code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride=1,
      groups=1,
      num_conv_branches=1,
      lr_mult=1.0,
      lab_lr=0.1,
  ):
      super().__init__()
      self.is_repped = False
      self.groups = groups
      self.stride = stride
      self.kernel_size = kernel_size
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.num_conv_branches = num_conv_branches
      self.padding = (kernel_size - 1) // 2
  
      self.identity = (
          BatchNorm2D(
              num_features=in_channels,
              weight_attr=ParamAttr(learning_rate=lr_mult),
              bias_attr=ParamAttr(learning_rate=lr_mult),
          )
          if out_channels == in_channels and stride == 1
          else None
      )
  
      self.conv_kxk = nn.LayerList(
          [
              ConvBNLayer(
                  in_channels,
                  out_channels,
                  kernel_size,
                  stride,
                  groups=groups,
                  lr_mult=lr_mult,
              )
              for _ in range(self.num_conv_branches)
          ]
      )
  
      self.conv_1x1 = (
          ConvBNLayer(
              in_channels,
              out_channels,
              1,
              stride,
              groups=groups,
              lr_mult=lr_mult,
          )
          if kernel_size > 1
          else None
      )
  
      self.lab = LearnableAffineBlock(lr_mult=lr_mult, lab_lr=lab_lr)
      self.act = Act(lr_mult=lr_mult, lab_lr=lab_lr)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Constant, KaimingNormal
  from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Hardsigmoid, Hardswish, Identity, Linear, ReLU
  from paddle.regularizer import L2Decay
label_desc: Use image node_id 1 to create a new LearnableRepLayer object from inherited base classes: nn with example: obj = LearnableRepLayer(in_channels, out_channels, kernel_size, stride, groups, num_conv_branches, lr_mult, lab_lr)