input: 
  from typing import List
  
  
  def identify_author(samples: List[str], labels: List[str]) -> None:
      """ Develops and evaluates a machine learning model to identify the author of given articles.
      The model uses TfidfVectorizer for text feature extraction and Gaussian Naive Bayes for classification.
      The model is trained and evaluated using 30% of the data as a test set.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      model score: 0.5
      """
  
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
  
      vectorizer = TfidfVectorizer()
      X = vectorizer.fit_transform(samples)
      y = labels
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
  
      model = GaussianNB()
      model.fit(X_train.toarray(), y_train)
  
      y_pred = model.predict(X_test.toarray())
      score = accuracy_score(y_test, y_pred)
  
      print(f"model score: {score}")