hunk.code_window:     """Lists experiments in the directory subtree."""
       commands.list_experiments(project_path, sort)
   @cli.command()
   @click.argument("path", required=True, type=str)
  
hunk.labels: keep replace keep keep keep keep
hunk.after_edit:     commands.list_experiments(project_path, sort, output)
  
hunk.file_path: python/ray/tune/scripts.py
hunk.type: replace
file: # This can reach 18-19 reward within 10 minutes on a Tesla M60 GPU (e.g., G3 EC2 node)
  # with 32 workers and 10 envs per worker. This is more efficient than the non-vectorized
  # configuration which requires 128 workers to achieve the same performance.
  pong-impala-vectorized:
      env: PongNoFrameskip-v4
      run: IMPALA
      config:
          sample_batch_size: 50
          train_batch_size: 500
          num_workers: 32
          num_envs_per_worker: 10
  