docstr: Collect prints from this rank in distributed mode to avoid interleaved output
code: def defer_prints(cls):
      """Collect prints from this rank in distributed mode to avoid interleaved output"""
  
      if (
          torch.distributed.is_initialized()
          and torch.distributed.get_world_size() > 1
      ):
          cls.__new_stdout = io.StringIO(
              str(torch.distributed.get_rank())
          )
          cls.__builtin_print = __builtin__.print
  
          def deferred_print(*args, **kwargs):
              try:  # see if torchvision examples has suppressed other ranks with the force argument
                  cls.__builtin_print(
                      *args, file=cls.__new_stdout, force=True, **kwargs
                  )
              except:
                  cls.__builtin_print(
                      *args, file=cls.__new_stdout, **kwargs
                  )
  
          __builtin__.print = deferred_print
  
imports: import os
  import torch
  import json
  import string
  import time
  import numpy
  import sys
  import builtins
  import io
label_desc: Use image node_id 19 for calling the Permutation obj's underlying member method code with example usage: obj.defer_prints(cls) without return types