input: Write a Python function to preprocess email data, transforming text into numerical values using the CountVectorizer and splitting data into training and test sets.
  assert len(X_train) == int(0.7 * len(dataset))
  assert len(X_test) == int(0.3 * len(dataset))
  assert isinstance(X_train, type(X_test))
  assert isinstance(y_train, type(y_test))
output: def preprocess_email_data(dataset, labels):
      from sklearn.feature_extraction.text import CountVectorizer
      from sklearn.model_selection import train_test_split
  
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(dataset)
      
      X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.3, random_state=42)
      
      return X_train, X_test, y_train, y_test