input: Write a python function to perform KMeans clustering on a dataset and return the labels and centroids.
  assert kmeans_clustering([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]]) == ([0, 0, 0, 1, 1, 1], [[1.0, 2.0], [4.0, 2.0]])
  assert kmeans_clustering([[2, 3], [3, 3], [6, 6], [8, 9], [7, 7], [1, 4], [5, 5], [9, 1]]) == ([0, 0, 1, 2, 1, 0, 1, 2], [[2.0, 3.33333333], [6.0, 6.0], [8.5, 5.0]])
output: def kmeans_clustering(data, n_clusters=2):
      from sklearn.cluster import KMeans
      import numpy as np
  
      kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(data)
      labels = kmeans.labels_.tolist()
      centroids = kmeans.cluster_centers_.tolist()
      
      return labels, centroids