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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def worker():
 """ Initialize the distributed environment. """


  import torch
  import torch.distributed as dist
  from torch.multiprocessing import Process
  import numpy as np

  print("Initializing distributed pytorch")
  os.environ['MASTER_ADDR'] = str(args.master_addr)
  os.environ['MASTER_PORT'] = str(args.master_port)
  # Use TCP backend. Gloo needs nightly, where it currently fails with
  #     dist.init_process_group('gloo', rank=args.rank,
  #   AttributeError: module 'torch.distributed' has no attribute 'init_process_group'
  dist.init_process_group('tcp', rank=args.rank,
                          world_size=args.size)

  tensor = torch.ones(args.size_mb*250*1000)*(args.rank+1)
  time_list = []
  outfile = 'out' if args.rank == 0 else '/dev/null'
  log = util.FileLogger(outfile)
  for i in range(args.iters):
    # print('before: rank ', args.rank, ' has data ', tensor[0])

    start_time = time.perf_counter()
    if args.rank == 0:
      dist.send(tensor=tensor, dst=1)
    else:
      dist.recv(tensor=tensor, src=0)
      
    elapsed_time_ms = (time.perf_counter() - start_time)*1000
    time_list.append(elapsed_time_ms)
    # print('after: rank ', args.rank, ' has data ', tensor[0])
    rate = args.size_mb/(elapsed_time_ms/1000)

    log('%03d/%d added %d MBs in %.1f ms: %.2f MB/second' % (i, args.iters, args.size_mb, elapsed_time_ms, rate))

  min = np.min(time_list)
  median = np.median(time_list)
  log(f"min: {min:8.2f}, median: {median:8.2f}, mean: {np.mean(time_list):8.2f}")