Why normalize pixel locations

Hi everyone,

In the week 3 lab of mnist digit localization, while transforming the images why we are dividing pixel location like xmax, xmin by 75?

'''
Transforms each image in dataset by pasting it on a 75x75 canvas at random locations.
'''
def read_image_tfds(image, label):
    xmin = tf.random.uniform((), 0 , 48, dtype=tf.int32)
    ymin = tf.random.uniform((), 0 , 48, dtype=tf.int32)
    image = tf.reshape(image, (28,28,1,))
    image = tf.image.pad_to_bounding_box(image, ymin, xmin, 75, 75)
    image = tf.cast(image, tf.float32)/255.0
    xmin = tf.cast(xmin, tf.float32)
    ymin = tf.cast(ymin, tf.float32)
   
    xmax = (xmin + 28) / 75
    ymax = (ymin + 28) / 75
    xmin = xmin / 75
    ymin = ymin / 75
    return image, (tf.one_hot(label, 10), [xmin, ymin, xmax, ymax])

This is because you are placing those images in a bigger canvas at 75 by 75 pixels.

Thank you for the reply.
Could you please look into this too.
https://community.deeplearning.ai/t/why-nomalize-images/181632/2