code: def fixed_offset(data, num_nodes, scale):
      """Test"""
      len_data = len(data)
      len_per_part = int(len_data / scale)
      offset = np.arange(0, scale, dtype="int64")
      offset = offset * num_nodes
      offset = np.repeat(offset, len_per_part)
      if len(data.shape) > 1:
          data += offset.reshape([-1, 1])
      else:
          data += offset
  
file_path: PGL/legacy/examples/graphsage/train_scale.py
imports: import argparse
  import time
  import numpy
  import scipy.sparse
  from sklearn.preprocessing import StandardScaler
  import pgl
  from pgl.utils.logger import log
  from pgl.utils import paddle_helper
  import paddle
  import paddle.fluid
  import reader
  from model import graphsage_mean, graphsage_meanpool, graphsage_maxpool, graphsage_lstm
label_desc: Use image node_id 1 for calling a global function with example usage: fixed_offset(data, num_nodes, scale) without return types