v1.10.0

Added

• HybridModel A wrapper class to combine deep learning models and conceptual hydrology models, where the deep learning model parameterizes the conceptual model, which also needs to be implemented in PyTorch. In the current implementation, the deep learning model is always a standard LSTM, as commonly used in literature.
• BaseConceptualModel a parent class to facilitate adding new conceptual models into the NeuralHydrology framework.
• SHM one implementation of a BaseConceptualModel that adds a modified version of the SHM to the modelzoo. See the documentation for details and references about this model.

Fixes

• A solution that removes the circular import error of #157 As a result of that, load_scaler.py has been moved to datautils/utils.py
• An update of the documentation to resolve #138
• Some corner cases in the sampling utils related to #154
• Minor changes to the Tester class to resolve #158

Huge thanks to @eduardoAcunaEspinoza for contributing the HybridModel, BaseConceptualModel and SHM implementation.