class_docstr: Implementation of GIN + SAGPool.
      
class_docstr_tok: ["Implementation","of","GIN","+","SAGPool","."]
code: def __init__(self, args, num_class):
      super(GINModel, self).__init__()
      self.args = args
      self.input_size = self.args.feat_size
      self.output_size = self.args.hidden_size
      self.init_eps = self.args.init_eps
      self.train_eps = self.args.train_eps
      self.ratio = self.args.pool_ratio
      self.num_class = num_class
      self.min_score = self.args.min_score
      self.conv1 = pgl.nn.GINConv(
          self.input_size,
          self.output_size,
          "relu",
          self.init_eps,
          self.train_eps,
      )
      self.pool1 = SAGPool(
          self.output_size,
          min_score=self.min_score,
          ratio=self.ratio,
          gnn=pgl.nn.GCNConv,
      )
      self.conv2 = pgl.nn.GINConv(
          self.output_size,
          self.output_size,
          "relu",
          self.init_eps,
          self.train_eps,
      )
      self.pool2 = SAGPool(
          self.output_size,
          min_score=self.min_score,
          ratio=self.ratio,
          gnn=pgl.nn.GCNConv,
      )
      self.conv3 = pgl.nn.GINConv(
          self.output_size,
          self.output_size,
          "relu",
          self.init_eps,
          self.train_eps,
      )
      self.out_layer = nn.Linear(self.output_size, self.num_class)
  
file_path: PGL/examples/sag_pool/model.py
imports: import os
  import sys
  import time
  import argparse
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  from pgl.nn import SAGPool, GINConv
  from pgl.math import segment_max
label_desc: Use image node_id 1 to create a new GINModel object from inherited base classes: nn with example: obj = GINModel(args, num_class)