code: def conv_bn_swish(
      out, in_channels, channels, kernel=1, stride=1, pad=0, num_group=1
  ):
      out.append(
          nn.Conv2D(
              in_channels,
              channels,
              kernel,
              stride,
              pad,
              groups=num_group,
              bias_attr=False,
          )
      )
      out.append(nn.BatchNorm2D(channels))
      out.append(nn.Swish())
  
imports: import os
  from math import ceil
  from typing import Union
  import paddle
  import paddle.nn
  import numpy
  import paddlehub.vision.transforms
  from paddle import ParamAttr
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 2 for calling a global function with example usage: conv_bn_swish(out, in_channels, channels, kernel, stride, pad, num_group) without return types