code: def get_leaderboard(
      finalize_models: bool = False,
      model_only: bool = False,
      fit_kwargs: Optional[dict] = None,
      groups: Optional[Union[str, Any]] = None,
      verbose: bool = True,
  ) -> pd.DataFrame:
      """
      This function returns the leaderboard of all models trained in the
      current setup.
  
  
      Example
      -------
      >>> from pycaret.regression import get_leaderboard
      >>> leaderboard = get_leaderboard()
  
  
      finalize_models: bool, default = False
          If True, will finalize all models in the 'Model' column.
  
  
      model_only: bool, default = False
          When set to False, only model object is returned, instead
          of the entire pipeline.
  
  
      fit_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the fit method of the model.
          Ignored if finalize_models is False.
  
  
      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.
          Ignored if finalize_models is False.
  
  
      verbose: bool, default = True
          Progress bar is not printed when verbose is set to False.
  
  
      Returns:
          pandas.DataFrame
  
      """
      return _CURRENT_EXPERIMENT.get_leaderboard(
          finalize_models=finalize_models,
          model_only=model_only,
          fit_kwargs=fit_kwargs,
          groups=groups,
          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.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 29 for calling a global function with example usage: get_leaderboard(finalize_models, model_only, fit_kwargs, groups, verbose) and returns: _CURRENT_EXPERIMENT