input: 
  from typing import List, Tuple
  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
  
  def author_identifier(samples: List[str], labels: List[str]) -> Pipeline:
      """ Build a machine learning model to identify the author of a given article.
      The function will take a list of text samples and their corresponding author labels,
      build a machine learning model using a pipeline that includes TfidfVectorizer and
      GaussianNB, train the model, and return the trained model.
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> model = author_identifier(samples, labels)
      >>> X_test = ["This is a test sample from AuthorB"]
      >>> model.predict(X_test)
      array(['AuthorB'], dtype=object)
      """
      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)
      
      return model
  
output:     return model