code: def check_classifier_data_not_an_array(name, estimator_orig):
      X, y = _create_large_ts_dataset()
      y = enforce_estimator_tags_y(estimator_orig, y)
      for obj_type in ["NotAnArray", "PandasDataframe"]:
          if obj_type == "PandasDataframe":
              X_ = X[:, :, 0]  # pandas df cant be 3d
          else:
              X_ = X
          check_estimators_data_not_an_array(
              name, estimator_orig, X_, y, obj_type
          )
  
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 13 for calling a global function with example usage: check_classifier_data_not_an_array(name, estimator_orig) without return types