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 __init__(
      self,
      num_classes,
      in_channels=3,
      enable_auxiliary_loss=True,
      align_corners=False,
      pretrained=None,
  ):
      super().__init__()
  
      self.learning_to_downsample = LearningToDownsample(
          in_channels, 32, 48, 64
      )
      self.global_feature_extractor = GlobalFeatureExtractor(
          in_channels=64,
          block_channels=[64, 96, 128],
          out_channels=128,
          expansion=6,
          num_blocks=[3, 3, 3],
          align_corners=True,
      )
      self.feature_fusion = FeatureFusionModule(
          64, 128, 128, align_corners
      )
      self.classifier = Classifier(128, num_classes)
  
      if enable_auxiliary_loss:
          self.auxlayer = layers.AuxLayer(64, 32, num_classes)
  
      self.enable_auxiliary_loss = enable_auxiliary_loss
      self.align_corners = align_corners
      self.pretrained = pretrained
      self.init_weight()
  
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 1 to create a new FastSCNN object from inherited base classes: nn with example: obj = FastSCNN(num_classes, in_channels, enable_auxiliary_loss, align_corners, pretrained)