def METHOD_NAME():
    env = ForagerEnv()
    reward = 0
    done = False
    if RENDER:
        env.render()  # If visualization is desired
    else:
        print("Running simulation...")
    steps = 0
    start_time = timeit.default_timer()
    for _ in range(num_trials):
        observation = env.reset()
        while True:
            run_results = agent_comp.run(inputs={
                player_obs:[observation[player_coord_slice]],
                predator_obs:[observation[predator_coord_slice]],
                prey_obs:[observation[prey_coord_slice]],
                Panicky_control_mech:[0]
                # values:[observation[player_value_idx],observation[prey_value_idx],observation[predator_value_idx]],
                # reward:[reward],
            })
            action= np.where(run_results[0] == 0, 0, run_results[0] / np.abs(run_results[0]))
            observation, reward, done, _ = env.step(action)
            steps +=1
            if done:
                break
    stop_time = timeit.default_timer()
    print(f'{steps / (stop_time - start_time):.1f} steps/second, {steps} total steps in '
          f'{stop_time - start_time:.2f} seconds')
    if RENDER:
        env.render()  # If visualization is desired