code: def main():
      # Create the experiment
      experiment.create(name="ppo")
      # Configurations
      configs = {
          # Number of updates
          "updates": 10000,
          # ⚙️ Number of epochs to train the model with sampled data.
          # You can change this while the experiment is running.
          "epochs": IntDynamicHyperParam(8),
          # Number of worker processes
          "n_workers": 8,
          # Number of steps to run on each process for a single update
          "worker_steps": 128,
          # Number of mini batches
          "batches": 4,
          # ⚙️ Value loss coefficient.
          # You can change this while the experiment is running.
          "value_loss_coef": FloatDynamicHyperParam(0.5),
          # ⚙️ Entropy bonus coefficient.
          # You can change this while the experiment is running.
          "entropy_bonus_coef": FloatDynamicHyperParam(0.01),
          # ⚙️ Clip range.
          "clip_range": FloatDynamicHyperParam(0.1),
          # You can change this while the experiment is running.
          # ⚙️ Learning rate.
          "learning_rate": FloatDynamicHyperParam(1e-3, (0, 1e-3)),
      }
  
      experiment.configs(configs)
  
      # Initialize the trainer
      m = Trainer(**configs)
  
      # Run and monitor the experiment
      with experiment.start():
          m.run_training_loop()
      # Stop the workers
      m.destroy()
  
imports: from typing import Dict
  import numpy
  import torch
  from torch import nn
  from torch import optim
  from torch.distributions import Categorical
  from labml import monit, tracker, logger, experiment
  from labml.configs import FloatDynamicHyperParam, IntDynamicHyperParam
  from labml_helpers.module import Module
  from labml_nn.rl.game import Worker
  from labml_nn.rl.ppo import ClippedPPOLoss, ClippedValueFunctionLoss
  from labml_nn.rl.ppo.gae import GAE
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types