code: def render_slicing_metrics(data, config, event_handlers=None):
      """Renders the slicing metrics view in Jupyter.
  
      Args:
        data: A list of dictionary containing metrics for correpsonding slices.
        config: A dictionary of the configuration.
        event_handlers: A dictionary of where keys are event types and values are
          event handlers.
  
      Returns:
        A SlicingMetricsViewer.
      """
      view = tfma_widget.SlicingMetricsViewer()
      view.data = data
      view.config = config
      view.event_handlers = event_handlers
  
      return view
  
imports: import tensorflow_model_analysis.notebook.jupyter.tfma_widget
label_desc: Use image node_id 1 for calling a global function with example usage: render_slicing_metrics(data, config, event_handlers) and returns: view