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)
docstr: Specify the layer types to exclude from manifold mixup
  
  Args:
      layer_types (list or str): Layer types, eg [nn.RelU]:
  
  Returns: self
docstr_tok: ["Specify","the","layer","types","to","exclude","from","manifold","mixup","Args",":","layer_types","(","list","or","str",")",":","Layer","types",",","eg","[","nn.RelU","]",":","Returns",":","self"]
code: def with_layer_type_filter(self, layer_types=()):
      """Specify the layer types to exclude from manifold mixup
  
      Args:
          layer_types (list or str): Layer types, eg [nn.RelU]:
  
      Returns: self
      """
      layer_types = self._single_to_list(layer_types)
      self._layer_types = layer_types
      return self
  
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 6 for calling the ManifoldMixup obj's underlying member method code with example usage: obj.with_layer_type_filter(layer_types) and returns: self