How to use the tensorboard.plugins.custom_scalar.layout_pb2.Layout function in tensorboard

def layout_dashboard(writer):
    from tensorboard import summary
    from tensorboard.plugins.custom_scalar import layout_pb2
    
    # This action does not have to be performed at every step, so the action is not
    # taken care of by an op in the graph. We only need to specify the layout once. 
    # We only need to specify the layout once (instead of per step).
    layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
        category=[
            layout_pb2.Category(
            title='losses',
            chart=[
                layout_pb2.Chart(
                    title='losses',
                    multiline=layout_pb2.MultilineChartContent(
                    tag=[r'loss.*'],
                )),
                layout_pb2.Chart(
                    title='baz',
                    margin=layout_pb2.MarginChartContent(
                    series=[
                        layout_pb2.MarginChartContent.Series(
                        value='loss/baz/scalar_summary',
                        lower='baz_lower/baz/scalar_summary',

def layout_dashboard(writer):
    from tensorboard import summary
    from tensorboard.plugins.custom_scalar import layout_pb2
    
    # This action does not have to be performed at every step, so the action is not
    # taken care of by an op in the graph. We only need to specify the layout once. 
    # We only need to specify the layout once (instead of per step).
    layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
        category=[
            layout_pb2.Category(
            title='losses',
            chart=[
                layout_pb2.Chart(
                    title='losses',
                    multiline=layout_pb2.MultilineChartContent(
                    tag=[r'loss.*'],
                )),
                layout_pb2.Chart(
                    title='baz',
                    margin=layout_pb2.MarginChartContent(
                    series=[
                        layout_pb2.MarginChartContent.Series(
                        value='loss/baz/scalar_summary',
                        lower='baz_lower/baz/scalar_summary',

def layout_dashboard(writer):
    from tensorboard import summary
    from tensorboard.plugins.custom_scalar import layout_pb2
    
    # This action does not have to be performed at every step, so the action is not
    # taken care of by an op in the graph. We only need to specify the layout once. 
    # We only need to specify the layout once (instead of per step).
    layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
        category=[
            layout_pb2.Category(
            title='losses',
            chart=[
                layout_pb2.Chart(
                    title='losses',
                    multiline=layout_pb2.MultilineChartContent(
                    tag=[r'loss.*'],
                )),
                layout_pb2.Chart(
                    title='baz',
                    margin=layout_pb2.MarginChartContent(
                    series=[
                        layout_pb2.MarginChartContent.Series(
                        value='loss/baz/scalar_summary',
                        lower='baz_lower/baz/scalar_summary',

layout_categories = []

        for scope in self._scope_tensor:
            chart = []
            for name in self._scope_tensor[scope]:
                chart.append(
                    layout_pb2.Chart(
                        title=name,
                        multiline=layout_pb2.MultilineChartContent(
                            tag=[r'name(?!.*margin.*)'.replace('name', name)
                                 ])))
            category = layout_pb2.Category(title=scope, chart=chart)
            layout_categories.append(category)

        if layout_categories:
            layout_proto_to_write = layout_pb2.Layout(
                category=layout_categories)

            try:
                # Load former layout_proto from self._layout_writer_dir.
                multiplexer = event_multiplexer.EventMultiplexer()
                multiplexer.AddRunsFromDirectory(self._layout_writer_dir)
                multiplexer.Reload()
                tensor_events = multiplexer.Tensors(
                    '.', metadata.CONFIG_SUMMARY_TAG)
                shutil.rmtree(self._layout_writer_dir)

                # Parse layout proto from disk.
                string_array = tf.make_ndarray(tensor_events[0].tensor_proto)
                content = np.asscalar(string_array)
                layout_proto_from_disk = layout_pb2.Layout()
                layout_proto_from_disk.ParseFromString(

tensor_events = multiplexer.Tensors(
                    '.', metadata.CONFIG_SUMMARY_TAG)
                shutil.rmtree(self._layout_writer_dir)

                # Parse layout proto from disk.
                string_array = tf.make_ndarray(tensor_events[0].tensor_proto)
                content = np.asscalar(string_array)
                layout_proto_from_disk = layout_pb2.Layout()
                layout_proto_from_disk.ParseFromString(
                    tf.compat.as_bytes(content))

                # Merge two layout proto.
                merged_layout_json = merge(
                    json_format.MessageToJson(layout_proto_from_disk),
                    json_format.MessageToJson(layout_proto_to_write))
                merged_layout_proto = layout_pb2.Layout()
                json_format.Parse(str(merged_layout_json), merged_layout_proto)

                self._layout_writer = tf.summary.FileWriter(
                    self._layout_writer_dir)
                layout_summary = summary_lib.custom_scalar_pb(
                    merged_layout_proto)
                self._layout_writer.add_summary(layout_summary)
                self._layout_writer.close()
            except KeyError:
                # Write the current layout proto into disk
                # when there is no layout.
                self._layout_writer = tf.summary.FileWriter(
                    self._layout_writer_dir)
                layout_summary = summary_lib.custom_scalar_pb(
                    layout_proto_to_write)
                self._layout_writer.add_summary(layout_summary)

def layout_dashboard(writer):
    from tensorboard import summary
    from tensorboard.plugins.custom_scalar import layout_pb2

    # This action does not have to be performed at every step, so the action is not
    # taken care of by an op in the graph. We only need to specify the layout once. 
    # We only need to specify the layout once (instead of per step).
    layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
        category=[
            layout_pb2.Category(
                title='losses',
                chart=[
                    layout_pb2.Chart(
                        title='losses',
                        multiline=layout_pb2.MultilineChartContent(
                            tag=[r'loss.*'],
                        )),
                    layout_pb2.Chart(
                        title='baz',
                        margin=layout_pb2.MarginChartContent(
                            series=[
                                layout_pb2.MarginChartContent.Series(
                                    value='loss/baz/scalar_summary',
                                    lower='baz_lower/baz/scalar_summary',

("Clip Fraction", [r"clipfrac"]),
            ]
        ),
    )

    # Intentionally unused:
    # + serial_timesteps (just total_timesteps / num_envs)
    # + time_elapsed (TensorBoard already logs wall-clock time)
    # + nupdates (this is already logged as step)
    time = layout_pb2.Category(
        title="Time",
        chart=gen_multiline_charts([("Total Timesteps", [r"total_timesteps"]), ("FPS", [r"fps"])]),
    )

    categories = [episode_rewards, game_outcome, training, time]
    return summary_lib.custom_scalar_pb(layout_pb2.Layout(category=categories))

def layout_dashboard(writer):
    from tensorboard import summary
    from tensorboard.plugins.custom_scalar import layout_pb2
    
    # This action does not have to be performed at every step, so the action is not
    # taken care of by an op in the graph. We only need to specify the layout once. 
    # We only need to specify the layout once (instead of per step).
    layout_summary = summary.custom_scalar_pb(layout_pb2.Layout(
        category=[
            layout_pb2.Category(
            title='losses',
            chart=[
                layout_pb2.Chart(
                    title='losses',
                    multiline=layout_pb2.MultilineChartContent(
                    tag=[r'loss.*'],
                )),
                layout_pb2.Chart(
                    title='baz',
                    margin=layout_pb2.MarginChartContent(
                    series=[
                        layout_pb2.MarginChartContent.Series(
                        value='loss/baz/scalar_summary',
                        lower='baz_lower/baz/scalar_summary',