В России ответили на имитирующие высадку на Украине учения НАТО18:04
both, and neverthelesse it looketh still as like bread as ever it did;
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The mypy project itself -- ~100k+ lines of Python -- achieved a 4x end-to-end speedup by compiling with mypyc. The official docs say "1.5x to 5x" for existing annotated code, "5x to 10x" for code tuned for compilation. The spectral-norm result (14x) lands above that range because the inner loop is pure arithmetic that mypyc compiles directly to C. On our dict-heavy JSON pipeline, mypyc hit 2.3x on pre-parsed dicts -- closer to the expected floor.
Again, to understand the class distribution of the various sports, one would have to take account of the representation which, in terms of their specific schemes of perception and appreciation, the different classes have of the costs (economic, cultural and ‘physical’) and benefits attached to the different sports—immediate or deferred ‘physical’ benefits (health, beauty, strength, whether visible, through ‘body-building’ or invisible through ‘keep-fit’ exercises), economic and social benefits (upward mobility etc.), immediate or deferred symbolic benefits linked to the distributional or positional value of each of the sports considered (i.e., all that each of them receives from its greater or lesser rarity, and its more or less clear association with a class, with boxing, football, rugby or body-building evoking the working classes, tennis and skiing the bourgeoisie and golf the upper bourgeoisie), gains in distinction accruing from the effects on the body itself (e.g., slimness, sun-tan, muscles obviously or discreetly visible etc.) or from the access to highly selective groups which some of these sports give (golf, polo etc.).