input: Write a python function to cluster a given dataset using K-Means clustering.
  assert (cluster_dataset(np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]), n_clusters=2) == np.array([1, 1, 1, 0, 0, 0])).all()
  assert (cluster_dataset(np.array([[1, 2], [2, 3], [1, 4], [2, 1], [3, 2], [3, 4], [10, 10], [10, 11], [11, 10], [11, 11]]), n_clusters=3) == np.array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0])).all()
  assert (cluster_dataset(np.array([[1, 1], [1, 2], [1, 3], [10, 10], [10, 11], [10, 12]]), n_clusters=2) == np.array([1, 1, 1, 0, 0, 0])).all()
output: import numpy as np
  from sklearn.cluster import KMeans
  
  def cluster_dataset(data, n_clusters):
      kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(data)
      return kmeans.labels_