human
Solve the following python programming problem given the following code:
# Load data 
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=False)

# build graph
class Graph:
    def __init__(self, is_training=False):
        # Inputs and labels
        self.x = tf.placeholder(tf.float32, shape=[None, 784])
        self.y = tf.placeholder(tf.int32, shape=[None])

        # Layer 1
        w1 = tf.get_variable("w1", shape=[784, 100], initializer=tf.truncated_normal_initializer())
        output1 = tf.matmul(self.x, w1)
        output1 = tf.contrib.layers.batch_norm(...)

        #Layer 2
        w2 = tf.get_variable("w2", shape=[100, 10], initializer=tf.truncated_normal_initializer())
        logits = tf.matmul(output1, w2)
        preds = tf.to_int32(tf.arg_max(logits, dimension=1))

        # training
        loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.y, logits=logits)
        self.train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
        self.acc = tf.reduce_mean(tf.to_float(tf.equal(self.y, preds)))

# Training
tf.reset_default_graph()
g = Graph(is_training=True)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    saver = tf.train.Saver()
    for i in range(1, 10000+1):
        batch = mnist.train.next_batch(60)
        sess.run(g.train_op, {g.x: batch[0], g.y: batch[1]})
        # Evaluation
        if i % 100 == 0:
            print("training steps=", i, "Acc. =", sess.run(g.acc, {g.x: mnist.test.images, g.y: mnist.test.labels}))
    save_path = saver.save(sess, './my-model')
        
# Inference
tf.reset_default_graph()
g2 = Graph(is_training=False)
with tf.Session() as sess:
    saver = tf.train.Saver()
    saver.restore(sess, save_path)
    hits = 0
    for i in range(100):
        hits += sess.run(g2.acc, {g2.x: [mnist.test.images[i]], g2.y: [mnist.test.labels[i]]})
    print(hits)



Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Q5. The code below is to implement the mnist classification task. Complete it by adding batch normalization.