* Rewrite the `declearn.main.utils.Checkpointer` util.
  - Implement Model, Optimizer and Metric-output results saving and loading methods.
  - Add a `max_history` parameter to garbage-collect past files.
  - Revise the labeling of output files to ease collection and re-use.
* Revise the use of `Checkpointer` in `FederatedClient` and `FederatedServer`.
  - Have both entities checkpoint at the end of evaluation rounds.
  - For now, clients save the global model (not local one), local optimizer and metrics.
  - For now, server saves the global model (not local ones), server optimizer and all
    metrics: aggregated ones and client-wise ones (in separate files).
  - Move lowest-loss-yielding weights tracking and restoring to the private backend
    of `FederatedServer`, and ensure they are still saved and sent to clients at the
    end of the federated learning process.
* Add unit tests for the `Checkpointer` class.