class_docstr: Masked Cross Entropy
  
  This criterion can be used for instance in Class Incremental
  Learning Problems when no examplars are used
  (i.e LwF in Class Incremental Learning would need to use mask="new").
code: def __call__(self, logits, targets):
      oh_targets = F.one_hot(targets, num_classes=logits.shape[1])
  
      oh_targets = oh_targets[:, self.current_mask(logits.shape[1])]
      logits = logits[:, self.current_mask(logits.shape[1])]
  
      return cross_entropy_with_oh_targets(
          logits,
          oh_targets.float(),
          reduction=self.reduction,
      )
  
imports: import copy
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn import BCELoss
  from avalanche.training.plugins import SupervisedPlugin
  from avalanche.training.regularization import cross_entropy_with_oh_targets
label_desc: Use image node_id 2 for calling the MaskedCrossEntropy obj's underlying member method code with example usage: obj.__call__(logits, targets) and returns: cross_entropy_with_oh_targets