Try avoid saving PTL trainer (and redefine it in fit() and predict() calls)

[hrzn]

No description provided.

[alexcolpitts96]

TorchForecastingModel._setup_trainer() could be adjusted to not save the trainer to self but rather return a Trainer instance.

I have found that I use different trainer args for training versus inference. That being said, it is possible on model initialization to save the trainer args and create a trainer for fit() and predict() based on these with the option to override when calling fit() or predict().

[hrzn]

Ping @dennisbader

[madtoinou]

I think that this issue is solved by #1371.

[dennisbader]

Yes, this will be solved with #1371.

@alexcolpitts96, the PR adjusts TorchForecastingModel._setup_trainer() but we cannot avoid storing the trainer as an attribute in the model object due to week reference errors (the trainer would get dropped after fit/predict calls if we don't store it -> PyTorch Lightning will lsoe the reference to the trainer).