class_docstr: The ENCNet implementation based on PaddlePaddle.
  
  The original article refers to
  Hang Zhang, Kristin Dana, et, al. "Context Encoding for Semantic Segmentation".
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (Paddle.nn.Layer): A backbone network.
      backbone_indices (tuple): The values in the tuple indicate the indices of
          output of backbone.
      num_codes (int): The number of encoded words. Default: 32.
      mid_channels (int): The channels of middle layers. Default: 512.
      use_se_loss (int): Whether use semantic encoding loss. Default: True.
      add_lateral (int): Whether use lateral convolution layers. 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 paddle
  import paddle.nn
  import paddle.nn.functional
  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 ENCNet obj's underlying member method code with example usage: obj.init_weight() without return types