Why is detach() used in this return statement?

I saw the following bit of code on Github and was curious about the behavior of the return statement in the forward() function. Specifically I’m wondering why something is subtracted, detached, and then added back. Is this a standard technique or some hack to get the computation graph to behave a certain way?

import math
import torch
import torch.nn as nn

from torch.distributions.multivariate_normal import MultivariateNormal

class DoE(nn.Module):

    def __init__(self, dim, hidden, layers, pdf):
        super(DoE, self).__init__()
        self.qY = PDF(dim, pdf)
        self.qY_X = ConditionalPDF(dim, hidden, layers, pdf)

    def forward(self, X, Y, XY_package):
        hY = self.qY(Y)
        hY_X = self.qY_X(Y, X)

        loss = hY + hY_X
        mi_loss = hY_X - hY
        return (mi_loss - loss).detach() + loss

Do you have the link to the original Github repo? It might be explained there?

The code snippet would return the mi_loss in the forward pass (the loss tensor is subtracted) and would use loss the backward pass (the left-hand side is detached).