From: Edward Yang <ezyang@fb.com>
Date: Tue, 12 Mar 2019 15:46:52 +0000 (-0700)
Subject: Fix lint errors in test_autograd
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~866
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=4073e3c2f24945d9a93b8e45f45d6350fdf81ced;p=platform%2Fupstream%2Fpytorch.git

Fix lint errors in test_autograd

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17812

Reviewed By: eellison

Differential Revision: D14388897

fbshipit-source-id: 6e2671805dc8d57af68eb0a0cd6ccb24d9db45e2
---

diff --git a/test/test_autograd.py b/test/test_autograd.py
index f118d75..aa2dbbe 100644
--- a/test/test_autograd.py
+++ b/test/test_autograd.py
@@ -284,7 +284,7 @@ class TestAutograd(TestCase):
         def fn(x):
             return x ** 2 + y * x + y ** 2
 
-        for i in range(5):
+        for _ in range(5):
             grad_x, = torch.autograd.grad(
                 fn(x), x, grad_outputs=grad_output, create_graph=True)
 
@@ -753,7 +753,7 @@ class TestAutograd(TestCase):
             y = x.clone()
 
             # build a "chain" computation graph
-            for i in range(depth):
+            for _ in range(depth):
                 y = y + y * 0.000001
 
             # graph deletion occurs when the above locals go out of scope.
@@ -774,7 +774,7 @@ class TestAutograd(TestCase):
             prev_values = [None, None]
 
             # Build a "chain with skip connections" graph
-            for i in range(depth):
+            for _ in range(depth):
                 prev_tensors = [tensor for tensor in prev_values[:-1]
                                 if tensor is not None]
                 prev_values.append(y)
@@ -809,7 +809,7 @@ class TestAutograd(TestCase):
             y = x.clone()
 
             # build deeply nested computation graph
-            for i in range(depth):
+            for _ in range(depth):
                 y = MyOp.apply(y, y)
 
             # graph deletion occurs when the above locals go out of scope.
@@ -1609,7 +1609,7 @@ class TestAutograd(TestCase):
             def __del__(self):
                 gc.collect()
 
-        for i in range(10):
+        for _ in range(10):
             Variable(torch.randn(10, 10), _grad_fn=CollectOnDelete())
 
     @unittest.skipIf(torch.cuda.device_count() < 2, "no multi-GPU")
@@ -1965,7 +1965,7 @@ class TestAutograd(TestCase):
         add1 = a + b
         add2 = add1 + c
         # Simulate a long branch, so grad_output will get buffered.
-        for i in range(4):
+        for _ in range(4):
             a = a * 2
             b = b * 2
             c = c * 2