code: def setup_method(self):
      super().setup_method()
  
      def circle(r, n):
          """Generate n points on a circle of radius r.
  
          Args:
              r (float): Radius of the circle.
              n (int): Number of points to generate.
  
          Yields:
              tuple(float, float): Coordinate of a point.
  
          """
          for t in np.arange(0, 2 * np.pi, 2 * np.pi / n):
              yield r * np.sin(t), r * np.cos(t)
  
      N = 4
      goals = circle(3.0, N)
      tasks = {
          str(i + 1): {
              "args": [],
              "kwargs": {
                  "goal": g,
                  "never_done": False,
                  "done_bonus": 0.0,
              },
          }
          for i, g in enumerate(goals)
      }
  
      latent_length = 1
      inference_window = 2
      self.batch_size = 100 * len(tasks)
      self.policy_ent_coeff = 2e-2
      self.encoder_ent_coeff = 2.2e-3
      self.inference_ce_coeff = 5e-2
      embedding_init_std = 1.0
      embedding_max_std = 2.0
      embedding_min_std = 0.38
      policy_init_std = 1.0
      policy_max_std = None
      policy_min_std = None
  
      task_names = sorted(tasks.keys())
      task_args = [tasks[t]["args"] for t in task_names]
      task_kwargs = [tasks[t]["kwargs"] for t in task_names]
  
      task_envs = [
          PointEnv(*t_args, **t_kwargs, max_episode_length=100)
          for t_args, t_kwargs in zip(task_args, task_kwargs)
      ]
      self.env = env = MultiEnvWrapper(
          task_envs, round_robin_strategy, mode="vanilla"
      )
      self.max_episode_length = self.env.spec.max_episode_length
  
      latent_lb = np.zeros(
          latent_length,
      )
      latent_ub = np.ones(
          latent_length,
      )
      latent_space = akro.Box(latent_lb, latent_ub)
  
      obs_lb, obs_ub = env.observation_space.bounds
      obs_lb_flat = env.observation_space.flatten(obs_lb)
      obs_ub_flat = env.observation_space.flatten(obs_ub)
      traj_lb = np.stack([obs_lb_flat] * inference_window)
      traj_ub = np.stack([obs_ub_flat] * inference_window)
      traj_space = akro.Box(traj_lb, traj_ub)
  
      task_embed_spec = InOutSpec(env.task_space, latent_space)
      traj_embed_spec = InOutSpec(traj_space, latent_space)
  
      self.inference = GaussianMLPEncoder(
          name="inference",
          embedding_spec=traj_embed_spec,
          hidden_sizes=[20, 10],
          std_share_network=True,
          init_std=2.0,
          output_nonlinearity=tf.nn.tanh,
          min_std=embedding_min_std,
      )
  
      task_encoder = GaussianMLPEncoder(
          name="embedding",
          embedding_spec=task_embed_spec,
          hidden_sizes=[20, 20],
          std_share_network=True,
          init_std=embedding_init_std,
          max_std=embedding_max_std,
          output_nonlinearity=tf.nn.tanh,
          min_std=embedding_min_std,
      )
  
      self.policy = GaussianMLPTaskEmbeddingPolicy(
          name="policy",
          env_spec=env.spec,
          encoder=task_encoder,
          hidden_sizes=[32, 16],
          std_share_network=True,
          max_std=policy_max_std,
          init_std=policy_init_std,
          min_std=policy_min_std,
      )
  
      self.baseline = LinearMultiFeatureBaseline(
          env_spec=env.spec,
          features=["observations", "tasks", "latents"],
      )
  
file_path: garage/tests/garage/tf/algos/test_te.py
imports: import akro
  import numpy
  import tensorflow
  from garage import InOutSpec
  from garage.envs import MultiEnvWrapper, PointEnv
  from garage.envs.multi_env_wrapper import round_robin_strategy
  from garage.np.baselines import LinearMultiFeatureBaseline
  from garage.sampler import LocalSampler
  from garage.tf.algos import TEPPO
  from garage.tf.algos.te import TaskEmbeddingWorker
  from garage.tf.embeddings import GaussianMLPEncoder
  from garage.tf.policies import GaussianMLPTaskEmbeddingPolicy
  from garage.trainer import TFTrainer
  from tests.fixtures import snapshot_config, TfGraphTestCase
label_desc: Use image node_id 1 for calling the TestTE obj's underlying member method code with example usage: obj.setup_method() without return types