code: def act(self, inputs, states, masks, deterministic=False):
      value, actor_features, states = self.base(inputs, states, masks)
      distribution = self.distribution(actor_features)
  
      if deterministic:
          action = distribution.mode()
      else:
          action = distribution.sample()
  
      action_log_probs = distribution.log_probs(action)
  
      return value, action, action_log_probs, states
  
file_path: SerpentAI/serpent/machine_learning/reinforcement_learning/ppo/policy.py
imports: import torch
  from .cnn_base import CNNBase
  from .distributions import Categorical
label_desc: Use image node_id 3 for calling the Policy obj's underlying member method code with example usage: obj.act(inputs, states, masks, deterministic) and returns: value,  action,  action_log_probs,  states