class_docstr: Apply mixup and/or cutmix for videos at batch level.
  mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
  CutMix: Regularization Strategy to Train Strong Classifiers with Localizable
      Features (https://arxiv.org/abs/1905.04899)
class_docstr_tok: ["Apply","mixup","and\/or","cutmix","for","videos","at","batch","level",".","mixup",":","Beyond","Empirical","Risk","Minimization","(","https",":","\/\/arxiv.org\/abs\/1710.09412",")","CutMix",":","Regularization","Strategy","to","Train","Strong","Classifiers","with","Localizable","Features","(","https",":","\/\/arxiv.org\/abs\/1905.04899",")"]
code: def _mix_batch(self, x):
      lam, use_cutmix = self._get_mixup_params()
      if lam == 1.0:
          return 1.0
      if use_cutmix:
          (yl, yh, xl, xh), lam = get_cutmix_bbox(
              x.shape,
              lam,
              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: SlowFast/slowfast/datasets/mixup.py
imports: import numpy
  import torch
label_desc: Use image node_id 3 for calling the MixUp obj's underlying member method code with example usage: obj._mix_batch(x) and returns: lam, int