class_docstr: Base class of DeepLab V3+.
  
  Args:
      fature_extractor (callable): Feature extractor network.
          This network should return lowlevel and highlevel feature maps
          as :obj:`(lowlevel, highlevel)`.
      aspp (callable): ASPP network.
      decoder (callable): Decoder network.
      min_input_size (int or tuple of ints): Minimum image size of inputs.
          if height or width is lower than this values, input images are
          padded to be this shape. The default value is :obj:`(513, 513)`
      scales (tuple of floats): Scales for multi-scale prediction.
          Final outputs are averaged after softmax activation.
          The default value is :obj:`(1.0,)`.
      flip (bool): When this is true, a left-right flipped images are
          also input and finally averaged. When :obj:`len(scales)` are
          more than 1, flipped prediction is performed in each scales.
          The default value is :obj:`False`
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docstr: Conduct semantic segmentation from images.
  
  Args:
      imgs (iterable of numpy.ndarray): Arrays holding images.
          All images are in CHW and RGB format
          and the range of their values are :math:`[0, 255]`.
  
  Returns:
      list of numpy.ndarray:
  
      List of integer labels predicted from each image in the input list.
docstr_tok: ["Conduct","semantic","segmentation","from","images",".","Args",":","imgs","(","iterable","of","numpy.ndarray",")",":","Arrays","holding","images",".","All","images","are","in","CHW","and","RGB","format","and","the","range","of","their","values","are",":","math",":","`","[","0",",","255","]","`",".","Returns",":","list","of","numpy.ndarray",":","List","of","integer","labels","predicted","from","each","image","in","the","input","list","."]
code: def predict(self, imgs):
      """Conduct semantic segmentation from images.
  
      Args:
          imgs (iterable of numpy.ndarray): Arrays holding images.
              All images are in CHW and RGB format
              and the range of their values are :math:`[0, 255]`.
  
      Returns:
          list of numpy.ndarray:
  
          List of integer labels predicted from each image in the input list.
  
      """
  
      with chainer.using_config(
          "train", False
      ), chainer.function.no_backprop_mode():
          labels = []
          n_aug = (
              len(self.scales) if self.flip else len(self.scales) * 2
          )
  
          for img in imgs:
              score = 0
              for scale in self.scales:
                  score += self._get_proba(img, scale, False) / n_aug
                  if self.flip:
                      score += self._get_proba(img, scale, True) / n_aug
  
              label = np.argmax(score, axis=0).astype(np.int32)
              labels.append(label)
      return labels
  
imports: from __future__ import division
  import chainer
  import chainer.functions
  import chainer.links
  from chainercv.links.connection import Conv2DBNActiv
  from chainercv.links.connection import SeparableConv2DBNActiv
  from chainercv.links.model.deeplab.aspp import SeparableASPP
  from chainercv.links.model.deeplab.xception import Xception65
  from chainercv.transforms import resize
  from chainercv import utils
  import numpy
label_desc: Use image node_id 5 for calling the DeepLabV3plus obj's underlying member method code with example usage: obj.predict(imgs) and returns: labels