code: def get_batch(self):
      balanced_batch_images = []
      balanced_batch_texts = []
  
      for i, data_loader_iter in enumerate(self.dataloader_iter_list):
          try:
              image, text = data_loader_iter.next()
              balanced_batch_images.append(image)
              balanced_batch_texts += text
          except StopIteration:
              self.dataloader_iter_list[i] = iter(
                  self.data_loader_list[i]
              )
              image, text = self.dataloader_iter_list[i].next()
              balanced_batch_images.append(image)
              balanced_batch_texts += text
          except ValueError:
              pass
  
      balanced_batch_images = torch.cat(balanced_batch_images, 0)
  
      return balanced_batch_images, balanced_batch_texts
  
imports: import os
  import sys
  import re
  import six
  import math
  import torch
  import pandas
  from natsort import natsorted
  from PIL import Image
  import numpy
  from torch.utils.data import Dataset, ConcatDataset, Subset
  from torch._utils import _accumulate
  import torchvision.transforms
label_desc: Use image node_id 2 for calling the Batch_Balanced_Dataset obj's underlying member method code with example usage: obj.get_batch() and returns: balanced_batch_images,  balanced_batch_texts