I have a binary classification problem and I have a unbalanced dataset. I decided to use  WeightedRandomSampler  from  torch.utils.data
class_weights = [0.35,0.65]
class_weights_initialize = []
for i in train_readyframe['target']:
class_weights_initialize.append(class_weights[i])
weighted_sampler=WeightedRandomSampler(weights=class_weights_initialize,num_samples=len(class_weights_initiaze),replacement=True)
I have given a weight of 0.35 to the 0th class and 0.65 to the other.
Does it means that the DATALOADER will select 65% of 1st class and 35% of 0th class in a single batch of training data?