class_docstr: Multi-argument transformation represented as tuples.
code: def flat_transforms(self, position: int):
      if position < len(self.transforms):
          return flat_transforms_recursive(
              self.transforms[position], position
          )
      return []
  
imports: from abc import ABC, abstractmethod
  import warnings
  from typing import Any, Callable, Iterable, List, Sequence, Tuple, Union
  from inspect import signature, Parameter
  from torchvision.transforms import Compose
label_desc: Use image node_id 6 for calling the TupleTransform obj's underlying member method code with example usage: obj.flat_transforms(position) and returns: list, flat_transforms_recursive