code: def bn_drop(self, feat, drop_rate):
      # feat = L.batch_norm(feat)
      feat = L.dropout(feat, dropout_prob=drop_rate)
      return feat
  
imports: import os
  import re
  import time
  from random import random
  from functools import reduce, partial
  import numpy
  import multiprocessing
  import paddle
  import paddle.fluid
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.graph_wrapper import GraphWrapper
  from pgl.layers.conv import gcn, gat
  from pgl.utils import paddle_helper
label_desc: Use image node_id 4 for calling the GraphSageModel obj's underlying member method code with example usage: obj.bn_drop(feat, drop_rate) and returns: feat