class_docstr: Configure annotations (for pie and bar charts).
  
  Examples
  --------
  .. jupyter-execute::
      :linenos:
      :emphasize-lines: 6
  
      from lets_plot import *
      LetsPlot.setup_html()
      data = {'name': ['a', 'b', 'c', 'd', 'b'], 'value': [40, 90, 10, 50, 20 ] }
      ggplot(data) + geom_pie(aes(slice='value', fill='name'), size=15, hole=0.4, \
                              stat='identity', tooltips = 'none', \
                              labels=layer_labels().line('@value'))
docstr: Return the dictionary of all properties of the object.
  
  Returns
  -------
  dict
      Dictionary of properties.
  
  Examples
  --------
  .. jupyter-execute::
      :linenos:
      :emphasize-lines: 6
  
      from lets_plot import *
      LetsPlot.setup_html()
      layer_labels().format('@{..prop..}', '.0%') \
                   .line('@name') \
                   .line('(@{..prop..})') \
                   .as_dict()
code: def as_dict(self):
      """
          Return the dictionary of all properties of the object.
  
          Returns
          -------
          dict
              Dictionary of properties.
  
          Examples
          --------
          .. jupyter-execute::
              :linenos:
              :emphasize-lines: 6
  
              from lets_plot import *
              LetsPlot.setup_html()
              layer_labels().format('@{..prop..}', '.0%') \\
                           .line('@name') \\
                           .line('(@{..prop..})') \\
                           .as_dict()
  
          """
      d = super().as_dict()
      d["formats"] = self._formats
      d["lines"] = self._lines
      d["variables"] = self._variables
      d["annotation_size"] = self._size
      return _filter_none(d)
  
file_path: lets-plot/python-package/lets_plot/plot/annotation.py
imports: from typing import List
  from lets_plot.plot.core import FeatureSpec, _filter_none
label_desc: Use image node_id 2 for calling the layer_labels obj's underlying member method code with example usage: obj.as_dict() and returns: _filter_none