code: def render_slicing_metrics(
      data: List[Dict[str, Union[Dict[str, Any], str]]],
      config: Dict[str, str],
      event_handlers: Optional[
          Callable[[Dict[str, Union[str, float]]], None]
      ] = None,
  ) -> None:
      """Renders the slicing metrics view in Colab.
  
      Args:
        data: A list of dictionary containing metrics for correpsonding slices.
        config: A dictionary of the configuration.
        event_handlers: event handlers
      """
      util.render_tfma_component(
          "tfma-nb-slicing-metrics",
          data,
          config,
          event_handlers=event_handlers,
          trusted_html_for_vulcanized_tfma_js=get_trusted_html_for_vulcanized_js(),
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Union
  from tensorflow_model_analysis.notebook.colab import util
label_desc: Use image node_id 2 for calling a global function with example usage: render_slicing_metrics(data, config, event_handlers) without return types