code: def __init__(self, nIn, nOut):
      super().__init__()
      n = int(nOut / 5)
      n1 = nOut - 4 * n
      self.c1 = C(nIn, n, 3, 2)
      self.d1 = CDilated(n, n1, 3, 1, 1)
      self.d2 = CDilated(n, n, 3, 1, 2)
      self.d4 = CDilated(n, n, 3, 1, 4)
      self.d8 = CDilated(n, n, 3, 1, 8)
      self.d16 = CDilated(n, n, 3, 1, 16)
      self.bn = nn.BatchNorm2d(nOut, eps=1e-3)
      self.act = nn.PReLU(nOut)
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new DownSamplerB object from inherited base classes: nn with example: obj = DownSamplerB(nIn, nOut)