input: from typing import List
  
  def identify_article_author(samples: List[str], labels: List[str]) -> Pipeline:
      """
      Develop a machine learning model in Python to identify the author of given articles.
      The function uses a pipeline that combines TF-IDF vectorization with a Gaussian Naive Bayes classifier.
      
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> model = identify_article_author(samples, labels)
      >>> X_test = ["sample 3 from AuthorA", "sample 3 from AuthorB"]
      >>> y_test = ["AuthorA", "AuthorB"]
      >>> print(f'model score: {model.score(X_test, y_test)}')
      model score: 1.0
      """
      
      # 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)
      
      return model
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