input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use TF-IDF vectorization and a Gaussian Naive Bayes classifier, and it should evaluate the model's performance.
  assert predict_author(['sample 1 from AuthorA', 'sample 2 from AuthorA', 'sample 1 from AuthorB'], ['AuthorA', 'AuthorA', 'AuthorB'])==None
  assert predict_author(['sample 1 from AuthorA', 'sample 2 from AuthorB'], ['AuthorA', 'AuthorB'])==None
output: def predict_author(articles, authors):
      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 = 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, y_train)
  
      y_pred = model.predict(X_test)
      accuracy = accuracy_score(y_test, y_pred)
  
      return None