input: Write a function to create a k-means clustering algorithm in Python and test the algorithm with provided data.
  
  from numpy import array, allclose
  assert kmeans(2, array([[1,2], [2,3], [4,5], [5,6], [3,4]])) == (array([0, 0, 1, 1, 1]), array([[1.5, 2.5], [4.5, 5.5]]))
  assert kmeans(3, array([[0,0], [3,3], [6,0], [0,6], [6,6]])) == (array([0, 1, 2, 0, 1]), array([[0, 0], [4.5, 3], [6, 6]]))
  assert kmeans(1, array([[1,2], [2,3], [4,5], [5,6], [3,4]])) == (array([0, 0, 0, 0, 0]), array([[3, 4]]))
  assert kmeans(4, array([[1,1], [9,9], [1,9], [9,1]])) == (array([2, 3, 1, 0]), array([[9, 1], [1, 9], [1, 1], [9, 9]]))
  assert kmeans(1, array([[1]])) == (array([0]), array([1]))
output: 
  def kmeans(k, data):
      import numpy as np
      from sklearn.cluster import KMeans
      
      kmeans = KMeans(n_clusters=k, n_init=10).fit(data)
      labels = kmeans.labels_
      centers = kmeans.cluster_centers_
      
      return labels, centers
  