code: def aggregate_var(h):
      """variance aggregation"""
      h_mean_squares = torch.mean(h * h, dim=1)
      h_mean = torch.mean(h, dim=1)
      var = torch.relu(h_mean_squares - h_mean * h_mean)
      return var
  
imports: from typing import List
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn import AvgPooling, SumPooling
  from ogb.graphproppred.mol_encoder import AtomEncoder
label_desc: Use image node_id 5 for calling a global function with example usage: aggregate_var(h) and returns: var