human
Complete the below
def main_lstm_generate_text():

    ''''Generate text by Synced sequence input and output.'
    '''
assistant
#Here is the Python program you requested.
    init_scale = 0.1
    learning_rate = 1.0
    max_grad_norm = 5
    num_steps = 4
    hidden_size = 200
    max_epoch = 4
    max_max_epoch = 100
    keep_prob = 0.8
    lr_decay = 0.9
    batch_size = 20
    vocab_size = 10000
    embedding_size = 200
    top_k_list = [5, 10, 50, 100]
    print_length = 100
    resume = False
    model_file_name = 'model_generate_text'
    if resume:
        print ('Load    existing    data    and    dictionaries' + ('!' * 10))
        all_var = tl.files.load_npy_to_any(name=(model_file_name + '.npy'))
        data = all_var['data']
        count = all_var['count']
        dictionary = all_var['dictionary']
        reverse_dictionary = all_var['reverse_dictionary']
    else:
        print 'Load    data    and    creat    dictionaries    ....'
        words = tl.files.load_nietzsche_dataset()
        words = basic_clean_str(words)
        words = words.split()
        (data, count, dictionary, reverse_dictionary) = tl.nlp.build_words_dataset(words, vocab_size, True, _UNK)
        data = np.asarray(data)
        del words
    print ('Data    size    %d' % len(data))
    print ('Most    5    common    words    (+UNK)', count[:5])
    print ('Sample    data', data[:10], [reverse_dictionary[i] for i in data[:10]])
    train_data = data
    print 'len(train_data)    {}'.format(len(train_data))
    seed = 'it    should    be    good'
    seed = basic_clean_str(seed).split()
    print ('seed    :    %s' % seed)
    sess = tf.InteractiveSession()
    input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
    targets = tf.placeholder(tf.int32, [batch_size, num_steps])
    input_data_test = tf.placeholder(tf.int32, [1, 1])
    targets_test = tf.placeholder(tf.int32, [1, 1])
    def inference(x, is_training, num_steps, reuse=None):
        'If    reuse    is    True,    the    inferences    use    the    existing    parameters,\n                                then    different    inferences    share    the    same    parameters.\n                                '
       ...