Create custom tuning profiles that take advantage of the inherent quantities of the input data and CPU thread saturation/scheduling/parallelization to optimize the crate such that ALL benchmarks run 60% or quicker (1.4x faster). You can use the flamegraph crate to help with the profiling
tee() splits a stream into two branches. It seems straightforward, but the implementation requires buffering: if one branch is read faster than the other, the data must be held somewhere until the slower branch catches up.
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