input: Write a function to develop a machine learning model in Python to identify the author of a given article. The function should return the accuracy of the model on a test set. The input to the function is a list of sample articles and a list of corresponding author labels.
  
  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
  
  def identify_author_accuracy(samples, labels):
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      score = model.score(X_test, y_test)
      return score
  
  assert identify_author_accuracy([
    "sample 1 from AuthorA",
    "sample 2 from AuthorA",
    "sample 1 from AuthorB",
    "sample 2 from AuthorB"
  ], [
    "AuthorA",
    "AuthorA",
    "AuthorB",
    "AuthorB"
  ]) >= 0.5
  
  assert identify_author_accuracy([
    "sample 1 from AuthorC",
    "sample 2 from AuthorC",
    "sample 1 from AuthorD",
    "sample 2 from AuthorD"
  ], [
    "AuthorC",
    "AuthorC",
    "AuthorD",
    "AuthorD"
  ]) >= 0.5
output: def identify_author_accuracy(samples, labels):
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      score = model.score(X_test, y_test)
      return score