code: def mtsac_metaworld_mt50(
      ctxt=None, *, seed, use_gpu, _gpu, n_tasks, timesteps
  ):
      """Train MTSAC with MT50 environment.
  
      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.
          use_gpu (bool): Used to enable ussage of GPU in training.
          _gpu (int): The ID of the gpu (used on multi-gpu machines).
          n_tasks (int): Number of tasks to use. Should be a multiple of 50.
          timesteps (int): Number of timesteps to run.
  
      """
      deterministic.set_seed(seed)
      trainer = Trainer(ctxt)
      mt50 = metaworld.MT50()  # pylint: disable=no-member
      mt50_test = metaworld.MT50()  # pylint: disable=no-member
      train_task_sampler = MetaWorldTaskSampler(
          mt50,
          "train",
          lambda env, _: normalize(env, normalize_reward=True),
          add_env_onehot=True,
      )
      test_task_sampler = MetaWorldTaskSampler(
          mt50_test,
          "train",
          lambda env, _: normalize(env),
          add_env_onehot=True,
      )
      assert n_tasks % 50 == 0
      assert n_tasks <= 2500
      mt50_train_envs = train_task_sampler.sample(n_tasks)
      env = mt50_train_envs[0]()
      mt50_test_envs = [
          env_up() for env_up in test_task_sampler.sample(n_tasks)
      ]
  
      policy = TanhGaussianMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=[400, 400, 400],
          hidden_nonlinearity=nn.ReLU,
          output_nonlinearity=None,
          min_std=np.exp(-20.0),
          max_std=np.exp(2.0),
      )
  
      qf1 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[400, 400, 400],
          hidden_nonlinearity=F.relu,
      )
  
      qf2 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[400, 400, 400],
          hidden_nonlinearity=F.relu,
      )
  
      replay_buffer = PathBuffer(
          capacity_in_transitions=int(1e6),
      )
  
      sampler = LocalSampler(
          agents=policy,
          envs=mt50_train_envs,
          max_episode_length=env.spec.max_episode_length,
          # 1 sampler worker for each environment
          n_workers=50,
          worker_class=FragmentWorker,
          # increasing n_envs increases the vectorization of a sampler worker
          # which improves runtime performance, but you will need to adjust this
          # depending on your memory constraints. For reference, each worker by
          # default uses n_envs=8. Each environment is approximately ~50mb large
          # so creating 50 envs with 8 copies comes out to 20gb of memory. Many
          # users want to be able to run multiple seeds on 1 machine, so I have
          # reduced this to n_envs = 2 for 2 copies in the meantime.
          worker_args=dict(n_envs=2),
      )
  
      batch_size = int(env.spec.max_episode_length * n_tasks)
      num_evaluation_points = 500
      epochs = timesteps // batch_size
      epoch_cycles = epochs // num_evaluation_points
      epochs = epochs // epoch_cycles
      mtsac = MTSAC(
          policy=policy,
          qf1=qf1,
          qf2=qf2,
          sampler=sampler,
          gradient_steps_per_itr=env.spec.max_episode_length,
          eval_env=mt50_test_envs,
          env_spec=env.spec,
          num_tasks=50,
          steps_per_epoch=epoch_cycles,
          replay_buffer=replay_buffer,
          min_buffer_size=7500,
          target_update_tau=5e-3,
          discount=0.99,
          buffer_batch_size=6400,
      )
      set_gpu_mode(use_gpu, _gpu)
      mtsac.to()
      trainer.setup(algo=mtsac, env=mt50_train_envs)
  
      trainer.train(n_epochs=epochs, batch_size=batch_size)
  
file_path: garage/src/garage/examples/torch/mtsac_metaworld_mt50.py
imports: import click
  import metaworld
  import numpy
  from torch import nn
  from torch.nn import functional
  from garage import wrap_experiment
  from garage.envs import normalize
  from garage.experiment import deterministic
  from garage.experiment.task_sampler import MetaWorldTaskSampler
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.torch import set_gpu_mode
  from garage.torch.algos import MTSAC
  from garage.torch.policies import TanhGaussianMLPPolicy
  from garage.torch.q_functions import ContinuousMLPQFunction
  from garage.trainer import Trainer
label_desc: Use image node_id 1 for calling a global function with example usage: mtsac_metaworld_mt50(ctxt) without return types