class_docstr: Down sampler.
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
class_docstr_tok: ["Down","sampler",".","Args",":","in_channels","(","int",")",":","Number","of","input","channels",".","out_channels","(","int",")",":","Number","of","output","channels","."]
code: def __init__(self, in_channels, out_channels):
      super().__init__()
      branch_channels = out_channels // 5
      remain_channels = out_channels - branch_channels * 4
      self.conv1 = nn.Conv2D(
          in_channels,
          branch_channels,
          3,
          stride=2,
          padding=1,
          bias_attr=False,
      )
      self.d_conv1 = nn.Conv2D(
          branch_channels,
          remain_channels,
          3,
          padding=1,
          bias_attr=False,
      )
      self.d_conv2 = nn.Conv2D(
          branch_channels,
          branch_channels,
          3,
          padding=2,
          dilation=2,
          bias_attr=False,
      )
      self.d_conv4 = nn.Conv2D(
          branch_channels,
          branch_channels,
          3,
          padding=4,
          dilation=4,
          bias_attr=False,
      )
      self.d_conv8 = nn.Conv2D(
          branch_channels,
          branch_channels,
          3,
          padding=8,
          dilation=8,
          bias_attr=False,
      )
      self.d_conv16 = nn.Conv2D(
          branch_channels,
          branch_channels,
          3,
          padding=16,
          dilation=16,
          bias_attr=False,
      )
      self.bn = layers.SyncBatchNorm(out_channels)
      self.act = nn.PReLU(out_channels)
  
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 DownSampler object from inherited base classes: nn with example: obj = DownSampler(in_channels, out_channels)