code: def __getitem__(self, idx):
      batch_idx = int(idx / self.batch_size)
      item_idx = idx % self.batch_size
      data = tuple([data[item_idx] for data in self.data[batch_idx][0]])
  
      if self.has_labels:
          label = self.data[batch_idx][1]
          if label is not None:
              return data, self.data[batch_idx][1][item_idx]
          else:
              return data, torch.tensor([0])
      else:
          return data
  
imports: from typing import Sequence, List, Tuple, Any, Union, Iterable
  import numpy
  from loguru import logger
  from nebullvm.config import MIN_DIM_INPUT_DATA
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch, Dataset, DataLoader
  from nebullvm.tools.onnx import convert_to_numpy
label_desc: Use image node_id 3 for calling the PytorchDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: data, data,  self, data,  torch