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 names on which to apply manifold mixup.
  
  Args:
      layers (list or str): Layer names, eg ['conv1', 'fc1']
  
  Returns: self
docstr_tok: ["Specify","the","layer","names","on","which","to","apply","manifold","mixup",".","Args",":","layers","(","list","or","str",")",":","Layer","names",",","eg","[","'conv1","'",",","'fc1","'","]","Returns",":","self"]
code: def for_layers(self, layers):
      """Specify the layer names on which to apply manifold mixup.
  
      Args:
          layers (list or str): Layer names, eg ['conv1', 'fc1']
  
      Returns: self
      """
      layers = self._single_to_list(layers)
      self._mixup_layers = layers
      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 4 for calling the ManifoldMixup obj's underlying member method code with example usage: obj.for_layers(layers) and returns: self