diff --git a/keras_cv/layers/preprocessing_3d/input_format_test.py b/keras_cv/layers/preprocessing_3d/input_format_test.py
index ca1545babd..63e03b974a 100644
--- a/keras_cv/layers/preprocessing_3d/input_format_test.py
+++ b/keras_cv/layers/preprocessing_3d/input_format_test.py
@@ -21,103 +21,102 @@
 from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
 from keras_cv.tests.test_case import TestCase
 
-if not keras_3():
-    POINT_CLOUDS = base_augmentation_layer_3d.POINT_CLOUDS
-    BOUNDING_BOXES = base_augmentation_layer_3d.BOUNDING_BOXES
+POINT_CLOUDS = base_augmentation_layer_3d.POINT_CLOUDS
+BOUNDING_BOXES = base_augmentation_layer_3d.BOUNDING_BOXES
 
-    TEST_CONFIGURATIONS = [
-        (
-            "FrustrumRandomDroppingPoints",
-            preprocessing_3d.FrustumRandomDroppingPoints(
-                r_distance=0, theta_width=1, phi_width=1, drop_rate=0.5
-            ),
+TEST_CONFIGURATIONS = [
+    (
+        "FrustrumRandomDroppingPoints",
+        preprocessing_3d.FrustumRandomDroppingPoints(
+            r_distance=0, theta_width=1, phi_width=1, drop_rate=0.5
         ),
-        (
-            "FrustrumRandomPointFeatureNoise",
-            preprocessing_3d.FrustumRandomPointFeatureNoise(
-                r_distance=10,
-                theta_width=np.pi,
-                phi_width=1.5 * np.pi,
-                max_noise_level=0.5,
-            ),
+    ),
+    (
+        "FrustrumRandomPointFeatureNoise",
+        preprocessing_3d.FrustumRandomPointFeatureNoise(
+            r_distance=10,
+            theta_width=np.pi,
+            phi_width=1.5 * np.pi,
+            max_noise_level=0.5,
         ),
-        (
-            "GlobalRandomDroppingPoints",
-            preprocessing_3d.GlobalRandomDroppingPoints(drop_rate=0.5),
+    ),
+    (
+        "GlobalRandomDroppingPoints",
+        preprocessing_3d.GlobalRandomDroppingPoints(drop_rate=0.5),
+    ),
+    (
+        "GlobalRandomFlip",
+        preprocessing_3d.GlobalRandomFlip(),
+    ),
+    (
+        "GlobalRandomRotation",
+        preprocessing_3d.GlobalRandomRotation(
+            max_rotation_angle_x=1.0,
+            max_rotation_angle_y=1.0,
+            max_rotation_angle_z=1.0,
         ),
-        (
-            "GlobalRandomFlip",
-            preprocessing_3d.GlobalRandomFlip(),
+    ),
+    (
+        "GlobalRandomScaling",
+        preprocessing_3d.GlobalRandomScaling(
+            x_factor=(0.5, 1.5),
+            y_factor=(0.5, 1.5),
+            z_factor=(0.5, 1.5),
         ),
-        (
-            "GlobalRandomRotation",
-            preprocessing_3d.GlobalRandomRotation(
-                max_rotation_angle_x=1.0,
-                max_rotation_angle_y=1.0,
-                max_rotation_angle_z=1.0,
-            ),
+    ),
+    (
+        "GlobalRandomTranslation",
+        preprocessing_3d.GlobalRandomTranslation(
+            x_stddev=1.0, y_stddev=1.0, z_stddev=1.0
         ),
-        (
-            "GlobalRandomScaling",
-            preprocessing_3d.GlobalRandomScaling(
-                x_factor=(0.5, 1.5),
-                y_factor=(0.5, 1.5),
-                z_factor=(0.5, 1.5),
-            ),
+    ),
+    (
+        "RandomDropBox",
+        preprocessing_3d.RandomDropBox(
+            label_index=1, max_drop_bounding_boxes=4
         ),
-        (
-            "GlobalRandomTranslation",
-            preprocessing_3d.GlobalRandomTranslation(
-                x_stddev=1.0, y_stddev=1.0, z_stddev=1.0
-            ),
-        ),
-        (
-            "RandomDropBox",
-            preprocessing_3d.RandomDropBox(
-                label_index=1, max_drop_bounding_boxes=4
-            ),
-        ),
-    ]
+    ),
+]
 
-    def convert_to_model_format(inputs):
-        point_clouds = {
-            "point_xyz": inputs["point_clouds"][..., :3],
-            "point_feature": inputs["point_clouds"][..., 3:-1],
-            "point_mask": tf.cast(inputs["point_clouds"][..., -1], tf.bool),
-        }
-        boxes = {
-            "boxes": inputs["bounding_boxes"][..., :7],
-            "classes": inputs["bounding_boxes"][..., 7],
-            "difficulty": inputs["bounding_boxes"][..., -1],
-            "mask": tf.cast(inputs["bounding_boxes"][..., 8], tf.bool),
-        }
-        return {
-            "point_clouds": point_clouds,
-            "3d_boxes": boxes,
-        }
+def convert_to_model_format(inputs):
+    point_clouds = {
+        "point_xyz": inputs["point_clouds"][..., :3],
+        "point_feature": inputs["point_clouds"][..., 3:-1],
+        "point_mask": tf.cast(inputs["point_clouds"][..., -1], tf.bool),
+    }
+    boxes = {
+        "boxes": inputs["bounding_boxes"][..., :7],
+        "classes": inputs["bounding_boxes"][..., 7],
+        "difficulty": inputs["bounding_boxes"][..., -1],
+        "mask": tf.cast(inputs["bounding_boxes"][..., 8], tf.bool),
+    }
+    return {
+        "point_clouds": point_clouds,
+        "3d_boxes": boxes,
+    }
 
-    @pytest.skip(
-        reason="values are not matching because of changes to random.py"
-    )
-    class InputFormatTest(TestCase):
-        @parameterized.named_parameters(*TEST_CONFIGURATIONS)
-        def test_equivalent_results_with_model_format(self, layer):
-            point_clouds = np.random.random(size=(3, 2, 50, 10)).astype(
-                "float32"
-            )
-            bounding_boxes = np.random.random(size=(3, 2, 10, 9)).astype(
-                "float32"
-            )
-            inputs = {
-                POINT_CLOUDS: point_clouds,
-                BOUNDING_BOXES: bounding_boxes,
-            }
+@pytest.mark.skip(
+    reason="values are not matching because of changes to random.py"
+)
+class InputFormatTest(TestCase):
+    @parameterized.named_parameters(*TEST_CONFIGURATIONS)
+    def test_equivalent_results_with_model_format(self, layer):
+        point_clouds = np.random.random(size=(3, 2, 50, 10)).astype(
+            "float32"
+        )
+        bounding_boxes = np.random.random(size=(3, 2, 10, 9)).astype(
+            "float32"
+        )
+        inputs = {
+            POINT_CLOUDS: point_clouds,
+            BOUNDING_BOXES: bounding_boxes,
+        }
 
-            tf.random.set_seed(123)
-            outputs_with_legacy_format = convert_to_model_format(layer(inputs))
-            tf.random.set_seed(123)
-            outputs_with_model_format = layer(convert_to_model_format(inputs))
+        tf.random.set_seed(123)
+        outputs_with_legacy_format = convert_to_model_format(layer(inputs))
+        tf.random.set_seed(123)
+        outputs_with_model_format = layer(convert_to_model_format(inputs))
 
-            self.assertAllClose(
-                outputs_with_legacy_format, outputs_with_model_format
-            )
+        self.assertAllClose(
+            outputs_with_legacy_format, outputs_with_model_format
+        )