Ivy Framework Agnostic Machine Learning Build, Transpile, and Benchmark Across All Major Backends

Ivy introduces a groundbreaking framework that enables the development of machine learning models to be entirely framework-agnostic, supporting seamless execution across NumPy, PyTorch, TensorFlow, and JAX. This innovation leverages code transpilation, unified APIs, and advanced features like Ivy Containers and graph tracing to facilitate portable, efficient, and backend-independent deep learning workflows, significantly simplifying model creation, optimization, and benchmarking without being tied to a specific ecosystem. By providing a fully compatible neural network implementation that operates uniformly across multiple backends, Ivy demonstrates how developers can write once and deploy everywhere, reducing complexity and increasing