class_docstr: Mixup class from timm.
  https://github.com/rwightman/pytorch-image-models/blob/d30685c283137b4b91ea43c4e595c964cd2cb6f0/timm/data/mixup.py
  The parameters here are correspond to the mixup config in data.
  The mixup in timm only produce image mixup and cutmix with one-hot class target.
  This module helps to take the lambda from the Mixup.
  Lambda is added to the function to produce the mixup with specific lambda.
code: def __call__(self, x, target, lam=None):
      if self.mode == "elem":
          lam = self._mix_elem(x, lam)
      elif self.mode == "pair":
          lam = self._mix_pair(x, lam)
      else:
          lam = self._mix_batch(x, lam)
      target = mixup_target(
          target, self.num_classes, lam, self.label_smoothing, x.device
      )
      return x, target, lam
  
file_path: autogluon/multimodal/src/autogluon/multimodal/data/mixup.py
imports: import numpy
  import torch
  from timm.data.mixup import Mixup, cutmix_bbox_and_lam, mixup_target
label_desc: Use image node_id 5 for calling the MixupModule obj's underlying member method code with example usage: obj.__call__(x, target, lam) and returns: x,  target,  lam