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: Get input sample as function of multiplicative factor of random vector.
  
  :param var_t: Multiplicative factor of second random vector for critical point search.
  :return: Input sample of shape `(1, num_features)`.
docstr_tok: ["Get","input","sample","as","function","of","multiplicative","factor","of","random","vector",".",":","param","var_t",":","Multiplicative","factor","of","second","random","vector","for","critical","point","search",".",":","return",":","Input","sample","of","shape","`","(","1",",","num_features",")","`","."]
code: def _get_x(self, var_t: float) -> np.ndarray:
      """
      Get input sample as function of multiplicative factor of random vector.
  
      :param var_t: Multiplicative factor of second random vector for critical point search.
      :return: Input sample of shape `(1, num_features)`.
      """
      return self.vector_u + var_t * self.vector_v
  
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 4 for calling the FunctionallyEquivalentExtraction obj's underlying member method code with example usage: obj._get_x(var_t) and returns: unknown