class_docstr: This module implements the Functionally Equivalent Extraction attack for neural networks with two dense layers,
  ReLU activation at the first layer and logits output after the second layer.
  
  | Paper link: https://arxiv.org/abs/1909.01838
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docstr: Predict the target model.
  
  :param x: Samples of input data of shape `(num_samples, num_features)`.
  :param e_j: Additive delta vector of shape `(1, num_features)`.
  :return: Prediction of the target model of shape `(num_samples, num_classes)`.
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code: def _o_l(
      self, x: np.ndarray, e_j: Optional[np.ndarray] = None
  ) -> np.ndarray:
      """
      Predict the target model.
  
      :param x: Samples of input data of shape `(num_samples, num_features)`.
      :param e_j: Additive delta vector of shape `(1, num_features)`.
      :return: Prediction of the target model of shape `(num_samples, num_classes)`.
      """
      if e_j is not None:
          x = x + e_j
      return self.estimator.predict(x).astype(NUMPY_DTYPE)
  
file_path: adversarial-robustness-toolbox/art/attacks/extraction/functionally_equivalent_extraction.py
imports: import logging
  import os
  from typing import List, Optional, TYPE_CHECKING
  import numpy
  from scipy.optimize import least_squares
  from art.attacks.attack import ExtractionAttack
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.estimators.classification.keras import KerasClassifier
  from art.estimators.classification.blackbox import BlackBoxClassifier
label_desc: Use image node_id 3 for calling the FunctionallyEquivalentExtraction obj's underlying member method code with example usage: obj._o_l(x, e_j) and returns: self