input: 
  def classify_handwritten_digits() -> tf.Session:
      """Generate a neural network model with TensorFlow and Python for classifying hand-written digits from the MNIST dataset.
      >>> classify_handwritten_digits()
      <tensorflow.python.client.session.Session object at 0x...>
      """
  
output:     
      import tensorflow as tf
      from tensorflow.examples.tutorials.mnist import input_data
  
      mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
  
      x = tf.placeholder(tf.float32, [None, 784])
      W = tf.Variable(tf.zeros([784, 10]))
      b = tf.Variable(tf.zeros([10]))
  
      y = tf.nn.softmax(tf.matmul(x, W) + b)
      y_ = tf.placeholder(tf.float32, [None, 10])
  
      cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
      train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
  
      sess = tf.Session()
      sess.run(tf.global_variables_initializer())
  
      for _ in range(1000):
          batch_xs, batch_ys = mnist.train.next_batch(100)
          sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  
      correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
      accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
  
      print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))
  
      return sess
  