code: def _prop_size(n, counts, output_range=(0.00, 0.3)):
      min_samples = min(counts)
      max_samples = max(counts)
      sample_count_range = max_samples - min_samples
  
      if sample_count_range > 0:
          zero_to_one = (n - min_samples) / sample_count_range
          return (
              zero_to_one * (output_range[1] - output_range[0])
              + output_range[0]
          )
      else:
          return output_range[0]
  
imports: import os
  import tempfile
  from typing import Mapping, List, Callable
  import matplotlib
  import matplotlib.patches
  import matplotlib.pyplot
  import numpy
  import pandas
  from colour import Color, rgb2hex
  from sklearn import tree
  from dtreeviz.colors import adjust_colors
  from dtreeviz.interpretation import explain_prediction_plain_english, explain_prediction_sklearn_default
  from dtreeviz.models.shadow_decision_tree import ShadowDecTree
  from dtreeviz.models.shadow_decision_tree import ShadowDecTreeNode
  from dtreeviz.utils import myround, DTreeVizRender, add_classifier_legend, _format_axes, _draw_wedge, _set_wedge_ticks, tessellate, is_numeric
label_desc: Use image node_id 8 for calling a global function with example usage: _prop_size(n, counts, output_range) and returns: unknown, output_range