class_docstr: RepLK Block.
  
  Args:
      in_channels (`int`): input dimension
      dw_channels (`int`): output or input dimension used in depthwise conv layers
      block_lk_size (`int`): kernel size of ReparamLargeKernelConv
      small_kernel (`int`): small kernel size of ReparamLargeKernelConv
      drop_rate (`float`): drop rate of drop path
      small_kernel_merged (`bool`): flag to merge small kernels in ReparamLargeKernelConv
class_docstr_tok: ["RepLK","Block",".","Args",":","in_channels","(","`","int","`",")",":","input","dimension","dw_channels","(","`","int","`",")",":","output","or","input","dimension","used","in","depthwise","conv","layers","block_lk_size","(","`","int","`",")",":","kernel","size","of","ReparamLargeKernelConv","small_kernel","(","`","int","`",")",":","small","kernel","size","of","ReparamLargeKernelConv","drop_rate","(","`","float","`",")",":","drop","rate","of","drop","path","small_kernel_merged","(","`","bool","`",")",":","flag","to","merge","small","kernels","in","ReparamLargeKernelConv"]
code: def forward(self, x):
      out = self.prelkb_bn(x)
      out = self.pw1(out)
      out = self.large_kernel(out)
      out = self.lk_nonlinear(out)
      out = self.pw2(out)
      return x + self.drop_path(out)
  
imports: from torch import nn
  from towhee.models.layers.droppath import DropPath
  from towhee.models.layers.conv_bn_activation import Conv2dBNActivation
label_desc: Use image node_id 2 for calling the RepLKBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown