class_docstr: Create plots for `InteractTargetPlot` or `InteractPredictPlot`
  
  Methods
  -------
  plot_matplotlib : plot using matplotlib
  plot_plotly : plot using plotly
class_docstr_tok: ["Create","plots","for","`","InteractTargetPlot","`","or","`","InteractPredictPlot","`","Methods","--","--","--","-","plot_matplotlib",":","plot","using","matplotlib","plot_plotly",":","plot","using","plotly"]
code: def _get_interact_marker_sizes(self):
      df = self.plot_obj.plot_df
      marker_style = self.plot_style.marker
      count_min, count_max = df["count"].min(), df["count"].max()
  
      if count_min == count_max:
          marker_sizes = [marker_style["min_size"]] * len(
              df["count"].values
          )
      else:
          marker_sizes = [
              (
                  (count - count_min)
                  / (count_max - count_min)
                  * (
                      marker_style["max_size"]
                      - marker_style["min_size"]
                  )
                  + marker_style["min_size"]
              )
              for count in df["count"].values
          ]
  
      return count_min, count_max, marker_sizes
  
imports: from .styles import _prepare_plot_style, _axes_modify, _display_percentile, _modify_legend_axes, _get_axes_label
  import numpy
  from itertools import cycle
  import plotly.express
  import matplotlib
  import matplotlib.pyplot
  from matplotlib.gridspec import GridSpecFromSubplotSpec
  from mpl_toolkits.axes_grid1.inset_locator import inset_axes
  import matplotlib.patheffects
  import plotly.graph_objects
label_desc: Use image node_id 2 for calling the InteractInfoPlotEngine obj's underlying member method code with example usage: obj._get_interact_marker_sizes() and returns: count_min,  count_max,  marker_sizes