code: def seperate(self):
      small_dataset = []
      for large_batch in self.dataset:
          if type(large_batch) == list or type(large_batch) == tuple:
              large_size = len(large_batch[0])
          elif type(large_batch) == dict:
              large_size = len(large_batch[list(large_batch.keys())[0]])
          else:
              large_size = len(large_batch)
          for i in range(0, large_size, self.small_batch_size):
              if (
                  type(large_batch) == list
                  or type(large_batch) == tuple
              ):
                  small_dataset.append(
                      [
                          x[i : i + self.small_batch_size]
                          for x in large_batch
                      ]
                  )
              elif type(large_batch) == dict:
                  small_dataset.append(
                      {
                          k: v[i : i + self.small_batch_size]
                          for k, v in large_batch.items()
                      }
                  )
              else:
                  small_dataset.append(
                      large_batch[i : i + self.small_batch_size]
                  )
      self.free()
  
      return small_dataset
  
imports: import torch
  from torch.utils.data import Dataset, Subset, ConcatDataset
  from torch.nn.utils.rnn import pad_sequence
  import torch.nn.functional
  from datasets import load_dataset
  import numpy
  import os
  import hashlib
  from itertools import chain
  from dschat.utils.data import raw_datasets
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the MiniDataset obj's underlying member method code with example usage: obj.seperate() and returns: small_dataset