you could maybe write the whole thing in a few hours, but debugging what you wrote to recreate prior results will probably take much longer depending on choice of problem
sorry should've been more specific, but ML is general. from what i've seen it's not hard to reimplement the pseudo-code in any ML paper. it's just that it gets tricky when you actually try to utilize the code you've written, usually in trying to recreate performance results in the implementing paper. it's very common for authors to leave out / downplay the role of tricks or implementation details that greatly contributed to the performance of the model, in addition to just how finicky machine learning is in general
