tensorflow 1x why save session run optimizer as a variable? - tensorflow

Question

I am learning tensorflow from a DataCamp tutorial, at the final training phase I have this code below. My question is the line
opt = sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
why are we saving sess.run(optimizer, ...) as a variable? the variable "opt" isn't used again anywhere... some explanation as to how tensorflow's optimizer works under the hood would be awesome thanks!
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
training_iters = 20
saver = tf.train.Saver()
# tf.reset_default_graph()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
for batch in range(len(train_X)//batch_size):
batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
batch_y = train_y[batch*batch_size:min((batch+1)*batch_size,len(train_y))]
opt = sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
# print(type(opt)) # class 'NoneType'
loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x, y: batch_y})
print("Iter " + str(i) + \
", Loss= " + "{:.6f}".format(loss) + \
", Training Accuracy= " + "{:.5f}".format(acc)) # prints the loss and training accuracy after each epoch
print("Optimization Finished!")
# Calculate accuracy for all 10000 mnist test images
test_acc, valid_loss = sess.run([accuracy,cost], feed_dict={x: val_X, y: val_y})
train_loss.append(loss)
test_loss.append(valid_loss)
train_accuracy.append(acc)
test_accuracy.append(test_acc)
print("Testing Accuracy:","{:.5f}".format(test_acc))
saved_path = saver.save(sess, './my-model', global_step=training_iters)
summary_writer.close()