input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should use a pipeline that first vectorizes the text data using TfidfVectorizer and then classifies it using GaussianNB. The function should return the model's accuracy score.
  
  def identify_author(samples, labels):
      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
  
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) is not None
  assert identify_author(["sample 1 from AuthorC", "sample 2 from AuthorC", "sample 1 from AuthorD", "sample 2 from AuthorD"], ["AuthorC", "AuthorC", "AuthorD", "AuthorD"]) is not None
output: def identify_author(samples, labels):
      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
  
      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)
  
      score = model.score(X_test, y_test)
      return score