class_docstr: Performs manifold mixup on desired layers. Requires use of :meth:`MixupInputs.loss`, otherwise lambdas can be found in
  state under :attr:`.MIXUP_LAMBDA`. Model targets will be a tuple containing the original target and permuted target.
  
  Args:
      lam: Mixup lambda. If RANDOM, choose lambda from Beta(alpha, alpha). Else, lambda=lam
      alpha: Mixup alpha. Alpha used to sample lambda from Beta(alpha, alpha)
code: def _sample_lam(self):
      if self.lam is self.RANDOM:
          if self.alpha > 0:
              lam = self.distrib.sample()
          else:
              lam = 1.0
      else:
          lam = self.lam
      return lam
  
imports: from torchbearer import Callback
  import torchbearer
  from torch.distributions.beta import Beta
  import random
  import torch
  import types
  from torchbearer import cite
label_desc: Use image node_id 2 for calling the ManifoldMixup obj's underlying member method code with example usage: obj._sample_lam() and returns: lam