class_docstr: Test class for MAML-VPG.
class_docstr_tok: ["Test","class","for","MAML-VPG","."]
docstr: Setup method which is called before every test.
docstr_tok: ["Setup","method","which","is","called","before","every","test","."]
code: def setup_method(self):
      """Setup method which is called before every test."""
      max_episode_length = 100
      self.env = normalize(
          GymEnv(
              HalfCheetahDirEnv(), max_episode_length=max_episode_length
          ),
          expected_action_scale=10.0,
      )
      self.task_sampler = SetTaskSampler(
          HalfCheetahDirEnv,
          wrapper=lambda env, _: normalize(
              GymEnv(env, max_episode_length=max_episode_length),
              expected_action_scale=10.0,
          ),
      )
      self.policy = GaussianMLPPolicy(
          env_spec=self.env.spec,
          hidden_sizes=(64, 64),
          hidden_nonlinearity=torch.tanh,
          output_nonlinearity=None,
      )
      self.value_function = GaussianMLPValueFunction(
          env_spec=self.env.spec, hidden_sizes=(32, 32)
      )
  
file_path: garage/tests/garage/torch/algos/test_maml_vpg.py
imports: import pytest
  import torch
  from garage.envs import GymEnv, normalize
  from garage.experiment import deterministic, MetaEvaluator, SetTaskSampler
  from garage.sampler import LocalSampler
  from garage.torch.algos import MAMLVPG
  from garage.torch.policies import GaussianMLPPolicy
  from garage.torch.value_functions import GaussianMLPValueFunction
  from garage.trainer import Trainer
  from tests.fixtures import snapshot_config
  from garage.envs.mujoco import HalfCheetahDirEnv
label_desc: Use image node_id 1 for calling the TestMAMLVPG obj's underlying member method code with example usage: obj.setup_method() without return types