code: def check_pipeline_consistency(name, estimator_orig):
      if estimator_orig._get_tags()["non_deterministic"]:
          msg = name + " is non deterministic"
          raise SkipTest(msg)
  
      # check that make_pipeline(est) gives same score as est
      X, y = make_blobs(
          n_samples=30,
          centers=[[0, 0, 0], [1, 1, 1]],
          random_state=0,
          n_features=2,
          cluster_std=0.1,
      )
      X -= X.min()
      X = pairwise_estimator_convert_X(
          X, estimator_orig, kernel=rbf_kernel
      )
      estimator = clone(estimator_orig)
      y = multioutput_estimator_convert_y_2d(estimator, y)
      set_random_state(estimator)
      pipeline = make_pipeline(estimator)
      estimator.fit(X, y)
      pipeline.fit(X, y)
  
      funcs = ["score", "fit_transform"]
  
      for func_name in funcs:
          func = getattr(estimator, func_name, None)
          if func is not None:
              func_pipeline = getattr(pipeline, func_name)
              result = func(X, y)
              result_pipe = func_pipeline(X, y)
              assert_allclose_dense_sparse(result, result_pipe)
  
imports: from tslearn.generators import random_walk_blobs
  from tslearn.preprocessing import TimeSeriesScalerMeanVariance
  import sklearn
  import warnings
  from sklearn.base import clone
  from sklearn.base import is_classifier, is_outlier_detector, is_regressor
  from sklearn.base import ClusterMixin
  from sklearn.exceptions import DataConversionWarning
  from sklearn.datasets import make_blobs
  from sklearn.metrics.pairwise import rbf_kernel, linear_kernel, pairwise_distances
  from sklearn.utils import shuffle
  from sklearn.utils.estimator_checks import check_classifiers_predictions, check_fit2d_1sample, check_fit2d_1feature, check_fit1d, check_get_params_invariance, check_set_params, check_dict_unchanged, check_dont_overwrite_parameters, check_estimators_data_not_an_array, check_fit2d_predict1d, check_methods_subset_invariance, check_regressors_int
  from sklearn.exceptions import SkipTestWarning
  from sklearn.utils.estimator_checks import _yield_classifier_checks, _yield_regressor_checks, _yield_transformer_checks, _yield_clustering_checks, _yield_outliers_checks, _is_pairwise_metric
  from sklearn.metrics import adjusted_rand_score, accuracy_score
  from sklearn.model_selection import ShuffleSplit
  from sklearn.model_selection._validation import _safe_split
  from sklearn.pipeline import make_pipeline
  from tslearn.clustering import TimeSeriesKMeans
  import warnings
  import numpy
label_desc: Use image node_id 17 for calling a global function with example usage: check_pipeline_consistency(name, estimator_orig) without return types