code: def evaluate_actions(self, inputs, states, masks, action):
      value, actor_features, states = self.base(inputs, states, masks)
      distribution = self.distribution(actor_features)
  
      action_log_probs = distribution.log_probs(action)
      distribution_entropy = distribution.entropy().mean()
  
      return value, action_log_probs, distribution_entropy, 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 5 for calling the Policy obj's underlying member method code with example usage: obj.evaluate_actions(inputs, states, masks, action) and returns: value,  action_log_probs,  distribution_entropy,  states