How to use the lagom.utils.tensorify function in lagom

assert np.allclose(x, y)
    del x, y

    x = np.random.randn(10, 20, 30)
    y = tensorify(x, 'cpu')
    assert np.allclose(x, y)
    del x, y

    # raw list
    x = [2.43]
    y = tensorify(x, 'cpu')
    assert np.allclose(x, y.item())
    del x, y

    x = [1, 2, 3, 4, 5, 6]
    y = tensorify(x, 'cpu')
    assert np.allclose(x, y)
    del x, y

    x = [[1, 2], [3, 4], [5, 6]]
    y = tensorify(x, 'cpu')
    assert np.allclose(x, y)
    del x, y

solutions = es.ask()
            data = [(config, seed, device, solution) for solution in solutions]
            out = pool.map(CloudpickleWrapper(fitness), data, chunksize=config['train.worker_chunksize'])
            Rs, Hs = zip(*out)
            es.tell(solutions, [-R for R in Rs])
            logger = Logger()
            logger('generation', generation+1)
            logger('num_seconds', round(time.perf_counter() - t0, 1))
            logger('Returns', describe(Rs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('Horizons', describe(Hs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('fbest', es.result.fbest)
            train_logs.append(logger.logs)
            if generation == 0 or (generation+1) % config['log.freq'] == 0:
                logger.dump(keys=None, index=0, indent=0, border='-'*50)
            if (generation+1) >= int(config['train.generations']*(checkpoint_count/(config['checkpoint.num'] - 1))):
                agent.from_vec(tensorify(es.result.xbest, 'cpu'))
                agent.checkpoint(logdir, generation+1)
                checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

def choose_action(self, obs, **kwargs):
        obs = tensorify(obs, self.device)
        out = {}
        if kwargs['mode'] == 'train':
            dist = self.actor(obs)
            action = dist.rsample()
            out['action'] = action
            out['action_logprob'] = dist.log_prob(action)
        elif kwargs['mode'] == 'stochastic':
            with torch.no_grad():
                out['action'] = numpify(self.actor(obs).sample(), 'float')
        elif kwargs['mode'] == 'eval':
            with torch.no_grad():
                out['action'] = numpify(torch.tanh(self.actor.mean_forward(obs)), 'float')
        else:
            raise NotImplementedError
        return out

def fitness(data):
    torch.set_num_threads(1)  # VERY IMPORTANT TO AVOID GETTING STUCK
    config, seed, device, param = data
    env = make_env(config, seed, 'train')
    agent = Agent(config, env, device)
    agent.from_vec(tensorify(param, 'cpu'))
    runner = EpisodeRunner()
    with torch.no_grad():
        D = runner(agent, env, 10)
    R = np.mean([sum(traj.rewards) for traj in D])
    H = np.mean([traj.T for traj in D])
    return R, H

def choose_action(self, obs, **kwargs):
        obs = tensorify(obs, self.device)
        with torch.no_grad():
            action = numpify(self.actor(obs), 'float')
        if kwargs['mode'] == 'train':
            eps = np.random.normal(0.0, self.config['agent.action_noise'], size=action.shape)
            action = np.clip(action + eps, self.env.action_space.low, self.env.action_space.high)
        out = {}
        out['action'] = action
        return out

def choose_action(self, x, **kwargs):
        obs = tensorify(x.observation, self.device).unsqueeze(0)
        features = self.feature_network(obs)
        action_dist = self.action_head(features)
        V = self.V_head(features)
        action = action_dist.sample()
        out = {}
        out['action_dist'] = action_dist
        out['V'] = V
        out['entropy'] = action_dist.entropy()
        out['action'] = action
        out['raw_action'] = numpify(action, self.env.action_space.dtype).squeeze(0)
        out['action_logprob'] = action_dist.log_prob(action.detach())
        return out

start_time = time.perf_counter()
            solutions = es.ask()
            out = list(executor.map(fitness, solutions, chunksize=2))
            Rs, Hs = zip(*out)
            es.tell(solutions, [-R for R in Rs])
            logger = Logger()
            logger('generation', generation+1)
            logger('num_seconds', round(time.perf_counter() - start_time, 1))
            logger('Returns', describe(Rs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('Horizons', describe(Hs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('fbest', es.result.fbest)
            train_logs.append(logger.logs)
            if generation == 0 or (generation+1)%config['log.freq'] == 0:
                logger.dump(keys=None, index=0, indent=0, border='-'*50)
            if (generation+1) >= int(config['train.generations']*(checkpoint_count/(config['checkpoint.num'] - 1))):
                agent.from_vec(tensorify(es.result.xbest, 'cpu'))
                agent.checkpoint(logdir, generation+1)
                checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

solutions = es.ask()
            data = [(config, seed, device, solution) for solution in solutions]
            out = pool.map(CloudpickleWrapper(fitness), data, chunksize=config['train.worker_chunksize'])
            Rs, Hs = zip(*out)
            es.tell(solutions, [-R for R in Rs])
            logger = Logger()
            logger('generation', generation+1)
            logger('num_seconds', round(time.perf_counter() - t0, 1))
            logger('Returns', describe(Rs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('Horizons', describe(Hs, axis=-1, repr_indent=1, repr_prefix='\n'))
            logger('fbest', es.result.fbest)
            train_logs.append(logger.logs)
            if generation == 0 or (generation+1) % config['log.freq'] == 0:
                logger.dump(keys=None, index=0, indent=0, border='-'*50)
            if (generation+1) >= int(config['train.generations']*(checkpoint_count/(config['checkpoint.num'] - 1))):
                agent.from_vec(tensorify(es.result.xbest, 'cpu'))
                agent.checkpoint(logdir, generation+1)
                checkpoint_count += 1
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    return None

def fitness(data):
    torch.set_num_threads(1)  # VERY IMPORTANT TO AVOID GETTING STUCK
    config, seed, device, param = data
    env = make_env(config, seed, 'train')
    agent = Agent(config, env, device)
    agent.from_vec(tensorify(param, 'cpu'))
    runner = EpisodeRunner()
    with torch.no_grad():
        D = runner(agent, env, 10)
    R = np.mean([sum(traj.rewards) for traj in D])
    H = np.mean([traj.T for traj in D])
    return R, H