class_docstr: Implementation of Adversarial Embedding attack by Tan, Shokri (2019).
  "Bypassing Backdoor Detection Algorithms in Deep Learning"
  
  This attack trains a classifier with an additional discriminator and loss function that aims
  to create non-differentiable latent representations between backdoored and benign examples.
  
  | Paper link: https://arxiv.org/abs/1905.13409
class_docstr_tok: ["Implementation","of","Adversarial","Embedding","attack","by","Tan",",","Shokri","(","2019",")",".","``","Bypassing","Backdoor","Detection","Algorithms","in","Deep","Learning","''","This","attack","trains","a","classifier","with","an","additional","discriminator","and","loss","function","that","aims","to","create","non-differentiable","latent","representations","between","backdoored","and","benign","examples",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1905.13409"]
code: def _check_valid_label_shape(self, label: np.ndarray) -> None:
      if label.shape != self.estimator.model.output_shape[1:]:
          raise ValueError(
              f"Invalid shape for target array. Should be {self.estimator.model.output_shape[1:]} "
              f"received {label.shape}"
          )
  
file_path: adversarial-robustness-toolbox/art/attacks/poisoning/adversarial_embedding_attack.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Tuple, Union, List, Optional, TYPE_CHECKING
  import numpy
  from art.attacks.attack import PoisoningAttackTransformer
  from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
  from art.estimators.classification.keras import KerasClassifier
label_desc: Use image node_id 6 for calling the PoisoningAttackAdversarialEmbedding obj's underlying member method code with example usage: obj._check_valid_label_shape(label) without return types