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 _wrap_layer_check(self, module, name, nname):
      # Check for exclusions
      name_check = (
          self._mixup_layers is None or nname in self._mixup_layers
      )
  
      filters = [
          any([f == nname for f in self._layer_filter]),
          any([isinstance(module, t) for t in self._layer_types]),
      ]
      return name_check and not any(filters)
  
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 11 for calling the ManifoldMixup obj's underlying member method code with example usage: obj._wrap_layer_check(module, name, nname) and returns: unknown