message: Fixing up bucketized_column docstring. Making it a bit more informative and removing references to LinearModel which isn't part of TF2 anymore.
diff: @@ -1340,7 +1340,7 @@ def numeric_column(key,
  @tf_export('feature_column.bucketized_column')
  def bucketized_column(source_column, boundaries):
  - """Represents discretized dense input.
  + """Represents discretized dense input bucketed by `boundaries`.
  Buckets include the left boundary, and exclude the right boundary. Namely,
  `boundaries=[0., 1., 2.]` generates buckets `(-inf, 0.)`, `[0., 1.)`,
  @@ -1366,30 +1366,30 @@ def bucketized_column(source_column, boundaries):
  Example:
  ```python
  - price = numeric_column('price')
  - bucketized_price = bucketized_column(price, boundaries=[...])
  - columns = [bucketized_price, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - linear_prediction = linear_model(features, columns)
  -
  - # or
  + price = tf.feature_column.numeric_column('price')
  + bucketized_price = tf.feature_column.bucketized_column(
  + price, boundaries=[...])
  columns = [bucketized_price, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - dense_tensor = input_layer(features, columns)
  - ```
  + features = tf.io.parse_example(
  + ..., features=tf.feature_column.make_parse_example_spec(columns))
  + dense_tensor = tf.keras.layers.DenseFeatures(columns)(features)
  `bucketized_column` can also be crossed with another categorical column using
  `crossed_column`:
  ```python
  - price = numeric_column('price')
  + price = tf.feature_column.numeric_column('price')
  # bucketized_column converts numerical feature to a categorical one.
  - bucketized_price = bucketized_column(price, boundaries=[...])
  + bucketized_price = tf.feature_column.bucketized_column(
  + price, boundaries=[...])
  # 'keywords' is a string feature.
  - price_x_keywords = crossed_column([bucketized_price, 'keywords'], 50K)
  + price_x_keywords = tf.feature_column.crossed_column(
  + [bucketized_price, 'keywords'], 50K)
  columns = [price_x_keywords, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - linear_prediction = linear_model(features, columns)
  + features = tf.io.parse_example(
  + ..., features=tf.feature_column.make_parse_example_spec(columns))
  + dense_tensor = tf.keras.layers.DenseFeatures(columns)(features)
  + linear_model = tf.keras.experimental.LinearModel(units=...)(dense_tensor)
  ```
  Args:
  