hunk.code_window:     if not isinstance(element, dict):
           if base_env:
               error = (
                   f"The element returned by {function_string} has values "
                   f"that are not MultiAgentDicts. Instead, they are of "
                   f"type: {type(element)}"
               )
  
hunk.labels: keep keep keep replace keep keep keep
hunk.after_edit:                 f"The element returned by {function_string} contains values "
  
hunk.file_path: rllib/utils/pre_checks/env.py
hunk.type: replace
file: import json
  import os
  import time
  
  import ray
  from ray.util.ray_lightning.simple_example import main
  
  if __name__ == "__main__":
      start = time.time()
  
      addr = os.environ.get("RAY_ADDRESS")
      job_name = os.environ.get("RAY_JOB_NAME", "ray_lightning_user_test")
  
      # Manually set NCCL_SOCKET_IFNAME to "ens3" so NCCL training works on
      # anyscale_default_cloud.
      # See https://github.com/pytorch/pytorch/issues/68893 for more details.
      # Passing in runtime_env to ray.init() will also set it for all the
      # workers.
      runtime_env = {"env_vars": {"NCCL_SOCKET_IFNAME": "ens3"}}
  
      if addr is not None and addr.startswith("anyscale://"):
          ray.init(address=addr, job_name=job_name, runtime_env=runtime_env)
      else:
          ray.init(address="auto", runtime_env=runtime_env)
  
      main(num_workers=6, use_gpu=True, max_steps=50)
  
      taken = time.time() - start
      result = {
          "time_taken": taken,
      }
      test_output_json = os.environ.get(
          "TEST_OUTPUT_JSON", "/tmp/ray_lightning_user_test.json"
      )
      with open(test_output_json, "wt") as f:
          json.dump(result, f)
  
      print("Test Successful!")
  