class_docstr: Considers those perturbed inputs adversarial whose predicted class
  differs from the label.
  
  Args:
      labels: Tensor with labels of the unperturbed inputs ``(batch,)``.
code: def __init__(self, labels: Any):
      super().__init__()
      self.labels: ep.Tensor = ep.astensor(labels)
  
imports: from typing import TypeVar, Any
  from abc import ABC, abstractmethod
  import eagerpy
label_desc: Use image node_id 1 to create a new Misclassification object from inherited base classes: Criterion with example: obj = Misclassification(labels)