class_docstr: Expectation Maximization Attention Networks for Semantic Segmentation based on PaddlePaddle.
  
  The original article refers to
  Xia Li, et al. "Expectation-Maximization Attention Networks for Semantic Segmentation"
  (https://arxiv.org/abs/1907.13426)
  
  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.
      ema_channels (int): EMA module channels.
      gc_channels (int): The input channels to Global Context Block.
      num_bases (int): Number of bases.
      stage_num (int): The iteration number for EM.
      momentum (float): The parameter for updating bases.
      concat_input (bool): Whether concat the input and output of convs before classification layer. Default: True
      enable_auxiliary_loss (bool, optional): A bool value indicates whether adding auxiliary loss. Default: True.
      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.
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code: def init_weight(self):
      if self.pretrained is not None:
          utils.load_entire_model(self, self.pretrained)
  
file_path: PaddleSeg/paddleseg/models/emanet.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.cvlibs import manager
  from paddleseg.utils import utils
label_desc: Use image node_id 3 for calling the EMANet obj's underlying member method code with example usage: obj.init_weight() without return types