This repository implements the Learning to Learn meta-learning approach from the paper by Andrychowicz et al. It trains a learned optimizer (implemented as a neural network) that can learn to optimize other neural networks on various tasks. The framework includes support for simple quadratic functions, MNIST, and CIFAR-10 classification problems, allowing users to train and evaluate learned optimizers on standard benchmarks.