text: Title: [API] redesign of logging and monitoring for 2.0
  Body: Discussion on API design of monitoring and logging layer for version 2.0.
  
  Top level umbrella issue: https://github.com/sktime/pytorch-forecasting/issues/1736
  
  Converted from original issue below, by @dr-upsilon.
  
  ---
  `pytorch-forecasting` seemingly unnecessarily saves `loss` and `logging_metrics` multiple times
  ---
  - PyTorch-Forecasting version: 1.1.1
  - PyTorch version: 2.4.1
  - Python version: 3.12.5
  - Operating System: windows
  
  
  `C:\...miniconda3\envs\envpt\Lib\site-packages\lightning\pytorch\utilities\parsing.py:208: Attribute 'logging_metrics' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['logging_metrics'])`.
  `
  
  This is caused by `self.save_hyperparameters()` in __init__ method of TemporalFusionTransformer, because save_hyperparameters() uses inspect and frame to identify all the hyperparameters, 
  
  What's the reason to keep it or shall we add handling in __init__?