How was the neg_sqr_dist formulated? Could you point out the equation in the paper?

[kalyanainala]

In the tensorflow folder, the python code resnet_model.py has the following lines
"
XY = tf.matmul(feat, means, transpose_b=True)
XX = tf.reduce_sum(tf.square(feat), axis=1, keep_dims=True)
YY = tf.reduce_sum(tf.square(tf.transpose(means)), axis=0, keep_dims=True)
neg_sqr_dist = -0.5 * (XX - 2.0 * XY + YY)
"
I have read the paper but couldn't understand how the neg_sqr_dist = -0.5 * (XX - 2.0 * XY + YY) equation was formed. Could you point out the equation in the paper which gave -0.5 * (XX - 2.0 * XY + YY) ?

[kalyanainala]

I believe it is the equation 18 that gave the formula for neg_sqr_dist. ((a-b)2 = (a2 - 2ab + b2)) where a, b are the xi and mean respectively.

If I am right, Why was the covariance matrix not considered in the equation?