input: Write a function to develop a machine learning model in Python to identify the author of a given article using the sklearn library. The function should load provided text samples and their corresponding author labels, split the data into training and testing sets, build a pipeline using TfidfVectorizer and GaussianNB, train the model, and print the model's score on the test set.
  
  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)
      print(f'model score: {score}')
  
  # Test cases
  samples = [
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ]
  labels = [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]
  
  identify_author(samples, labels)
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)
      print(f'model score: {score}')