class_docstr: RepLK Block.
  
  Args:
      in_channels (`int`): input dimension
      dw_channels (`int`): output or input dimension used in depthwise conv layers
      block_lk_size (`int`): kernel size of ReparamLargeKernelConv
      small_kernel (`int`): small kernel size of ReparamLargeKernelConv
      drop_rate (`float`): drop rate of drop path
      small_kernel_merged (`bool`): flag to merge small kernels in ReparamLargeKernelConv
class_docstr_tok: ["RepLK","Block",".","Args",":","in_channels","(","`","int","`",")",":","input","dimension","dw_channels","(","`","int","`",")",":","output","or","input","dimension","used","in","depthwise","conv","layers","block_lk_size","(","`","int","`",")",":","kernel","size","of","ReparamLargeKernelConv","small_kernel","(","`","int","`",")",":","small","kernel","size","of","ReparamLargeKernelConv","drop_rate","(","`","float","`",")",":","drop","rate","of","drop","path","small_kernel_merged","(","`","bool","`",")",":","flag","to","merge","small","kernels","in","ReparamLargeKernelConv"]
code: def __init__(
      self,
      in_channels,
      dw_channels,
      block_lk_size,
      small_kernel,
      drop_rate,
      small_kernel_merged=False,
  ):
      super().__init__()
      self.pw1 = conv_bn_relu(
          in_channels=in_channels,
          out_channels=dw_channels,
          kernel_size=1,
          stride=1,
          padding=0,
          groups=1,
      )
      self.pw2 = conv_bn(
          in_channels=dw_channels,
          out_channels=in_channels,
          kernel_size=1,
          stride=1,
          padding=0,
          groups=1,
      )
      self.large_kernel = ReparamLargeKernelConv(
          in_channels=dw_channels,
          out_channels=dw_channels,
          kernel_size=block_lk_size,
          stride=1,
          groups=dw_channels,
          small_kernel=small_kernel,
          small_kernel_merged=small_kernel_merged,
      )
      self.lk_nonlinear = nn.ReLU()
      self.prelkb_bn = nn.BatchNorm2d(in_channels)
      self.drop_path = (
          DropPath(drop_rate) if drop_rate > 0.0 else nn.Identity()
      )
  
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 1 to create a new RepLKBlock object from inherited base classes: nn with example: obj = RepLKBlock(in_channels, dw_channels, block_lk_size, small_kernel, drop_rate, small_kernel_merged)