code: def __init__(self, in_channels: int, out_channels: int) -> None:
      super().__init__()
      self.conv1 = ConvNormAct(in_channels, out_channels, 3, act="none")
      self.conv2 = ConvNormAct(in_channels, out_channels, 1, act="none")
      self.act = nn.SiLU(inplace=True)
      self.conv: Optional[nn.Conv2d] = None
  
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 1 to create a new RepVggBlock object from inherited base classes: Module with example: obj = RepVggBlock(in_channels, out_channels)