class_docstr: Densenet-BC model from the
  `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper.
  
  Parameters
  ----------
  num_init_features : int
      Number of filters to learn in the first convolution layer.
  growth_rate : int
      Number of filters to add each layer (`k` in the paper).
  block_config : list of int
      List of integers for numbers of layers in each pooling block.
  bn_size : int, default 4
      Multiplicative factor for number of bottle neck layers.
      (i.e. bn_size * k features in the bottleneck layer)
  dropout : float, default 0
      Rate of dropout after each dense layer.
  classes : int, default 1000
      Number of classification classes.
  norm_layer : object
      Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
      Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
  norm_kwargs : dict
      Additional `norm_layer` arguments, for example `num_devices=4`
      for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
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code: def hybrid_forward(self, F, x):
      x = self.features(x)
      x = self.output(x)
      return x
  
file_path: gluon-cv/gluoncv/model_zoo/densenet.py
imports: from mxnet.context import cpu
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
  from mxnet.gluon.contrib.nn import HybridConcurrent, Identity
label_desc: Use image node_id 2 for calling the DenseNet obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: x