With this challenge in mind, we introduce Torch-Struct with three specific contributions:

Modularity: models are represented as distributions with a standard flexible API integrated into a deep learning framework.

Completeness: a broad array of classical algorithms are implemented and new models can easily be added in Python.

Efficiency: implementations target computational/memory efficiency for GPUs and the backend includes extensions for optimization. 
 Question: Is this library implemented into Torch or is framework agnostic?
It uses deep learning framework (pytorch)