class_docstr: Implementation of a database reconstruction attack. In this case, the adversary is assumed to have in his/her
  possession a model trained on a dataset, and all but one row of that training dataset. This attack attempts to
  reconstruct the missing row.
class_docstr_tok: ["Implementation","of","a","database","reconstruction","attack",".","In","this","case",",","the","adversary","is","assumed","to","have","in","his\/her","possession","a","model","trained","on","a","dataset",",","and","all","but","one","row","of","that","training","dataset",".","This","attack","attempts","to","reconstruct","the","missing","row","."]
docstr: Create a DatabaseReconstruction instance.
  
  :param estimator: Trained target estimator.
docstr_tok: ["Create","a","DatabaseReconstruction","instance",".",":","param","estimator",":","Trained","target","estimator","."]
code: def __init__(self, estimator):
      """
      Create a DatabaseReconstruction instance.
  
      :param estimator: Trained target estimator.
      """
      super().__init__(estimator=estimator)
  
      self.params = self.estimator.get_trainable_attribute_names()
  
file_path: adversarial-robustness-toolbox/art/attacks/inference/reconstruction/white_box.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple
  import numpy
  import sklearn
  from scipy.optimize import fmin_l_bfgs_b
  from art.attacks.attack import ReconstructionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.estimators.classification.scikitlearn import ScikitlearnEstimator
label_desc: Use image node_id 1 to create a new DatabaseReconstruction object from inherited base classes: ReconstructionAttack with example: obj = DatabaseReconstruction(estimator)