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","."]
code: def fprop(self, x, y, **kwargs):
      mix = tf_distributions.Beta(self.beta, self.beta)
      mix = mix.sample([tf.shape(x)[0]] + [1] * (len(x.shape) - 1))
      xm = x + mix * (x[::-1] - x)
      ym = y + mix * (y[::-1] - y)
      logits = self.model.get_logits(xm, **kwargs)
      loss = softmax_cross_entropy_with_logits(labels=ym, logits=logits)
      warnings.warn(
          "LossMixUp is deprecated, switch to "
          "MixUp. LossFeaturePairing may be removed "
          "on or after 2019-03-06."
      )
      return loss
  
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 2 for calling the LossMixUp obj's underlying member method code with example usage: obj.fprop(x, y) and returns: loss