input: Write a python function to perform KMeans clustering on a given set of data points and return the cluster labels. 
  ```python
  from sklearn.cluster import KMeans
  import numpy as np
  
  def kmeans_clustering(data_points, n_clusters=2, random_state=0):
      data_points = np.array(data_points)
      kmeans = KMeans(n_clusters=n_clusters, random_state=random_state).fit(data_points)
      return kmeans.labels_
  
  assert (kmeans_clustering([[1, 2], [1, 4], [1, 0], [4, 2], [4, 4], [4, 0]]) == np.array([1, 1, 1, 0, 0, 0])).all()
  assert (kmeans_clustering([[1, 2], [1, 4], [1, 0], [10, 10], [10, 12], [10, 8]]) == np.array([1, 1, 1, 0, 0, 0])).all()
  assert (kmeans_clustering([[1, 2], [1, 4], [1, 0], [8, 8], [8, 10], [8, 6]]) == np.array([1, 1, 1, 0, 0, 0])).all()
output: def kmeans_clustering(data_points, n_clusters=2, random_state=0):
      data_points = np.array(data_points)
      kmeans = KMeans(n_clusters=n_clusters, random_state=random_state).fit(data_points)
      return kmeans.labels_