class_docstr: This class defines overparameterized large kernel conv block in `RepLKNet <https://arxiv.org/abs/2203.06717>`_
  Reference: https://github.com/DingXiaoH/RepLKNet-pytorch
  
  Args:
      opts: Command-line arguments.
      in_channels: Number of input channels.
      out_channels: Number of output channels.
      kernel_size: Kernel size of the large kernel conv branch.
      stride: Stride size. Default: 1
      dilation: Kernel dilation factor. Default: 1
      groups: Group number. Default: 1
      small_kernel_size: Kernel size of small kernel conv branch.
      inference_mode: If True, instantiates model in inference mode. Default: ``False``
      use_act: If True, activation is used. Default: ``True``
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docstr: Forward pass implements inference logic for module
  before and after reparameterization.
  
  Args:
      x: Input tensor of shape :math:`(B, C, H, W)`.
  
  Returns:
      torch.Tensor of shape :math:`(B, C, H, W)`.
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code: def forward(self, x: torch.Tensor, *args, **kwargs) -> torch.Tensor:
      """
      Forward pass implements inference logic for module
      before and after reparameterization.
  
      Args:
          x: Input tensor of shape :math:`(B, C, H, W)`.
  
      Returns:
          torch.Tensor of shape :math:`(B, C, H, W)`.
      """
      if hasattr(self, "lkb_reparam"):
          out = self.lkb_reparam(x)
      else:
          out = self.lkb_origin(x)
          if hasattr(self, "small_conv"):
              out += self.small_conv(x)
  
      self.activation(out)
      return out
  
imports: import argparse
  from typing import Tuple, Union
  import torch
  import torch.nn
  from cvnets.layers import ConvLayer2d, Identity
  from cvnets.layers.activation import build_activation_layer
  from cvnets.layers.normalization.batch_norm import BatchNorm2d
  from cvnets.modules import BaseModule, SqueezeExcitation
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: out