code: def td3_half_cheetah(ctxt=None, seed=1):
      """Train TD3 with InvertedDoublePendulum-v2 environment.
  
      Args:
          ctxt (garage.experiment.ExperimentContext): The experiment
              configuration used by LocalRunner to create the snapshotter.
          seed (int): Used to seed the random number generator to produce
          determinism.
      """
      set_seed(seed)
  
      n_epochs = 500
      steps_per_epoch = 20
      sampler_batch_size = 250
      num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
  
      trainer = Trainer(ctxt)
      env = normalize(GymEnv("HalfCheetah-v2"))
  
      policy = DeterministicMLPPolicy(
          env_spec=env.spec,
          hidden_sizes=[256, 256],
          hidden_nonlinearity=F.relu,
          output_nonlinearity=torch.tanh,
      )
  
      exploration_policy = AddGaussianNoise(
          env.spec,
          policy,
          total_timesteps=num_timesteps,
          max_sigma=0.1,
          min_sigma=0.1,
      )
  
      uniform_random_policy = UniformRandomPolicy(env.spec)
  
      qf1 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[256, 256],
          hidden_nonlinearity=F.relu,
      )
  
      qf2 = ContinuousMLPQFunction(
          env_spec=env.spec,
          hidden_sizes=[256, 256],
          hidden_nonlinearity=F.relu,
      )
  
      replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
  
      sampler = LocalSampler(
          agents=exploration_policy,
          envs=env,
          max_episode_length=env.spec.max_episode_length,
          worker_class=FragmentWorker,
      )
  
      td3 = TD3(
          env_spec=env.spec,
          policy=policy,
          qf1=qf1,
          qf2=qf2,
          replay_buffer=replay_buffer,
          sampler=sampler,
          policy_optimizer=torch.optim.Adam,
          qf_optimizer=torch.optim.Adam,
          exploration_policy=exploration_policy,
          uniform_random_policy=uniform_random_policy,
          target_update_tau=0.005,
          discount=0.99,
          policy_noise_clip=0.5,
          policy_noise=0.2,
          policy_lr=1e-3,
          qf_lr=1e-3,
          steps_per_epoch=40,
          start_steps=1000,
          grad_steps_per_env_step=50,
          min_buffer_size=1000,
          buffer_batch_size=100,
      )
  
      trainer.setup(algo=td3, env=env)
      trainer.train(n_epochs=750, batch_size=100)
  
file_path: garage/src/garage/examples/torch/td3_halfcheetah.py
imports: import torch
  from torch.nn import functional
  from garage import wrap_experiment
  from garage.envs import GymEnv, normalize
  from garage.experiment.deterministic import set_seed
  from garage.np.exploration_policies import AddGaussianNoise
  from garage.np.policies import UniformRandomPolicy
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.torch.algos import TD3
  from garage.torch.policies import DeterministicMLPPolicy
  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: td3_half_cheetah(ctxt, seed) without return types