input: Write a python function to implement a k-nearest neighbor model using the SciKit-Learn library.
  assert knn_predict([[1, 2], [2, 3], [3, 4], [4, 5]], [0, 0], 3) == 0
  assert knn_predict([[1, 2], [2, 3], [3, 4], [4, 5]], [5, 5], 2) == 1
  assert knn_predict([[1, 2], [2, 3], [3, 4], [4, 5]], [2, 2], 1) == 0
output: def knn_predict(data, point, k):
      from sklearn.neighbors import KNeighborsClassifier
      import numpy as np
  
      X = np.array(data)
      y = np.array([0 if i < len(data) // 2 else 1 for i in range(len(data))])
  
      knn = KNeighborsClassifier(n_neighbors=k)
      knn.fit(X, y)
  
      prediction = knn.predict([point])
      return prediction[0]