ndata/edata
         del part.ndata["orig_id"]
         del part.edata["orig_id"]
@@ -1570,17 +1570,20 @@ def convert_partition(part_id, graph_formats):
         )
         torch.save(csc_graph, csc_graph_path)
 
-
         return os.path.relpath(csc_graph_path, os.path.dirname(part_config))
         # Update graph path.
 
     # Iterate over partitions.
-    convert_with_format = partial(convert_partition, graph_formats=graph_formats)
-    with concurrent.futures.ProcessPoolExecutor(max_workers=min(num_parts, n_jobs)) as executor:
-        for part_id, part_path in enumerate(executor.map(convert_with_format, range(num_parts))):
-            new_part_meta[f"part-{part_id}"][
-                "part_graph_graphbolt"
-            ] = part_path
+    convert_with_format = partial(
+        convert_partition, graph_formats=graph_formats
+    )
+    with concurrent.futures.ProcessPoolExecutor(
+        max_workers=min(num_parts, n_jobs)
+    ) as executor:
+        for part_id, part_path in enumerate(
+            executor.map(convert_with_format, range(num_parts))
+        ):
+            new_part_meta[f"part-{part_id}"]["part_graph_graphbolt"] = part_path
 
     # Save dtype info into partition config.
     # [TODO][Rui] Always use int64_t for node/edge IDs in GraphBolt. See more