code: def round_filters(filters, global_params):
      """Calculate and round number of filters based on width multiplier.
         Use width_coefficient, depth_divisor and min_depth of global_params.
  
      Args:
          filters (int): Filters number to be calculated.
          global_params (namedtuple): Global params of the model.
  
      Returns:
          new_filters: New filters number after calculating.
      """
      multiplier = global_params.width_coefficient
      if not multiplier:
          return filters
      # TODO: modify the params names.
      #       maybe the names (width_divisor,min_width)
      #       are more suitable than (depth_divisor,min_depth).
      divisor = global_params.depth_divisor
      min_depth = global_params.min_depth
      filters *= multiplier
      min_depth = (
          min_depth or divisor
      )  # pay attention to this line when using min_depth
      # follow the formula transferred from official TensorFlow implementation
      new_filters = max(
          min_depth, int(filters + divisor / 2) // divisor * divisor
      )
      if (
          new_filters < 0.9 * filters
      ):  # prevent rounding by more than 10%
          new_filters += divisor
      return int(new_filters)
  
imports: import re
  import math
  import collections
  from functools import partial
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.utils import model_zoo
label_desc: Use image node_id 1 for calling a global function with example usage: round_filters(filters, global_params) and returns: int, filters