code: def merge_files(full_output, suffix, n_workers):
      output = full_output + f".{suffix}"
      with open(output, "w") as full:
          for worker_id in range(n_workers):
              partial_path = worker_shard_path(
                  full_output, suffix, worker_id
              )
              partial = open(partial_path, "r")
              for line in partial:
                  print(line.strip(), file=full)
              partial_path.unlink()
  
file_path: textlesslib/tools/distributed_transcribe/transcribe.py
imports: import torch.distributed
  import torch
  import pathlib
  from data_handler import ManifestDataset
  from distributed import init_distributed_context
  import logging
  from textless.data.speech_encoder import SpeechEncoder
label_desc: Use image node_id 5 for calling a global function with example usage: merge_files(full_output, suffix, n_workers) without return types