input:    
  def identify_author(samples: List[str], labels: List[str]) -> float:
      """Develop a machine learning model in Python to identify the author of a given article.
      The function takes a list of article samples and a corresponding list of labels (authors).
      It uses a TF-IDF vectorizer and a Gaussian Naive Bayes classifier to train and evaluate
      the model. The function returns the model's accuracy score on a test set.
      >>> identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB"])
      0.5
      >>> identify_author(["text by A", "another by A", "B's text", "more by B"], ["A", "A", "B", "B"])
      1.0
      """
      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
      return model.score(X_test, y_test)
  
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
      return model.score(X_test, y_test)