input: Write a python function to classify a given sentence based on a trained Naive Bayes classifier using CountVectorizer.
  assert classify_sentence("The derivative of x^2 is 2x.") == 1
  assert classify_sentence("Water is a molecule composed of two hydrogen atoms and one oxygen atom.") == 0
output: def classify_sentence(sentence):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.naive_bayes import MultinomialNB
      import numpy as np
  
      # Example data
      X_train = [
          "The integral of x squared is x cubed over three plus a constant.",
          "The derivative of x^2 is 2x.",
          "The limit of a function is the value that the function approaches as the input approaches some value.",
          "Water is a molecule composed of two hydrogen atoms and one oxygen atom.",
          "Photosynthesis is a process used by plants to convert light energy into chemical energy."
      ]
      y_train = [1, 1, 1, 0, 0]  # 1 for math, 0 for science
  
      # Transform the text data to feature vectors
      vectorizer = CountVectorizer()
      X_train_counts = vectorizer.fit_transform(X_train)
  
      # Train the Naive Bayes classifier
      clf = MultinomialNB()
      clf.fit(X_train_counts, y_train)
  
      # Transform the input sentence to the same feature space
      X_test_counts = vectorizer.transform([sentence])
  
      # Predict the class of the input sentence
      predicted = clf.predict(X_test_counts)
  
      return predicted[0]