WeightedRandomSampler for dataset balance

Could you please help me to understand WeightedRandomSampler ?
I have unbalanced dataset, where target is either 0 or 1. Number of zeroes is 1871, number of ones is 229.

Is it possible to generate new balanced dataset with WeightedRandomSampler,such that I have overall number of zeroes is for example 200 and ones is also 200.

in my code weights=[0.1,0.9]
sampler = torch.utils.data.sampler.WeightedRandomSampler(weights, 400,replacement=False)

but it doesn’t work, it gives error
RuntimeError: invalid argument 2: cannot sample n_sample > prob_dist.size(1) samples without replacement
I just want to include in my dataset all ones and only part of zeroes.
Thank you very much in advance!

weights should be given as sample weights (i.e. each sample in your Dataset will get a weight) not as class weights. Have a look at this small code snippet to see an example.