code: def _make_dense(
      self, nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
  ):
      layers = []
      for i in range(int(nDenseBlocks)):
          if bottleneck:
              layers.append(
                  Bottleneck(nChannels, growthRate, use_dropout)
              )
          else:
              layers.append(
                  SingleLayer(nChannels, growthRate, use_dropout)
              )
          nChannels += growthRate
      return nn.Sequential(*layers)
  
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 2 for calling the DenseNet obj's underlying member method code with example usage: obj._make_dense(nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout) and returns: nn