I have a need to use a BatchSampler that is exactly as described in the Pytorch documentation, yet, I cannot understand how to use the batchsampler with any given dataset.
e.g
class MyDataset(Dataset):

    def __init__(self, remote_ddf, ):
        self.ddf = remote_ddf

    def __len__(self):
        return len(self.ddf)

    def __getitem__(self, idx):
        return self.ddf[idx] --------> This is as expensive as a batch call

    def get_batch(self, batch_idx):
        return self.ddf[batch_idx]

my_loader = DataLoader(MyDataset(remote_ddf), 
           batch_sampler=BatchSampler(Sampler(), batch_size=3))


The thing I do not understand, neither found any example online or in torch docs, is how do I use my get_batch function instead of the __getitem__ function.
Practically, I would even prefer to not implement the __getitem__, and let my custom dataset create the whole batched sample.