input: def author_identification_model() -> Pipeline:
      """Build a machine learning model in Python to identify the author of a given
      article using the given samples and labels. The model should include TfidfVectorizer
      for text feature extraction and Gaussian Naive Bayes for classification. Train the 
      model and evaluate its performance. The output should be a classification score.
      
      >>> samples = [
          "sample 1 from AuthorA",
          "sample 2 from AuthorA",
          "sample 1 from AuthorB",
          "sample 2 from AuthorB"
      ]
      >>> labels = [
          "AuthorA",
          "AuthorA",
          "AuthorB",
          "AuthorB"
      ]
      >>> model = author_identification_model()
      >>> X_train, X_test, y_train, y_test = train_test_split(\
          samples, labels, test_size=0.3)
      >>> model.fit(X_train, y_train)
      Pipeline(...)
      >>> score = model.score(X_test, y_test)
      >>> score > 0.0
      True
      """
      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
      
      # load data
      samples = [
        "sample 1 from AuthorA",
        "sample 2 from AuthorA",
        "sample 1 from AuthorB",
        "sample 2 from AuthorB"
      ]
      labels = [
        "AuthorA",
        "AuthorA",
        "AuthorB",
        "AuthorB"
      ]
  
      # 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}')
      
      return model
output:     return model