code: def optimize_for_deployment(self) -> None:
      def _fuse_conv_bn_weights(
          m: ConvNormAct,
      ) -> tuple[nn.Parameter, nn.Parameter]:
          return fuse_conv_bn_weights(
              m.conv.weight,
              m.conv.bias,
              m.norm.running_mean,
              m.norm.running_var,
              m.norm.eps,
              m.norm.weight,
              m.norm.bias,
          )
  
      kernel3x3, bias3x3 = _fuse_conv_bn_weights(self.conv1)
      kernel1x1, bias1x1 = _fuse_conv_bn_weights(self.conv2)
      kernel3x3.add_(pad(kernel1x1, [1, 1, 1, 1]))
      bias3x3.add_(bias1x1)
  
      self.conv = nn.Conv2d(
          kernel3x3.shape[1], kernel3x3.shape[0], 3, 1, 1
      )
      self.conv.weight = kernel3x3
      self.conv.bias = bias3x3
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from kornia.contrib.models.common import ConvNormAct
  from kornia.core import Module, Tensor, concatenate, pad
label_desc: Use image node_id 3 for calling the RepVggBlock obj's underlying member method code with example usage: obj.optimize_for_deployment() and returns: fuse_conv_bn_weights