code: def __init__(
      self, in_planes, out_planes, kernel_size=1, stride=1, padding=0
  ):
      super(Conv_BN_ReLU, self).__init__()
      self.conv = nn.Conv2D(
          in_planes,
          out_planes,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          bias_attr=False,
      )
      self.bn = nn.BatchNorm2D(out_planes)
      self.relu = nn.ReLU()
  
      for m in self.sublayers():
          if isinstance(m, nn.Conv2D):
              n = (
                  m._kernel_size[0]
                  * m._kernel_size[1]
                  * m._out_channels
              )
              normal_ = Normal(mean=0.0, std=math.sqrt(2.0 / n))
              normal_(m.weight)
          elif isinstance(m, nn.BatchNorm2D):
              zeros_(m.bias)
              ones_(m.weight)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from paddle import ParamAttr
  import os
  import sys
  import math
  from paddle.nn.initializer import TruncatedNormal, Constant, Normal
label_desc: Use image node_id 1 to create a new Conv_BN_ReLU object from inherited base classes: nn with example: obj = Conv_BN_ReLU(in_planes, out_planes, kernel_size, stride, padding)