class_docstr: Aggregate the context features according to the initial
  predicted probability distribution.
  Employ the soft-weighted method to aggregate the context.
  
  Output:
    The correlation of every class map with every feature map
    shape = [n, num_feats, num_classes, 1]
class_docstr_tok: ["Aggregate","the","context","features","according","to","the","initial","predicted","probability","distribution",".","Employ","the","soft-weighted","method","to","aggregate","the","context",".","Output",":","The","correlation","of","every","class","map","with","every","feature","map","shape","=","[","n",",","num_feats",",","num_classes",",","1","]"]
code: def __init__(self, cls_num=0, scale=1):
      super().__init__()
      self.cls_num = cls_num
      self.scale = scale
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.models import layers
  from paddleseg.utils import utils
  from paddleseg.cvlibs import manager, param_init
label_desc: Use image node_id 1 to create a new SpatialGatherModule object from inherited base classes: nn with example: obj = SpatialGatherModule(cls_num, scale)