class_docstr: Each sample will be given a integer random seed to apply transform.
  Return (aux_uid/seed, origin_sample).
  
  Parameters
  ----------
  dataset
      The original dataset or a `_UidDataset` instance.
      If dataset is a `_UidDataset` instance and `aux_dataset_cls` is None, this dataset will be took as the aux_dataset.
  transform
      The transform function for each sample from dataset.
  seed
      This seed controls the random generator used in transform.
  get_rngs_state
      () -> states. A callable function to get all the random generator states in current environment.
      By default, the random generator states of ``built-in random``, ``numpy.random``, ``torch.random`` will be got,
      used to recover the random state after transform to before tranform.
      If other random library is used, please customize ``get_rngs_state``.
  set_rngs_state
      (states) -> None. A callable function to set all the random generators in current environment with given states.
      By default, the random generator states of ``built-in random``, ``numpy.random``, ``torch.random`` will be set,
      used to recover the random state after transform to before tranform.
      If other random library is used, please customize ``set_rngs_state``.
      The input of ``set_rngs_state`` is the state got from ``get_rngs_state``.
  manual_seed
      (int) -> None. A callable function to manual seed for all random library.
      By default, ``built-in random``, ``numpy.random``, ``torch.random`` will be set seed.
      In ``__getitem__``, the calling sequence is ``get_rngs_state -> manual_seed -> transform -> set_rngs_state``.
  aux_dataset_cls
      `aux_dataset_cls` is used to wrap the dataset to generate the aux_uid.
  aux_args
      Additional position arguments for `aux_dataset_cls` initialization.
  aux_kwargs
      Additional keyword arguments for `aux_dataset_cls` initialization.
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code: def get_origin_dataset(self):
      return self._dataset.get_origin_dataset()
  
imports: from __future__ import annotations
  from collections import defaultdict
  from typing import Any, Callable, Dict, List, Type
  import torch
  from torch.utils.data import Dataset, IterableDataset
  from .utils import _default_get_rngs_state, _default_set_rngs_state, _default_manual_seed
label_desc: Use image node_id 4 for calling the AugmentationDataset obj's underlying member method code with example usage: obj.get_origin_dataset() and returns: self