code: def __init__(self):
      super().__init__()
      c = 64  # The base channel count
      cs = [c, c * 2, c * 2, c * 4, c * 4, c * 8]
  
      self.timestep_embed = FourierFeatures(1, 16)
      self.down = nn.AvgPool2D(2)
      self.up = nn.Upsample(
          scale_factor=2, mode="bilinear", align_corners=False
      )
  
      self.net = nn.Sequential(
          ConvBlock(3 + 16, cs[0]),
          ConvBlock(cs[0], cs[0]),
          SkipBlock(
              [
                  self.down,
                  ConvBlock(cs[0], cs[1]),
                  ConvBlock(cs[1], cs[1]),
                  SkipBlock(
                      [
                          self.down,
                          ConvBlock(cs[1], cs[2]),
                          ConvBlock(cs[2], cs[2]),
                          SkipBlock(
                              [
                                  self.down,
                                  ConvBlock(cs[2], cs[3]),
                                  ConvBlock(cs[3], cs[3]),
                                  SkipBlock(
                                      [
                                          self.down,
                                          ConvBlock(cs[3], cs[4]),
                                          ConvBlock(cs[4], cs[4]),
                                          SkipBlock(
                                              [
                                                  self.down,
                                                  ConvBlock(
                                                      cs[4], cs[5]
                                                  ),
                                                  ConvBlock(
                                                      cs[5], cs[5]
                                                  ),
                                                  ConvBlock(
                                                      cs[5], cs[5]
                                                  ),
                                                  ConvBlock(
                                                      cs[5], cs[4]
                                                  ),
                                                  self.up,
                                              ]
                                          ),
                                          ConvBlock(cs[4] * 2, cs[4]),
                                          ConvBlock(cs[4], cs[3]),
                                          self.up,
                                      ]
                                  ),
                                  ConvBlock(cs[3] * 2, cs[3]),
                                  ConvBlock(cs[3], cs[2]),
                                  self.up,
                              ]
                          ),
                          ConvBlock(cs[2] * 2, cs[2]),
                          ConvBlock(cs[2], cs[1]),
                          self.up,
                      ]
                  ),
                  ConvBlock(cs[1] * 2, cs[1]),
                  ConvBlock(cs[1], cs[0]),
                  self.up,
              ]
          ),
          ConvBlock(cs[0] * 2, cs[0]),
          nn.Conv2D(cs[0], 3, 3, padding=1),
      )
  
imports: import math
  from dataclasses import dataclass
  from functools import partial
  import paddle
  import paddle.nn
label_desc: Use image node_id 1 to create a new SecondaryDiffusionImageNet2 object from inherited base classes: nn with example: obj = SecondaryDiffusionImageNet2()