class_docstr: We need this class to be able to distinguish between a single argument
  transformation and multi-argument ones.
  
  Transformations are callable objects.
docstr: Returns a flat list of transformations.
  
  A flat list of transformations is a list in which
  all intermediate wrappers (such as torchvision Compose,
  Avalanche MultiParamCompose, ...) are removed.
  
  The position parameter is used to control which transformations
  are to be returned based on the position of the tranformed element.
  Position 0 means transformations on the "x" value,
  1 means "target" (or y) transformations, and so on.
  
  Please note that transformations acting on multiple parameters
  may be returned when appropriate. This is common for object
  detection augmentations that transform x (image) and y (bounding boxes)
  inputs at the same time.
  
  :position: The position of the tranformed element.
  :return: A list of transformations for the given position.
code: def flat_transforms(self, position: int) -> List[Any]:
      """
      Returns a flat list of transformations.
  
      A flat list of transformations is a list in which
      all intermediate wrappers (such as torchvision Compose,
      Avalanche MultiParamCompose, ...) are removed.
  
      The position parameter is used to control which transformations
      are to be returned based on the position of the tranformed element.
      Position 0 means transformations on the "x" value,
      1 means "target" (or y) transformations, and so on.
  
      Please note that transformations acting on multiple parameters
      may be returned when appropriate. This is common for object
      detection augmentations that transform x (image) and y (bounding boxes)
      inputs at the same time.
  
      :position: The position of the tranformed element.
      :return: A list of transformations for the given position.
      """
      pass
  
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 2 for calling the MultiParamTransform obj's underlying member method code with example usage: obj.flat_transforms(position) without return types