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 forward(self, inputs):
      if hasattr(self, "lkb_reparam"):
          out = self.lkb_reparam(inputs)
      else:
          out = self.lkb_origin(inputs)
          if hasattr(self, "small_conv"):
              out += self.small_conv(inputs)
      return 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 ReparamLargeKernelConv obj's underlying member method code with example usage: obj.forward(inputs) and returns: out