input: Write a function to apply KMeans clustering to a dataset and return the cluster labels.
  assert kmeans_clustering(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), 2).tolist() == [0, 1, 1]
  assert kmeans_clustering(np.array([[1, 1], [1, 4], [4, 1], [4, 4]]), 2).tolist() == [0, 1, 0, 1]
  assert kmeans_clustering(np.array([[1.1, 2.2], [3.3, 4.4], [5.5, 6.6], [7.7, 8.8]]), 3).tolist() == [0, 0, 1, 2]
output: def kmeans_clustering(data, n_clusters):
      from sklearn.cluster import KMeans
      kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(data)
      return kmeans.labels_