class_docstr: The ENet implementation based on PaddlePaddle.
  
  The original article refers to
      Adam Paszke, Abhishek Chaurasia, Sangpil Kim, Eugenio Culurciello, et al."ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation"
      (https://arxiv.org/abs/1606.02147).
  
  Args:
      num_classes (int): The unique number of target classes.
      in_channels (int, optional): The channels of input image. Default: 3.
      pretrained (str, optional): The path or url of pretrained model. Default: None.
      encoder_relu (bool, optional): When ``True`` ReLU is used as the activation
          function; otherwise, PReLU is used. Default: False.
      decoder_relu (bool, optional): When ``True`` ReLU is used as the activation
          function; otherwise, PReLU is used. Default: True.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_pretrained_model(self, self.pretrained)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg import utils
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 3 for calling the ENet obj's underlying member method code with example usage: obj.init_weight() without return types