input: from typing import List
  
  
  def identify_article_author(samples: List[str], labels: List[str]) -> float:
      """
      Develop a machine learning model in Python to identify the author of a given article.
      The function takes a list of article samples and corresponding author labels, builds
      a machine learning model using a pipeline that includes TF-IDF vectorization and Gaussian
      Naive Bayes classifier, trains the model, and returns the score of the model on a test set.
      >>> identify_article_author(["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"], ["AuthorA", "AuthorA", "AuthorB", "AuthorB"])
      1.0
      """
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=0.2, random_state=42)
  
      # Create a pipeline with TF-IDF vectorizer and Gaussian Naive Bayes classifier
      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)
  
      # Return the accuracy score
      return accuracy_score(y_test, y_pred)