input: Write a function to develop a machine learning model in Python to identify the author of a given article. Use TfidfVectorizer for text feature extraction and GaussianNB as the classifier. Return the score of the model after evaluating it on the test set.
  assert develop_author_model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) > 0.5
  assert develop_author_model(["text by AuthorC", "another text by AuthorC", "text by AuthorD", "another text by AuthorD"], ["AuthorC", "AuthorC", "AuthorD", "AuthorD"]) > 0.5
  assert develop_author_model([], []) is None
  assert develop_author_model(["text", "another text"], ["AuthorX", "AuthorX"]) > 0.5
  assert develop_author_model(["text by different authors", "more text by the same"], ["AuthorY", "AuthorZ"]) < 0.5
output: def develop_author_model(articles, authors):
      if not articles or not authors:
          return None
      
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.model_selection import train_test_split
      from sklearn.metrics import accuracy_score
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(articles)
      y = authors
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      model = GaussianNB()
      model.fit(X_train.toarray(), y_train)
  
      y_pred = model.predict(X_test.toarray())
      return accuracy_score(y_test, y_pred)