How to use the tf2onnx.utils.port_name function in tf2onnx

# Same principle should work but we need to implement our own eye.
        raise ValueError("onehot op: only rank1 is supported")
    logit_name = node.input[0]
    logit_dtype = ctx.get_dtype(logit_name)
    logit_shape = ctx.get_shape(logit_name)
    utils.make_sure(logit_dtype, "Dtype of {} is None".format(logit_name))
    indices_dtype = ctx.get_dtype(indices_name)
    if indices_dtype != TensorProto.INT64:
        indices_cast = ctx.make_node("Cast", [indices_name], attr={"to": TensorProto.INT64})
        indices_name = indices_cast.output[0]
    indices_size = ctx.make_node("Size", [indices_name])
    indices_unsqueeze = ctx.make_node("Unsqueeze", [indices_name], attr={"axes": [1]})
    zero_const = ctx.make_const(utils.make_name("zero"), np.array(0, dtype=np.int64))
    one_const = ctx.make_const(utils.make_name("one"), np.array(1, dtype=np.int64))
    id_name = utils.make_name("sparse_softmax_id")
    id_output = utils.port_name(id_name)
    controlflow.make_range(ctx, zero_const.output[0], indices_size.output[0], one_const.output[0],
                           id_output, id_name, shape=[-1], dtype=TensorProto.INT64)
    id_unsqueeze = ctx.make_node("Unsqueeze", [id_output], attr={"axes": [1]})
    indices_with_id = ctx.make_node("Concat",
                                    [id_unsqueeze.output[0], indices_unsqueeze.output[0]],
                                    attr={"axis": 1})
    log_softmax = ctx.make_node(op_type="LogSoftmax",
                                inputs=[logit_name], dtypes=[logit_dtype], shapes=[logit_shape])
    gathernd_name = utils.make_name("sparse_softmax_gathernd")
    gathernd_output = utils.port_name(gathernd_name)
    tensor.make_gathernd(ctx, log_softmax.output[0], indices_with_id.output[0], gathernd_output,
                         gathernd_name, logit_dtype, [logit_shape], [logit_dtype])
    const_name = utils.make_name("const_negative_one")
    const_negative_one = ctx.make_const(const_name, np.array(-1).astype(utils.map_onnx_to_numpy_type(logit_dtype)))
    mul2 = ctx.make_node(op_type="Mul", inputs=[const_negative_one.output[0], gathernd_output])
    shapes = node.output_shapes

output_id = loop_1.output[1]
    elif rank == 0:
        _, if_node_output_id = create_if_op(g, input_ids_for_current_iter, output_data_type, output_shape[1:])
        output_id = if_node_output_id

    output_identity_name = utils.make_name("loop_output")
    loop_output_id = utils.port_name(output_identity_name)
    g.make_node(
        'Identity',
        [output_id],
        outputs=[loop_output_id],
        name=output_identity_name
    )

    cond_identity_name = utils.make_name("cond_output")
    cond_output_id = utils.port_name(cond_identity_name)
    g.make_node(
        'Identity',
        [cond_name],
        outputs=[cond_output_id],
        name=cond_identity_name
    )

    fake_var_identity_name = utils.make_name("fake_var_output")
    fake_var_output_id = utils.port_name(fake_var_identity_name)
    g.make_node(
        'Identity',
        [fake_var_name],
        outputs=[fake_var_output_id],
        name=fake_var_identity_name
    )

false_input_id_for_current_iter = get_inputs_for_current_iteration(g, false_input_id, iter_name)

    input_ids_for_current_iter = [cond_input_id_for_current_iter, true_input_id_for_current_iter,
                                  false_input_id_for_current_iter]
    output_id = None
    rank -= 1
    if rank >= 1:
        loop_1 = create_loop_op(g, input_ids_for_current_iter, output_data_type, output_shape[1:],
                                trip_count_input_ids, rank)
        output_id = loop_1.output[1]
    elif rank == 0:
        _, if_node_output_id = create_if_op(g, input_ids_for_current_iter, output_data_type, output_shape[1:])
        output_id = if_node_output_id

    output_identity_name = utils.make_name("loop_output")
    loop_output_id = utils.port_name(output_identity_name)
    g.make_node(
        'Identity',
        [output_id],
        outputs=[loop_output_id],
        name=output_identity_name
    )

    cond_identity_name = utils.make_name("cond_output")
    cond_output_id = utils.port_name(cond_identity_name)
    g.make_node(
        'Identity',
        [cond_name],
        outputs=[cond_output_id],
        name=cond_identity_name
    )

[output_id],
        outputs=[loop_output_id],
        name=output_identity_name
    )

    cond_identity_name = utils.make_name("cond_output")
    cond_output_id = utils.port_name(cond_identity_name)
    g.make_node(
        'Identity',
        [cond_name],
        outputs=[cond_output_id],
        name=cond_identity_name
    )

    fake_var_identity_name = utils.make_name("fake_var_output")
    fake_var_output_id = utils.port_name(fake_var_identity_name)
    g.make_node(
        'Identity',
        [fake_var_name],
        outputs=[fake_var_output_id],
        name=fake_var_identity_name
    )

    g.add_graph_output(cond_output_id, TensorProto.BOOL, ())
    g.add_graph_output(fake_var_output_id, TensorProto.FLOAT, ())

    # use None for all dims, just keep original rank. Because it is observed, dims might be changed in loop.
    g.add_graph_output(loop_output_id, output_data_type, utils.create_vague_shape_like(output_shape[1:]))

    return g

"""Create and insert a new node into the graph.
        Args:
            op_type: type for new operation
            output_name: the names of the outputs above us
            name: the name of the new op
            kwargs: attributes of the new node

        Returns:
            node that was inserted
        """
        utils.make_sure(isinstance(output_name, six.text_type), "output_name's type is not expected: %s",
                        type(output_name))
        utils.make_sure(isinstance(op_type, six.text_type), "op_type's type is not expected: %s",
                        type(op_type))

        new_output = port_name(name)
        new_node = self.make_node(op_type, [output_name], attr=kwargs, outputs=[new_output], name=name, domain=domain)

        to_replace = [n for n in self.get_nodes() if n != new_node]
        self.replace_all_inputs(to_replace, output_name, new_output)
        return new_node

def create_if_op(g, input_ids, output_data_type, output_shape):
    op_name = utils.make_name("If")
    true_graph = create_body_graph_for_if_branch(g, output_data_type, output_shape, input_ids[1], op_name)
    false_graph = create_body_graph_for_if_branch(g, output_data_type, output_shape, input_ids[2], op_name)
    out_name = utils.port_name(op_name)

    # output a scalar
    if_node = g.make_node("If", [input_ids[0]], outputs=[out_name], name=op_name, skip_conversion=False)
    if_node.set_body_graph_as_attr("then_branch", true_graph)
    if_node.set_body_graph_as_attr("else_branch", false_graph)
    return if_node, out_name

def create_if_op(g, input_ids, output_data_type, output_shape):
    op_name = utils.make_name("If")
    true_graph = create_body_graph_for_if_branch(g, output_data_type, output_shape, input_ids[1], op_name)
    false_graph = create_body_graph_for_if_branch(g, output_data_type, output_shape, input_ids[2], op_name)
    out_name = utils.port_name(op_name)

    # output a scalar
    if_node = g.make_node("If", [input_ids[0]], outputs=[out_name], name=op_name, skip_conversion=False)
    if_node.set_body_graph_as_attr("then_branch", true_graph)
    if_node.set_body_graph_as_attr("else_branch", false_graph)
    return if_node, out_name

false_input_id_for_current_iter = get_inputs_for_current_iteration(g, false_input_id, iter_name)

    input_ids_for_current_iter = [cond_input_id_for_current_iter, true_input_id_for_current_iter,
                                  false_input_id_for_current_iter]
    output_id = None
    rank = rank - 1
    if rank >= 1:
        loop_1 = create_loop_op(g, input_ids_for_current_iter, output_data_type, output_shape[1:],
                                trip_count_input_ids, rank)
        output_id = loop_1.output[1]
    elif rank == 0:
        _, if_node_output_id = create_if_op(g, input_ids_for_current_iter, output_data_type, output_shape[1:])
        output_id = if_node_output_id

    output_identity_name = utils.make_name("loop_output")
    loop_output_id = utils.port_name(output_identity_name)
    g.make_node(
        'Identity',
        [output_id],
        outputs=[loop_output_id],
        name=output_identity_name
    )

    cond_identity_name = utils.make_name("cond_output")
    cond_output_id = utils.port_name(cond_identity_name)
    g.make_node(
        'Identity',
        [cond_name],
        outputs=[cond_output_id],
        name=cond_identity_name
    )

"""Create and insert a new node into the graph.
        Args:
            op_type: type for new operation
            output_name: the names of the outputs above us
            name: the name of the new op
            kwargs: attributes of the new node

        Returns:
            node that was inserted
        """
        utils.make_sure(isinstance(output_name, six.text_type), "output_name's type is not expected: %s",
                        type(output_name))
        utils.make_sure(isinstance(op_type, six.text_type), "op_type's type is not expected: %s",
                        type(op_type))

        new_output = port_name(name)
        new_node = self.make_node(op_type, [output_name], attr=kwargs, outputs=[new_output], name=name, domain=domain)

        to_replace = [n for n in self.get_nodes() if n != new_node]
        self.replace_all_inputs(to_replace, output_name, new_output)
        return new_node