code: def __init__(
      self,
      growthRate,
      reduction,
      bottleneck,
      use_dropout,
      input_channel,
      **kwargs,
  ):
      super(DenseNet, self).__init__()
  
      nDenseBlocks = 16
      nChannels = 2 * growthRate
  
      self.conv1 = nn.Conv2D(
          input_channel,
          nChannels,
          kernel_size=7,
          padding=3,
          stride=2,
          bias_attr=False,
      )
      self.dense1 = self._make_dense(
          nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
      )
      nChannels += nDenseBlocks * growthRate
      out_channels = int(math.floor(nChannels * reduction))
      self.trans1 = Transition(nChannels, out_channels, use_dropout)
  
      nChannels = out_channels
      self.dense2 = self._make_dense(
          nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
      )
      nChannels += nDenseBlocks * growthRate
      out_channels = int(math.floor(nChannels * reduction))
      self.trans2 = Transition(nChannels, out_channels, use_dropout)
  
      nChannels = out_channels
      self.dense3 = self._make_dense(
          nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
      )
      self.out_channels = out_channels
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new DenseNet object from inherited base classes: nn with example: obj = DenseNet(growthRate, reduction, bottleneck, use_dropout, input_channel)