human
Complete the below
def main(_):

    ''''The core of the model consists of an LSTM cell that processes one word at
    a time and computes probabilities of the possible continuations of the
    sentence. The memory state of the network is initialized with a vector
    of zeros and gets updated after reading each word. Also, for computational
    reasons, we will process data in mini-batches of size batch_size.'
    '''

=====
Give me a the Python program you requested?
assistant
#Here is the Python program you requested.
    if (FLAGS.model == 'small'):
        init_scale = 0.1
        learning_rate = 1.0
        max_grad_norm = 5
        num_steps = 20
        hidden_size = 200
        max_epoch = 4
        max_max_epoch = 13
        keep_prob = 1.0
        lr_decay = 0.5
        batch_size = 20
        vocab_size = 10000
    elif (FLAGS.model == 'medium'):
        init_scale = 0.05
        learning_rate = 1.0
        max_grad_norm = 5
        num_layers = 2
        num_steps = 35
        hidden_size = 650
        max_epoch = 6
        max_max_epoch = 39
        keep_prob = 0.5
        lr_decay = 0.8
        batch_size = 20
        vocab_size = 10000
    elif (FLAGS.model == 'large'):
        init_scale = 0.04
        learning_rate = 1.0
        max_grad_norm = 10
        num_layers = 2
        num_steps = 35
        hidden_size = 1500
        max_epoch = 14
        max_max_epoch = 55
        keep_prob = 0.35
        lr_decay = (1 / 1.15)
        batch_size = 20
        vocab_size = 10000
    else:
        raise ValueError('Invalid    model:    %s', FLAGS.model)
    (train_data, valid_data, test_data, vocab_size) = tl.files.load_ptb_dataset()
    print 'len(train_data)    {}'.format(len(train_data))
    print 'len(valid_data)    {}'.format(len(valid_data))
    print 'len(test_data)        {}'.format(len(test_data))
    print 'vocab_size                        {}'.format(vocab_size)
    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\n                                Note    :\n                                -    For    DynamicRNNLayer,    you    can  ...