#Gin Config

Gin-config is a Python framework that simplifies the configuration of machine learning experiments, enabling dynamic and flexible parameter management through dependency injection and intuitive syntax. It supports the effortless assignment of default parameter values directly from configuration files or command line inputs, particularly benefiting projects using TensorFlow or PyTorch. With capabilities like scoped configurations and references, Gin-config enhances project flexibility without the need for extensive boilerplate code.