There are two different models dqn.gz und c51.gz. Both models are taken from https://github.com/floringogianu/atari-agents.
The DQN model refers to the modern DQN (or M-DQN) model from the repo, while the c51 model is a implementation of the architecture introduced by http://proceedings.mlr.press/v70/bellemare17a.html, also important from Gogianu.
The easiest way is to load an agent based on its path, see example below. After loading an agent, one can calculate a policy bases on the current observation state and use this max(policy) as potential next action.
opts.path = f"../models/{opts.game}/dqn.gz"
dqn_agent = load_agent(opts, env.action_space.n)
action = dqn_agent.draw_action(env.dqn_obs)
obs, reward, terminated, truncated, info = env.step(action)