human
Complete this python program:
def main_lstm_generate_text2():
assistant
#Here is the Python program you requested.

    """Generate text by Synced sequence input and output.
    """
    init_scale = 0.05
    learning_rate = 1.0
    max_grad_norm = 5
    sequence_length = 35
    hidden_size = 650
    max_epoch = 6
    max_max_epoch = 39
    keep_prob = 0.5
    lr_decay = 0.8
    batch_size = 20
    top_k_list = [1, 3, 5, 10]
    print_length = 30
    model_file_name = 'model_generate_text.npz'
    path = 'data/ptb/'
    filename = 'simple-examples.tgz'
    url = 'http://www.fit.vutbr.cz/~imikolov/rnnlm/'
    tl.files.maybe_download_and_extract(filename, path, url, extract=True)
    data_path = os.path.join(path, 'simple-examples', 'data')
    train_path = os.path.join(data_path, 'ptb.train.txt')
    words = tl.nlp.read_words(train_path)
    vocab = tl.nlp.create_vocab([words], word_counts_output_file='vocab.txt', min_word_count=1)
    vocab = tl.nlp.Vocabulary('vocab.txt', unk_word='<UNK>')
    vocab_size = (vocab.unk_id + 1)
    train_data = [vocab.word_to_id(word) for word in words]
    seed = 'the    balance    is    supplied'
    seed = nltk.tokenize.word_tokenize(seed)
    print ('seed    :    %s' % seed)
    sess = tf.InteractiveSession()
    input_data = tf.placeholder(tf.int32, [batch_size, sequence_length])
    targets = tf.placeholder(tf.int32, [batch_size, sequence_length])
    input_data_test = tf.placeholder(tf.int32, [1, 1])
    def inference(x, is_train, sequence_length, reuse=None):
        'If    reuse    is    True,    the    inferences    use    the    existing    parameters,\n                                then    different    inferences    share    the    same    parameters.\n                                '
        print ('\nsequence_length:    %d,    is_train:    %s,    reuse:    %s' % (sequence_length, is_train, reuse))
        rnn_init = tf.random_uniform_initializer((- init_scale), init_scale)
        with tf.variable_scope('model', reuse=reuse):
            tl.layers.set_name_reuse(reuse)
            network = EmbeddingInputlayer(inputs=x, vocabulary_size=vocab_size, embedding_size=hidden_size, E_init=rnn_init, name='embedding')
            network =...