input: 
  def identify_author(text: str) -> str:
      """ This function constructs a machine learning model to predict the author of a given 
      article text. It uses a pipeline combining TfidfVectorizer and GaussianNB. The model 
      is trained on predefined text samples and evaluates its performance. 
      >>> identify_author("text sample from AuthorA")
      'AuthorA'
      >>> identify_author("text sample from AuthorB")
      'AuthorB'
      """
      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, _, y_train, _ = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # predict the author
      prediction = model.predict([text])[0]
      return prediction
  
output:     return prediction