code: def assign_model(
      model, transformation: bool = False, verbose: bool = True
  ) -> pd.DataFrame:
      """
      This function assigns cluster labels to the dataset for a given model.
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> jewellery = get_data('jewellery')
      >>> from pycaret.clustering import *
      >>> exp_name = setup(data = jewellery)
      >>> kmeans = create_model('kmeans')
      >>> kmeans_df = assign_model(kmeans)
  
  
  
      model: scikit-learn compatible object
          Trained model object
  
  
      transformation: bool, default = False
          Whether to apply cluster labels on the transformed dataset.
  
  
      verbose: bool, default = True
          Status update is not printed when verbose is set to False.
  
  
      Returns:
          pandas.DataFrame
  
      """
  
      return _CURRENT_EXPERIMENT.assign_model(
          model, transformation=transformation, verbose=verbose
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.clustering.oop import ClusteringExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 5 for calling a global function with example usage: assign_model(model, transformation, verbose) and returns: _CURRENT_EXPERIMENT