Does equal probabilities not summing to one in torch.utils.data.WeightedRandomSampler still make it uniform?
In pytorch, there is a sampler class called WeightedRandomSampler (https://pytorch.org/docs/stable/data.html#torch.utils.data.WeightedRandomSampler). It ('weights' parameter) expects probabilities for N samples. For uniform distribution, I believe it expects array with 1/N value.
But if I put say 0.5 for each sample, where N*0.5 is not equal to 1, does it still make the sampling uniform, given equal probabilities are there for each sample?

Yes, the sampling will still be uniform. Only the relative magnitude of the weights with respect to the other weights is important, not the absolute magnitude, as pytorch normalizes the weights.
If we look under the hood of WeightedRandomSampler, it makes a call to torch.multinomial which itself makes a call to torch.distributions.Categorical, which we can see here (line 57) normalizes the weights such that they sum to one.
