MemCNN is a PyTorch framework that simplifies the application of reversible functions by removing the need for a customized backpropagation. The framework contains a set of practical generalized tools, which can wrap common operations like convolutions and batch normalization and which take care of memory management. We validate the presented framework by reproducing state-of-the-art experiments using MemCNN and by comparing classification accuracy and training time on Cifar-10 and Cifar-100. Our MemCNN implementations achieved similar classification accuracy and faster training times while retaining compatibility with the default backpropagation facilities of PyTorch. This version is described in the JOSS paper: S.C. van de Leemput, J. Teuwen, B. van Ginneken, and R. Manniesing: MemCNN: A Python/PyTorch package for creating memory-efficient invertible neural networks, Journal of Open Source Software, 4, 1576, https://doi.org/10.21105/joss.01576, 2019.