code: def draw_date_vars_hv(
      df,
      dep,
      datevars,
      nums,
      chart_format,
      modeltype="Regression",
      mk_dir="AutoViz_Plots",
      verbose=0,
  ):
      ensure_hvplot_imported()
      #### Now you want to display 2 variables at a time to see how they change over time
      ### Don't change the number of cols since you will have to change rows formula as well
      df = copy.deepcopy(df)
      imgdata_list = list()
      image_count = 0
      N = len(nums)
      cols = 2
      width_size = 600
      height_size = 400
      jitter = 0.05
      alpha = 0.5
      size = 5
      transparent = 0.5
      colortext = "brycgkbyrcmgkbyrcmgkbyrcmgkbyr"
      colors = cycle("brycgkbyrcmgkbyrcmgkbyrcmgkbyr")
      plot_name = "timeseries_plots"
      #####################################################
      ###### Draw the time series for Regression and DepVar
      #####################################################
      if modeltype == "Regression":
          if isinstance(dep, str):
              if dep not in nums:
                  nums.append(dep)
          else:
              nums += dep
              nums = find_remove_duplicates(nums)
      else:
          nums = find_remove_duplicates(nums)
      ### This is where you draw the widgets #####################
      quantileable = nums[:]
      cmap_list = [
          "Blues",
          "rainbow",
          "viridis",
          "plasma",
          "inferno",
          "magma",
          "cividis",
      ]
  
      x = pnw.Select(name="X-Axis", value=datevars[0], options=datevars)
      y = pnw.Select(
          name="Y-Axis", value=quantileable[0], options=quantileable
      )
  
      ## you need to decorate this function with depends to make the widgets change axes real time ##
      @pn.depends(x.param.value, y.param.value)
      def create_figure(x, y):
          # opts = dict(cmap=cmap_list[0], line_color='black')
          opts = dict(
              cmap=cmap_list[0],
              width=width_size,
              height=height_size,
              line_color="black",
              line_width=1,
              line_dash="dotted",
              line_alpha=0.5,
          )
          # opts['size'] = bubble_size
          opts["alpha"] = alpha
          opts["tools"] = ["hover"]
          opts["toolbar"] = "above"
          opts["colorbar"] = True
          # opts['color'] = next(colors)
          opts["title"] = title = "Time Series plots of Numeric vars"
          dft = df.set_index(df[x])
          conti_df = df[[x, y]].set_index(df[x]).drop(x, axis=1)
          return hv.Curve(conti_df).opts(**opts)
  
      widgets = pn.WidgetBox(x, y)
  
      hv_panel = pn.Row(widgets, create_figure).servable(
          "Cross-selector"
      )
      #####################################################
      ##### Save all the chart objects here ##############
      if chart_format in ["server", "bokeh_server", "bokeh-server"]:
          # server = pn.serve(hv_panel, start=True, show=True)
          print("%s can be found in URL below:" % plot_name)
          hv_panel.show()
      elif chart_format == "html":
          save_html_data(hv_panel, chart_format, plot_name, mk_dir)
      else:
          ### This will display it in a Jupyter Notebook. If you want it on a server, you use drawobj.show()
          display(hv_panel)
      return hv_panel
  
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 16 for calling a global function with example usage: draw_date_vars_hv(df, dep, datevars, nums, chart_format, modeltype, mk_dir, verbose) and returns: hv_panel, hv