MONAI Dataset objects incompatible with `torch.utils.data.BatchSampler`

Describe the bug
The default collate function fails when using a BatchSampler because datasets given lists of indices or slices return Subset instances from __getitem__. This doesn't appear to be the protocol Pytorch expected.

To Reproduce

Run the following:

import torch
from monai.data import Dataset, DataLoader
from monai.transforms import Compose

def load_data(i):
    print(i)
    return torch.full((1,2,2),ord(i))

data=["a","b","c"]
sampler=torch.utils.data.BatchSampler([0,0,1,1,2,2],batch_size=2,drop_last=False)
ds=Dataset(data,Compose([load_data]))
dl=DataLoader(dataset=ds,sampler=sampler)
print(list(dl))

Output:

> E: unsupported type in collate [<torch.utils.data.dataset.Subset object at 0x7fc4fe751f70>].

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~/workspace/monai/MONAI_mine/monai/data/utils.py in list_data_collate(batch)
    409         else:
--> 410             ret = default_collate(data)
    411             if isinstance(ret, MetaObj) and all(isinstance(d, MetaObj) for d in data):

~/miniconda3/envs/monai/lib/python3.9/site-packages/torch/utils/data/_utils/collate.py in default_collate(batch)
     85 
---> 86     raise TypeError(default_collate_err_msg_format.format(elem_type))

TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'torch.utils.data.dataset.Subset'>

The above exception was the direct cause of the following exception:

TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_411606/696157708.py in <module>
     10 ds=Dataset(data,Compose([load_data]))
     11 dl=DataLoader(dataset=ds,sampler=sampler)
---> 12 print(list(dl))

~/miniconda3/envs/monai/lib/python3.9/site-packages/torch/utils/data/dataloader.py in __next__(self)
    519             if self._sampler_iter is None:
    520                 self._reset()
--> 521             data = self._next_data()
    522             self._num_yielded += 1
    523             if self._dataset_kind == _DatasetKind.Iterable and \

~/miniconda3/envs/monai/lib/python3.9/site-packages/torch/utils/data/dataloader.py in _next_data(self)
    559     def _next_data(self):
    560         index = self._next_index()  # may raise StopIteration
--> 561         data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    562         if self._pin_memory:
    563             data = _utils.pin_memory.pin_memory(data)

~/miniconda3/envs/monai/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
     50         else:
     51             data = self.dataset[possibly_batched_index]
---> 52         return self.collate_fn(data)

~/workspace/monai/MONAI_mine/monai/data/utils.py in list_data_collate(batch)
    436             )
    437         _ = dev_collate(data)
--> 438         raise TypeError(re_str) from re
    439 
    440 

TypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found <class 'torch.utils.data.dataset.Subset'>

Expected behavior
Dataset should be compatible with Dataloader and produce desired batches. The function used in place of a transform should also print out the raw data "a", "b", "c".

Environment

================================
Printing MONAI config...
================================
MONAI version: 0.9.0rc1+19.gfe5fb747.dirty
Numpy version: 1.21.2
Pytorch version: 1.10.2
MONAI flags: HAS_EXT = True, USE_COMPILED = False
MONAI rev id: fe5fb7475ec84cc5dddd1194b82be706fdf1c9a5
MONAI __file__: /home/localek10/workspace/monai/MONAI_mine/monai/__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.8
Nibabel version: 3.2.1
scikit-image version: 0.18.3
Pillow version: 8.4.0
Tensorboard version: 2.6.0
gdown version: 4.2.1
TorchVision version: 0.11.3
tqdm version: 4.62.3
lmdb version: 1.2.1
psutil version: 5.8.0
pandas version: 1.3.5
einops version: 0.4.0
transformers version: 4.14.1
mlflow version: 1.23.1
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies


================================
Printing system config...
================================
System: Linux
Linux version: Ubuntu 20.04.3 LTS
Platform: Linux-5.4.0-96-generic-x86_64-with-glibc2.31
Processor: x86_64
Machine: x86_64
Python version: 3.9.7
Process name: python
Command: ['/home/localek10/miniconda3/envs/monai/bin/python', '-m', 'ipykernel_launcher', '-f', '/home/localek10/.local/share/jupyter/runtime/kernel-d0f602db-d30c-4cde-8eff-31b94f28fb4f.json']
Open files: [popenfile(path='/home/localek10/nohup.out', fd=40, position=3147127, mode='a', flags=558081), popenfile(path='/home/localek10/nohup.out', fd=43, position=3147127, mode='a', flags=558081), popenfile(path='/home/localek10/.ipython/profile_default/history.sqlite', fd=47, position=132070400, mode='r+', flags=688130), popenfile(path='/home/localek10/.ipython/profile_default/history.sqlite', fd=49, position=132389888, mode='r+', flags=688130), popenfile(path='/home/localek10/.ipython/profile_default/history.sqlite-journal', fd=64, position=12, mode='r+', flags=688130), popenfile(path='/home/localek10/workspace/BMEISWorkshops/15_CPP/cufile.log', fd=68, position=221, mode='a', flags=33793)]
Num physical CPUs: 6
Num logical CPUs: 12
Num usable CPUs: 12
CPU usage (%): [1.1, 1.0, 0.9, 1.0, 0.9, 0.9, 0.9, 0.9, 0.9, 1.0, 1.5, 0.9]
CPU freq. (MHz): 1495
Load avg. in last 1, 5, 15 mins (%): [0.9, 0.8, 0.8]
Disk usage (%): 71.7
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 31.3
Available memory (GB): 23.4
Used memory (GB): 7.2

================================
Printing GPU config...
================================
Num GPUs: 2
Has CUDA: True
CUDA version: 11.3
cuDNN enabled: True
cuDNN version: 8200
Current device: 0
Library compiled for CUDA architectures: ['sm_37', 'sm_50', 'sm_60', 'sm_61', 'sm_70', 'sm_75', 'sm_80', 'sm_86', 'compute_37']
GPU 0 Name: NVIDIA TITAN X (Pascal)
GPU 0 Is integrated: False
GPU 0 Is multi GPU board: False
GPU 0 Multi processor count: 28
GPU 0 Total memory (GB): 11.9
GPU 0 CUDA capability (maj.min): 6.1
GPU 1 Name: NVIDIA GeForce GTX 980
GPU 1 Is integrated: False
GPU 1 Is multi GPU board: False
GPU 1 Multi processor count: 16
GPU 1 Total memory (GB): 3.9
GPU 1 CUDA capability (maj.min): 5.2

Additional context
What should be done, change Dataset to return lists of results rather than Subset or change collate functions?

Start with the provided Dataset, DataLoader, BatchSampler, and Compose reproduction, then inspect how Dataset handles list or slice indices and how batching reaches the collate function. Confirm the intended PyTorch protocol and make the example produce batches while the transform receives raw values such as "a", "b", and "c"; no source file or test is named in the issue.