code: def check_different_length_fit_predict_transform(name, estimator):
      # Check if classifier can predict a dataset that does not have the same
      # number of timestamps as the data passed at fit time
      X, y = _create_small_ts_dataset()
  
      # Default for kmeans is Euclidean
      if isinstance(estimator, TimeSeriesKMeans):
          new_estimator = clone(estimator)
          new_estimator.metric = "dtw"
      elif name == "LearningShapelets":
          # Prepare shapelet model for long series
          new_estimator = clone(estimator)
          new_estimator.max_size = 2 * X.shape[1]
      else:
          new_estimator = estimator
  
      new_estimator.fit(X, y)
  
      X2 = np.hstack((X, X))
      X3 = np.stack((X[:, :, 0], X[:, :, 0]), axis=-1)
      check_methods = [
          "predict",
          "transform",
          "decision_function",
          "predict_proba",
      ]
      for func_name in check_methods:
          func = getattr(estimator, func_name, None)
          if func is not None:
              method = getattr(new_estimator, func_name)
              method(X2)
  
              with assert_raises(
                  ValueError,
                  msg="The estimator {} does not raise an error when number of "
                  "features (last dimension) is different between "
                  "fit and {}.".format(name, func_name),
              ):
                  method(X3)
  
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 18 for calling a global function with example usage: check_different_length_fit_predict_transform(name, estimator) without return types