code: def __init__(
      self,
      block,
      nblocks,
      growth_rate=12,
      reduction=0.5,
      num_classes=10,
  ):
      super(DenseNet, self).__init__()
      self.growth_rate = growth_rate
  
      num_planes = 2 * growth_rate
      self.conv1 = nn.Conv2d(
          3, num_planes, kernel_size=3, padding=1, bias=False
      )
  
      self.dense1 = self._make_dense_layers(
          block, num_planes, nblocks[0]
      )
      num_planes += nblocks[0] * growth_rate
      out_planes = int(math.floor(num_planes * reduction))
      self.trans1 = Transition(num_planes, out_planes)
      num_planes = out_planes
  
      self.dense2 = self._make_dense_layers(
          block, num_planes, nblocks[1]
      )
      num_planes += nblocks[1] * growth_rate
      out_planes = int(math.floor(num_planes * reduction))
      self.trans2 = Transition(num_planes, out_planes)
      num_planes = out_planes
  
      self.dense3 = self._make_dense_layers(
          block, num_planes, nblocks[2]
      )
      num_planes += nblocks[2] * growth_rate
      out_planes = int(math.floor(num_planes * reduction))
      self.trans3 = Transition(num_planes, out_planes)
      num_planes = out_planes
  
      self.dense4 = self._make_dense_layers(
          block, num_planes, nblocks[3]
      )
      num_planes += nblocks[3] * growth_rate
  
      self.bn = nn.BatchNorm2d(num_planes)
      self.linear = nn.Linear(num_planes, num_classes)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from ..tools.custom_modules import FakeReLU
label_desc: Use image node_id 1 to create a new DenseNet object from inherited base classes: nn with example: obj = DenseNet(block, nblocks, growth_rate, reduction, num_classes)