import tensorflow as tf

        biases = tf.Variable(tf.zeros([32]),
                             name="biases")
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
      # Linear
      with tf.name_scope("softmax_linear"):
        weights = tf.Variable(
            tf.truncated_normal([32, 10],
                                stddev=1.0 / math.sqrt(float(32))),
            name="weights")
        biases = tf.Variable(tf.zeros([10]),
                             name="biases")
        logits = tf.matmul(hidden2, weights) + biases
        tf.add_to_collection("logits", logits)

      # Runs to logit.
      tf.initialize_all_variables().run()
      sess.run(logits)
      # Creates a saver.
      saver0 = tf.train.Saver()
      saver0.save(sess, saver0_ckpt)
      # Generates MetaGraphDef.
      saver0.export_meta_graph(filename)
