class_docstr: VGG Atrous multi layer feature extractor which produces multiple output
  feature maps.
  
  Parameters
  ----------
  layers : list of int
      Number of layer for vgg base network.
  filters : list of int
      Number of convolution filters for each layer.
  extras : list of list
      Extra layers configurations.
  batch_norm : bool
      If `True`, will use BatchNorm layers.
class_docstr_tok: ["VGG","Atrous","multi","layer","feature","extractor","which","produces","multiple","output","feature","maps",".","Parameters","--","--","--","--","--","layers",":","list","of","int","Number","of","layer","for","vgg","base","network",".","filters",":","list","of","int","Number","of","convolution","filters","for","each","layer",".","extras",":","list","of","list","Extra","layers","configurations",".","batch_norm",":","bool","If","`","True","`",",","will","use","BatchNorm","layers","."]
code: def __init__(
      self, layers, filters, extras, batch_norm=False, **kwargs
  ):
      super(VGGAtrousExtractor, self).__init__(
          layers, filters, batch_norm, **kwargs
      )
      with self.name_scope():
          self.extras = nn.HybridSequential()
          for i, config in enumerate(extras):
              extra = nn.HybridSequential(prefix="extra%d_" % (i))
              with extra.name_scope():
                  for f, k, s, p in config:
                      extra.add(nn.Conv2D(f, k, s, p, **self.init))
                      if batch_norm:
                          extra.add(nn.BatchNorm())
                      extra.add(nn.Activation("relu"))
              self.extras.add(extra)
  
imports: from __future__ import division
  import os
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from mxnet.initializer import Xavier
label_desc: Use image node_id 1 to create a new VGGAtrousExtractor object from inherited base classes: VGGAtrousBase with example: obj = VGGAtrousExtractor(layers, filters, extras, batch_norm)