text: Title: Documentation Inconsistency: `num_loader_workers` Parameter
  Body: The documentation for deep learning models in Darts is outdated regarding the `num_loader_workers` parameter.
  
  ### Current Documentation
  [This page](https://github.com/unit8co/darts/blob/master/docs/userguide/torch_forecasting_models.md#tune-num_loader_workers), states:
  >All deep learning models in Darts have a parameter `num_loader_workers` in their `fit()` and `predict()` functions, which
  configures the `num_workers` parameter in the PyTorch `DataLoaders`. By default it is set to 0, which means that the main process will also take care of loading the data. 
  
  ### Actual Implementation (as of v0.30.0)
  According to the changelog, this is no longer accurate:
  > Removed parameter num_loader_workers from fit*(), predict*(), find_lr(). You can now set the parameter through the dataloader_kwargs dict.
  
  ### Proposed Update
  I suggest updating the documentation to reflect this change:
  > All deep learning models in Darts have a parameter `dataloader_kwargs` in their `fit()` and `predict()` functions, which configures the PyTorch DataLoaders. The `num_workers` parameter for PyTorch DataLoaders can be set using the `num_workers` key in the `dataloader_kwargs` dictionary.
  
  Note: The default value for `num_workers` is (I think) the PyTorch default (0), and so the second sentence can be omitted, but this should be confirmed.