class_docstr: Compose transformation for multi-argument transformations.
  
  Differently from torchvision Compose, this transformation can handle both
  single-element and multi-elements transformations.
  
  For instance, single-element transformations are commonly used in
  classification tasks where there is no need to transform the class label.
  Multi-element transformations are used to transform the image and
  bounding box annotations at the same timein object detection tasks. This
  is needed as applying augmentations (such as flipping) may change the
  position of objects in the image.
  
  This class automatically detects the type of augmentation by inspecting
  its signature. Keyword-only arguments are never filled.
code: def __eq__(self, other):
      if self is other:
          return True
  
      if not isinstance(other, MultiParamCompose):
          return False
  
      return (
          self.transforms == other.transforms
          and self.param_def == other.param_def
          and self.min_params == other.min_params
          and self.max_params == other.max_params
      )
  
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 MultiParamCompose obj's underlying member method code with example usage: obj.__eq__(other) and returns: unknown, True, False