Enable skipping frozen weights when using `Model.get_weights` and `Model.set_weights`

This MR tackles issue #15 (closed), namely the fact that the current support for frozen neural network weights is imperfect.

Currently:

• Model.compute_batch_gradients properly ignores frozen (i.e. non-trainable) weights
• Model.get_weights / Model.set_weights returns/expects all weights, including frozen ones

This causes a core issue: weight-decay and loss Regularizer instances cause bugs when using models with frozen weights.

This merge request:

• Adds a boolean flag to Model.get_weights and Model.set_weights that enables skipping frozen weights.
• Fixes the identified issue thanks to it: weight-decay and loss-regularization terms are computed for trainable weights only.
• Optimizes communications by excluding frozen weights when sending model updates past the initialization phase.
• Fixes potential issues with server-side optimization, that would receive zero-valued updates for non-trainable weights, which in some cases might have resulted in non-zero updates (e.g. due to the use of weight decay).

Implementation tasklist:

• Implement Model.get_weights(trainable: bool = False).
• Deploy the former to Optimizer backend, fixing the current bug.
• Implement Model.set_weights(trainable: bool = False).
• Deploy the former to reduce communication costs as part of the FL process.

Closes #15 (closed)