It’s to transform inputs normalized one way into inputs normalized differently.  The Inceptionv3 model uses the pre-trained weights from Google. They assume images are normalized by: (img - 0.5) / 0.5 so that inputs are between -1 and 1.
The PyTorch data loader transforms images using (roughly):
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
img[c] = (img[c] - mean[c]) / std[c]

This makes the ImageNet training set have zero-mean and unit-variance.
The code in the inception model reverses the PyTorch transformation and applies the transform from Inceptionv3.