code: def mttrpo_metaworld_mt1_push(ctxt, seed, epochs, batch_size):
      """Set up environment and algorithm and run the task.
  
      Args:
          ctxt (garage.experiment.ExperimentContext): The experiment
              configuration used by Trainer to create the snapshotter.
          seed (int): Used to seed the random number generator to produce
              determinism.
          epochs (int): Number of training epochs.
          batch_size (int): Number of environment steps in one batch.
  
      """
      set_seed(seed)
      n_tasks = 50
      mt1 = metaworld.MT1("push-v1")
      train_task_sampler = MetaWorldTaskSampler(
          mt1, "train", lambda env, _: normalize(env)
      )
      envs = [env_up() for env_up in train_task_sampler.sample(n_tasks)]
      env = MultiEnvWrapper(
          envs, sample_strategy=round_robin_strategy, mode="vanilla"
      )
  
      policy = GaussianMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=(64, 64),
          hidden_nonlinearity=torch.tanh,
          output_nonlinearity=None,
      )
  
      value_function = GaussianMLPValueFunction(
          env_spec=env.spec,
          hidden_sizes=(32, 32),
          hidden_nonlinearity=torch.tanh,
          output_nonlinearity=None,
      )
  
      sampler = RaySampler(
          agents=policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
      )
  
      algo = TRPO(
          env_spec=env.spec,
          policy=policy,
          value_function=value_function,
          sampler=sampler,
          discount=0.99,
          gae_lambda=0.95,
      )
  
      trainer = Trainer(ctxt)
      trainer.setup(algo, env)
      trainer.train(n_epochs=epochs, batch_size=batch_size)
  
file_path: garage/src/garage/examples/torch/mttrpo_metaworld_mt1_push.py
imports: import click
  import metaworld
  import torch
  from garage import wrap_experiment
  from garage.envs import normalize
  from garage.envs.multi_env_wrapper import MultiEnvWrapper, round_robin_strategy
  from garage.experiment.deterministic import set_seed
  from garage.experiment.task_sampler import MetaWorldTaskSampler
  from garage.sampler import RaySampler
  from garage.torch.algos import TRPO
  from garage.torch.policies import GaussianMLPPolicy
  from garage.torch.value_functions import GaussianMLPValueFunction
  from garage.trainer import Trainer
label_desc: Use image node_id 1 for calling a global function with example usage: mttrpo_metaworld_mt1_push(ctxt, seed, epochs, batch_size) without return types