class_docstr: Implementation of DeepZ to certify neural network robustness. We use the zonotope representation of a datapoint as
  it travels through the network to then verify if it can have its class changed given a certain perturbation.
  
  | Paper link: https://papers.nips.cc/paper/2018/file/f2f446980d8e971ef3da97af089481c3-Paper.pdf
class_docstr_tok: ["Implementation","of","DeepZ","to","certify","neural","network","robustness",".","We","use","the","zonotope","representation","of","a","datapoint","as","it","travels","through","the","network","to","then","verify","if","it","can","have","its","class","changed","given","a","certain","perturbation",".","|","Paper","link",":","https",":","\/\/papers.nips.cc\/paper\/2018\/file\/f2f446980d8e971ef3da97af089481c3-Paper.pdf"]
docstr: Access function to get the classifier loss
  
  :param output: model predictions
  :param target: ground truth labels
  
  :return: loss value
docstr_tok: ["Access","function","to","get","the","classifier","loss",":","param","output",":","model","predictions",":","param","target",":","ground","truth","labels",":","return",":","loss","value"]
code: def concrete_loss(
      self, output: "torch.Tensor", target: "torch.Tensor"
  ) -> "torch.Tensor":
      """
      Access function to get the classifier loss
  
      :param output: model predictions
      :param target: ground truth labels
  
      :return: loss value
      """
      return self._loss(output, target)
  
imports: from typing import List, Optional, Tuple, Union, Callable, Any, TYPE_CHECKING
  import logging
  import math
  import warnings
  import sys
  import numpy
  import torch
  from art.estimators.certification.deep_z.deep_z import ZonoConv, ZonoDenseLayer, ZonoReLU, ZonoBounds
  from art.estimators.classification.pytorch import PyTorchClassifier
label_desc: Use image node_id 4 for calling the PytorchDeepZ obj's underlying member method code with example usage: obj.concrete_loss(output, target) and returns: self