code: def get_config(variable: Optional[str] = None):
      """
      This function retrieves the global variables created when initializing the
      ``setup`` function. Following variables are accessible:
  
      - X: Period/Index of X
      - y: Time Series as pd.Series
      - X_train: Period/Index of X_train
      - y_train: Time Series as pd.Series (Train set only)
      - X_test: Period/Index of X_test
      - y_test: Time Series as pd.Series  (Test set only)
      - fh: forecast horizon
      - enforce_pi: enforce prediction interval in models
      - seed: random state set through session_id
      - prep_pipe: Transformation pipeline
      - n_jobs_param: n_jobs parameter used in model training
      - html_param: html_param configured through setup
      - _master_model_container: model storage container
      - _display_container: results display container
      - exp_name_log: Name of experiment
      - logging_param: log_experiment param
      - log_plots_param: log_plots param
      - USI: Unique session ID parameter
      - data_before_preprocess: data before preprocessing
      - gpu_param: use_gpu param configured through setup
      - fold_generator: CV splitter configured in fold_strategy
      - fold_param: fold params defined in the setup
      - seasonality_present: seasonality as detected in the setup
      - seasonality_period: seasonality_period as detected in the setup
  
  
      Example
      -------
      >>> from pycaret.datasets import get_data
      >>> airline = get_data('airline')
      >>> from pycaret.time_series import *
      >>> exp_name = setup(data = airline,  fh = 12)
      >>> X_train = get_config('X_train')
  
  
      variable : str, default = None
          Name of the variable to return the value of. If None,
          will return a list of possible names.
  
  
      Returns:
          Global variable
  
  
      """
  
      return _CURRENT_EXPERIMENT.get_config(variable=variable)
  
imports: import logging
  import os
  from typing import TYPE_CHECKING, Any, BinaryIO, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from pycaret.internal.parallel.parallel_backend import ParallelBackend
  from pycaret.time_series.forecasting.oop import TSForecastingExperiment
  from pycaret.utils.generic import check_if_global_is_not_none
label_desc: Use image node_id 20 for calling a global function with example usage: get_config(variable) and returns: _CURRENT_EXPERIMENT