[ENH] Add Feature load_model to Deep Regressor Ensembles

[rwtarpit]

Reference Issues/PRs

Fixes #2770

What does this implement/fix? Explain your changes.

This PR implements the missing load_model functionality for deep regressor ensembles and corrects a systemic documentation error found in the deep classifier ensembles.

Primary Feature (Feat):
Implements the @classmethod load_model for LITETimeRegressor and InceptionTimeRegressor.

• The implementation uses a loop over a list of model paths (model_path: list[str]).
• New unit tests are included in test_lite_time.py and test_inception_time.py to verify lossless save/load functionality using regression_target=True.

Systemic Fixes (Docs/Fix):
Based on mentor feedback, this PR includes essential fixes for code consistency and correctness:

1. Documentation Correction (Docs): Corrects misleading documentation across the base classes (BaseDeepClassifier, BaseDeepRegressor) and the deep classifier ensembles. The model_path parameter is corrected to reflect that it is the path from which the model is loaded, not where it is "will be saved."
2. Code Consistency (Fix): Resolves a NameError in the load_model return type hint in the InceptionTimeClassifier and LITETimeClassifier by wrapping the class name in quotes (-> "InceptionTimeClassifier").

Does your contribution introduce a new dependency? If yes, which one?

No.

Any other comments?

1. The implementation for regressors correctly omits the classes parameter in the load_model signature, as regressors do not deal with discrete labels.
2. The implementation follows the non-standard @classmethod signature (self instead of cls) to maintain consistency with the existing Aeon deep learning module structure.

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[hadifawaz1999]

Thanks for taking care of this
Just a few comments

[hadifawaz1999]

My bad !

[hadifawaz1999]

LGTM Thanks !