text: Title: Support (> 10000)-token texts in `infer_vector()`
  Body: As `infer_vector()` uses the same optimized Cython functions as training behind-the-scenes, it also suffers from the same fixed-token-buffer size as training, where texts with more than 10000 tokens have all overflow tokens ignored. 
  
  But, this might be easier to fix for inference, as the it could be easy to call the training functions with a mini-batch that just reuses the same temporary candidate vector-in-training. 
  
  (And in fact, thinking about this makes me wonder if we should just auto-chunk documents during training, too – perhaps with a warning to the user the 1st time this happens. Previously, I'd wanted to fix this limitation by using `alloca()` to replace our fixed 10000-slot on-stack arrays with variable-length on-stack arrays - which worked in my tests, and perhaps even offered a memory-compactness advantage/speedup for all the cases where texts were *smaller* than 10000 tokens – but, `alloca()` isn't guaranteed to be available everywhere, even though in practice it seems to be everywhere we support.)