Hi, first off thank you for your contributions, and this goes to the entire team. Keras is a wonderful tool and this was definitely the right move to do. No oth

Hi, first off thank you for your contributions, and this goes to the entire team. Keras is a wonderful tool and this was definitely the right move to do. No other package nails the “progressive disclosure” philosophy like Keras. This caught my eye: > “Right now, we use tf.nest (a Python data structure processing utility) extensively across the codebase, which requires the TensorFlow package. In the near future, we intend to turn tf.nest into a standalone package, so that you could use Keras Core without installing TensorFlow.” I recently migrated a TF project to PyTorch (would have been great to have keras_core at the time) and used torch.nested. Could this not be an option? A second question. For “customizing what happens in fit()”. Must this be written in either TF/PyTorch/Jax only, or can this be done with keras_core.ops, similar to the example shown for custom components? The idea would be you can reuse the same training loop logic across frameworks, like for custom components.