input: Write a function to develop a machine learning model in Python to identify the author of a given article using the provided code. The function should load data, build the model, train it, and evaluate its performance.
  
  
  def identify_author():
      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
  
      # load data
      samples = [
        "sample 1 from AuthorA",
        "sample 2 from AuthorA",
        "sample 1 from AuthorB",
        "sample 2 from AuthorB"
      ]
      labels = [
        "AuthorA",
        "AuthorA",
        "AuthorB",
        "AuthorB"
      ]
  
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  
  identify_author()
  
  assert identify_author()==None
output: 
  def identify_author():
      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
  
      # load data
      samples = [
        "sample 1 from AuthorA",
        "sample 2 from AuthorA",
        "sample 1 from AuthorB",
        "sample 2 from AuthorB"
      ]
      labels = [
        "AuthorA",
        "AuthorA",
        "AuthorB",
        "AuthorB"
      ]
  
      # build the model
      model = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', GaussianNB())
      ])
  
      # train the model
      X_train, X_test, y_train, y_test = train_test_split(samples, labels, test_size=0.3)
      model.fit(X_train, y_train)
  
      # evaluate the model
      score = model.score(X_test, y_test)
      print(f'model score: {score}')
  