class_docstr: Deprecated version of `MixUp` that returns per-example loss
  rather than mean loss.
class_docstr_tok: ["Deprecated","version","of","`","MixUp","`","that","returns","per-example","loss","rather","than","mean","loss","."]
docstr: Constructor.
  :param model: Model instance, the model on which to apply the loss.
  :param beta: float, beta distribution parameter for MixUp.
docstr_tok: ["Constructor",".",":","param","model",":","Model","instance",",","the","model","on","which","to","apply","the","loss",".",":","param","beta",":","float",",","beta","distribution","parameter","for","MixUp","."]
code: def __init__(self, model, beta, **kwargs):
      """Constructor.
      :param model: Model instance, the model on which to apply the loss.
      :param beta: float, beta distribution parameter for MixUp.
      """
      del kwargs
      Loss.__init__(self, model, locals())
      self.beta = beta
  
imports: import copy
  import json
  import os
  import warnings
  import numpy
  import tensorflow
  from cleverhans.attacks import Attack
  from cleverhans.compat import softmax_cross_entropy_with_logits
  from cleverhans.model import Model
  from cleverhans.utils import safe_zip
label_desc: Use image node_id 1 to create a new LossMixUp object from inherited base classes: Loss with example: obj = LossMixUp(model, beta)