From: Edward Yang <ezyang@fb.com>
Date: Thu, 14 Mar 2019 16:53:05 +0000 (-0700)
Subject: Fix lint in test_multiprocessing.
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~815
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=84c30398c7e76a5eded207f6a69de6bcda63d5a7;p=platform%2Fupstream%2Fpytorch.git

Fix lint in test_multiprocessing.

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18016

Reviewed By: eellison

Differential Revision: D14458177

fbshipit-source-id: f17b3e06223ab399e9ce24be6988effe04dad636
---

diff --git a/test/test_multiprocessing.py b/test/test_multiprocessing.py
index f5fa8ee..44d86da 100644
--- a/test/test_multiprocessing.py
+++ b/test/test_multiprocessing.py
@@ -183,7 +183,7 @@ class leak_checker(object):
 
     def _has_shm_files(self):
         gc.collect()
-        names = list('torch_' + str(pid) for pid in self.checked_pids)
+        names = ['torch_' + str(pid) for pid in self.checked_pids]
         for filename in os.listdir('/dev/shm'):
             for name in names:
                 if filename.startswith(name):
@@ -245,7 +245,7 @@ class TestMultiprocessing(TestCase):
                 self.assertEqual(t.storage()._cdata, storage_cdata)
 
         with leak_checker(self):
-            for i in range(repeat):
+            for _ in range(repeat):
                 do_test()
 
     def _test_pool(self, ctx=mp, repeat=1):
@@ -266,7 +266,7 @@ class TestMultiprocessing(TestCase):
             p.join()
 
         with leak_checker(self) as lc:
-            for i in range(repeat):
+            for _ in range(repeat):
                 do_test()
 
     @unittest.skipIf(platform == 'darwin', "file descriptor strategy is not supported on macOS")
@@ -346,11 +346,11 @@ class TestMultiprocessing(TestCase):
         p.start()
 
         results = []
-        for i in range(5):
+        for _ in range(5):
             results.append(outq.get())
         p.join()
 
-        for i, tensor in enumerate(tensors):
+        for i, _tensor in enumerate(tensors):
             v, device, tensor_size, storage_size = results[i]
             self.assertEqual(v, torch.arange(i * 5., (i + 1) * 5).sum())
             self.assertEqual(device, i % 2)