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 _get_mixup_params(self):
      lam = 1.0
      use_cutmix = False
      if np.random.rand() < self.mix_prob:
          if self.mixup_alpha > 0.0 and self.cutmix_alpha > 0.0:
              use_cutmix = np.random.rand() < self.switch_prob
              lam_mix = (
                  np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
                  if use_cutmix
                  else np.random.beta(
                      self.mixup_alpha, self.mixup_alpha
                  )
              )
          elif self.mixup_alpha > 0.0:
              lam_mix = np.random.beta(
                  self.mixup_alpha, self.mixup_alpha
              )
          elif self.cutmix_alpha > 0.0:
              use_cutmix = True
              lam_mix = np.random.beta(
                  self.cutmix_alpha, self.cutmix_alpha
              )
          lam = float(lam_mix)
      return lam, use_cutmix
  
file_path: SlowFast/slowfast/datasets/mixup.py
imports: import numpy
  import torch
label_desc: Use image node_id 2 for calling the MixUp obj's underlying member method code with example usage: obj._get_mixup_params() and returns: lam,  use_cutmix