input: 
  def Author_Identification_Model(samples: List[str], labels: List[str]) -> float:
      """Develop a machine learning model using Sklearn's Pipeline to identify the author of given articles.
      The model will use a TfidfVectorizer and GaussianNB classifier. After training, the model will print the
      evaluation score.
      
      Example:
      for Author_Identification_Model(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]) == "model score: <score>"
      """
  
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
  
      # Create the pipeline
      pipeline = Pipeline([
          ('tfidf', TfidfVectorizer()),
          ('clf', GaussianNB())
      ])
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.2, random_state=42)
  
      # 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)
  
      # Print the evaluation score
      print(f"model score: {score}")
  
      return score