input: def kmeans(k, data):
      """
      Perform k-means clustering on data
      >>> kmeans(2, np.array([[1, 1], [2, 2], [4, 4], [5, 5]]))
      (array([0, 0, 1, 1]), array([[1.5, 1.5], [4.5, 4.5]]))
      >>> kmeans(3, np.array([[1, 1], [2, 2], [4, 4], [5, 5], [8, 8]]))
      (array([0, 0, 1, 1, 2]), array([[1.5, 1.5], [4.5, 4.5], [8., 8.]]))
      """
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