code: def test_predict_on_toy_problem(global_random_seed):
      """Manually check predicted class labels for toy dataset."""
      clf1 = LogisticRegression(random_state=global_random_seed)
      clf2 = RandomForestClassifier(
          n_estimators=10, random_state=global_random_seed
      )
      clf3 = GaussianNB()
  
      X = np.array(
          [
              [-1.1, -1.5],
              [-1.2, -1.4],
              [-3.4, -2.2],
              [1.1, 1.2],
              [2.1, 1.4],
              [3.1, 2.3],
          ]
      )
  
      y = np.array([1, 1, 1, 2, 2, 2])
  
      assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
      assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
      assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
  
      eclf = VotingClassifier(
          estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
          voting="hard",
          weights=[1, 1, 1],
      )
      assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
  
      eclf = VotingClassifier(
          estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)],
          voting="soft",
          weights=[1, 1, 1],
      )
      assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])
  
imports: import re
  import numpy
  import pytest
  from sklearn import datasets
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.datasets import make_multilabel_classification
  from sklearn.dummy import DummyRegressor
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier, VotingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.model_selection import GridSearchCV, cross_val_score, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
label_desc: Use image node_id 8 for calling a global function with example usage: test_predict_on_toy_problem(global_random_seed) without return types