input: Write a python function to perform k-means clustering on a given DataFrame with 'age' and 'height' columns and return the cluster labels.
  assert list(kmeans_clustering(pd.DataFrame({'age': [25, 30, 45, 50], 'height': [5.5, 5.9, 5.7, 5.8]}), 2)) == [0, 0, 1, 1]
  assert list(kmeans_clustering(pd.DataFrame({'age': [20, 22, 35, 40], 'height': [5.4, 5.6, 5.8, 5.7]}), 2)) == [0, 0, 1, 1]
  assert list(kmeans_clustering(pd.DataFrame({'age': [18, 19, 65, 70], 'height': [5.2, 5.3, 6.0, 6.1]}), 2)) == [0, 0, 1, 1]
output: def kmeans_clustering(df, k):
      from sklearn.cluster import KMeans
      kmeans = KMeans(n_clusters=k, random_state=0).fit(df[['age', 'height']])
      return kmeans.labels_