class_docstr: Large Kernel Convolutions with new parameters.
  
  Args:
      in_channels (`int`): input dimension
      out_channels (`int`): output dimension
      kernel_size (`int`): kernel size of conv
      stride (`int`): stride of conv
      groups (`int`): groups of conv
      small_kernel (`int`): small kernel size
      small_kernel_merged (`bool`): flag to merge
class_docstr_tok: ["Large","Kernel","Convolutions","with","new","parameters",".","Args",":","in_channels","(","`","int","`",")",":","input","dimension","out_channels","(","`","int","`",")",":","output","dimension","kernel_size","(","`","int","`",")",":","kernel","size","of","conv","stride","(","`","int","`",")",":","stride","of","conv","groups","(","`","int","`",")",":","groups","of","conv","small_kernel","(","`","int","`",")",":","small","kernel","size","small_kernel_merged","(","`","bool","`",")",":","flag","to","merge"]
code: def merge_kernel(self):
      eq_k, eq_b = self.get_equivalent_kernel_bias()
      self.lkb_reparam = nn.Conv2d(
          in_channels=self.lkb_origin.conv2d.in_channels,
          out_channels=self.lkb_origin.conv2d.out_channels,
          kernel_size=self.lkb_origin.conv2d.kernel_size,
          stride=self.lkb_origin.conv2d.stride,
          padding=self.lkb_origin.conv2d.padding,
          dilation=self.lkb_origin.conv2d.dilation,
          groups=self.lkb_origin.conv2d.groups,
          bias=True,
      )
      self.lkb_reparam.weight.data = eq_k
      self.lkb_reparam.bias.data = eq_b
      self.__delattr__("lkb_origin")
      if hasattr(self, "small_conv"):
          self.__delattr__("small_conv")
  
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 4 for calling the ReparamLargeKernelConv obj's underlying member method code with example usage: obj.merge_kernel() without return types