code: def evaluate_model(
      estimator,
      fold: Optional[Union[int, Any]] = None,
      fit_kwargs: Optional[dict] = None,
      plot_kwargs: Optional[dict] = None,
      groups: Optional[Union[str, Any]] = None,
  ):
      """
      This function displays a user interface for analyzing performance of a trained
      model. It calls the ``plot_model`` function internally.
  
      Example
      --------
      >>> from pycaret.datasets import get_data
      >>> boston = get_data('boston')
      >>> from pycaret.regression import *
      >>> exp_name = setup(data = boston,  target = 'medv')
      >>> lr = create_model('lr')
      >>> evaluate_model(lr)
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      fold: int or scikit-learn compatible CV generator, default = None
          Controls cross-validation. If None, the CV generator in the ``fold_strategy``
          parameter of the ``setup`` function is used. When an integer is passed,
          it is interpreted as the 'n_splits' parameter of the CV generator in the
          ``setup`` function.
  
  
      fit_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the fit method of the model.
  
  
      plot_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the visualizer class.
  
  
      groups: str or array-like, with shape (n_samples,), default = None
          Optional group labels when GroupKFold is used for the cross validation.
          It takes an array with shape (n_samples, ) where n_samples is the number
          of rows in training dataset. When string is passed, it is interpreted as
          the column name in the dataset containing group labels.
  
  
      Returns:
          None
  
  
      Warnings
      --------
      -   This function only works in IPython enabled Notebook.
  
      """
  
      return _CURRENT_EXPERIMENT.evaluate_model(
          estimator=estimator,
          fold=fold,
          fit_kwargs=fit_kwargs,
          plot_kwargs=plot_kwargs,
          groups=groups,
      )
  
imports: import logging
  import os
  from typing import Any, BinaryIO, Callable, Dict, List, Optional, Union
  import pandas
  from joblib.memory import Memory
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.regression.oop import RegressionExperiment
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 11 for calling a global function with example usage: evaluate_model(estimator, fold, fit_kwargs, plot_kwargs, groups) and returns: _CURRENT_EXPERIMENT