input: Write a function to perform KMeans clustering on a given set of features and return the cluster labels.
  assert (KMeansCluster(np.array([[1.8, 2.4], [3.2, 1.3], [1.1, 8.2], [8.2, 1.2]]), 2) == np.array([0, 0, 1, 0])).all()
  assert (KMeansCluster(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]), 2) == np.array([1, 1, 0, 0])).all()
  assert (KMeansCluster(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]), 2) == np.array([1, 1, 0, 0])).all()
output: def KMeansCluster(features, k):
      from sklearn.cluster import KMeans
      kmeans = KMeans(n_clusters=k, random_state=0).fit(features)
      return kmeans.labels_