class_docstr: A simple implementation for FCN based on PaddlePaddle.
  
  The original article refers to
  Evan Shelhamer, et, al. "Fully Convolutional Networks for Semantic Segmentation"
  (https://arxiv.org/abs/1411.4038).
  
  Args:
      num_classes (int): The unique number of target classes.
      backbone (paddle.nn.Layer): Backbone networks.
      backbone_indices (tuple, optional): The values in the tuple indicate the indices of output of backbone.
          Default: (-1, ).
      channels (int, optional): The channels between conv layer and the last layer of FCNHead.
          If None, it will be the number of channels of input features. Default: None.
      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_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/fcn.py
imports: import paddle.nn
  import paddle.nn.functional
  import paddle
  from paddleseg import utils
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
label_desc: Use image node_id 3 for calling the FCN obj's underlying member method code with example usage: obj.init_weight() without return types