code: def maml_trpo_metaworld_ml10(
      ctxt, seed, epochs, episodes_per_task, meta_batch_size
  ):
      """Set up environment and algorithm and run the task.
  
      Args:
          ctxt (ExperimentContext): The experiment configuration used by
              :class:`~Trainer: to create the :class:`~Snapshotter:.
          seed (int): Used to seed the random number generator to produce
              determinism.
          epochs (int): Number of training epochs.
          episodes_per_task (int): Number of episodes per epoch per task
              for training.
          meta_batch_size (int): Number of tasks sampled per batch.
  
      """
      set_seed(seed)
      ml10 = metaworld.ML10()
      tasks = MetaWorldTaskSampler(ml10, "train")
      env = tasks.sample(10)[0]()
      test_sampler = SetTaskSampler(
          MetaWorldSetTaskEnv, env=MetaWorldSetTaskEnv(ml10, "test")
      )
  
      policy = GaussianMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=(100, 100),
          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,
      )
  
      meta_evaluator = MetaEvaluator(test_task_sampler=test_sampler)
  
      sampler = RaySampler(
          agents=policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
          n_workers=meta_batch_size,
      )
  
      trainer = Trainer(ctxt)
      algo = MAMLTRPO(
          env=env,
          policy=policy,
          sampler=sampler,
          task_sampler=tasks,
          value_function=value_function,
          meta_batch_size=meta_batch_size,
          discount=0.99,
          gae_lambda=1.0,
          inner_lr=0.1,
          num_grad_updates=1,
          meta_evaluator=meta_evaluator,
      )
  
      trainer.setup(algo, env)
      trainer.train(
          n_epochs=epochs,
          batch_size=episodes_per_task * env.spec.max_episode_length,
      )
  
file_path: garage/src/garage/examples/torch/maml_trpo_metaworld_ml10.py
imports: import click
  import metaworld
  import torch
  from garage import wrap_experiment
  from garage.envs import MetaWorldSetTaskEnv
  from garage.experiment import MetaEvaluator, MetaWorldTaskSampler, SetTaskSampler
  from garage.experiment.deterministic import set_seed
  from garage.sampler import RaySampler
  from garage.torch.algos import MAMLTRPO
  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: maml_trpo_metaworld_ml10(ctxt, seed, epochs, episodes_per_task, meta_batch_size) without return types