class_docstr: Tests for timeline
code: def test_timeline_push_sum(self):
      # Use win_accumulate to simulate the push-sum algorithm (sync).
      outdegree = len(bf.out_neighbor_ranks())
      indegree = len(bf.in_neighbor_ranks())
      # we append the p at the last of data.
      x = torch.Tensor(
          [bf.rank() / (indegree + 1), 1.0 / bf.size() / (indegree + 1)]
      )
  
      # Remember we do not create buffer with 0.
      bf.win_create(x, name="x_buff")
      x = bf.win_update_then_collect(name="x_buff")
  
      for _ in range(10):
          bf.win_accumulate(
              x,
              name="x_buff",
              dst_weights={
                  rank: 1.0 / (outdegree + 1)
                  for rank in bf.out_neighbor_ranks()
              },
              require_mutex=True,
          )
          x.div_(1 + outdegree)
          x = bf.win_update_then_collect(name="x_buff")
  
      bf.barrier()
      # Do not forget to sync at last!
      x = bf.win_update_then_collect(name="x_buff")
  
      file_name = f"{self.temp_file}{bf.rank()}.json"
      with open(file_name, "r") as tf:
          timeline_text = tf.read()
          assert "MPI_WIN_ACCUMULATE" in timeline_text, timeline_text
          assert (
              "ENQUEUE_WIN_ACCUMULATE" in timeline_text
          ), timeline_text
  
      bf.win_free()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import time
  import threading
  import unittest
  import warnings
  import torch
  import bluefog.torch
  from bluefog.common import topology_util
  from bluefog.common.util import env
label_desc: Use image node_id 8 for calling the TimelineTests obj's underlying member method code with example usage: obj.test_timeline_push_sum() without return types