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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code: def __init__(
      self,
      feature_extractor,
      aspp,
      decoder,
      min_input_size,
      scales=(1.0,),
      flip=False,
  ):
      super(DeepLabV3plus, self).__init__()
  
      if not isinstance(min_input_size, (list, tuple)):
          min_input_size = (int(min_input_size), int(min_input_size))
      self.min_input_size = min_input_size
      self.scales = scales
      self.flip = flip
  
      with self.init_scope():
          self.feature_extractor = feature_extractor
          self.aspp = aspp
          self.decoder = decoder
  
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 1 to create a new DeepLabV3plus object from inherited base classes: chainer with example: obj = DeepLabV3plus(feature_extractor, aspp, decoder, min_input_size, scales, flip)