code: def measure_misclassification(
      classifier: "CLASSIFIER_NEURALNETWORK_TYPE",
      x_test: np.ndarray,
      y_test: np.ndarray,
  ) -> float:
      """
      Computes 1-accuracy given x_test and y_test
  
      :param classifier: Classifier to be used for predictions.
      :param x_test: Test set.
      :param y_test: Labels for test set.
      :return: 1-accuracy.
      """
      predictions = np.argmax(classifier.predict(x_test), axis=1)
      return (
          1.0
          - np.sum(predictions == np.argmax(y_test, axis=1))
          / y_test.shape[0]
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import pickle
  import time
  from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  from sklearn.cluster import KMeans, MiniBatchKMeans
  from art import config
  from art.data_generators import DataGenerator
  from art.defences.detector.poison.clustering_analyzer import ClusteringAnalyzer
  from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
  from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
  from art.utils import segment_by_class
  from art.visualization import create_sprite, save_image, plot_3d
label_desc: Use image node_id 1 for calling a global function with example usage: measure_misclassification(classifier, x_test, y_test) and returns: unknown