Amir Moradzadeh enhanced the NVIDIA/NeMo repository by delivering a feature that improves compatibility between the MR layer’s back-to-back filter and the FFTConv mixer. He introduced a parameter to control the flipping of mixer weights, ensuring the MR layer’s output aligns with the baseline FFTConv behavior and reducing integration risk in the MR+FFTConv inference path. This work, implemented in Python using PyTorch and deep learning techniques, focused on enabling smoother end-to-end inference and more reliable integration of neural network components. The contribution demonstrates a targeted engineering approach, addressing a specific compatibility challenge within a complex machine learning framework.