input: Write a python function to create a KMeans model, fit it with provided data, and make predictions.
  assert kmeans_predictions([[1.5, 2.6, 3.2], [4.3, 5.2, 6.2], [-1.2, 0.2, 4.3]], 3).tolist() == [1, 0, 2]
  assert kmeans_predictions([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0]], 2).tolist() == [1, 1, 0, 0]
  assert kmeans_predictions([[8.0, 3.0], [7.0, 4.0], [5.0, 1.0]], 3).tolist() == [0, 2, 1]
output: from sklearn.cluster import KMeans
  import numpy as np
  
  def kmeans_predictions(data, n_clusters):
      kmeans = KMeans(n_clusters=n_clusters, random_state=0)
      kmeans.fit(data)
      predictions = kmeans.predict(data)
      return np.array(predictions)