code: def test_estimator_checks_classification(test_fn):
      if test_fn.__name__ == "check_classifiers_classes":
          prior = {
              "one": 0.1,
              "two": 0.1,
              "three": 0.1,
              -1: 0.1,
              1: 0.6,
          }  # nonsensical prior to make sklearn check pass
      else:
          prior = {0: 0.7, 1: 0.2, 2: 0.1}
  
      # Some of the sklearn checkers generate random y data with 3 classes, so prior needs to have these classes
      estimator = SubjectiveClassifier(LogisticRegression(), prior)
      test_fn(SubjectiveClassifier.__name__, estimator)
  
file_path: scikit-lego/tests/test_meta/test_subjective_classifier.py
imports: import numpy
  import pytest
  from sklearn.cluster import DBSCAN
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LogisticRegression, Ridge
  from sklego.common import flatten
  from sklego.meta import SubjectiveClassifier
  from tests.conftest import classifier_checks, general_checks, select_tests
label_desc: Use image node_id 1 for calling a global function with example usage: test_estimator_checks_classification(test_fn) without return types