code: def __init__(
      self, in_channels, out_channels, kernel_size, shortcut=True
  ):
      super(RSELayer, self).__init__()
      weight_attr = paddle.nn.initializer.KaimingUniform()
      self.out_channels = out_channels
      self.in_conv = nn.Conv2D(
          in_channels=in_channels,
          out_channels=self.out_channels,
          kernel_size=kernel_size,
          padding=int(kernel_size // 2),
          weight_attr=ParamAttr(initializer=weight_attr),
          bias_attr=False,
      )
      self.se_block = SEModule(self.out_channels)
      self.shortcut = shortcut
  
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
  from ppocr.modeling.necks.intracl import IntraCLBlock
  from ppocr.modeling.backbones.det_mobilenet_v3 import SEModule
label_desc: Use image node_id 1 to create a new RSELayer object from inherited base classes: nn with example: obj = RSELayer(in_channels, out_channels, kernel_size, shortcut)