code: def _generate_meta_estimator_instances_with_pipeline():
      """Generate instances of meta-estimators fed with a pipeline
  
      Are considered meta-estimators all estimators accepting one of "estimator",
      "base_estimator" or "estimators".
      """
      for _, Estimator in sorted(all_estimators()):
          sig = set(signature(Estimator).parameters)
  
          if (
              "estimator" in sig
              or "base_estimator" in sig
              or "regressor" in sig
          ):
              if is_regressor(Estimator):
                  estimator = make_pipeline(TfidfVectorizer(), Ridge())
                  param_grid = {"ridge__alpha": [0.1, 1.0]}
              else:
                  estimator = make_pipeline(
                      TfidfVectorizer(), LogisticRegression()
                  )
                  param_grid = {"logisticregression__C": [0.1, 1.0]}
  
              if "param_grid" in sig or "param_distributions" in sig:
                  # SearchCV estimators
                  extra_params = (
                      {"n_iter": 2} if "n_iter" in sig else {}
                  )
                  yield Estimator(estimator, param_grid, **extra_params)
              else:
                  yield Estimator(estimator)
  
          elif "transformer_list" in sig:
              # FeatureUnion
              transformer_list = [
                  (
                      "trans1",
                      make_pipeline(TfidfVectorizer(), MaxAbsScaler()),
                  ),
                  (
                      "trans2",
                      make_pipeline(
                          TfidfVectorizer(),
                          StandardScaler(with_mean=False),
                      ),
                  ),
              ]
              yield Estimator(transformer_list)
  
          elif "estimators" in sig:
              # stacking, voting
              if is_regressor(Estimator):
                  estimator = [
                      (
                          "est1",
                          make_pipeline(
                              TfidfVectorizer(), Ridge(alpha=0.1)
                          ),
                      ),
                      (
                          "est2",
                          make_pipeline(
                              TfidfVectorizer(), Ridge(alpha=1)
                          ),
                      ),
                  ]
              else:
                  estimator = [
                      (
                          "est1",
                          make_pipeline(
                              TfidfVectorizer(),
                              LogisticRegression(C=0.1),
                          ),
                      ),
                      (
                          "est2",
                          make_pipeline(
                              TfidfVectorizer(), LogisticRegression(C=1)
                          ),
                      ),
                  ]
              yield Estimator(estimator)
  
          else:
              continue
  
imports: import functools
  from inspect import signature
  import numpy
  import pytest
  from sklearn.base import BaseEstimator, is_regressor
  from sklearn.datasets import make_classification
  from sklearn.ensemble import BaggingClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.feature_selection import RFE, RFECV
  from sklearn.linear_model import LogisticRegression, Ridge
  from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import MaxAbsScaler, StandardScaler
  from sklearn.semi_supervised import SelfTrainingClassifier
  from sklearn.utils import all_estimators
  from sklearn.utils._testing import set_random_state
  from sklearn.utils.estimator_checks import _enforce_estimator_tags_X, _enforce_estimator_tags_y
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: _generate_meta_estimator_instances_with_pipeline() without return types