input: Write a python function to create a Gaussian Naive Bayes model, fit it with given data, and make a prediction. 
  assert make_prediction([[1, 0, 0]]) == 'This review is positive.'
  assert make_prediction([[0, 0, 0]]) == 'This review is negative.'
  assert make_prediction([[1, 1, 1]]) == 'This review is positive.'
output: def make_prediction(input_data):
      from sklearn.naive_bayes import GaussianNB
      import numpy as np
      
      # Dummy dataset
      X = np.array([[1, 0, 0], [0, 1, 0], [1, 1, 1], [0, 0, 0]])
      y = np.array([1, 0, 1, 0])
      
      # Create and train the model
      model = GaussianNB()
      model.fit(X, y)
      
      # Make a prediction
      prediction = model.predict(input_data)
      
      # Return the appropriate result
      return 'This review is positive.' if prediction[0] == 1 else 'This review is negative.'