import tensorflow as tf

    print(f"Obs_phs space: {obs_phs}")
    #assert 5 == 1
    #######################
    for var in tf.all_variables():
        print(var)
    
    batch_size = tf.shape(policy.obs_ph)[0]
    n_actions = ac_space.nvec if isinstance(ac_space, MultiDiscrete) else ac_space.n
    random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=n_actions, dtype=tf.int64)
    chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
    stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)

    output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
    update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
    _act = tf_util.function(inputs=[policy.obs_ph, stochastic_ph, update_eps_ph],
                            outputs=output_actions,
