message: Add clarification to tf.clip_by_norm docs
diff: @@ -171,12 +171,33 @@ def clip_by_norm(t, clip_norm, axes=None, name=None):
  of the output will have L2-norm less than or equal to `clip_norm`. If
  `axes == [0]` instead, each column of the output will be clipped.
  + Code example:
  +
  + >>> some_nums = tf.constant([[1, 2, 3, 4, 5]], dtype=tf.float32)
  + >>> tf.clip_by_norm(some_nums, 2.0).numpy()
  + array([[0.26967996, 0.5393599 , 0.80903983, 1.0787199 , 1.3483998 ]],
  + dtype=float32)
  +
  This operation is typically used to clip gradients before applying them with
  - an optimizer.
  + an optimizer. Most gradient data is a collection of different shaped tensors
  + for different parts of the model. Thus, this is a common usage:
  +
  + ```
  + # Get your gradients after training
  + loss_value, grads = grad(model, features, labels)
  +
  + # Apply some clipping
  + grads = [tf.clip_by_norm(g, norm)
  + for g in grads]
  +
  + # Continue on with training
  + optimizer.apply_gradients(grads)
  + ```
  Args:
  - t: A `Tensor` or `IndexedSlices`.
  - clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value.
  + t: A `Tensor` or `IndexedSlices`. This must be a floating point type.
  + clip_norm: A 0-D (scalar) `Tensor` > 0. A maximum clipping value, also
  + floating point
  axes: A 1-D (vector) `Tensor` of type int32 containing the dimensions
  to use for computing the L2-norm. If `None` (the default), uses all
  dimensions.
  