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@@ -51,6 +51,7 @@ This section contains libraries that are well-made and useful, but have not nece
     - [FedJAX](https://github.com/google/fedjax) - Federated learning in JAX, built on Optax and Haiku. <img src="https://img.shields.io/github/stars/google/fedjax?style=social" align="center">
     - [Equivariant MLP](https://github.com/mfinzi/equivariant-MLP) - Construct equivariant neural network layers. <img src="https://img.shields.io/github/stars/mfinzi/equivariant-MLP?style=social" align="center">
     - [jax-resnet](https://github.com/n2cholas/jax-resnet/) - Implementations and checkpoints for ResNet variants in Flax. <img src="https://img.shields.io/github/stars/n2cholas/jax-resnet?style=social" align="center">
+    - [Parallax](https://github.com/srush/parallax) - Immutable Torch Modules for JAX. <img src="https://img.shields.io/github/stars/srush/parallax?style=social" align="center">
 - [jax-unirep](https://github.com/ElArkk/jax-unirep) - Library implementing the [UniRep model](https://www.nature.com/articles/s41592-019-0598-1) for protein machine learning applications. <img src="https://img.shields.io/github/stars/ElArkk/jax-unirep?style=social" align="center">
 - [jax-flows](https://github.com/ChrisWaites/jax-flows) - Normalizing flows in JAX. <img src="https://img.shields.io/github/stars/ChrisWaites/jax-flows?style=social" align="center">
 - [sklearn-jax-kernels](https://github.com/ExpectationMax/sklearn-jax-kernels) - `scikit-learn` kernel matrices using JAX. <img src="https://img.shields.io/github/stars/ExpectationMax/sklearn-jax-kernels?style=social" align="center">