code: def check_class_proba(
      proba: np.ndarray, known_labels: Sequence, all_labels: Sequence
  ) -> np.ndarray:
      """
      Checks the class probabilities and reshapes it if not all labels are present in the classifier.
  
      Args:
          proba: The class probabilities of a classifier.
          known_labels: The class labels known by the classifier.
          all_labels: All class labels.
  
      Returns:
          Class probabilities augmented such that the probability of all classes is present. If the classifier is unaware
          of a particular class, all probabilities are zero.
      """
      # TODO: rewrite this function using numpy.insert
  
      label_idx_map = -np.ones(len(all_labels), dtype="int")
  
      for known_label_idx, known_label in enumerate(known_labels):
          # finds the position of label in all_labels
          for label_idx, label in enumerate(all_labels):
              if np.array_equal(label, known_label):
                  label_idx_map[label_idx] = known_label_idx
                  break
  
      aug_proba = np.hstack(
          (proba, np.zeros(shape=(proba.shape[0], 1)))
      )
      return aug_proba[:, label_idx_map]
  
imports: from typing import Sequence
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.exceptions import NotFittedError
label_desc: Use image node_id 2 for calling a global function with example usage: check_class_proba(proba, known_labels, all_labels) and returns: aug_proba