docstr: 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
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code: def dashboard(
      self,
      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
      """
  
      # soft dependencies check
      super().dashboard(
          estimator,
          display_format,
          dashboard_kwargs,
          run_kwargs,
          **kwargs,
      )
  
      dashboard_kwargs = dashboard_kwargs or {}
      run_kwargs = run_kwargs or {}
  
      from explainerdashboard import (
          ExplainerDashboard,
          RegressionExplainer,
      )
  
      # Replaceing chars which dash doesnt accept for column name `.` , `{`, `}`
      X_test_df = self.X_test_transformed.copy()
      X_test_df.columns = [
          col.replace(".", "__").replace("{", "__").replace("}", "__")
          for col in X_test_df.columns
      ]
      explainer = RegressionExplainer(
          estimator, X_test_df, self.y_test_transformed, **kwargs
      )
      return ExplainerDashboard(
          explainer, mode=display_format, **dashboard_kwargs
      ).run(**run_kwargs)
  
imports: import logging
  import re
  import time
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from joblib.memory import Memory
  from pycaret.containers.metrics.regression import get_all_metric_containers
  from pycaret.containers.models.regression import ALL_ALLOWED_ENGINES, get_all_model_containers, get_container_default_engines
  from pycaret.internal.display import CommonDisplay
  from pycaret.internal.logging import get_logger
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.internal.pipeline import Pipeline
  from pycaret.internal.preprocess.preprocessor import Preprocessor
  from pycaret.internal.pycaret_experiment.non_ts_supervised_experiment import _NonTSSupervisedExperiment
  from pycaret.loggers.base_logger import BaseLogger
  from pycaret.utils.constants import DATAFRAME_LIKE, SEQUENCE_LIKE, TARGET_LIKE
  from pycaret.utils.generic import MLUsecase, highlight_setup
label_desc: Use image node_id 26 for calling the RegressionExperiment obj's underlying member method code with example usage: obj.dashboard(estimator, display_format, dashboard_kwargs, run_kwargs) and returns: ExplainerDashboard