class_docstr: ##############################################################################
  #############       This is not an Officially Supported Google Product! ######
  ##############################################################################
  #Copyright 2019 Google LLC                                              ######
  #                                                                       ######
  #Licensed under the Apache License, Version 2.0 (the "License");        ######
  #you may not use this file except in compliance with the License.       ######
  #You may obtain a copy of the License at                                ######
  #                                                                       ######
  #    https://www.apache.org/licenses/LICENSE-2.0                        ######
  #                                                                       ######
  #Unless required by applicable law or agreed to in writing, software    ######
  #distributed under the License is distributed on an "AS IS" BASIS,      ######
  #WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.#####
  #See the License for the specific language governing permissions and    ######
  #limitations under the License.                                         ######
  ##############################################################################
  ###########             AutoViz Class                                   ######
  ###########             by Ram Seshadri                                 ######
  ###########      AUTOMATICALLY VISUALIZE ANY DATA SET                   ######
  ###########            Version V0.0.68 1/10/20                          ######
  ##############################################################################
  ##### AUTOVIZ PERFORMS AUTOMATIC VISUALIZATION OF ANY DATA SET WITH ONE CLICK.
  #####    Give it any input file (CSV, txt or json) and AV will visualize it.##
  ##### INPUTS:                                                            #####
  #####    A FILE NAME OR A DATA FRAME AS INPUT.                           #####
  ##### AutoViz will visualize any sized file using a statistically valid sample.
  #####  - COMMA is assumed as default separator in file. But u can change it.##
  #####  - Assumes first row as header in file but you can change it.      #####
  #####  - First instantiate an AutoViz class to  hold output of charts, plots.#
  #####  - Then call the Autoviz program with inputs as defined below.       ###
  ##############################################################################
docstr: This is a simple program to append the input chart to the right variable named plotname
  which is an attribute of class AV. So make sure that the plotname var matches an exact
  variable name defined in class AV. Otherwise, this will give an error.
docstr_tok: ["This","is","a","simple","program","to","append","the","input","chart","to","the","right","variable","named","plotname","which","is","an","attribute","of","class","AV",".","So","make","sure","that","the","plotname","var","matches","an","exact","variable","name","defined","in","class","AV",".","Otherwise",",","this","will","give","an","error","."]
code: def add_subheading(self, plotname, X):
      """
      This is a simple program to append the input chart to the right variable named plotname
      which is an attribute of class AV. So make sure that the plotname var matches an exact
      variable name defined in class AV. Otherwise, this will give an error.
      """
      if X is None:
          ### If there is nothing to add, leave it as it is.
          pass
      else:
          getattr(self, plotname)["subheading"].append(X)
  
imports: import pandas
  import numpy
  from pathlib import Path
  import os
  import warnings
  import warnings
  from sklearn.exceptions import DataConversionWarning
  import matplotlib
  import matplotlib.pyplot
  import io
  import seaborn
  import re
  import pprint
  import matplotlib
  from itertools import cycle, combinations
  from collections import defaultdict
  import copy
  import time
  import sys
  import random
  import xlrd
  import statsmodels
  from io import BytesIO
  import base64
  from functools import reduce
  import traceback
  import xgboost
  from xgboost.sklearn import XGBClassifier
  from xgboost.sklearn import XGBRegressor
  from sklearn.model_selection import train_test_split
  from autoviz.AutoViz_Holo import AutoViz_Holo
  from autoviz.AutoViz_Utils import save_image_data, analyze_problem_type, draw_pivot_tables, draw_scatters
  from autoviz.AutoViz_Utils import draw_pair_scatters, plot_fast_average_num_by_cat, draw_barplots, draw_heatmap
  from autoviz.AutoViz_Utils import draw_distplot, draw_violinplot, draw_date_vars, catscatter, draw_catscatterplots
  from autoviz.AutoViz_Utils import list_difference, search_for_word_in_list, analyze_ID_columns, start_classifying_vars
  from autoviz.AutoViz_Utils import analyze_columns_in_dataset, find_remove_duplicates, load_file_dataframe, classify_print_vars
  from autoviz.AutoViz_Utils import marthas_columns, EDA_find_remove_columns_with_infinity, return_dictionary_list
  from autoviz.AutoViz_Utils import remove_variables_using_fast_correlation, count_freq_in_list, find_corr_vars, left_subtract
  from autoviz.AutoViz_Utils import convert_train_test_cat_col_to_numeric, return_factorized_dict, convert_a_column_to_numeric
  from autoviz.AutoViz_Utils import convert_all_object_columns_to_numeric, find_top_features_xgb, convert_a_mixed_object_column_to_numeric
  from autoviz.AutoViz_NLP import draw_word_clouds
  from pandas_dq import Fix_DQ
  from pandas_dq import dq_report
label_desc: Use image node_id 3 for calling the AutoViz_Class obj's underlying member method code with example usage: obj.add_subheading(plotname, X) without return types