diff --git a/setup.cfg b/setup.cfg
index 1141dc0ef8..0069214de3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -173,6 +173,7 @@ max_line_length = 120
 # B028 https://github.com/Project-MONAI/MONAI/issues/5855
 # B907 https://github.com/Project-MONAI/MONAI/issues/5868
 # B908 https://github.com/Project-MONAI/MONAI/issues/6503
+# B036 https://github.com/Project-MONAI/MONAI/issues/7396
 ignore =
     E203
     E501
@@ -186,6 +187,7 @@ ignore =
     B028
     B907
     B908
+    B036
 per_file_ignores = __init__.py: F401, __main__.py: F401
 exclude = *.pyi,.git,.eggs,monai/_version.py,versioneer.py,venv,.venv,_version.py
 
diff --git a/tests/test_flexible_unet.py b/tests/test_flexible_unet.py
index 1218ce6e85..1d831f0976 100644
--- a/tests/test_flexible_unet.py
+++ b/tests/test_flexible_unet.py
@@ -39,7 +39,7 @@ class DummyEncoder(BaseEncoder):
     def get_encoder_parameters(cls):
         basic_dict = {"spatial_dims": 2, "in_channels": 3, "pretrained": False}
         param_dict_list = [basic_dict]
-        for key in basic_dict:
+        for key in basic_dict.keys():
             cur_dict = basic_dict.copy()
             del cur_dict[key]
             param_dict_list.append(cur_dict)
diff --git a/tests/test_invertd.py b/tests/test_invertd.py
index cd2e91257a..2e6ee35981 100644
--- a/tests/test_invertd.py
+++ b/tests/test_invertd.py
@@ -112,15 +112,15 @@ def test_invert(self):
                 self.assertTupleEqual(i.shape[1:], (101, 100, 107))
 
                 # check the case that different items use different interpolation mode to invert transforms
-                d = item["image_inverted1"]
+                j = item["image_inverted1"]
                 # if the interpolation mode is nearest, accumulated diff should be smaller than 1
-                self.assertLess(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 1.0)
-                self.assertTupleEqual(d.shape, (1, 101, 100, 107))
+                self.assertLess(torch.sum(j.to(torch.float) - j.to(torch.uint8).to(torch.float)).item(), 1.0)
+                self.assertTupleEqual(j.shape, (1, 101, 100, 107))
 
-                d = item["label_inverted1"]
+                k = item["label_inverted1"]
                 # if the interpolation mode is not nearest, accumulated diff should be greater than 10000
-                self.assertGreater(torch.sum(d.to(torch.float) - d.to(torch.uint8).to(torch.float)).item(), 10000.0)
-                self.assertTupleEqual(d.shape, (1, 101, 100, 107))
+                self.assertGreater(torch.sum(k.to(torch.float) - k.to(torch.uint8).to(torch.float)).item(), 10000.0)
+                self.assertTupleEqual(k.shape, (1, 101, 100, 107))
 
         # check labels match
         reverted = item["label_inverted"].detach().cpu().numpy().astype(np.int32)