input: def identify_author(samples: List[str], labels: List[str]) -> float:
      """ Develops a machine learning model using a pipeline of TF-IDF vectorizer and Gaussian Naive Bayes classifier
      to identify the author of a given article. Returns the evaluation score of 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"
      ... ]
      >>> identify_author(samples, labels)
      1.0
      """
output:     from sklearn.feature_extraction.text import TfidfVectorizer
      from sklearn.naive_bayes import GaussianNB
      from sklearn.pipeline import make_pipeline
      from sklearn.model_selection import cross_val_score
  
      # Create a pipeline with TF-IDF vectorizer and Gaussian Naive Bayes classifier
      model = make_pipeline(TfidfVectorizer(), GaussianNB())
  
      # Evaluate the model using cross-validation
      scores = cross_val_score(model, samples, labels, cv=5)
  
      # Return the mean evaluation score
      return scores.mean()