code: def process_train(self, *args):
      super(MixupAcc, self).process_train(*args)
      state = args[0]
  
      target1, target2 = state[torchbearer.Y_TRUE]
      _state = args[0].copy()
      _state[torchbearer.Y_TRUE] = target1
      acc1 = self.cat_acc.process(_state)
  
      _state = args[0].copy()
      _state[torchbearer.Y_TRUE] = target2
      acc2 = self.cat_acc.process(_state)
  
      return acc1 * state[torchbearer.MIXUP_LAMBDA] + acc2 * (
          1 - state[torchbearer.MIXUP_LAMBDA]
      )
  
file_path: torchbearer/torchbearer/callbacks/mixup.py
imports: import torch
  import torch.nn.functional
  from torch.distributions.beta import Beta
  import torchbearer
  from torchbearer import cite
  from torchbearer.callbacks import Callback
  from torchbearer.metrics import CategoricalAccuracy, AdvancedMetric, running_mean, mean, super
  from torchbearer.metrics import default
label_desc: Use image node_id 2 for calling the MixupAcc obj's underlying member method code with example usage: obj.process_train() and returns: unknown