message: Add note about enclosing GradientTape in for better performance, especially when jabobians are involved.
diff: @@ -1111,7 +1111,13 @@ class GradientTape(object):
  Note: Unless you set `persistent=True` a GradientTape can only be used to
  compute one set of gradients (or jacobians).
  - See[wikipedia article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant)
  + Note: By default the jacobian implementation uses parallel for (pfor), which
  + creates a tf.function under the hood for each jacobian call. For better
  + performance, and to avoid recompilation and vectorization rewrites on each
  + call, enclose GradientTape code in @tf.function.
  +
  + See[wikipedia
  + article](http://en.wikipedia.org/wiki/jacobian_matrix_and_determinant)
  for the definition of a Jacobian.
  Example usage:
  @@ -1243,6 +1249,12 @@ class GradientTape(object):
  Note: Unless you set `persistent=True` a GradientTape can only be used to
  compute one set of gradients (or jacobians).
  + Note: By default the batch_jacobian implementation uses parallel for (pfor),
  + which creates a tf.function under the hood for each batch_jacobian call.
  + For better performance, and to avoid recompilation and vectorization
  + rewrites on each call, enclose GradientTape code in @tf.function.
  +
  +
  Example usage:
  ```python
  