code: def get_mixup_cutmix(
      *, mixup_alpha, cutmix_alpha, num_categories, use_v2
  ):
      transforms_module = get_module(use_v2)
  
      mixup_cutmix = []
      if mixup_alpha > 0:
          mixup_cutmix.append(
              transforms_module.MixUp(
                  alpha=mixup_alpha, num_categories=num_categories
              )
              if use_v2
              else RandomMixUp(
                  num_classes=num_categories, p=1.0, alpha=mixup_alpha
              )
          )
      if cutmix_alpha > 0:
          mixup_cutmix.append(
              transforms_module.CutMix(
                  alpha=mixup_alpha, num_categories=num_categories
              )
              if use_v2
              else RandomCutMix(
                  num_classes=num_categories, p=1.0, alpha=mixup_alpha
              )
          )
      if not mixup_cutmix:
          return None
  
      return transforms_module.RandomChoice(mixup_cutmix)
  
file_path: vision/references/classification/transforms.py
imports: import math
  from typing import Tuple
  import torch
  from presets import get_module
  from torch import Tensor
  from torchvision.transforms import functional
label_desc: Use image node_id 1 for calling a global function with example usage: get_mixup_cutmix() and returns: transforms_module, None