class_docstr: The CPS DeeplabV3P implementation based on PaddlePaddle.
  
  The original article refers to
  Xiaokang Chen, et, al. "Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision"
   (https://arxiv.org/abs/2106.01226)
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone_l (paddle.nn.Layer): Backbone network for branch1, currently support Resnet50/Resnet101.
      backbone_r (paddle.nn.Layer): Backbone network for branch2, currently support Resnet50/Resnet101.
      bn_eps (float): CPS model's batchnorm initialization used.
      bn_monentum (float): CPS model's batchnorm initialization used.
      backbone_indices (tuple, optional): Two values in the tuple indicate the indices of output of backbone.
         Default: (0, 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: True.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
      data_format(str, optional): Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".
code: def forward(self, data):
      if not self.training:
          pred1 = self.branch1(data)
          return pred1
  
      return self.branch1(data), self.branch2(data)
  
file_path: PaddleSeg/contrib/CrossPseudoSupervision/models/cps_deeplabv3p.py
imports: import paddle
  import paddle.nn
  from cvlibs import manager
  import models.initializer
  from paddleseg.models.deeplab import DeepLabV3P
label_desc: Use image node_id 2 for calling the CPSDeeplabV3P obj's underlying member method code with example usage: obj.forward(data) and returns: self,  self, pred1