code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      groups=1,
  ):
      super(ConvBN, self).__init__()
      self.conv = nn.Conv2D(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          groups=groups,
          bias_attr=False,
      )
      self.bn = nn.BatchNorm2D(num_features=out_channels)
  
imports: import os
  from typing import Union
  import paddle
  import paddle.nn
  import paddlehub.vision.transforms
  import numpy
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.cv_module import ImageClassifierModule
label_desc: Use image node_id 1 to create a new ConvBN object from inherited base classes: nn with example: obj = ConvBN(in_channels, out_channels, kernel_size, stride, padding, groups)