input: def kmeans(k, data):
      """
      Perform k-means clustering on data
      >>> kmeans(2, np.array([[1, 1], [2, 1], [4, 3], [5, 4]]))
      (array([0, 0, 1, 1]), array([[ 1.5,  1. ], [ 4.5,  3.5]]))
      >>> kmeans(3, np.array([[1, 1], [2, 1], [4, 3], [5, 4], [9, 1]]))
      (array([0, 0, 1, 1, 2]), array([[ 1.5,  1. ], [ 4.5,  3.5], [ 9. ,  1. ]]))
      """
output:     import numpy as np
      from sklearn.cluster import KMeans
  
      kmeans = KMeans(n_clusters=k, random_state=0).fit(data)
      labels = kmeans.labels_
      centers = kmeans.cluster_centers_
  
      return labels, centers