All visualization callbacks now contain a `visualize` method that takes an
appropriate PyTorchLightning Module and visualizes it without the need for a
Trainer. This is to encourage users to perform one-off visualizations after
training.
* chore: update pre-commit versions
* ci: remove old configurations
* ci: copy workflow from prototorch
* ci: run precommit for all files
* ci: add examples CPU test
* ci(test): failing example test
* ci: fix workflow definition
* ci(test): repeat failing example test
* ci: fix workflow definition
* ci(test): repeat failing example test II
* ci: fix test command
* ci: cleanup example test
* ci: remove travis badge
Pass the component initializer as an hparam slows down the script very much. The
API has now been changed to pass it as a kwarg to the models instead.
The example scripts have also been updated to reflect the new changes.
Also, ImageGMLVQ and an example script `gmlvq_mnist.py` that uses it have also
been added.
