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`.

def _index_impl(self):
    """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`.
    """
    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