docs: def predict(dataset, fitmodel_url, save_results=True, show=False):
      """
      Predict the closest clusters for the datapoints in input.
      """
  
      from disco.job import Job
      from disco.worker.pipeline.worker import Worker, Stage
      from disco.core import result_iterator
  
      if "kmeans_fitmodel" not in fitmodel_url:
          raise Exception("Incorrect fit model.")
  
      job = Job(worker=Worker(save_results=save_results))
      job.params = dict(dataset.params.items() + mean_point_center.items())
      job.params["centers"] = [(i, c) for i, c in result_iterator(fitmodel_url["kmeans_fitmodel"])]
  
      job.pipeline = [("split", Stage("kmeans_predict", input_chain=dataset.params["input_chain"], init=simple_init,
                                      process=predict_map))]
  
      job.run(input=dataset.params["data_tag"], name="kmeans_predict")
  
      return job.wait(show=show)