Hi fellow libtorchers! We are working on a project solely using the C++ API. Recently we started profiling our models, running on CPU, as we want to get a feel for the parallelization on CPU before moving to GPU. It seems that libtorch provides two main parallelization options that the user can specify when computing on CPU: the intra-op thread count and the inter-op thread count. After some  testing, we are still unsure where difference between the inter- and intra- operations lie. As we moniter the time on our machine, we see a performance difference when specifying intra-op thread count, while specifying the inter-op thread count seems to make no difference. So we are wondering what the difference between these two are, how one can utilize the inter-op thread pool, or if you have some nice resources on this topic!  Some of the articles we looked at: CPU threading and TorchScript inference — PyTorch 1.9.0 documentation 5 Intra- and inter-operator parallelism in PyTorch (work plan) · Issue #19002 · pytorch/pytorch · GitHub 8