code: def assign_model(
      model,
      transformation: bool = False,
      score: bool = True,
      verbose: bool = True,
  ) -> pd.DataFrame:
      """
      This function assigns anomaly labels to the dataset for a given model.
      (1 = outlier, 0 = inlier).
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> anomaly = get_data('anomaly')
      >>> from pycaret.anomaly import *
      >>> exp_name = setup(data = anomaly)
      >>> knn = create_model('knn')
      >>> knn_df = assign_model(knn)
  
  
      model: scikit-learn compatible object
          Trained model object
  
  
      transformation: bool, default = False
          Whether to apply anomaly labels on the transformed dataset.
  
  
      score: bool, default = True
          Whether to show outlier score or not.
  
  
      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,
          score=score,
          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.anomaly.oop import AnomalyExperiment
  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 3 for calling a global function with example usage: assign_model(model, transformation, score, verbose) and returns: _CURRENT_EXPERIMENT