code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      padding=0,
      dilation=1,
      groups=1,
      padding_mode="zeros",
  ):
      super(RepVGGBlock, self).__init__()
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.kernel_size = kernel_size
      self.stride = stride
      self.padding = padding
      self.dilation = dilation
      self.groups = groups
      self.padding_mode = padding_mode
  
      assert kernel_size == 3
      assert padding == 1
  
      padding_11 = padding - kernel_size // 2
  
      self.nonlinearity = nn.ReLU()
  
      self.rbr_identity = (
          nn.BatchNorm2D(num_features=in_channels)
          if out_channels == in_channels and stride == 1
          else None
      )
      self.rbr_dense = ConvBN(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          groups=groups,
      )
      self.rbr_1x1 = ConvBN(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=1,
          stride=stride,
          padding=padding_11,
          groups=groups,
      )
  
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 RepVGGBlock object from inherited base classes: nn with example: obj = RepVGGBlock(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, padding_mode)