input: Write a python function to load data and labels from given file paths and preprocess them using CountVectorizer.
  assert isinstance(load_and_preprocess('data.txt', 'labels.txt'), tuple)
  assert len(load_and_preprocess('data.txt', 'labels.txt')) == 2
  assert load_and_preprocess('data.txt', 'labels.txt')[0].shape[1] > 0
output: def load_and_preprocess(data_path, labels_path):
      from sklearn.feature_extraction.text import CountVectorizer
      import numpy as np
  
      # Load data from file
      with open(data_path, 'r') as data_file:
          data = data_file.readlines()
      
      # Load labels from file
      with open(labels_path, 'r') as labels_file:
          labels = labels_file.readlines()
      
      # Preprocess data using CountVectorizer
      vectorizer = CountVectorizer()
      X = vectorizer.fit_transform(data)
  
      # Convert labels to numpy array
      y = np.array(labels).astype(int)
  
      return X, y