input: Write a python function to classify a given sentence into predefined topics using Naive Bayes classifier.
  ```python
  import sklearn
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.preprocessing import LabelEncoder
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_sentence(sentences, topics, sample_sentence):
      # Create feature vectors 
      vectorizer = CountVectorizer(stop_words='english') 
      X = vectorizer.fit_transform(sentences).toarray()
  
      # Create label encoder 
      label_encoder = LabelEncoder()
      y = label_encoder.fit_transform(topics)
  
      # Instantiate the model 
      classifier = MultinomialNB() 
      classifier.fit(X, y) 
  
      # Classify new sample 
      sample = vectorizer.transform([sample_sentence]).toarray()
      prediction = classifier.predict(sample) 
      return label_encoder.inverse_transform(prediction)[0]
  
  assert classify_sentence(["The quick brown fox jumps over the lazy dog.", 
                            "This is a sample text used to determine how well the classification model works.", 
                            "This text belongs to the Animal topic."], 
                           ["Animal", "Sample", "Animal"], 
                           "A warm and sunny day") == "Sample"
  assert classify_sentence(["The match was exciting.", 
                            "The sky is clear today.", 
                            "The cat is very playful."], 
                           ["Sports", "Weather", "Animal"], 
                           "It is raining heavily outside") == "Weather"
  assert classify_sentence(["Dogs are loyal animals.", 
                            "Soccer is a popular sport.", 
                            "Climate change is affecting weather patterns."], 
                           ["Animal", "Sports", "Weather"], 
                           "Basketball is fun to watch") == "Sports"
output: import sklearn
  from sklearn.feature_extraction.text import CountVectorizer
  from sklearn.preprocessing import LabelEncoder
  from sklearn.naive_bayes import MultinomialNB
  
  def classify_sentence(sentences, topics, sample_sentence):
      # Create feature vectors 
      vectorizer = CountVectorizer(stop_words='english') 
      X = vectorizer.fit_transform(sentences).toarray()
  
      # Create label encoder 
      label_encoder = LabelEncoder()
      y = label_encoder.fit_transform(topics)
  
      # Instantiate the model 
      classifier = MultinomialNB() 
      classifier.fit(X, y) 
  
      # Classify new sample 
      sample = vectorizer.transform([sample_sentence]).toarray()
      prediction = classifier.predict(sample) 
      return label_encoder.inverse_transform(prediction)[0]
  
  assert classify_sentence(["The quick brown fox jumps over the lazy dog.", 
                            "This is a sample text used to determine how well the classification model works.", 
                            "This text belongs to the Animal topic."], 
                           ["Animal", "Sample", "Animal"], 
                           "A warm and sunny day") == "Sample"
  assert classify_sentence(["The match was exciting.", 
                            "The sky is clear today.", 
                            "The cat is very playful."], 
                           ["Sports", "Weather", "Animal"], 
                           "It is raining heavily outside") == "Weather"
  assert classify_sentence(["Dogs are loyal animals.", 
                            "Soccer is a popular sport.", 
                            "Climate change is affecting weather patterns."], 
                           ["Animal", "Sports", "Weather"], 
                           "Basketball is fun to watch") == "Sports"