class_docstr: This class defines the dilated convolution.
class_docstr_tok: ["This","class","defines","the","dilated","convolution","."]
code: def __init__(self, nIn, nOut, kSize, avgsize):
      super().__init__()
  
      self.resolution_down = False
      if avgsize > 1:
          self.resolution_down = True
          self.down_res = nn.AvgPool2D(avgsize, avgsize)
          self.up_res = nn.UpsamplingBilinear2D(scale_factor=avgsize)
          self.avgsize = avgsize
  
      padding = int((kSize - 1) / 2)
      self.conv = nn.Sequential(
          nn.Conv2D(
              nIn,
              nIn,
              kernel_size=(kSize, kSize),
              stride=1,
              padding=(padding, padding),
              groups=nIn,
              bias_attr=False,
          ),
          nn.BatchNorm(nIn),
      )
  
      self.act_conv1x1 = nn.Sequential(
          nn.PReLU(nIn),
          nn.Conv2D(
              nIn, nOut, kernel_size=1, stride=1, bias_attr=False
          ),
      )
  
      self.bn = nn.BatchNorm(nOut)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new S2block object from inherited base classes: nn with example: obj = S2block(nIn, nOut, kSize, avgsize)