The sampler applies weighted sampling on the dataset and returns the sampled view. It creates a discrete distribution with given weights and randomly picks samples based on it. The resulting view is generated in such a way that when creating a dataloader from the view and training on it, the performance impact is minimal. See the sample_by method on how to use this feature:


Returns a sliced Dataset with given weighted sampler applied.