Using multiple dataloaders at training time
I Try to train on two dataloaders, one attached to a dataset where each __get_item__ call fetches a predefined batch of varying length (thus the batch_size I transfer to the dataloader object is 1), and one where I sample randomly from a set of sequences, thus __get_item__ call fetches one sample each time.
I'm looking for something like
            loader = DataLoader(
                batched_dataset,
                batch_size=1,
            )
            loader_tdm = DataLoader(
                random_samples_dataset,
                batch_size=8,
            )
            return [loader1,loader2]

I see this option is available for the validation and test dataloaders, while for training you suggested (previous issues) to use the ConcatDataset.
Is there a workaround?