class_docstr: An NER visualizer that renders NER prediction as a string of HTML
  inline to any Python class Jupyter notebooks.
class_docstr_tok: ["An","NER","visualizer","that","renders","NER","prediction","as","a","string","of","HTML","inline","to","any","Python","class","Jupyter","notebooks","."]
docstr: Generate an HTML template for the given text and its label.
  
  Parameters
  ----------
  text
      The text to be highlighted.
  label
      The predicted label for the given text.
  color
      The background color of the mark tag.
code: def html_template(self, text, label, color):
      """
      Generate an HTML template for the given text and its label.
  
      Parameters
      ----------
      text
          The text to be highlighted.
      label
          The predicted label for the given text.
      color
          The background color of the mark tag.
      """
      text = '<mark style="background-color:{}; color:white; border-radius: .6em .6em; padding: .1em;">{} \
           <b style="background-color:white; color:black; font-size:x-small; border-radius: 0.5em .5em; padding: .0em;">{} </b> \
           </mark>'.format(
          color, self.escape_html(text), self.escape_html(label)
      )
      return text
  
imports: import collections
  import colorsys
  import logging
  import re
  from enum import Enum, unique
  from typing import List
  import matplotlib
  import matplotlib.colors
  import matplotlib.figure
  import matplotlib.pyplot
  import numpy
  import pandas
  from matplotlib.backends.backend_agg import FigureCanvasAgg
  from PIL import Image
  from .colormap import random_color
  from .misc import merge_spans
label_desc: Use image node_id 3 for calling the NERVisualizer obj's underlying member method code with example usage: obj.html_template(text, label, color) and returns: text