class_docstr: A VGG-16 based feature extractor for SSD300.
  
  This is a feature extractor for :class:`~chainercv.links.model.ssd.SSD300`.
  This extractor is based on :class:`~chainercv.links.model.ssd.VGG16`.
class_docstr_tok: ["A","VGG-16","based","feature","extractor","for","SSD300",".","This","is","a","feature","extractor","for",":","class",":","`","~chainercv.links.model.ssd.SSD300","`",".","This","extractor","is","based","on",":","class",":","`","~chainercv.links.model.ssd.VGG16","`","."]
code: def __init__(self):
      init = {
          "initialW": initializers.LeCunUniform(),
          "initial_bias": initializers.Zero(),
      }
      super(VGG16Extractor300, self).__init__()
      with self.init_scope():
          self.conv8_1 = L.Convolution2D(256, 1, **init)
          self.conv8_2 = L.Convolution2D(
              512, 3, stride=2, pad=1, **init
          )
  
          self.conv9_1 = L.Convolution2D(128, 1, **init)
          self.conv9_2 = L.Convolution2D(
              256, 3, stride=2, pad=1, **init
          )
  
          self.conv10_1 = L.Convolution2D(128, 1, **init)
          self.conv10_2 = L.Convolution2D(256, 3, **init)
  
          self.conv11_1 = L.Convolution2D(128, 1, **init)
          self.conv11_2 = L.Convolution2D(256, 3, **init)
  
imports: from __future__ import division
  import numpy
  import chainer
  import chainer.functions
  from chainer import initializers
  import chainer.links
  from chainercv.links.model.ssd import Multibox
  from chainercv.links.model.ssd import Normalize
  from chainercv.links.model.ssd import SSD
  from chainercv import utils
label_desc: Use image node_id 1 to create a new VGG16Extractor300 object from inherited base classes: VGG16 with example: obj = VGG16Extractor300()