message: Add more usage examples to tf.keras.utils.to_categorical.
diff: @@ -29,17 +29,33 @@ def to_categorical(y, num_classes=None, dtype='float32'):
  Usage Example:
  - >>> y = [0, 1, 2, 3]
  - >>> tf.keras.utils.to_categorical(y, num_classes=4)
  - array([[1., 0., 0., 0.],
  - [0., 1., 0., 0.],
  - [0., 0., 1., 0.],
  - [0., 0., 0., 1.]], dtype=float32)
  + >>> a = tf.keras.utils.to_categorical([0, 1, 2, 3], num_classes=4)
  + >>> a = tf.constant(a, shape=[4, 4])
  + >>> print(a)
  + tf.Tensor(
  + [[1. 0. 0. 0.]
  + [0. 1. 0. 0.]
  + [0. 0. 1. 0.]
  + [0. 0. 0. 1.]], shape=(4, 4), dtype=float32)
  +
  + >>> b = tf.constant([.9, .04, .03, .03,
  + ... .3, .45, .15, .13,
  + ... .04, .01, .94, .05,
  + ... .12, .21, .5, .17],
  + ... shape=[4, 4])
  + >>> loss = tf.keras.backend.categorical_crossentropy(a, b)
  + >>> print(np.around(loss, 5))
  + [0.10536 0.82807 0.1011 1.77196]
  +
  + >>> loss = tf.keras.backend.categorical_crossentropy(a, a)
  + >>> print(np.around(loss, 5))
  + [0. 0. 0. 0.]
  Arguments:
  y: class vector to be converted into a matrix
  (integers from 0 to num_classes).
  - num_classes: total number of classes.
  + num_classes: total number of classes. If `None`, this would be inferred
  + as the (largest number in `y`) + 1.
  dtype: The data type expected by the input. Default: `'float32'`.
  Returns:
  