diff --git a/tests/python/relay/test_pass_convert_op_layout.py b/tests/python/relay/test_pass_convert_op_layout.py
index d2a13298d6ea..86687eac6b67 100644
--- a/tests/python/relay/test_pass_convert_op_layout.py
+++ b/tests/python/relay/test_pass_convert_op_layout.py
@@ -751,44 +751,50 @@ def expected():
 
 def test_qnn_conv_nhwc_convert_layout():
     def before():
-        x = relay.var("x", shape=(1, 64, 56, 56), dtype='int8')
-        weight = relay.var('weight', shape=(64, 64, 3, 3), dtype='int8')
-        y = relay.qnn.op.conv2d(x, weight,
-                                relay.const(1, 'int32'),
-                                relay.const(1, 'int32'),
-                                relay.const(1, 'float32'),
-                                relay.const(1, 'float32'),
-                                channels=64,
-                                kernel_size=(3, 3),
-                                padding=(1, 1),
-                                data_layout='NCHW',
-                                kernel_layout='OIHW')
+        x = relay.var("x", shape=(1, 64, 56, 56), dtype="int8")
+        weight = relay.var("weight", shape=(64, 64, 3, 3), dtype="int8")
+        y = relay.qnn.op.conv2d(
+            x,
+            weight,
+            relay.const(1, "int32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "float32"),
+            channels=64,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NCHW",
+            kernel_layout="OIHW",
+        )
         y = relay.nn.relu(y)
         y = relay.Function([x, weight], y)
         return y
 
     def expected():
-        x = relay.var("x", shape=(1, 64, 56, 56), dtype='int8')
-        weight = relay.var('weight', shape=(64, 64, 3, 3), dtype='int8')
-        x = relay.layout_transform(x, 'NCHW', 'NHWC')
-        weight = relay.layout_transform(weight, 'OIHW', 'HWIO')
-        y = relay.qnn.op.conv2d(x, weight,
-                                relay.const(1, 'int32'),
-                                relay.const(1, 'int32'),
-                                relay.const(1, 'float32'),
-                                relay.const(1, 'float32'),
-                                channels=64,
-                                kernel_size=(3, 3),
-                                padding=(1, 1),
-                                data_layout="NHWC",
-                                kernel_layout="HWIO")
+        x = relay.var("x", shape=(1, 64, 56, 56), dtype="int8")
+        weight = relay.var("weight", shape=(64, 64, 3, 3), dtype="int8")
+        x = relay.layout_transform(x, "NCHW", "NHWC")
+        weight = relay.layout_transform(weight, "OIHW", "HWIO")
+        y = relay.qnn.op.conv2d(
+            x,
+            weight,
+            relay.const(1, "int32"),
+            relay.const(1, "int32"),
+            relay.const(1, "float32"),
+            relay.const(1, "float32"),
+            channels=64,
+            kernel_size=(3, 3),
+            padding=(1, 1),
+            data_layout="NHWC",
+            kernel_layout="HWIO",
+        )
         y = relay.nn.relu(y)
-        y = relay.layout_transform(y, 'NHWC', 'NCHW')
+        y = relay.layout_transform(y, "NHWC", "NCHW")
         y = relay.Function(relay.analysis.free_vars(y), y)
         return y
 
     a = before()
-    a = run_opt_pass(a, transform.ConvertLayout({'qnn.conv2d': ['NHWC', 'default']}))
+    a = run_opt_pass(a, transform.ConvertLayout({"qnn.conv2d": ["NHWC", "default"]}))
     b = run_opt_pass(expected(), transform.InferType())
 
     assert tvm.ir.structural_equal(a, b), "Actual = \n" + str(a)