class_docstr: Loads image/segmentation pairs of files from the given filename lists. Transformations can be specified
  for the image and segmentation arrays separately.
  The difference between this dataset and `ArrayDataset` is that this dataset can apply transform chain to images
  and segs and return both the images and metadata, and no need to specify transform to load images from files.
  For more information, please see the image_dataset demo in the MONAI tutorial repo,
  https://github.com/Project-MONAI/tutorials/blob/master/modules/image_dataset.ipynb
code: def randomize(self, data: Any | None = None) -> None:
      self._seed = self.R.randint(MAX_SEED, dtype="uint32")
  
file_path: MONAI/monai/data/image_dataset.py
imports: from __future__ import annotations
  from collections.abc import Callable, Sequence
  from typing import Any
  import numpy
  from torch.utils.data import Dataset
  from monai.config import DtypeLike
  from monai.data.image_reader import ImageReader
  from monai.transforms import LoadImage, Randomizable, apply_transform
  from monai.utils import MAX_SEED, get_seed
label_desc: Use image node_id 3 for calling the ImageDataset obj's underlying member method code with example usage: obj.randomize(data) without return types