message: Add documentation for numeric_column api.
diff: @@ -1026,18 +1026,46 @@ def numeric_column(key,
  Example:
  - ```python
  - price = numeric_column('price')
  - columns = [price, ...]
  - features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  - dense_tensor = input_layer(features, columns)
  -
  - # or
  - 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)
  - ```
  + Assume we have a dataset with two features `a` and `b`.
  + Feature `a` is of numeric in nature.
  +
  + >>> data = {'a': [15, 9, 17, 19, 21, 18, 25, 30],
  + ... 'b': [5.0, 6.4, 10.5, 13.6, 15.7, 19.9, 20.3 , 0.0]}
  +
  + Convert the data to `tf.data.Dataset` and batch the dataset.
  + >>> dataset = tf.data.Dataset.from_tensor_slices(data).batch(2)
  +
  + Let us represent the feature `a` as numerical feature.
  + >>> a = tf.feature_column.numeric_column('a')
  +
  + The output of the feature column will be used as an input to the model.
  + But before we apply the feature column as an input, we can bucketize the
  + column as well.
  + Here we are providing `5` bucket boundaries, the bucketized_column api
  + will bucket this feature in total of `6` buckets. One extra bucket for
  + the data that is greater then the last bucket boundary.
  +
  + >>> a_buckets = tf.feature_column.bucketized_column(a,
  + ... boundaries=[10, 15, 20, 25, 30])
  +
  + Creating the `DenseFeatures` which will receive the bucketized numerical
  + feature column that was just created above.
  +
  + >>> feature_layer = tf.keras.layers.DenseFeatures(a_buckets)
  + >>> for batch_record in dataset.as_numpy_iterator():
  + ... print(feature_layer(batch_record))
  + tf.Tensor(
  + [[0. 0. 1. 0. 0. 0.]
  + [1. 0. 0. 0. 0. 0.]], shape=(2, 6), dtype=float32)
  + tf.Tensor(
  + [[0. 0. 1. 0. 0. 0.]
  + [0. 0. 1. 0. 0. 0.]], shape=(2, 6), dtype=float32)
  + tf.Tensor(
  + [[0. 0. 0. 1. 0. 0.]
  + [0. 0. 1. 0. 0. 0.]], shape=(2, 6), dtype=float32)
  + tf.Tensor(
  + [[0. 0. 0. 0. 1. 0.]
  + [0. 0. 0. 0. 0. 1.]], shape=(2, 6), dtype=float32)
  Args:
  key: A unique string identifying the input feature. It is used as the
  