class_docstr: Darknet v3.
  
  Parameters
  ----------
  layers : iterable
      Description of parameter `layers`.
  channels : iterable
      Description of parameter `channels`.
  classes : int, default is 1000
      Number of classes, which determines the dense layer output channels.
  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`.
  
  Attributes
  ----------
  features : mxnet.gluon.nn.HybridSequential
      Feature extraction layers.
  output : mxnet.gluon.nn.Dense
      A classes(1000)-way Fully-Connected Layer.
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code: def hybrid_forward(self, F, x):
      x = self.features(x)
      x = F.Pooling(x, kernel=(7, 7), global_pool=True, pool_type="avg")
      return self.output(x)
  
file_path: gluon-cv/gluoncv/model_zoo/yolo/darknet.py
imports: from __future__ import absolute_import
  import os
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from mxnet.gluon.nn import BatchNorm
label_desc: Use image node_id 2 for calling the DarknetV3 obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: self