class_docstr: Considers those perturbed inputs adversarial whose predicted class
  matches the target class.
  
  Args:
      target_classes: Tensor with target classes ``(batch,)``.
code: def __call__(self, perturbed: T, outputs: T) -> T:
      outputs_, restore_type = ep.astensor_(outputs)
      del perturbed, outputs
  
      classes = outputs_.argmax(axis=-1)
      assert classes.shape == self.target_classes.shape
      is_adv = classes == self.target_classes
      return restore_type(is_adv)
  
imports: from typing import TypeVar, Any
  from abc import ABC, abstractmethod
  import eagerpy
label_desc: Use image node_id 3 for calling the TargetedMisclassification obj's underlying member method code with example usage: obj.__call__(perturbed, outputs) and returns: restore_type