code: def set_clusterer(cls, resample_id=None):
      """Construct a clusterer.
  
      Basic way of creating the clusterer to build using the default settings. This
      set up is to help with batch jobs for multiple problems to facilitate easy
      reproducibility through run_clustering_experiment. You can set up bespoke
      clusterers and pass them to run_clustering_experiment if you prefer. It also
      serves to illustrate the base clusterer parameters
  
      Parameters
      ----------
      cls : str
          indicating which clusterer you want
      resample_id : int or None, default = None
          clusterer random seed
  
      Return
      ------
      A clusterer.
      """
      name = cls.lower()
      # Distance based
      if name == "kmeans" or name == "k-means":
          return TimeSeriesKMeans(
              n_clusters=5,
              max_iter=50,
              averaging_algorithm="mean",
              random_state=resample_id,
          )
      if name == "kmedoids" or name == "k-medoids":
          return TimeSeriesKMedoids(
              n_clusters=5,
              max_iter=50,
              averaging_algorithm="mean",
              random_state=resample_id,
          )
      else:
          raise Exception("UNKNOWN CLUSTERER")
  
label_desc: Use image node_id 1 for calling a global function with example usage: set_clusterer(cls, resample_id) and returns: TimeSeriesKMeans, TimeSeriesKMedoids