class_docstr: The FastSCNN implementation based on PaddlePaddle.
  As mentioned in the original paper, FastSCNN is a real-time segmentation algorithm (123.5fps)
  even for high resolution images (1024x2048).
  The original article refers to
  Poudel, Rudra PK, et al. "Fast-scnn: Fast semantic segmentation network"
  (https://arxiv.org/pdf/1902.04502.pdf).
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): The channels of input image. Default: 3.
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss.
          If true, auxiliary loss will be added after LearningToDownsample module. Default: False.
      align_corners (bool): An argument of F.interpolate. It should be set to False when the output size of feature
          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.
code: def forward(self, x):
      logit_list = []
      input_size = paddle.shape(x)[2:]
      higher_res_features = self.learning_to_downsample(x)
      x = self.global_feature_extractor(higher_res_features)
      x = self.feature_fusion(higher_res_features, x)
      logit = self.classifier(x)
      logit = F.interpolate(
          logit,
          input_size,
          mode="bilinear",
          align_corners=self.align_corners,
      )
      logit_list.append(logit)
  
      if self.enable_auxiliary_loss:
          auxiliary_logit = self.auxlayer(higher_res_features)
          auxiliary_logit = F.interpolate(
              auxiliary_logit,
              input_size,
              mode="bilinear",
              align_corners=self.align_corners,
          )
          logit_list.append(auxiliary_logit)
  
      return logit_list
  
imports: import paddle.nn
  import paddle.nn.functional
  import paddle
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 2 for calling the FastSCNN obj's underlying member method code with example usage: obj.forward(x) and returns: logit_list