code: def find_closest_divisor(to_divide, closest_to):
    
    def find_closest(td, ct):
      min_distance = td
      best = td
      
      for divisor in divisors(td):
        if abs(divisor - ct) < min_distance:
          min_distance = abs(divisor - ct)
          best = divisor
      return best
    
    return [ find_closest(td, ct) for td, ct in zip(to_divide, closest_to) ]
docs: This is used to find the right chunk size for
    importing a neuroglancer dataset that has a
    chunk import size that's not evenly divisible by
    64,64,64. 
  
    e.g. 
      neuroglancer_chunk_size = find_closest_divisor(build_chunk_size, closest_to=[64,64,64])
  
    Required:
      to_divide: (tuple) x,y,z chunk size to rechunk
      closest_to: (tuple) x,y,z ideal chunk size
  
    Return: [x,y,z] chunk size that works for ingestion