code: def __init__(
      self,
      last_planes,
      in_planes,
      out_planes,
      dense_depth,
      stride,
      first_layer,
  ):
      super(Bottleneck, self).__init__()
      self.out_planes = out_planes
      self.dense_depth = dense_depth
  
      self.conv1 = nn.Conv2d(
          last_planes, in_planes, kernel_size=1, bias=False
      )
      self.bn1 = nn.BatchNorm2d(in_planes)
      self.conv2 = nn.Conv2d(
          in_planes,
          in_planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          groups=32,
          bias=False,
      )
      self.bn2 = nn.BatchNorm2d(in_planes)
      self.conv3 = nn.Conv2d(
          in_planes, out_planes + dense_depth, kernel_size=1, bias=False
      )
      self.bn3 = nn.BatchNorm2d(out_planes + dense_depth)
  
      self.shortcut = nn.Sequential()
      if first_layer:
          self.shortcut = nn.Sequential(
              nn.Conv2d(
                  last_planes,
                  out_planes + dense_depth,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(out_planes + dense_depth),
          )
  
file_path: nni/examples/trials/cifar10_pytorch/models/dpn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(last_planes, in_planes, out_planes, dense_depth, stride, first_layer)