code: def check_support(alg, config, test_eager=False, test_trace=True):
      config["framework"] = "tf2"
      config["log_level"] = "ERROR"
      # Test both continuous and discrete actions.
      for cont in [True, False]:
          if cont and alg == "DQN":
              continue
  
          if cont:
              config["env"] = "Pendulum-v1"
          else:
              config["env"] = "CartPole-v1"
  
          a = get_trainable_cls(alg)
          if test_eager:
              print("tf-eager: alg={} cont.act={}".format(alg, cont))
              config["eager_tracing"] = False
              tune.Tuner(
                  a,
                  param_space=config,
                  run_config=air.RunConfig(
                      stop={"training_iteration": 1}, verbose=1
                  ),
              ).fit()
          if test_trace:
              config["eager_tracing"] = True
              print(
                  "tf-eager-tracing: alg={} cont.act={}".format(
                      alg, cont
                  )
              )
              tune.Tuner(
                  a,
                  param_space=config,
                  run_config=air.RunConfig(
                      stop={"training_iteration": 1}, verbose=1
                  ),
              ).fit()
  
imports: import unittest
  import ray
  from ray import air
  from ray import tune
  from ray.rllib.utils.framework import try_import_tf
  from ray.tune.registry import get_trainable_cls
label_desc: Use image node_id 1 for calling a global function with example usage: check_support(alg, config, test_eager, test_trace) without return types