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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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", "samples": 3 }, }, } 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