code: def _tested_estimators():
      for name, Estimator in all_estimators():
          try:
              estimator = _construct_instance(Estimator)
              set_random_state(estimator)
          except SkipTest:
              continue
  
          if isinstance(estimator, NearMiss):
              # For NearMiss, let's check the three algorithms
              for version in (1, 2, 3):
                  yield clone(estimator).set_params(version=version)
          else:
              yield estimator
  
imports: import warnings
  from collections import OrderedDict
  import numpy
  import pytest
  from sklearn.base import clone
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.utils._testing import SkipTest, ignore_warnings, set_random_state
  from sklearn.utils.estimator_checks import _construct_instance, _get_check_estimator_ids
  from sklearn.utils.estimator_checks import parametrize_with_checks
  from imblearn.over_sampling import RandomOverSampler
  from imblearn.under_sampling import NearMiss, RandomUnderSampler
  from imblearn.utils.estimator_checks import _set_checking_parameters, check_dataframe_column_names_consistency, check_param_validation, parametrize_with_checks
  from imblearn.utils.testing import all_estimators
label_desc: Use image node_id 2 for calling a global function with example usage: _tested_estimators() without return types