class_docstr: Squeeze-and-Excitation (SE) block w/ Swish: AvgPool, FC, Swish, FC, Sigmoid.
docstr: Round width of filters based on width multiplier
  Args:
      width (int): the channel dimensions of the input.
      multiplier (float): the multiplication factor.
      min_width (int): the minimum width after multiplication.
      divisor (int): the new width should be dividable by divisor.
docstr_tok: ["Round","width","of","filters","based","on","width","multiplier","Args",":","width","(","int",")",":","the","channel","dimensions","of","the","input",".","multiplier","(","float",")",":","the","multiplication","factor",".","min_width","(","int",")",":","the","minimum","width","after","multiplication",".","divisor","(","int",")",":","the","new","width","should","be","dividable","by","divisor","."]
code: def _round_width(self, width, multiplier, min_width=8, divisor=8):
      """
      Round width of filters based on width multiplier
      Args:
          width (int): the channel dimensions of the input.
          multiplier (float): the multiplication factor.
          min_width (int): the minimum width after multiplication.
          divisor (int): the new width should be dividable by divisor.
      """
      if not multiplier:
          return width
  
      width *= multiplier
      min_width = min_width or divisor
      width_out = max(
          min_width, int(width + divisor / 2) // divisor * divisor
      )
      if width_out < 0.9 * width:
          width_out += divisor
      return int(width_out)
  
file_path: SlowFast/slowfast/models/operators.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from slowfast.models.utils import get_gkern
  from pytorchvideo.layers.swish import Swish
label_desc: Use image node_id 1 for calling the SE obj's underlying member method code with example usage: obj._round_width(width, multiplier, min_width, divisor) and returns: int, width