I have the same question.  Can’t understand why there aren’t more examples of normalizing the inputs (and outputs potentially).  Looking at torchvision.transforms.Normalize it says it is for normalizing “a tensor image with mean and standard deviation” which I don’t think is the same as what we’re talking about here.
In Scikit-Learn you simply add a sklearn.preprocessing.StandardScaler to your pipeline and it normalizes your dataset before training starts.  As far as I can see there are no ‘built-in’ ways to do this in PyTorch or am I missing something?
Are there any options other than these:

Manually calculate the mean and std deviation of your data and convert it at the beginning and keep a record of those parameters.
Build above into your torch.utils.data.dataset if you are using a custom dataset and data loader.
For output normalization, attach the parameters to your model and build the conversion back to original units into your models forward() method (remembering to feed un-normalized target values to your test criterion).
Or, do all your predictions and test evaluations in normalized units and only convert them back when you finally plot/save the results.
Or import and use sklearn.preprocessing.StandardScaler?