class_docstr: A combination of GCN layer and SAGPool layer,
  followed by a concatenated (mean||sum) readout operation.
class_docstr_tok: ["A","combination","of","GCN","layer","and","SAGPool","layer",",","followed","by","a","concatenated","(","mean||sum",")","readout","operation","."]
code: def forward(self, graph, feature):
      out = F.relu(self.conv(graph, feature))
      graph, out, _ = self.pool(graph, out)
      g_out = torch.cat(
          [self.avgpool(graph, out), self.maxpool(graph, out)], dim=-1
      )
      return graph, out, g_out
  
file_path: dgl/examples/pytorch/sagpool/layer.py
imports: import dgl
  import torch
  import torch.nn.functional
  from dgl.nn import AvgPooling, GraphConv, MaxPooling
  from utils import get_batch_id, topk
label_desc: Use image node_id 2 for calling the ConvPoolBlock obj's underlying member method code with example usage: obj.forward(graph, feature) and returns: graph,  out,  g_out