class_docstr: This dataset class allows mini-batch formation pre-epoch, for greater speed
code: def __getitem__(self, idx):
      # we use SequentialSampler together with SuperLabelTrainDataset,
      # so idx==0 indicates the start of a new epoch
      batch_item = self.dataset[idx]
  
      if self.dataset_name == "Inaturalist":
          cls = int(batch_item[0].split("/")[1])
  
      else:
          cls = batch_item[0]
      img = Image.open(batch_item[1])
      return cls, self.transform(self.ensure_3dim(img))
  
imports: import pickle
  import warnings
  from numpy.core.arrayprint import IntegerFormat
  import copy
  import os
  import random
  import numpy
  import pandas
  import torch
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
label_desc: Use image node_id 4 for calling the TrainDatasetsmoothap obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: cls,  self