code: def _round_filters(
      filters: int,
      width_coefficient: float | None,
      depth_divisor: float,
  ) -> int:
      """
      Calculate and round number of filters based on width coefficient multiplier and depth divisor.
  
      Args:
          filters: number of input filters.
          width_coefficient: width coefficient for model.
          depth_divisor: depth divisor to use.
  
      Returns:
          new_filters: new number of filters after calculation.
      """
  
      if not width_coefficient:
          return filters
  
      multiplier: float = width_coefficient
      divisor: float = depth_divisor
      filters_float: float = filters * multiplier
  
      # follow the formula transferred from official TensorFlow implementation
      new_filters: float = max(
          divisor, int(filters_float + divisor / 2) // divisor * divisor
      )
      if (
          new_filters < 0.9 * filters_float
      ):  # prevent rounding by more than 10%
          new_filters += divisor
      return int(new_filters)
  
imports: from __future__ import annotations
  import math
  import operator
  import re
  from functools import reduce
  from typing import NamedTuple
  import torch
  from torch import nn
  from torch.utils import model_zoo
  from monai.networks.blocks import BaseEncoder
  from monai.networks.layers.factories import Act, Conv, Pad, Pool
  from monai.networks.layers.utils import get_norm_layer
  from monai.utils.module import look_up_option
label_desc: Use image node_id 6 for calling a global function with example usage: _round_filters(filters, width_coefficient, depth_divisor) and returns: int, filters