code: def dashboard(
      estimator,
      display_format: str = "dash",
      dashboard_kwargs: Optional[Dict[str, Any]] = None,
      run_kwargs: Optional[Dict[str, Any]] = None,
      **kwargs,
  ):
      """
      This function generates the interactive dashboard for a trained model. The
      dashboard is implemented using ExplainerDashboard (explainerdashboard.readthedocs.io)
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> juice = get_data('juice')
      >>> from pycaret.classification import *
      >>> exp_name = setup(data = juice,  target = 'Purchase')
      >>> lr = create_model('lr')
      >>> dashboard(lr)
  
  
      estimator: scikit-learn compatible object
          Trained model object
  
  
      display_format: str, default = 'dash'
          Render mode for the dashboard. The default is set to ``dash`` which will
          render a dashboard in browser. There are four possible options:
  
          - 'dash' - displays the dashboard in browser
          - 'inline' - displays the dashboard in the jupyter notebook cell.
          - 'jupyterlab' - displays the dashboard in jupyterlab pane.
          - 'external' - displays the dashboard in a separate tab. (use in Colab)
  
  
      dashboard_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the ``ExplainerDashboard`` class.
  
  
      run_kwargs: dict, default = {} (empty dict)
          Dictionary of arguments passed to the ``run`` method of ``ExplainerDashboard``.
  
  
      **kwargs:
          Additional keyword arguments to pass to the ``ClassifierExplainer`` or
          ``RegressionExplainer`` class.
  
  
      Returns:
          ExplainerDashboard
      """
  
      return _CURRENT_EXPERIMENT.dashboard(
          estimator,
          display_format,
          dashboard_kwargs,
          run_kwargs,
          **kwargs,
      )
  
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 30 for calling a global function with example usage: dashboard(estimator, display_format, dashboard_kwargs, run_kwargs) and returns: _CURRENT_EXPERIMENT