class_docstr: The GSCNN implementation based on PaddlePaddle.
  The original article refers to
  Towaki Takikawa, et, al. "Gated-SCNN: Gated Shape CNNs for Semantic Segmentation"
  (https://arxiv.org/pdf/1907.05740.pdf)
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone network, currently support Resnet50_vd/Resnet101_vd.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
         Default: (0, 1, 2, 3).
      aspp_ratios (tuple, optional): The dilation rate using in ASSP module.
          If output_stride=16, aspp_ratios should be set as (1, 6, 12, 18).
          If output_stride=8, aspp_ratios is (1, 12, 24, 36).
          Default: (1, 6, 12, 18).
      aspp_out_channels (int, optional): The output channels of ASPP module. Default: 256.
      align_corners (bool, optional): An argument of F.interpolate. It should be set to False when the feature size is even,
          e.g. 1024x512, otherwise it is True, e.g. 769x769. Default: False.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
imports: import cv2
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.models.backbones import resnet_vd
  from paddleseg.models import deeplab
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the GSCNN obj's underlying member method code with example usage: obj.init_weight() without return types