input: Write a function to develop a machine learning model in Python to identify the author of a given article using TF-IDF vectorization and Gaussian Naive Bayes. The function should return the score of the model after training and evaluating it on a test set.
  assert train_and_evaluate_model([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]) < 1.0
  assert train_and_evaluate_model([
    "This is a unique article",
    "An entirely different text here",
    "Yet another distinct article",
    "Completely separate piece of writing"
  ], [
    "AuthorX",
    "AuthorY",
    "AuthorX",
    "AuthorY"
  ]) < 1.0
output: 
  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
  
  def train_and_evaluate_model(articles, authors):
      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)
      score = accuracy_score(y_test, y_pred)
      
      return score
  