message: Revise docs of tf.random.truncated_normal.
diff: @@ -168,9 +168,17 @@ def truncated_normal(shape,
  name=None):
  """Outputs random values from a truncated normal distribution.
  - The generated values follow a normal distribution with specified mean and
  - standard deviation, except that values whose magnitude is more than 2 standard
  - deviations from the mean are dropped and re-picked.
  + The values are drawn from a normal distribution with specified mean and
  + standard deviation, discarding and re-drawing any samples that are more than
  + two standard deviations from the mean.
  +
  + Examples:
  +
  + >>> tf.random.truncated_normal(shape=[2])
  + <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
  +
  + >>> tf.random.truncated_normal(shape=[2], mean=3, stddev=1, dtype=tf.float32)
  + <tf.Tensor: shape=(2,), dtype=float32, numpy=array([..., ...], dtype=float32)>
  Args:
  shape: A 1-D integer Tensor or Python array. The shape of the output tensor.
  @@ -178,11 +186,10 @@ def truncated_normal(shape,
  truncated normal distribution.
  stddev: A 0-D Tensor or Python value of type `dtype`. The standard deviation
  of the normal distribution, before truncation.
  - dtype: The type of the output.
  + dtype: The type of the output. Restricted to floating-point types:
  + `tf.half`, `tf.float`, `tf.double`, etc.
  seed: A Python integer. Used to create a random seed for the distribution.
  - See
  - `tf.random.set_seed`
  - for behavior.
  + See `tf.random.set_seed` for more information.
  name: A name for the operation (optional).
  Returns:
  