input: 
  def identify_author(samples: List[str], labels: List[str]) -> float:
      """
      Develop a machine learning model in Python to identify the author of given articles.
      This function takes a list of article samples and their corresponding authors,
      trains a Naive Bayes classifier using TF-IDF vectorizer, and returns the model's accuracy score.
      
      >>> identify_author([
        "sample 1 from AuthorA",
        "sample 2 from AuthorA",
        "sample 1 from AuthorB",
        "sample 2 from AuthorB"
      ], [
        "AuthorA",
        "AuthorA",
        "AuthorB",
        "AuthorB"
      ])
      0.5
      """
      from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline
      from sklearn.model_selection import train_test_split
  
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      return score
  
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import Pipeline
      from sklearn.model_selection import train_test_split
  
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      return score