Minor bugfixes

Bug fixes and small changes

• enhanced loss: simple loss can take a gradient and hesse function and the default base loss provides fallbacks that work correctly between value_gradient and gradient. This maybe matters if you've implemented a custom loss and should fix any issues with it.
• multiprocessing would get stuck due to an upstream bug in TensorFlow <https://github.com/tensorflow/tensorflow/issues/66115>_. Working around it by disabling an unused piece of code.

Thanks

• acampoverde for finding the bug in the multiprocessing