class_docstr: ResidualConvUnit, pre-activate residual unit.
  Args:
      in_channels (int): number of channels in the input feature map.
      act_layer (nn.Module): activation layer.
      norm_layer (nn.Module): norm layer.
      stride (int): stride of the first block. Default: 1
      dilation (int): dilation rate for convs layers. Default: 1.
code: def __init__(
      self, in_channels, act_layer, norm_layer, stride=1, dilation=1
  ):
      super(PreActResidualConvUnit, self).__init__()
  
      self.conv1 = ConvModule(
          in_channels,
          in_channels,
          3,
          stride=stride,
          padding=dilation,
          dilation=dilation,
          norm_layer=norm_layer,
          act_layer=act_layer,
          bias=False,
          order=("act", "conv", "norm"),
      )
  
      self.conv2 = ConvModule(
          in_channels,
          in_channels,
          3,
          padding=1,
          norm_layer=norm_layer,
          act_layer=act_layer,
          bias=False,
          order=("act", "conv", "norm"),
      )
  
imports: import copy
  from functools import partial
  import math
  import warnings
  import torch
  import torch.nn
  from .ops import resize
label_desc: Use image node_id 1 to create a new PreActResidualConvUnit object from inherited base classes: nn with example: obj = PreActResidualConvUnit(in_channels, act_layer, norm_layer, stride, dilation)