code: def test_meta_evaluator_with_tf():
      set_seed(100)
      tasks = SetTaskSampler(PointEnv, wrapper=set_length)
      max_episode_length = 200
      env = PointEnv()
      n_eps = 3
      with tempfile.TemporaryDirectory() as log_dir_name:
          ctxt = SnapshotConfig(
              snapshot_dir=log_dir_name,
              snapshot_mode="none",
              snapshot_gap=1,
          )
          with TFTrainer(ctxt) as trainer:
              meta_eval = MetaEvaluator(
                  test_task_sampler=tasks,
                  n_test_tasks=10,
                  n_exploration_eps=n_eps,
              )
              policy = GaussianMLPPolicy(env.spec)
              algo = MockAlgo(
                  env, policy, max_episode_length, n_eps, meta_eval
              )
              trainer.setup(algo, env)
              log_file = tempfile.NamedTemporaryFile()
              csv_output = CsvOutput(log_file.name)
              logger.add_output(csv_output)
              meta_eval.evaluate(algo)
              algo_pickle = cloudpickle.dumps(algo)
          tf.compat.v1.reset_default_graph()
          with TFTrainer(ctxt) as trainer:
              algo2 = cloudpickle.loads(algo_pickle)
              trainer.setup(algo2, env)
              trainer.train(10, 0)
  
imports: import csv
  import tempfile
  import cloudpickle
  from dowel import CsvOutput, logger, tabular
  import numpy
  import pytest
  import tensorflow
  from garage.envs import PointEnv
  from garage.experiment import MetaEvaluator, SnapshotConfig
  from garage.experiment.deterministic import set_seed
  from garage.experiment.task_sampler import SetTaskSampler
  from garage.np.algos import MetaRLAlgorithm
  from garage.sampler import LocalSampler
  from garage.tf.policies import GaussianMLPPolicy
  from garage.trainer import TFTrainer, Trainer
label_desc: Use image node_id 4 for calling a global function with example usage: test_meta_evaluator_with_tf() without return types