code: def check_classifiers_classes(name, classifier_orig):
      # Case of shapelet models
      if name in ["LearningShapelets", "TimeSeriesMLPClassifier"]:
          X_multiclass, y_multiclass = _create_large_ts_dataset()
          classifier_orig = clone(classifier_orig)
          classifier_orig.max_iter = 1000
      else:
          X_multiclass, y_multiclass = _create_small_ts_dataset()
  
      X_multiclass, y_multiclass = shuffle(
          X_multiclass, y_multiclass, random_state=7
      )
  
      scaler = TimeSeriesScalerMeanVariance()
      X_multiclass = scaler.fit_transform(X_multiclass)
  
      X_multiclass = np.reshape(
          X_multiclass, (X_multiclass.shape[0], X_multiclass.shape[1])
      )
  
      X_binary = X_multiclass[y_multiclass != 2]
      y_binary = y_multiclass[y_multiclass != 2]
  
      X_multiclass = pairwise_estimator_convert_X(
          X_multiclass, classifier_orig
      )
      X_binary = pairwise_estimator_convert_X(X_binary, classifier_orig)
  
      labels_multiclass = ["one", "two", "three"]
      labels_binary = ["one", "two"]
  
      y_names_multiclass = np.take(labels_multiclass, y_multiclass)
      y_names_binary = np.take(labels_binary, y_binary)
  
      problems = [(X_binary, y_binary, y_names_binary)]
  
      if not classifier_orig._get_tags()["binary_only"]:
          problems.append(
              (X_multiclass, y_multiclass, y_names_multiclass)
          )
  
      for X, y, y_names in problems:
          for y_names_i in [y_names, y_names.astype("O")]:
              y_ = choose_check_classifiers_labels(name, y, y_names_i)
              check_classifiers_predictions(
                  X, y_, name, classifier_orig
              )
  
      labels_binary = [-1, 1]
      y_names_binary = np.take(labels_binary, y_binary)
      y_binary = choose_check_classifiers_labels(
          name, y_binary, y_names_binary
      )
      check_classifiers_predictions(
          X_binary, y_binary, name, classifier_orig
      )
  
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 9 for calling a global function with example usage: check_classifiers_classes(name, classifier_orig) without return types