input: def identify_author_MLmodel(samples: list, labels: list) -> float:
      """Build a machine learning model using Python to identify the author of given articles. 
      Use a pipeline combining TF-IDF vectorization and Gaussian Naive Bayes as the classifier.
      The function will output the model's score on test data.
      
      >>> samples = ["sample 1 from AuthorA", "sample 2 from AuthorA", "sample 1 from AuthorB", "sample 2 from AuthorB"]
      >>> labels = ["AuthorA", "AuthorA", "AuthorB", "AuthorB"]
      >>> identify_author_MLmodel(samples, labels)
      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 vectorization 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)
  
      # Calculate the accuracy score
      score = accuracy_score(y_test, y_pred)
  
      return score