message: categorical_column_with_hash_bucket => Modified the Example code such that it is executable
  Github Gist for working code is
  Fixes
diff: @@ -1142,16 +1142,27 @@ def categorical_column_with_hash_bucket(key,
  Example:
  ```python
  - keywords = categorical_column_with_hash_bucket("keywords", 10K)
  - columns = [keywords, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - linear_prediction = linear_model(features, columns)
  + import tensorflow as tf
  + keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
  + 10000)
  + columns = [keywords]
  + features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
  + 'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
  + 'LSTM', 'Keras', 'RNN']])}
  + linear_prediction, _, _ = tf.compat.v1.feature_column.linear_model(features,
  + columns)
  # or
  - keywords_embedded = embedding_column(keywords, 16)
  - columns = [keywords_embedded, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - dense_tensor = input_layer(features, columns)
  + import tensorflow as tf
  + keywords = tf.feature_column.categorical_column_with_hash_bucket("keywords",
  + 10000)
  + keywords_embedded = tf.feature_column.embedding_column(keywords, 16)
  + columns = [keywords_embedded]
  + features = {'keywords': tf.constant([['Tensorflow', 'Keras', 'RNN', 'LSTM',
  + 'CNN'], ['LSTM', 'CNN', 'Tensorflow', 'Keras', 'RNN'], ['CNN', 'Tensorflow',
  + 'LSTM', 'Keras', 'RNN']])}
  + input_layer = tf.keras.layers.DenseFeatures(columns)
  + dense_tensor = input_layer(features)
  ```
  Args:
  