code: def _loss(self, x: np.ndarray) -> float:
      output_adv = self._get_classifier_output(x)
      value = float(self.criterion(output_adv, self.label).item())
      return value * (1.0 if self.targeted else -1.0)
  
imports: import os
  import itertools
  from pathlib import Path
  import cv2
  import numpy
  import PIL.Image
  import torch.nn
  import torch
  import torchvision
  from torchvision.models import resnet50
  import torchvision.transforms
  import nevergrad
  import nevergrad.common.typing
  from nevergrad.common import errors
  from ..None import base
  from .None import imagelosses
label_desc: Use image node_id 2 for calling the ImageAdversarial obj's underlying member method code with example usage: obj._loss(x) and returns: unknown