class_docstr: This class implements the evaluation of Security Curves.
  
  Examples of Security Curves can be found in Figure 6 of Madry et al., 2017 (https://arxiv.org/abs/1706.06083).
class_docstr_tok: ["This","class","implements","the","evaluation","of","Security","Curves",".","Examples","of","Security","Curves","can","be","found","in","Figure","6","of","Madry","et","al.",",","2017","(","https",":","\/\/arxiv.org\/abs\/1706.06083",")","."]
docstr: Calculate accuracy of predicted labels.
  
  :param y: True labels.
  :param y_pred: Predicted labels.
  :return: Accuracy.
docstr_tok: ["Calculate","accuracy","of","predicted","labels",".",":","param","y",":","True","labels",".",":","param","y_pred",":","Predicted","labels",".",":","return",":","Accuracy","."]
code: def _get_accuracy(y: np.ndarray, y_pred: np.ndarray) -> float:
      """
      Calculate accuracy of predicted labels.
  
      :param y: True labels.
      :param y_pred: Predicted labels.
      :return: Accuracy.
      """
      return np.mean(
          np.argmax(y, axis=1) == np.argmax(y_pred, axis=1)
      ).item()
  
file_path: adversarial-robustness-toolbox/art/evaluations/security_curve/security_curve.py
imports: from typing import List, Optional, Tuple, TYPE_CHECKING, Union
  import numpy
  from art.evaluations.evaluation import Evaluation
  from art.attacks.evasion.projected_gradient_descent.projected_gradient_descent import ProjectedGradientDescent
label_desc: Use image node_id 6 for calling the SecurityCurve obj's underlying member method code with example usage: obj._get_accuracy(y, y_pred) and returns: np