class_docstr: Pooling layer from `An End-to-End Deep Learning Architecture for Graph Classification
  <https://www.cse.wustl.edu/~ychen/public/DGCNN.pdf>`__
  
  Parameters
  ----------
  k : int
      The number of nodes to hold for each graph.
code: def __init__(self, k):
      super(SortPooling, self).__init__()
      self.k = k
  
imports: from mxnet import gluon, nd
  from mxnet.gluon import nn
  from ...readout import broadcast_nodes, max_nodes, mean_nodes, softmax_nodes, sum_nodes, topk_nodes
label_desc: Use image node_id 1 to create a new SortPooling object from inherited base classes: nn with example: obj = SortPooling(k)