code: def _index_impl(self):
      
      runs = self._multiplexer.Runs()
      result = {run: {} for run in runs}
  
      mapping = self._multiplexer.PluginRunToTagToContent(metadata.PLUGIN_NAME)
      for (run, tag_to_content) in six.iteritems(mapping):
        for tag in tag_to_content:
          summary_metadata = self._multiplexer.SummaryMetadata(run, tag)
          tensor_events = self._multiplexer.Tensors(run, tag)
          samples = max([self._number_of_samples(event.tensor_proto)
                         for event in tensor_events] + [0])
          result[run][tag] = {'displayName': summary_metadata.display_name,
                              'description': plugin_util.markdown_to_safe_html(
                                  summary_metadata.summary_description),
                              'samples': samples}
  
      return result
docs: Return information about the tags in each run.
  
      Result is a dictionary of the form
  
          {
            "runName1": {
              "tagName1": {
                "displayName": "The first tag",
                "description": "<p>Long ago there was just one tag...</p>",
                "samples": 3
              },
              "tagName2": ...,
              ...
            },
            "runName2": ...,
            ...
          }
  
      For each tag, `samples` is the greatest number of audio clips that
      appear at any particular step. (It's not related to "samples of a
      waveform.") For example, if for tag `minibatch_input` there are
      five audio clips at step 0 and ten audio clips at step 1, then the
      dictionary for `"minibatch_input"` will contain `"samples": 10`.