class_docstr: A VGG-16 based feature extractor for SSD512.
  
  This is a feature extractor for :class:`~chainercv.links.model.ssd.SSD512`.
  This extractor is based on :class:`~chainercv.links.model.ssd.VGG16`.
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docstr: Compute feature maps from a batch of images.
  
  This method extracts feature maps from
  :obj:`conv4_3`, :obj:`conv7`, :obj:`conv8_2`,
  :obj:`conv9_2`, :obj:`conv10_2`, :obj:`conv11_2`, and :obj:`conv12_2`.
  
  Args:
      x (ndarray): An array holding a batch of images.
          The images should be resized to :math:`512\times 512`.
  
  Returns:
      list of Variable:
      Each variable contains a feature map.
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code: def forward(self, x):
      """Compute feature maps from a batch of images.
  
      This method extracts feature maps from
      :obj:`conv4_3`, :obj:`conv7`, :obj:`conv8_2`,
      :obj:`conv9_2`, :obj:`conv10_2`, :obj:`conv11_2`, and :obj:`conv12_2`.
  
      Args:
          x (ndarray): An array holding a batch of images.
              The images should be resized to :math:`512\\times 512`.
  
      Returns:
          list of Variable:
          Each variable contains a feature map.
      """
  
      ys = super(VGG16Extractor512, self).forward(x)
      for i in range(8, 12 + 1):
          h = ys[-1]
          h = F.relu(self["conv{:d}_1".format(i)](h))
          h = F.relu(self["conv{:d}_2".format(i)](h))
          ys.append(h)
      return ys
  
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 2 for calling the VGG16Extractor512 obj's underlying member method code with example usage: obj.forward(x) and returns: ys