input: from typing import List, Tuple
  
  
  def train_author_identification_model(samples: List[str], labels: List[str]) -> Tuple[float, Pipeline]:
      """Train a machine learning model to identify the author of a given article and return the model score and the trained model.
      >>> train_author_identification_model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      (0.5, Pipeline(steps=[('tfidf', TfidfVectorizer()), ('clf', GaussianNB())]))
      """
output:     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
      from sklearn.metrics import accuracy_score
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.5, random_state=42)
  
      # Create a pipeline with TfidfVectorizer and GaussianNB
      pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      # Train the model
      pipeline.fit(X_train, y_train)
  
      # Predict on the test set
      y_pred = pipeline.predict(X_test)
  
      # Calculate the accuracy score
      score = accuracy_score(y_test, y_pred)
  
      return score, pipeline