class_docstr: Mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)
class_docstr_tok: ["Mixup",":","Beyond","Empirical","Risk","Minimization","(","https",":","\/\/arxiv.org\/abs\/1710.09412",")"]
docstr: This implements MixUp for videos.
  
  Args:
      alpha (float): Mixup alpha value.
      label_smoothing (float): Label smoothing value.
      num_classes (int): Number of total classes.
docstr_tok: ["This","implements","MixUp","for","videos",".","Args",":","alpha","(","float",")",":","Mixup","alpha","value",".","label_smoothing","(","float",")",":","Label","smoothing","value",".","num_classes","(","int",")",":","Number","of","total","classes","."]
code: def __init__(
      self,
      alpha: float = 1.0,
      label_smoothing: float = 0.0,
      num_classes: int = 400,
      one_hot: bool = False,
  ) -> None:
      """
      This implements MixUp for videos.
  
      Args:
          alpha (float): Mixup alpha value.
          label_smoothing (float): Label smoothing value.
          num_classes (int): Number of total classes.
      """
      super().__init__()
      self.mixup_beta_sampler = torch.distributions.beta.Beta(
          alpha, alpha
      )
      self.label_smoothing = label_smoothing
      self.num_classes = num_classes
      self.one_hot = one_hot
  
file_path: pytorchvideo/pytorchvideo/transforms/mix.py
imports: from typing import Any, Dict, Tuple
  import torch
  from pytorchvideo.transforms.functional import convert_to_one_hot
label_desc: Use image node_id 1 to create a new MixUp object from inherited base classes: torch.nn with example: obj = MixUp(alpha, label_smoothing, num_classes, one_hot)