diff --git a/tensorflow/python/feature_column/feature_column_v2.py b/tensorflow/python/feature_column/feature_column_v2.py
index 749d54fc794..a57ba8450c3 100644
--- a/tensorflow/python/feature_column/feature_column_v2.py
+++ b/tensorflow/python/feature_column/feature_column_v2.py
@@ -1560,19 +1560,27 @@ def categorical_column_with_identity(key, num_buckets, default_value=None):
   Linear model:
 
   ```python
-  video_id = categorical_column_with_identity(
+  import tensorflow as tf
+  video_id = tf.feature_column.categorical_column_with_identity(
       key='video_id', num_buckets=1000000, default_value=0)
-  columns = [video_id, ...]
-  features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
-  linear_prediction, _, _ = linear_model(features, columns)
+  columns = [video_id]
+  features = {'video_id': tf.sparse.from_dense([[2, 85, 0, 0, 0],
+  [33,78, 2, 73, 1]])}
+  linear_prediction = tf.compat.v1.feature_column.linear_model(features,
+  columns)
   ```
 
   Embedding for a DNN model:
 
   ```python
-  columns = [embedding_column(video_id, 9),...]
-  features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
-  dense_tensor = input_layer(features, columns)
+  import tensorflow as tf
+  video_id = tf.feature_column.categorical_column_with_identity(
+      key='video_id', num_buckets=1000000, default_value=0)
+  columns = [tf.feature_column.embedding_column(video_id, 9)]
+  features = {'video_id': tf.sparse.from_dense([[2, 85, 0, 0, 0],
+  [33,78, 2, 73, 1]])}
+  input_layer = tf.keras.layers.DenseFeatures(columns)
+  dense_tensor = input_layer(features)
   ```
 
   Args: