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, momentum=0.1)
      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
              )
              m.weight = paddle.create_parameter(
                  shape=m.weight.shape,
                  dtype="float32",
                  default_initializer=paddle.nn.initializer.Normal(
                      0, math.sqrt(2.0 / n)
                  ),
              )
          elif isinstance(m, nn.BatchNorm2D):
              m.weight = paddle.create_parameter(
                  shape=m.weight.shape,
                  dtype="float32",
                  default_initializer=paddle.nn.initializer.Constant(
                      1.0
                  ),
              )
              m.bias = paddle.create_parameter(
                  shape=m.bias.shape,
                  dtype="float32",
                  default_initializer=paddle.nn.initializer.Constant(
                      0.0
                  ),
              )
  
file_path: PaddleOCR/ppocr/modeling/necks/fpn.py
imports: import paddle.nn
  import paddle
  import math
  import paddle.nn.functional
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)