message: categorical_column_with_identity => Modified the Example code such that it is executable
  Github Gist for working code is
  Fixes
diff: @@ -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:
  