text: from numpy import random, ascontiguousarray
  import minicore as mc
  from sklearn.datasets import make_blobs
  import numpy as np
  
  dat = np.random.rand(10000).reshape(1000, 10)
  
  res = mc.kmeanspp(dat, k=25, msr=2)
  out, asn, costs  = res
  
  cs = mc.CoresetSampler()
  
  sensid = mc.constants.SENSDICT["LBK"] # This uses coresets for Bregman divergences
  
  cs.make_sampler(25, costs=costs, assignments=asn, sens=4)
  
  weights, ids = cs.sample(100)
  top = sorted(zip(ids, weights), key=lambda x: -x[1])[:10]
  print("top 10: ", top)
  