How to use the tensorboard.compat.proto.summary_pb2.Summary function in tensorboard

def _ImageSummary(tag, height, width, colorspace, encoded_image):
    from tensorboard.compat.proto.summary_pb2 import Summary
    image = Summary.Image(
        height=height,
        width=width,
        colorspace=colorspace,
        encoded_image_string=encoded_image)
    return Summary(value=[Summary.Value(tag=tag, image=image)])

def _ScalarSummary(tag, val):
    from tensorboard.compat.proto.summary_pb2 import Summary
    return Summary(value=[Summary.Value(tag=tag, simple_value=val)])

summaries = []
    tensors = [
        (vertices, MeshPluginData.VERTEX),
        (faces, MeshPluginData.FACE),
        (colors, MeshPluginData.COLOR)
    ]
    tensors = [tensor for tensor in tensors if tensor[0] is not None]
    components = metadata.get_components_bitmask([
        content_type for (tensor, content_type) in tensors])

    for tensor, content_type in tensors:
        summaries.append(
            _get_tensor_summary(tag, display_name, description, tensor,
                                content_type, components, json_config))

    return Summary(value=summaries)

Args:
      name: A name for the generated node. Will also serve as the series name in
        TensorBoard.
      tensor: A real numeric Tensor containing a single value.
      collections: Optional list of graph collections keys. The new summary op is
        added to these collections. Defaults to `[GraphKeys.SUMMARIES]`.
    Returns:
      A scalar `Tensor` of type `string`. Which contains a `Summary` protobuf.
    Raises:
      ValueError: If tensor has the wrong shape or type.
    """
    name = _clean_tag(name)
    scalar = make_np(scalar)
    assert(scalar.squeeze().ndim == 0), 'scalar should be 0D'
    scalar = float(scalar)
    return Summary(value=[Summary.Value(tag=name, simple_value=scalar)])

tensor_proto = tensor_util.make_tensor_proto(
            tensor.data, dtype=tensor.data_type
        )
        summary_metadata = metadata.create_summary_metadata(
            tag,
            None,  # display_name
            tensor.content_type,
            components,
            shape,
            description,
            json_config=json_config,
        )
        instance_tag = metadata.get_instance_name(tag, tensor.content_type)
        summaries.append((instance_tag, summary_metadata, tensor_proto))

    summary = summary_pb2.Summary()
    for instance_tag, summary_metadata, tensor_proto in summaries:
        summary.value.add(
            tag=instance_tag, metadata=summary_metadata, tensor=tensor_proto
        )
    return summary

def _summary_pb(tag, hparams_plugin_data):
    """Create a summary holding the given `HParamsPluginData` message.

    Args:
      tag: The `str` tag to use.
      hparams_plugin_data: The `HParamsPluginData` message to use.

    Returns:
      A TensorBoard `summary_pb2.Summary` message.
    """
    summary = summary_pb2.Summary()
    summary_metadata = metadata.create_summary_metadata(hparams_plugin_data)
    summary.value.add(tag=tag, metadata=summary_metadata)
    return summary

Markdown is supported. Defaults to empty.

    Raises:
      TypeError: If the type of the data is unsupported.

    Returns:
      A `tf.Summary` protobuf object.
    """
    try:
        tensor = tensor_util.make_tensor_proto(data, dtype=np.object)
    except TypeError as e:
        raise TypeError("tensor must be of type string", e)
    summary_metadata = metadata.create_summary_metadata(
        display_name=None, description=description
    )
    summary = summary_pb2.Summary()
    summary.value.add(tag=tag, metadata=summary_metadata, tensor=tensor)
    return summary

def _ImageSummary(tag, height, width, colorspace, encoded_image):
    from tensorboard.compat.proto.summary_pb2 import Summary
    image = Summary.Image(
        height=height,
        width=width,
        colorspace=colorspace,
        encoded_image_string=encoded_image)
    return Summary(value=[Summary.Value(tag=tag, image=image)])