input: 
  def identify_author(samples: List[str], labels: List[str], test_size: float = 0.3) -> float:
      """ Develop and train a machine learning model to identify the author of a given article.
      The model uses a pipeline with TF-IDF vectorizer for text preprocessing and Gaussian Naive Bayes for classification.
      Return the accuracy score of the model on the test set.
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> score = identify_author(samples, labels)
      >>> print(f'model score: {score}')
      model score: 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=test_size)
      model.fit(X_train, y_train)
  
      # evalulate 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=test_size)
      model.fit(X_train, y_train)
  
      # evalulate the model
      score = model.score(X_test, y_test)
      return score
  