code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      activation: str = ACT_RELU,
      normalization: str = NORM_BATCH,
      normalization_kwargs=None,
      activation_kwargs=None,
      drop_path_rate: float = 0.0,
  ):
      super().__init__()
  
      if activation_kwargs is None:
          activation_kwargs = {"inplace": True}
      if normalization_kwargs is None:
          normalization_kwargs = {}
  
      self.residual = (
          nn.Conv2d(
              in_channels,
              out_channels,
              kernel_size=1,
              padding=0,
              stride=1,
              bias=False,
          )
          if in_channels != out_channels
          else nn.Identity()
      )
  
      self.conv1 = nn.Conv2d(
          in_channels,
          out_channels,
          kernel_size=3,
          padding=1,
          stride=1,
          bias=False,
      )
      self.norm1 = instantiate_normalization_block(
          normalization, out_channels, **normalization_kwargs
      )
      self.act1 = instantiate_activation_block(
          activation, **activation_kwargs
      )
  
      self.conv2 = nn.Conv2d(
          out_channels,
          out_channels,
          kernel_size=3,
          padding=1,
          stride=1,
          bias=False,
      )
      self.norm2 = instantiate_normalization_block(
          normalization, out_channels, **normalization_kwargs
      )
      self.act2 = instantiate_activation_block(
          activation, **activation_kwargs
      )
  
      self.drop_path = (
          DropPath(drop_path_rate)
          if drop_path_rate > 0.0
          else nn.Identity()
      )
  
file_path: pytorch-toolbelt/pytorch_toolbelt/modules/unet.py
imports: from torch import nn
  from pytorch_toolbelt.modules.drop_path import DropPath
  from pytorch_toolbelt.modules.activations import ACT_RELU, instantiate_activation_block
  from pytorch_toolbelt.modules.normalization import NORM_BATCH, instantiate_normalization_block
label_desc: Use image node_id 1 to create a new UnetResidualBlock object from inherited base classes: nn with example: obj = UnetResidualBlock(in_channels, out_channels, activation, normalization, normalization_kwargs, activation_kwargs, drop_path_rate)