Fix/historical forecasts torch models

[dennisbader]

Checklist before merging this PR:

• Mentioned all issues that this PR fixes or addresses.
• Summarized the updates of this PR under Summary.
• Added an entry under Unreleased in the Changelog.

Summary

• simplifies and improves historical forecast tests for torch models, by reducing input series length and checking for minimum length requirements. Speeds up tests from 2min to 1min 15 seconds
• Improvements to RNNModel:
  • 🔴 enforce training_length>input_chunk_length
  • the historical forecastable time index is now properly computed for RNNModels
• ForecastingModel.exteme_lags() now has an additional return element indicating the max_target_lag used during training (specific to RNNModel)
• simplifies and improves historical forecast tests for torch models, by reducing input series length and checking for minimum length requirements. Speeds up tests from 2min to 1min 15 seconds
• fixes a bug when using historical forecasts with untrained torch models and using covariates, where the historical forecastable time index computation did not take the covariates into account

[madtoinou]

Good job spotting and fixing the bug, just noticed some discrepancies in the tests between the comment and the value overlap_end.

I hope that we will find a more elegant solution than adding a 8th output to extreme_lags but this should do for now.