code: def yield_all_checks(name, estimator):
      tags = estimator._get_tags()
      if "2darray" not in tags["X_types"]:
          warnings.warn(
              "Can't test estimator {} which requires input "
              " of type {}".format(name, tags["X_types"]),
              SkipTestWarning,
          )
          return
      if tags["_skip_test"]:
          warnings.warn(
              "Explicit SKIP via _skip_test tag for estimator "
              "{}.".format(name),
              SkipTestWarning,
          )
          return
  
      yield from _yield_checks(estimator)
      if is_classifier(estimator):
          yield from _yield_classifier_checks(estimator)
      if is_regressor(estimator):
          yield from _yield_regressor_checks(estimator)
      if hasattr(estimator, "transform"):
          if not tags["allow_variable_length"]:
              # Transformer tests ensure that shapes are the same at fit and
              # transform time, hence we need to skip them for estimators that
              # allow variable-length inputs
              yield from _yield_transformer_checks(estimator)
      if isinstance(estimator, ClusterMixin):
          yield from _yield_clustering_checks(estimator)
      if is_outlier_detector(estimator):
          yield from _yield_outliers_checks(estimator)
      # We are not strict on presence/absence of the 3rd dimension
      # yield check_fit2d_predict1d
  
      if not tags["non_deterministic"]:
          yield check_methods_subset_invariance
  
      yield check_fit2d_1sample
      yield check_fit2d_1feature
      yield check_fit1d
      yield check_get_params_invariance
      yield check_set_params
      yield check_dict_unchanged
      yield check_dont_overwrite_parameters
      yield check_fit_idempotent
  
      if (
          is_classifier(estimator)
          or is_regressor(estimator)
          or isinstance(estimator, ClusterMixin)
      ):
          if tags["allow_variable_length"]:
              yield check_different_length_fit_predict_transform
  
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 19 for calling a global function with example usage: yield_all_checks(name, estimator) without return types