einops

Why we included this project

Tensor code is full of small, repetitive shape bookkeeping, and einops is the library many model builders reach for to get rid of it. Instead of chaining a reshape, a transpose, and a squeeze call, you write one readable expression using Einstein-style notation, and that same expression runs on NumPy, PyTorch, JAX, TensorFlow, and MLX without changes. That portability matters for teams that prototype in one framework and deploy in another. The package also includes einsum-style reductions and an EinMix layer for channel and feature mixing, so it covers a good share of the linear algebra you touch while building models. Beginners get readable syntax and worked examples, and anyone who has fought a silent shape mismatch will appreciate error messages that point to the mistake early.