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.
docstr: Parameters
  ----------
  mixup_alpha
      The mixup alpha value, it is active if > 0.
  cutmix_alpha
      The cutmix alpha value, cutmix is active if > 0.
  cutmix_minmax
      cutmix min/max image ratio. The para should be a list/tuple of float with size 2.
  prob
      The probability of conducting mixup/cutmix if enable.
  switch_prob
      The probability of switching mixup to cutmix if both enable.
  mode
      Perform mixup/cutmix on "batch" or "pair" or "elem".
  correct_lam
      Apply lambda correction when cutmix bbox clipped by image borders.
  label_smoothing
      Apply label smoothing to the mixed target.
  num_classes
      Number of classes for target.
code: def __init__(
      self,
      mixup_alpha=1.0,
      cutmix_alpha=0.0,
      cutmix_minmax=None,
      prob=1.0,
      switch_prob=0.5,
      mode="batch",
      correct_lam=True,
      label_smoothing=0.1,
      num_classes=1000,
  ):
      """
      Parameters
      ----------
      mixup_alpha
          The mixup alpha value, it is active if > 0.
      cutmix_alpha
          The cutmix alpha value, cutmix is active if > 0.
      cutmix_minmax
          cutmix min/max image ratio. The para should be a list/tuple of float with size 2.
      prob
          The probability of conducting mixup/cutmix if enable.
      switch_prob
          The probability of switching mixup to cutmix if both enable.
      mode
          Perform mixup/cutmix on "batch" or "pair" or "elem".
      correct_lam
          Apply lambda correction when cutmix bbox clipped by image borders.
      label_smoothing
          Apply label smoothing to the mixed target.
      num_classes
          Number of classes for target.
      """
      super().__init__(
          mixup_alpha,
          cutmix_alpha,
          cutmix_minmax,
          prob,
          switch_prob,
          mode,
          correct_lam,
          label_smoothing,
          num_classes,
      )
      self.lam = None
      self.target_a = None
      self.target_b = None
  
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 1 to create a new MixupModule object from inherited base classes: Mixup with example: obj = MixupModule(mixup_alpha, cutmix_alpha, cutmix_minmax, prob, switch_prob, mode, correct_lam, label_smoothing, num_classes)