input: 
  def kmeans(k, data):
      """
      Perform k-means clustering on data.
      >>> kmeans(2, np.array([[1, 2], [2, 3], [10, 11], [11, 12]]))
      (array([0., 0., 1., 1.]), array([[1.5, 2.5], [10.5, 11.5]]))
      >>> kmeans(3, np.array([[1, 2], [2, 3], [10, 11], [11, 12], [20, 21], [21, 22]]))
      (array([0., 0., 1., 1., 2., 2.]), array([[1.5, 2.5], [10.5, 11.5], [20.5, 21.5]]))
      """
  
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