code: def aggregate_node_features(graph):
      efeat = graph.edge_feat["feat"]
      graph.edge_feat["feat"] = efeat
      nfeat = np.zeros(
          (graph.num_nodes, efeat.shape[-1]), dtype="float32"
      )
      edges_dst = graph.edges[:, 1]
      np_scatter(edges_dst, efeat, nfeat)
      graph.node_feat["feat"] = nfeat
  
imports: import math
  import torch
  import paddle
  import pgl
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.contrib.ogb.nodeproppred.dataset_pgl import PglNodePropPredDataset
  import time
  import copy
  from ogb.nodeproppred import Evaluator
  from utils import to_undirected, add_self_loop, linear_warmup_decay
  from model import Proteins_baseline_model, Proteins_label_embedding_model
  from partition import random_partition_v2
  import argparse
  from tqdm import tqdm
label_desc: Use image node_id 6 for calling a global function with example usage: aggregate_node_features(graph) without return types