message: Improve example of 'mean' sparse_combiner in tf.feature_column.linear_model documentation.
diff: @@ -445,15 +445,16 @@ def linear_model(features,
  [0, 0]: "d"
  [1, 0]: "e"
  [1, 1]: "f"
  - [1, 2]: "g"
  + [1, 2]: "f"
  }
  ```
  - with `sparse_combiner` as "mean", the linear model outputs conceptly are:
  + with `sparse_combiner` as "mean", the linear model outputs consequently
  + are:
  ```
  - y_0 = 1.0 / 2.0 * ( w_a + w_ b) + w_c + b_0
  - y_1 = w_d + 1.0 / 3.0 * ( w_e + w_ f + w_g) + b_1
  + y_0 = 1.0 / 2.0 * ( w_a + w_b ) + w_d + b
  + y_1 = w_c + 1.0 / 3.0 * ( w_e + 2.0 * w_f ) + b
  ```
  - where `y_i` is the output, `b_i` is the bias, and `w_x` is the weight
  + where `y_i` is the output, `b` is the bias, and `w_x` is the weight
  assigned to the presence of `x` in the input features.
  weight_collections: A list of collection names to which the Variable will be
  added. Note that, variables will also be added to collections
  