class_docstr: A Darknet19 based feature extractor for YOLOv2.
  
  This is a feature extractor for :class:`~chainercv.links.model.yolo.YOLOv2`
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docstr: Compute a feature map from a batch of images.
  
  Args:
      x (ndarray): An array holding a batch of images.
          The images should be resized to :math:`416\times 416`.
  
  Returns:
      Variable:
docstr_tok: ["Compute","a","feature","map","from","a","batch","of","images",".","Args",":","x","(","ndarray",")",":","An","array","holding","a","batch","of","images",".","The","images","should","be","resized","to",":","math",":","`","416\\times","416","`",".","Returns",":","Variable",":"]
code: def forward(self, x):
      """Compute a feature map from a batch of images.
  
      Args:
          x (ndarray): An array holding a batch of images.
              The images should be resized to :math:`416\\times 416`.
  
      Returns:
          Variable:
      """
  
      h = x
      for i, link in enumerate(self):
          h = link(h)
          if i == 12:
              tmp = h
          elif i == 19:
              h, tmp = tmp, h
          elif i == 20:
              h = F.concat((_reorg(h), tmp))
          if i in {0, 1, 4, 7, 12}:
              h = _maxpool(h, 2)
      return h
  
imports: from __future__ import division
  import itertools
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer.links import Convolution2D
  from chainercv.links import Conv2DBNActiv
  from chainercv import utils
  from chainercv.links.model.yolo.yolo_base import YOLOBase
label_desc: Use image node_id 2 for calling the Darknet19Extractor obj's underlying member method code with example usage: obj.forward(x) and returns: h