code: def draw_kdeplot_hv(
      dfin,
      cats,
      nums,
      chart_format,
      problem_type,
      dep,
      ls_objects,
      mk_dir,
      verbose=0,
  ):
      ensure_hvplot_imported()
      dft = copy.deepcopy(dfin)
      image_count = 0
      imgdata_list = list()
      N = len(nums)
      cols = 2
      plot_name = "kde_plots"
      width_size = 600
      height_size = 400
  
      ########################################################################################
      def return_dynamic_objects(
          dfout, dep, title="Distribution of Target variable"
      ):
          width_size = 600
          height_size = 400
          pdf1 = pd.DataFrame(dfout[dep].value_counts().reset_index())
          pdf2 = pd.DataFrame(dfout[dep].value_counts(1).reset_index())
          drawobj41 = pdf1.hvplot(
              kind="bar", color="lightblue", title=title
          ).opts(height=height_size, width=width_size, xrotation=70)
          drawobj42 = pdf2.hvplot(
              kind="bar", color="lightgreen", title=title
          )
          return drawobj41 + drawobj42
  
      if problem_type.endswith("Classification"):
          colors = cycle("brycgkbyrcmgkbyrcmgkbyrcmgkbyr")
          dmap = hv.DynamicMap(
              return_dynamic_objects(
                  dfin,
                  dep,
                  title="Percent Distribution of Target variable",
              )
              .opts(shared_axes=False)
              .opts(title="Histogram and KDE of Target = %s" % dep)
          ).opts(height=height_size, width=width_size)
          dmap.opts(
              framewise=True, axiswise=True
          )  ## both must be True for your charts to have dynamically varying axes!
          hv_all = pn.pane.HoloViews(
              dmap
          )  # , sizing_mode="stretch_both")
          # ls_objects.append(drawobj41)
          # ls_objects.append(drawobj42)
      else:
          if not isinstance(dep, list):
              ### it means dep is a string ###
              if dep == "":
                  ### there is no target variable to draw ######
                  return ls_objects
              else:
                  dmap = hv.DynamicMap(
                      return_dynamic_objects(
                          dfin,
                          dep,
                          title=f"Histogram and KDE of Target = {dep}",
                      )
                  ).opts(width=width_size)
                  dmap.opts(
                      framewise=True, axiswise=True
                  )  ## both must be True for your charts to have dynamically varying axes!
                  hv_all = pn.pane.HoloViews(dmap)
                  # ls_objects.append(drawobj41)
                  # ls_objects.append(drawobj42)
      #### In this case we are using multiple objects in panel ###
      ##### Save all the chart objects here ##############
      if verbose == 2:
          imgdata_list = append_panels(
              hv_all, imgdata_list, chart_format
          )
          image_count += 1
      if chart_format.lower() in [
          "server",
          "bokeh_server",
          "bokeh-server",
      ]:
          ### If you want it on a server, you use drawobj.show()
          # (drawobj41+drawobj42).show()
          print("%s can be found in URL below:" % plot_name)
          server = pn.serve(hv_all, start=True, show=True)
      elif chart_format == "html":
          save_html_data(hv_all, chart_format, plot_name, mk_dir)
      else:
          ### This will display it in a Jupyter Notebook.
          display(hv_all)
      return ls_objects
  
imports: import numpy
  import pandas
  from autoviz.AutoViz_Utils import *
  import copy
  import warnings
  import logging
  import warnings
  from sklearn.exceptions import DataConversionWarning
  import matplotlib
  import matplotlib.pyplot
  import io
  import seaborn
  import re
  import pdb
  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
  import os
  from .classify_method import classify_columns
label_desc: Use image node_id 11 for calling a global function with example usage: draw_kdeplot_hv(dfin, cats, nums, chart_format, problem_type, dep, ls_objects, mk_dir, verbose) and returns: ls_objects, unknown, ls_objects