Ensure that the `leading_transpose` function can be used with the @tf.function decorator (workaround for bug in tensorflow<=2.2.0rc3)

Previously, GPflow's NaturalGradient optimizer would call the loss_function once for each (q_mu, q_sqrt) set in the var_list. This is a light refactor that separates out applying the natural gradient step from computing the gradients (`_natgrad_apply_gradients`), and changes `_natgrad_steps` to only evaluate the loss function once, computing the gradients for all (q_mu, q_sqrt) tuples passed in the var_list.

Other changes:
- The no-longer-used `_natgrad_step` method got removed.
- NaturalGradient now takes a `xi_transform` argument that is used for all parameter sets without explicitly specified xi transform (i.e. tuples rather than triplets).
- XiTransform has been changed to have staticmethods.

None of this should affect any downstream code; this PR is backwards-compatible.

Closes #1440

…1453)

* includes the repr() string in IPython/Jupyter notebook representation as well (i.e. fully-qualified class name and object hash (memory address), which helps distinguish objects from each other)
* only displays the parameter table when it is not empty
* makes use of default_summary_fmt() for IPython shell

Resolves #1439