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 _mix_batch(self, x, lam):
      if lam is None:
          lam, use_cutmix = self._params_per_batch()
      else:
          _, use_cutmix = self._params_per_batch()
      if lam == 1.0:
          return 1.0
      if use_cutmix:
          (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
              x.shape,
              lam,
              ratio_minmax=self.cutmix_minmax,
              correct_lam=self.correct_lam,
          )
          x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
      else:
          x_flipped = x.flip(0).mul_(1.0 - lam)
          x.mul_(lam).add_(x_flipped)
      return 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 4 for calling the MixupModule obj's underlying member method code with example usage: obj._mix_batch(x, lam) and returns: lam, int