class_docstr: A Darknet based feature extractor for YOLOv2Tiny.
  
  
  This is a feature extractor for
  :class:`~chainercv.links.model.yolo.YOLOv2Tiny`
class_docstr_tok: ["A","Darknet","based","feature","extractor","for","YOLOv2Tiny",".","This","is","a","feature","extractor","for",":","class",":","`","~chainercv.links.model.yolo.YOLOv2Tiny","`"]
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 < 5:
              h = _maxpool(h, 2)
          elif i == 5:
              h = _maxpool(h, 2, stride=1)
      return h
  
file_path: chainercv/chainercv/experimental/links/model/yolo/yolo_v2_tiny.py
imports: import chainer
  from chainercv.links import Conv2DBNActiv
  from chainercv.links.model.yolo.yolo_v2 import _leaky_relu
  from chainercv.links.model.yolo.yolo_v2 import _maxpool
  from chainercv.links.model.yolo import YOLOv2Base
label_desc: Use image node_id 2 for calling the DarknetExtractor obj's underlying member method code with example usage: obj.forward(x) and returns: h