def METHOD_NAME(
    summary_data: List[Tuple[str, List[str]]],
    total_parameters: int,
    trainable_parameters: int,
    model_size: float,
    **summarize_kwargs: Any,
) -> None:
    console = get_console()
    header_style: str = summarize_kwargs.get("header_style", "bold magenta")
    table = Table(header_style=header_style)
    table.add_column(" ", style="dim")
    table.add_column("Name", justify="left", no_wrap=True)
    table.add_column("Type")
    table.add_column("Params", justify="right")
    column_names = list(zip(*summary_data))[0]
    for column_name in ["In sizes", "Out sizes"]:
        if column_name in column_names:
            table.add_column(column_name, justify="right", style="white")
    rows = list(zip(*(arr[1] for arr in summary_data)))
    for row in rows:
        table.add_row(*row)
    console.print(table)
    parameters = []
    for param in [trainable_parameters, total_parameters - trainable_parameters, total_parameters, model_size]:
        parameters.append("{:<{}}".format(get_human_readable_count(int(param)), 10))
    grid = Table.grid(expand=True)
    grid.add_column()
    grid.add_column()
    grid.add_row(f"[bold]Trainable params[/]: {parameters[0]}")
    grid.add_row(f"[bold]Non-trainable params[/]: {parameters[1]}")
    grid.add_row(f"[bold]Total params[/]: {parameters[2]}")
    grid.add_row(f"[bold]Total estimated model params size (MB)[/]: {parameters[3]}")
    console.print(grid)