input: Write a python function to preprocess and normalize MNIST dataset, reshape it for a neural network, and return the processed train and test data.
  assert x_train.shape == (60000, 784)
  assert x_test.shape == (10000, 784)
  assert x_train.max() == 1.0
  assert x_train.min() == 0.0
output: def preprocess_mnist(x_train, x_test):
      # Normalize the dataset
      x_train = x_train.astype('float32') / 255.0
      x_test = x_test.astype('float32') / 255.0
      
      # Reshape the dataset
      x_train = x_train.reshape(-1, 28 * 28)
      x_test = x_test.reshape(-1, 28 * 28)
      
      return x_train, x_test