input: Write a function to use NaiveBayesClassifier to predict the class of given inputs.
  assert NaiveBayesClassifer(np.array([[1, 2], [2, 3], [3, 4], [4, 5]]), np.array([0, 0, 1, 1])).predict(np.array([[1.5, 2.5], [3.5, 4.5]])) == [0, 1]
  assert NaiveBayesClassifer(np.array([[1, 1], [2, 2], [3, 3], [4, 4]]), np.array([0, 0, 1, 1])).predict(np.array([[2, 2], [4, 4]])) == [0, 1]
  assert NaiveBayesClassifer(np.array([[1, 0], [2, 0], [3, 1], [4, 1]]), np.array([0, 0, 1, 1])).predict(np.array([[1, 0], [4, 1]])) == [0, 1]
output: from sklearn.naive_bayes import GaussianNB
  import numpy as np
  
  class NaiveBayesClassifer:
      def __init__(self, X_train, y_train):
          self.model = GaussianNB()
          self.model.fit(X_train, y_train)
      
      def predict(self, X_test):
          return self.model.predict(X_test).tolist()