Update and add torch models #94
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Create mains_stats.py to simplify obtaining mains average and standard deviation
Introduces new PyTorch-based disaggregator models for NILM, including Temporal Convolutional Network (TCN), ConvLSTM, MSDC (with and without CRF), Nilmformer, and Reformer.
Major refactor of WindowGRU, BERT, DAE, ResNet, and preprocessing modules to improve code clarity, add detailed docstrings, and ensure architectural and training behavior matches the original TensorFlow implementations.
Update mains_stats.py to accept a list of buildings as input
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This PR implements the following new models on torch: TCN, ConvLSTM, MSDC, NILMFormer and Reformer. In addition to that, it also improves the functionality as well as the documentation for the existing torch models.