diff --git a/tests/flytekit/unit/extras/tensorflow/test_native.py b/tests/flytekit/unit/extras/tensorflow/test_native.py
index 5a37f70dc5c..12bb853eaea 100644
--- a/tests/flytekit/unit/extras/tensorflow/test_native.py
+++ b/tests/flytekit/unit/extras/tensorflow/test_native.py
@@ -2,15 +2,23 @@
 
 from flytekit import task, workflow
 
+a = tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"foo", b"bar"]))
+b = tf.train.Feature(float_list=tf.train.FloatList(value=[1.0, 2.0]))
+c = tf.train.Feature(int64_list=tf.train.Int64List(value=[3, 4]))
+
 
 @task
-def generate_tf_example() -> tf.train.Example:
-    a = tf.train.Feature(bytes_list=tf.train.BytesList(value=[b"foo", b"bar"]))
-    b = tf.train.Feature(float_list=tf.train.FloatList(value=[1.0, 2.0]))
+def generate_tf_example_1() -> tf.train.Example:
     features = tf.train.Features(feature=dict(a=a, b=b))
     return tf.train.Example(features=features)
 
 
+@task
+def generate_tf_example_2() -> tf.train.Example:
+    features = tf.train.Features(feature=dict(a=a, b=b, c=c))
+    return tf.train.Example(features=features)
+
+
 @task
 def t1(example: tf.train.Example) -> tf.train.Example:
     assert example.features.feature["a"].bytes_list.value == [b"foo", b"bar"]
@@ -20,21 +28,28 @@ def t1(example: tf.train.Example) -> tf.train.Example:
 
 @task
 def t2(example: tf.train.Example) -> tf.train.Example:
-    # add a third feature
-    int_feat = tf.train.Feature(int64_list=tf.train.Int64List(value=[3, 4]))
-    example.features.feature.get_or_create("c")
-    example.features.feature.setdefault("c", int_feat)
+    assert example.features.feature["c"].int64_list.value == [3, 4]
     return example
 
 
+@task
+def t3(example: tf.train.Example):
+    feature_description = {
+        "b": tf.io.RaggedFeature(dtype=float),
+        "c": tf.io.RaggedFeature(dtype=tf.int64),
+    }
+    parsed = tf.io.parse_example(example.SerializeToString(), feature_description)
+    result = dict(map(lambda x: (x[0], x[1].numpy().tolist()), parsed.items()))
+    assert result == {"b": [1.0, 2.0], "c": [3, 4]}
+
+
 @workflow
-def wf() -> tf.train.Example:
-    t1(tensor=generate_tf_example())
-    result = t2(tensor=generate_tf_example())
-    return result
+def wf():
+    t1(example=generate_tf_example_1())
+    input_t3 = t2(example=generate_tf_example_2())
+    t3(example=input_t3)
 
 
 @workflow
 def test_wf():
-    example = wf()
-    assert example.features.feature["c"].int64_list.value == [3, 4]
+    wf()