input: 
  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
  
  def identify_author(samples, labels):
      """ Develop a machine learning model in Python to identify the author of a given article. 
      Given a list of text samples and their corresponding authors, the function builds a pipeline 
      using TF-IDF vectorizer and Gaussian Naive Bayes classifier, then trains the model and 
      returns the score of the model on a 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: 1.0
      """
      model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      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.score(X_test, y_test)
  
output:     model = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      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.score(X_test, y_test)