class_docstr: Given a batch of input images and labels, this class randomly applies the
  `MixUp transformation <https://arxiv.org/abs/1710.09412>`_
  
  Args:
      opts (argparse.Namespace): Arguments
      num_classes (int): Number of classes in the dataset
code: def __repr__(self) -> str:
      return "{}(num_classes={}, p={}, alpha={}, inplace={})".format(
          self.__class__.__name__,
          self.num_classes,
          self.p,
          self.alpha,
          self.inplace,
      )
  
imports: import argparse
  import math
  import random
  from typing import Dict, Tuple
  import torch
  from torch import Tensor
  from torch.nn import functional
  from torchvision.transforms import functional
  from data.transforms import TRANSFORMATIONS_REGISTRY, BaseTransformation
  from utils import logger
label_desc: Use image node_id 5 for calling the RandomMixup obj's underlying member method code with example usage: obj.__repr__() and returns: str