diff --git a/integration_tests/src/main/python/udf_cudf_test.py b/integration_tests/src/main/python/udf_cudf_test.py
index f13eb189b64..650561960e4 100644
--- a/integration_tests/src/main/python/udf_cudf_test.py
+++ b/integration_tests/src/main/python/udf_cudf_test.py
@@ -72,22 +72,19 @@ def _assert_cpu_gpu(cpu_func, gpu_func, cpu_conf={}, gpu_conf={}, is_sort=False)
 
 
 # ======= Test Scalar =======
-@pandas_udf('int')
-def _plus_one_cpu_func(v: pd.Series) -> pd.Series:
-    return v + 1
-
-
-@pandas_udf('int')
-def _plus_one_gpu_func(v: pd.Series) -> pd.Series:
-    import cudf
-    gpu_series = cudf.Series(v)
-    gpu_series = gpu_series + 1
-    return gpu_series.to_pandas()
-
-
 @cudf_udf
 @pytest.mark.parametrize('data', [small_data, large_data], ids=['small data', 'large data'])
 def test_with_column(enable_cudf_udf, data):
+    @pandas_udf('int')
+    def _plus_one_cpu_func(v: pd.Series) -> pd.Series:
+        return v + 1
+
+    @pandas_udf('int')
+    def _plus_one_gpu_func(v: pd.Series) -> pd.Series:
+        import cudf
+        gpu_series = cudf.Series(v)
+        gpu_series = gpu_series + 1
+        return gpu_series.to_pandas()
     def cpu_run(spark):
         df = _create_df(spark, data)
         return df.withColumn("v1", _plus_one_cpu_func(df.v)).collect()
@@ -101,6 +98,17 @@ def gpu_run(spark):
 
 @cudf_udf
 def test_sql(enable_cudf_udf):
+    @pandas_udf('int')
+    def _plus_one_cpu_func(v: pd.Series) -> pd.Series:
+        return v + 1
+
+    @pandas_udf('int')
+    def _plus_one_gpu_func(v: pd.Series) -> pd.Series:
+        import cudf
+        gpu_series = cudf.Series(v)
+        gpu_series = gpu_series + 1
+        return gpu_series.to_pandas()
+
     def cpu_run(spark):
         _ = spark.udf.register("add_one_cpu", _plus_one_cpu_func)
         _create_df(spark).createOrReplaceTempView("test_table_cpu")
@@ -115,23 +123,21 @@ def gpu_run(spark):
 
 
 # ======= Test Scalar Iterator =======
-@pandas_udf("long")
-def _plus_one_cpu_iter_func(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
-    for s in iterator:
-        yield s + 1
-
-
-@pandas_udf("long")
-def _plus_one_gpu_iter_func(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
-    import cudf
-    for s in iterator:
-        gpu_serises = cudf.Series(s)
-        gpu_serises = gpu_serises + 1
-        yield gpu_serises.to_pandas()
-
-
 @cudf_udf
 def test_select(enable_cudf_udf):
+    @pandas_udf("long")
+    def _plus_one_cpu_iter_func(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
+        for s in iterator:
+            yield s + 1
+
+    @pandas_udf("long")
+    def _plus_one_gpu_iter_func(iterator: Iterator[pd.Series]) -> Iterator[pd.Series]:
+        import cudf
+        for s in iterator:
+            gpu_serises = cudf.Series(s)
+            gpu_serises = gpu_serises + 1
+            yield gpu_serises.to_pandas()
+
     def cpu_run(spark):
         df = _create_df(spark)
         return df.select(_plus_one_cpu_iter_func(df.v)).collect()
@@ -169,23 +175,21 @@ def _filter_gpu_func(iterator):
 # ======= Test Grouped Map In Pandas =======
 # To solve: Invalid udf: the udf argument must be a pandas_udf of type GROUPED_MAP
 # need to add udf type
-@pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
-def _normalize_cpu_func(df):
-    v = df.v
-    return df.assign(v=(v - v.mean()) / v.std())
-
-
-@pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
-def _normalize_gpu_func(df):
-    import cudf
-    gdf = cudf.from_pandas(df)
-    v = gdf.v
-    return gdf.assign(v=(v - v.mean()) / v.std()).to_pandas()
-
-
 @allow_non_gpu('GpuFlatMapGroupsInPandasExec','PythonUDF')
 @cudf_udf
 def test_group_apply(enable_cudf_udf):
+    @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
+    def _normalize_cpu_func(df):
+        v = df.v
+        return df.assign(v=(v - v.mean()) / v.std())
+
+    @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)
+    def _normalize_gpu_func(df):
+        import cudf
+        gdf = cudf.from_pandas(df)
+        v = gdf.v
+        return gdf.assign(v=(v - v.mean()) / v.std()).to_pandas()
+
     def cpu_run(spark):
         df = _create_df(spark)
         return df.groupby("id").apply(_normalize_cpu_func).collect()
@@ -220,21 +224,19 @@ def _normalize_gpu_in_pandas_func(df):
 
 
 # ======= Test Aggregate In Pandas =======
-@pandas_udf("int")  
-def _sum_cpu_func(v: pd.Series) -> int:
-    return v.sum()
-
-
-@pandas_udf("integer")  
-def _sum_gpu_func(v: pd.Series) -> int:
-    import cudf
-    gpu_series = cudf.Series(v)
-    return gpu_series.sum()
-
-
 @allow_non_gpu('GpuAggregateInPandasExec','PythonUDF','Alias')
 @cudf_udf
 def test_group_agg(enable_cudf_udf):
+    @pandas_udf("int")
+    def _sum_cpu_func(v: pd.Series) -> int:
+        return v.sum()
+
+    @pandas_udf("integer")
+    def _sum_gpu_func(v: pd.Series) -> int:
+        import cudf
+        gpu_series = cudf.Series(v)
+        return gpu_series.sum()
+
     def cpu_run(spark):
         df = _create_df(spark)
         return df.groupby("id").agg(_sum_cpu_func(df.v)).collect()
@@ -249,6 +251,16 @@ def gpu_run(spark):
 @allow_non_gpu('GpuAggregateInPandasExec','PythonUDF','Alias')
 @cudf_udf
 def test_sql_group(enable_cudf_udf):
+    @pandas_udf("int")
+    def _sum_cpu_func(v: pd.Series) -> int:
+        return v.sum()
+
+    @pandas_udf("integer")
+    def _sum_gpu_func(v: pd.Series) -> int:
+        import cudf
+        gpu_series = cudf.Series(v)
+        return gpu_series.sum()
+
     def cpu_run(spark):
         _ = spark.udf.register("sum_cpu_udf", _sum_cpu_func)
         q = "SELECT sum_cpu_udf(v1) FROM VALUES (3, 0), (2, 0), (1, 1) tbl(v1, v2) GROUP BY v2"
@@ -267,6 +279,16 @@ def gpu_run(spark):
                'SpecifiedWindowFrame','UnboundedPreceding$', 'UnboundedFollowing$')
 @cudf_udf
 def test_window(enable_cudf_udf):
+    @pandas_udf("int")
+    def _sum_cpu_func(v: pd.Series) -> int:
+        return v.sum()
+
+    @pandas_udf("integer")
+    def _sum_gpu_func(v: pd.Series) -> int:
+        import cudf
+        gpu_series = cudf.Series(v)
+        return gpu_series.sum()
+
     def cpu_run(spark):
         df = _create_df(spark)
         w = Window.partitionBy('id').rowsBetween(Window.unboundedPreceding, Window.unboundedFollowing)