Add load forecasting example using LSTM. Add LSTM implementation consistent with blocks interface

[alpargun]

• This PR adds a load forecasting example using an LSTM model trained on a public dataset. Neuromancer's Problem formulation and Trainer are used to train the network, and the performance metrics and forecast plots are included for the evaluation. Both a Python and a Jupyter notebook file are included.
• Additionally, adds an LSTM implementation that complies with Neuromancer's blocks interface in blocks.py

[RBirmiwal]

Hi @alpargun for your contribution to NeuroMANCER! This is a great notebook. We will take a closer look how it fits into our release schedule and backlog and hopefully will incorporate into library soon. Thank you