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 layer names to exclude from manifold mixup.
  
  Args:
      layer_filter (list or str): Layer filter, eg ['conv1', 'fc1']
  
  Returns: self
docstr_tok: ["Specify","layer","names","to","exclude","from","manifold","mixup",".","Args",":","layer_filter","(","list","or","str",")",":","Layer","filter",",","eg","[","'conv1","'",",","'fc1","'","]","Returns",":","self"]
code: def with_layer_filter(self, layer_filter=()):
      """Specify layer names to exclude from manifold mixup.
  
      Args:
          layer_filter (list or str): Layer filter, eg ['conv1', 'fc1']
  
      Returns: self
      """
      layer_filter = self._single_to_list(layer_filter)
      self._layer_filter = layer_filter
      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 5 for calling the ManifoldMixup obj's underlying member method code with example usage: obj.with_layer_filter(layer_filter) and returns: self