code: def train(
      env_name="CartPole-v0",
      hidden_sizes=[32],
      lr=1e-2,
      epochs=50,
      batch_size=5000,
      render=False,
  ):
      # make environment, check spaces, get obs / act dims
      env = gym.make(env_name)
      assert isinstance(
          env.observation_space, Box
      ), "This example only works for envs with continuous state spaces."
      assert isinstance(
          env.action_space, Discrete
      ), "This example only works for envs with discrete action spaces."
  
      obs_dim = env.observation_space.shape[0]
      n_acts = env.action_space.n
  
      # make core of policy network
      logits_net = mlp(sizes=[obs_dim] + hidden_sizes + [n_acts])
  
      # make function to compute action distribution
      def get_policy(obs):
          logits = logits_net(obs)
          return Categorical(logits=logits)
  
      # make action selection function (outputs int actions, sampled from policy)
      def get_action(obs):
          return get_policy(obs).sample().item()
  
      # make loss function whose gradient, for the right data, is policy gradient
      def compute_loss(obs, act, weights):
          logp = get_policy(obs).log_prob(act)
          return -(logp * weights).mean()
  
      # make optimizer
      optimizer = Adam(logits_net.parameters(), lr=lr)
  
      # for training policy
      def train_one_epoch():
          # make some empty lists for logging.
          batch_obs = []  # for observations
          batch_acts = []  # for actions
          batch_weights = (
              []
          )  # for reward-to-go weighting in policy gradient
          batch_rets = []  # for measuring episode returns
          batch_lens = []  # for measuring episode lengths
  
          # reset episode-specific variables
          obs = (
              env.reset()
          )  # first obs comes from starting distribution
          done = False  # signal from environment that episode is over
          ep_rews = []  # list for rewards accrued throughout ep
  
          # render first episode of each epoch
          finished_rendering_this_epoch = False
  
          # collect experience by acting in the environment with current policy
          while True:
              # rendering
              if (not finished_rendering_this_epoch) and render:
                  env.render()
  
              # save obs
              batch_obs.append(obs.copy())
  
              # act in the environment
              act = get_action(
                  torch.as_tensor(obs, dtype=torch.float32)
              )
              obs, rew, done, _ = env.step(act)
  
              # save action, reward
              batch_acts.append(act)
              ep_rews.append(rew)
  
              if done:
                  # if episode is over, record info about episode
                  ep_ret, ep_len = sum(ep_rews), len(ep_rews)
                  batch_rets.append(ep_ret)
                  batch_lens.append(ep_len)
  
                  # the weight for each logprob(a_t|s_t) is reward-to-go from t
                  batch_weights += list(reward_to_go(ep_rews))
  
                  # reset episode-specific variables
                  obs, done, ep_rews = env.reset(), False, []
  
                  # won't render again this epoch
                  finished_rendering_this_epoch = True
  
                  # end experience loop if we have enough of it
                  if len(batch_obs) > batch_size:
                      break
  
          # take a single policy gradient update step
          optimizer.zero_grad()
          batch_loss = compute_loss(
              obs=torch.as_tensor(batch_obs, dtype=torch.float32),
              act=torch.as_tensor(batch_acts, dtype=torch.int32),
              weights=torch.as_tensor(
                  batch_weights, dtype=torch.float32
              ),
          )
          batch_loss.backward()
          optimizer.step()
          return batch_loss, batch_rets, batch_lens
  
      # training loop
      for i in range(epochs):
          batch_loss, batch_rets, batch_lens = train_one_epoch()
          print(
              "epoch: %3d \t loss: %.3f \t return: %.3f \t ep_len: %.3f"
              % (
                  i,
                  batch_loss,
                  np.mean(batch_rets),
                  np.mean(batch_lens),
              )
          )
  
file_path: spinningup/spinup/examples/pytorch/pg_math/2_rtg_pg.py
imports: import torch
  import torch.nn
  from torch.distributions.categorical import Categorical
  from torch.optim import Adam
  import numpy
  import gym
  from gym.spaces import Discrete, Box
label_desc: Use image node_id 3 for calling a global function with example usage: train(env_name, hidden_sizes, lr, epochs, batch_size, render) and returns: Categorical, get_policy, unknown, batch_loss,  batch_rets,  batch_lens