text: Title: Passing kwargs to the underlying fitting function
  Body: **Is your feature request related to a current problem? Please describe.**
  I wanted to pass some additional arguments to `Prophet`'s fit function (from now on called fit_kwargs), but this is not currently supported by darts. The only current possibility for the user to achieve this, is to subclass `Prophet` and overwrite the `._fit` function
  
  In general, there are multiple different strategies (depending on the model) in darts how passing through of fit_kwargs is currently handled.
  Looking through the code, I would summarize the current situation as follows (hope I haven't missed something):
  
  * `RegressionModels` support passing of fit_kwargs to the `.fit` function
  * `TorchForecastingModels` use the `pl_trainer_kwargs` argument in the constructor
  * `ExponentialSmoothing` supports passing of fit_kwargs to the constructor. It also allows passing of constructor_kwargs (that will be passed to the constructor of the underlying model) as a dict.
  * `Prophet` and `AutoARIMA` do not support passing of fit_kwargs (even though the underlying model has meaningful kwargs)
  * Other models do not support passing fit_kwargs, but that's fine since the underlying models do not support any meaningful additional arguments
  
  **Describe proposed solution**
  I propose to unify the behavior (except for `TorchForecastingModels` which makes sense be treated differently) to support passing of fit_kwargs through the `.fit` function, i.e.:
  * Add passing through of fit_kwargs to `Prophet` and `AutoARIMA`
  * Rework `ExponentialSmoothing` constructor and fit function signatures to the same style. This would be a breaking change.
  
  I think having the argument passing in the `.fit` function rather than the constructor function is better for two reasons: 
  1) Models often also support kwargs that are passed to the underlying models constructor, making a distinction between constructor_kwargs and fit_kwargs necessary. That means at least one of them has do be passed as a dict, which feels unintuive.
  2) I think the `.fit` method would be the more obvious place where users would look for the possibility to pass such kwargs.
  
  **Describe potential alternatives**
  * Add passing through of fit_kwargs to `Prophet` and `AutoARIMA` but keep `ExponentialSmoothing` as it is
  * Add a `fit_kwargs` argument to the constructors of `Prophet` and `AutoARIMA` and pass those to the fit function.
  
  **Additional context**
  I'm happy to prepare a PR for this issue, once it is decided which of these solutions should be implemented
  