message: categorical_column_with_vocabulary_file => Modified the Example code such that it is executable
  Github Gist for working code is
  Fixes
diff: @@ -1219,12 +1219,16 @@ def categorical_column_with_vocabulary_file(key,
  ID 50-54.
  ```python
  - states = categorical_column_with_vocabulary_file(
  - key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
  - num_oov_buckets=5)
  - columns = [states, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - linear_prediction = linear_model(features, columns)
  + import tensorflow as tf
  + states = tf.feature_column.categorical_column_with_vocabulary_file(
  + key='states', vocabulary_file='states.txt', vocabulary_size=5,
  + num_oov_buckets=1)
  + columns = [states]
  + features = {'states':tf.constant([['california', 'georgia', 'michigan',
  + 'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
  + 'texas']])}
  + linear_prediction = tf.compat.v1.feature_column.linear_model(features,
  + columns)
  ```
  Example with `default_value`:
  @@ -1234,20 +1238,31 @@ def categorical_column_with_vocabulary_file(key,
  others are assigned the corresponding line number 1-50.
  ```python
  - states = categorical_column_with_vocabulary_file(
  - key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
  + import tensorflow as tf
  + states = tf.feature_column.categorical_column_with_vocabulary_file(
  + key='states', vocabulary_file='states.txt', vocabulary_size=6,
  default_value=0)
  - columns = [states, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - linear_prediction, _, _ = linear_model(features, columns)
  + columns = [states]
  + features = {'states':tf.constant([['california', 'georgia', 'michigan',
  + 'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
  + 'texas']])}
  + linear_prediction = tf.compat.v1.feature_column.linear_model(features,
  + columns)
  ```
  And to make an embedding with either:
  ```python
  - columns = [embedding_column(states, 3),...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - dense_tensor = input_layer(features, columns)
  + import tensorflow as tf
  + states = tf.feature_column.categorical_column_with_vocabulary_file(
  + key='states', vocabulary_file='states.txt', vocabulary_size=5,
  + num_oov_buckets=1)
  + columns = [tf.feature_column.embedding_column(states, 3)]
  + features = {'states':tf.constant([['california', 'georgia', 'michigan',
  + 'texas', 'new york'], ['new york', 'georgia', 'california', 'michigan',
  + 'texas']])}
  + input_layer = tf.keras.layers.DenseFeatures(columns)
  + dense_tensor = input_layer(features)
  ```
  Args:
  