human
Complete the below
def main_lstm_generate_text():

    ''''Generate text by Synced sequence input and output.'
    '''
 Can you provide a the Python code you requested?
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#Here is the Python code you requested.
    init_scale = 0.1
    learning_rate = 1.0
    max_grad_norm = 5
    sequence_length = 20
    hidden_size = 200
    max_epoch = 4
    max_max_epoch = 100
    lr_decay = 0.9
    batch_size = 20
    top_k_list = [1, 3, 5, 10]
    print_length = 30
    model_file_name = 'model_generate_text.npz'
    words = customized_read_words(input_fpath='trump_text.txt')
    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 = 'it    is    a'
    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 = RNNLayer(network, cell_fn=tf.contrib.rnn.BasicLSTMCell, cell_init_args={'forget_bias': 0.0, 'state_is_tuple': True}, n_hidden=hidden_size, initializer=rnn_init, n_steps=sequence_length, return_last=False, return_seq_2d=True, name='lstm1')
            lstm1 = network
            network = DenseLayer(network, n_units=vocab_size,...