Add test for wrapping Isaac Gym preview environments in torch

tests/torch/test_wrapper_isaaclab.py

@@ -104,40 +104,3 @@ def test_env(capsys: pytest.CaptureFixture, num_states):
             assert isinstance(info, Mapping)
 
     env.close()
-
-# def test_vectorized_env(capsys: pytest.CaptureFixture):
-#     num_envs = 10
-#     action = torch.ones((num_envs, 1))
-
-#     # load wrap the environment
-#     original_env = gym.make_vec("Pendulum-v1", num_envs=num_envs)
-#     env = wrap_env(original_env, "gymnasium")
-#     assert isinstance(env, GymnasiumWrapper)
-
-#     # check properties
-#     assert env.state_space is None
-#     assert isinstance(env.observation_space, gym.Space) and env.observation_space.shape == (3,)
-#     assert isinstance(env.action_space, gym.Space) and env.action_space.shape == (1,)
-#     assert isinstance(env.num_envs, int) and env.num_envs == num_envs
-#     assert isinstance(env.num_agents, int) and env.num_agents == 1
-#     assert isinstance(env.device, torch.device)
-#     # check internal properties
-#     assert env._env is original_env
-#     assert env._unwrapped is original_env.unwrapped
-#     assert env._vectorized is True
-#     # check methods
-#     for _ in range(2):
-#         observation, info = env.reset()
-#         observation, info = env.reset()  # edge case: vectorized environments are autoreset
-#         assert isinstance(observation, torch.Tensor) and observation.shape == torch.Size([num_envs, 3])
-#         assert isinstance(info, Mapping)
-#         for _ in range(3):
-#             observation, reward, terminated, truncated, info = env.step(action)
-#             env.render()
-#             assert isinstance(observation, torch.Tensor) and observation.shape == torch.Size([num_envs, 3])
-#             assert isinstance(reward, torch.Tensor) and reward.shape == torch.Size([num_envs, 1])
-#             assert isinstance(terminated, torch.Tensor) and terminated.shape == torch.Size([num_envs, 1])
-#             assert isinstance(truncated, torch.Tensor) and truncated.shape == torch.Size([num_envs, 1])
-#             assert isinstance(info, Mapping)
-
-#     env.close()