hunk.code_window:                 address,
                   runtime_env={
                       "py_modules": [str(Path(tmp_working_dir) / "test_module")]
                   })
       call_ray_init()
       def reinit():
           ray.shutdown()
  
hunk.labels: keep keep keep add keep keep keep keep keep
hunk.after_edit:         elif option == "py_modules_path":
               ray.init(
                   address,
                   runtime_env={
                       "py_modules": [Path(tmp_working_dir) / "test_module"]
                   })
  
hunk.file_path: python/ray/tests/test_runtime_env_working_dir.py
hunk.type: add
file: """Test the send/recv API."""
  import pytest
  import numpy as np
  import ray
  
  from ray.util.collective.types import Backend
  from ray.util.collective.tests.cpu_util import create_collective_workers
  
  
  @pytest.mark.parametrize("backend", [Backend.GLOO])
  @pytest.mark.parametrize("group_name", ["default", "test", "123?34!"])
  @pytest.mark.parametrize("dst_rank", [0, 1])
  @pytest.mark.parametrize(
      "array_size", [2, 2**5, 2**10, 2**15, 2**20, [2, 2], [5, 9, 10, 85]])
  def test_reduce_different_name(ray_start_single_node, group_name, array_size,
                                 dst_rank, backend):
      world_size = 2
      actors, _ = create_collective_workers(
          num_workers=world_size, group_name=group_name, backend=backend)
      ray.wait([
          a.set_buffer.remote(np.ones(array_size, dtype=np.float32) * (i + 1))
          for i, a in enumerate(actors)
      ])
      src_rank = 1 - dst_rank
      refs = []
      for i, actor in enumerate(actors):
          if i != dst_rank:
              ref = actor.do_send.remote(group_name, dst_rank)
          else:
              ref = actor.do_recv.remote(group_name, src_rank)
          refs.append(ref)
      results = ray.get(refs)
      for i in range(world_size):
          assert (results[i] == np.ones(array_size, dtype=np.float32) *
                  (src_rank + 1)).all()
  
  
  @pytest.mark.parametrize("backend", [Backend.GLOO])
  @pytest.mark.parametrize("dst_rank", [0, 1])
  def test_sendrecv_torch_numpy(ray_start_single_node, dst_rank, backend):
      import torch
      world_size = 2
      actors, _ = create_collective_workers(world_size, backend=backend)
      ray.wait([actors[1].set_buffer.remote(torch.ones(10, ) * 2)])
      src_rank = 1 - dst_rank
  
      refs = []
      for i, actor in enumerate(actors):
          if i != dst_rank:
              ref = actor.do_send.remote(dst_rank=dst_rank)
          else:
              ref = actor.do_recv.remote(src_rank=src_rank)
          refs.append(ref)
      results = ray.get(refs)
      if dst_rank == 0:
          assert (results[0] == np.ones((10, )) * 2).all()
          assert (results[1] == torch.ones((10, )) * 2).all()
      else:
          assert (results[0] == np.ones((10, ))).all()
          assert (results[1] == torch.ones((10, ))).all()
  
  
  @pytest.mark.parametrize("backend", [Backend.GLOO])
  def test_sendrecv_invalid_rank(ray_start_single_node, backend, dst_rank=3):
      world_size = 2
      actors, _ = create_collective_workers(world_size, backend=backend)
      with pytest.raises(ValueError):
          ray.get([a.do_send.remote(dst_rank=dst_rank) for a in actors])
  
  
  if __name__ == "__main__":
      import pytest
      import sys
      sys.exit(pytest.main(["-v", "-x", __file__]))
  