As an update to this question:
Does Pytorch have a method of sampling from a dataset (who’s objects are given weights based on their frequency of appearance in the dataset) such that for every draw you have a roughly balanced sample set for regression?
For example: If you had a dataset who’s number of objects per label looks as follows:

(X-axis = label, Y-axis = number in the dataset)
Using WeightedRandomSampler with a controlled num_samples where the weights are given as  1-label, to my understanding this will not balance a sample’s number of objects with certain labels as they will be drawn from a distribution which favours the lower value labelled objects than the high value labelled objects.
So the distribution of labels in your sample will look like the plot above but mirrored about the y-axis.
Is there a way to sample such that from these labels you can draw a more balanced set given that your num_samples is smaller than the total number of objects in the dataset?
Please note that the values on the X-axis above are continuous as this is a regression problem and I have only binned the data to present my point.