class_docstr: Callback object that renders a bokeh chart in a jupyter notebook
  that gets updated as the training run proceeds.
  
  Requires a PandasLogger to collect the data it will render.
  
  This is an abstract base-class.  Sub-classes define the specific chart.
class_docstr_tok: ["Callback","object","that","renders","a","bokeh","chart","in","a","jupyter","notebook","that","gets","updated","as","the","training","run","proceeds",".","Requires","a","PandasLogger","to","collect","the","data","it","will","render",".","This","is","an","abstract","base-class",".","Sub-classes","define","the","specific","chart","."]
code: def __init__(
      self,
      pandas_logger,
      metric_name,
      display_freq=10,
      batch_size=None,
      frequent=50,
  ):
      if pandas_logger:
          self.pandas_logger = pandas_logger
      else:
          self.pandas_logger = PandasLogger(
              batch_size=batch_size, frequent=frequent
          )
      self.display_freq = display_freq
      self.last_update = time.time()
      # NOTE: would be nice to auto-detect the metric_name if there's only one.
      self.metric_name = metric_name
      bokeh.io.output_notebook()
      self.handle = self.setup_chart()
  
imports: import time
label_desc: Use image node_id 1 to create a new LiveBokehChart object from inherited base classes: object with example: obj = LiveBokehChart(pandas_logger, metric_name, display_freq, batch_size, frequent)