code: def _get_misclassifications_single(
      self, original_preds, generated_preds, attacks_applied
  ):
      results = zip(original_preds, generated_preds, attacks_applied)
      misclassifications_single = dict(
          [(att, 0) for att in flatten_unique(attacks_applied)]
      )
      always_wrong_single = copy(misclassifications_single)
      never_wrong_single = copy(misclassifications_single)
  
      for i, res in enumerate(results):
          original, generated, applied = res[0], res[1], res[2]
          if original == 1 and generated == 0:
              for attack in applied:
                  misclassifications_single[attack] += 1
          elif original == 1 and generated == 1:
              for attack in applied:
                  always_wrong_single[attack] += 1
          elif original == 0 and generated == 0:
              for attack in applied:
                  never_wrong_single[attack] += 1
  
      index_fancy = fancy_titles(misclassifications_single.keys())
      return pd.DataFrame(
          list(
              zip(
                  misclassifications_single.values(),
                  always_wrong_single.values(),
                  never_wrong_single.values(),
              )
          ),
          index=index_fancy,
          columns=[
              "Caused Misclassifications",
              "Always Misclassified",
              "Never Misclassified",
          ],
      ).rename_axis("Attack")
  
file_path: artificial-adversary/Adversary/adversary.py
imports: from copy import copy
  from functools import partial
  from random import random, seed
  import pandas
  from textblob import TextBlob
  from Adversary.attacks import *
  from Adversary.utils import *
label_desc: Use image node_id 7 for calling the Adversary obj's underlying member method code with example usage: obj._get_misclassifications_single(original_preds, generated_preds, attacks_applied) and returns: pd