input: 
  def author_identification_model() -> Pipeline:
      """ Create a machine learning model using Python's scikit-learn to identify the author of a given article.
      The model should use TF-IDF vectorization and Gaussian Naive Bayes as the classifier.
      >>> author_identification_model()
      Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', GaussianNB())])
      """
      
      # 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 Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  