class_docstr: Implements the Sign-OPT attack `SignOPTAttack`. This is a query-efficient
  hard-label adversarial attack.
  
  Paper link: https://arxiv.org/pdf/1909.10773.pdf
docstr: Helper method to predict label for x_0
  
  :param x_0: An array with the original input
  :return: Predicted label
docstr_tok: ["Helper","method","to","predict","label","for","x_0",":","param","x_0",":","An","array","with","the","original","input",":","return",":","Predicted","label"]
code: def _predict_label(self, x_0: np.ndarray) -> np.signedinteger:
      """
      Helper method to predict label for x_0
  
      :param x_0: An array with the original input
      :return: Predicted label
      """
      if self.enable_clipped:
          x_0 = np.clip(x_0, self.clip_min, self.clip_max)
      pred = self.estimator.predict(
          np.expand_dims(x_0, axis=0), batch_size=self.batch_size
      )
      return np.argmax(pred)
  
imports: import logging
  from typing import Optional, TYPE_CHECKING, Tuple
  import time
  import numpy
  from tqdm.auto import tqdm
  from art.attacks.attack import EvasionAttack
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import compute_success, check_and_transform_label_format, get_labels_np_array
label_desc: Use image node_id 6 for calling the SignOPTAttack obj's underlying member method code with example usage: obj._predict_label(x_0) and returns: np