input: Write a python function to train a KNeighborsClassifier and make predictions. 
  assert knn_predictions([[10, 20], [30, 40], [50, 20], [10, 70], [50, 50]], [0, 0, 1, 1, 1], [[10, 50], [20, 30]], 3) == [0, 0]
  assert knn_predictions([[5, 15], [25, 35], [45, 25], [5, 65], [45, 45]], [0, 1, 1, 0, 1], [[5, 45], [25, 20]], 3) == [0, 1]
output: def knn_predictions(X_train, y_train, X_test, n_neighbors):
      from sklearn.neighbors import KNeighborsClassifier
  
      # Initialize the classifier
      knn = KNeighborsClassifier(n_neighbors=n_neighbors)
  
      # Train the classifier
      knn.fit(X_train, y_train)
  
      # Make predictions
      predictions = knn.predict(X_test)
  
      return predictions