code: def _get_kernel_bias(self):
      kernel_conv_1x1, bias_conv_1x1 = self._fuse_bn_tensor(
          self.conv_1x1
      )
      kernel_conv_1x1 = self._pad_kernel_1x1_to_kxk(
          kernel_conv_1x1, self.kernel_size // 2
      )
  
      kernel_identity, bias_identity = self._fuse_bn_tensor(
          self.identity
      )
  
      kernel_conv_kxk = 0
      bias_conv_kxk = 0
      for conv in self.conv_kxk:
          kernel, bias = self._fuse_bn_tensor(conv)
          kernel_conv_kxk += kernel
          bias_conv_kxk += bias
  
      kernel_reparam = (
          kernel_conv_kxk + kernel_conv_1x1 + kernel_identity
      )
      bias_reparam = bias_conv_kxk + bias_conv_1x1 + bias_identity
      return kernel_reparam, bias_reparam
  
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 5 for calling the LearnableRepLayer obj's underlying member method code with example usage: obj._get_kernel_bias() and returns: kernel_reparam,  bias_reparam