input: Write a function to build and train a machine learning model in Python that identifies the author of a given article based on provided text samples. The function should take a list of text samples and corresponding author labels as input, split the data into training and testing sets, build a pipeline using TF-IDF vectorization and a Naive Bayes classifier, train the model, and finally evaluate and print the model's score. If the input is missing, the function should return "Input data missing".
  def identify_author(samples, labels):
      if not samples or not labels:
          return "Input data missing"
      
      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}')
  
  assert identify_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) == None
  assert identify_author([], []) == "Input data missing"
output: def identify_author(samples, labels):
      if not samples or not labels:
          return "Input data missing"
      
      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}')