class_docstr: Implementation of graph pooling
  
  This is an implementation of graph pooling
  
  Args:
      pool_type: The type of pooling ("sum", "mean" , "min", "max"). Default:None
class_docstr_tok: ["Implementation","of","graph","pooling","This","is","an","implementation","of","graph","pooling","Args",":","pool_type",":","The","type","of","pooling","(","``","sum","''",",","``","mean","''",",","``","min","''",",","``","max","''",")",".","Default",":","None"]
code: def __init__(self, pool_type=None):
      super(GraphPool, self).__init__()
      self.pool_type = pool_type
  
imports: import warnings
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  from pgl.math import segment_sum, segment_softmax, segment_topk, segment_pool
  from pgl.utils.transform import filter_adj
label_desc: Use image node_id 1 to create a new GraphPool object from inherited base classes: nn with example: obj = GraphPool(pool_type)