input: Write a python function to train a Gaussian Naive Bayes model and make predictions.
  assert (train_and_predict([[0, 0], [1, 1], [1, 0], [1, 1]], [0, 0, 1, 1], [[0, 1], [1, 1]]) == [1, 0]).all()
  assert (train_and_predict([[1, 0], [0, 1], [1, 1], [0, 0]], [1, 0, 1, 0], [[1, 1], [0, 0]]) == [1, 0]).all()
  assert (train_and_predict([[0, 1], [1, 0], [0, 0], [1, 1]], [0, 1, 0, 1], [[1, 0], [0, 1]]) == [1, 0]).all()
output: def train_and_predict(X_train, y_train, X_test):
      from sklearn.naive_bayes import GaussianNB
      import numpy as np
      
      # Create and train the model
      model = GaussianNB()
      model.fit(X_train, y_train)
      
      # Make predictions
      predictions = model.predict(X_test)
      
      return np.array(predictions)