input: Write a function to develop a machine learning model in Python that identifies the author of a given article using the TF-IDF vectorizer and a Gaussian Naive Bayes classifier. The function should return the model's accuracy score after training and evaluating the model on a test set. The input data should be a list of article samples and corresponding labels.
  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_modal([], []) is None
output: def develop_author_model(articles, labels):
      if not articles or not labels:
          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).toarray()
      y = labels
      
      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, y_train)
      
      y_pred = model.predict(X_test)
      return accuracy_score(y_test, y_pred)