code: def test_k_best_selects_best():
      # Tests that the labels added by st really are the 10 best labels.
      svc = SVC(gamma="scale", probability=True, random_state=0)
      st = SelfTrainingClassifier(
          svc, criterion="k_best", max_iter=1, k_best=10
      )
      has_label = y_train_missing_labels != -1
      st.fit(X_train, y_train_missing_labels)
  
      got_label = ~has_label & (st.transduction_ != -1)
  
      svc.fit(X_train[has_label], y_train_missing_labels[has_label])
      pred = svc.predict_proba(X_train[~has_label])
      max_proba = np.max(pred, axis=1)
  
      most_confident_svc = X_train[~has_label][
          np.argsort(max_proba)[-10:]
      ]
      added_by_st = X_train[np.where(got_label)].tolist()
  
      for row in most_confident_svc.tolist():
          assert row in added_by_st
  
imports: from math import ceil
  import numpy
  import pytest
  from numpy.testing import assert_array_equal
  from sklearn.datasets import load_iris, make_blobs
  from sklearn.ensemble import StackingClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.semi_supervised import SelfTrainingClassifier
  from sklearn.svm import SVC
label_desc: Use image node_id 14 for calling a global function with example usage: test_k_best_selects_best() without return types