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 __call__(self, x, target):
      if self.mix_prob > 0.0:
          assert (
              len(x) > 1
          ), "Batch size should be greater than 1 for mixup."
          lam = self._mix_batch(x)
      elif self.mix_prob == 0.0:
          lam = 1.0
      else:
          raise NotImplementedError
      target = mixup_target(
          target, self.num_classes, lam, self.label_smoothing
      )
      return x, target
  
file_path: SlowFast/slowfast/datasets/mixup.py
imports: import numpy
  import torch
label_desc: Use image node_id 4 for calling the MixUp obj's underlying member method code with example usage: obj.__call__(x, target) and returns: x,  target