LSTM inputs for Tensorflow

I'm trying to create an LSTM network in Tensorflow and I'm lost in terminology/basics. I have n time series examples so X=x_n, where x_i=[[x₁¹x₁²,x₁³],...,[x_m¹x_m²,x_m³]] and where x_iⁱ is a float. First of all I want to train a model that given the start of a sequence ([x₁¹x₁²,x₁³]) I can predict the rest of the sequence. Then later I hope to include a classifier to predict which binary class each x_i belongs to.

So my problem is what do I feed in to the start and pull out the end of my model? So far I have something that looks like the below

class ETLSTM(object):
    """docstring for ETLSTM"""
    def __init__(self, isTraining, config):
        super(ETLSTM, self).__init__()

        # This needs to be tidied
        self.batchSize = batchSize = config.batchSize
        self.numSteps = numSteps = config.numSteps
        self.numInputs = numInputs = config.numInputs
        self.numLayers = numLayers = config.numLayers

        lstmSize = config.lstm_size
        DORate = config.keep_prob

        self.input_data = tf.placeholder(tf.float32, [batchSize, numSteps,
                                                      numInputs])
        self.targets = tf.placeholder(tf.float32, [batchSize, numSteps,
                                                   numInputs])
        lstmCell = rnn_cell.BasicLSTMCell(lstmSize, forgetbias=0.0)
        if(isTraining and DORate < 1):
            lstmCell = tf.nn.rnn_cell.DropoutWrapper(lstmCell,
                                                     output_keep_prob=DORate)
        cell = tf.nn.rnn_cell.MultiRNNCell([lstmCell]*numLayers)

        self._initial_state = cell.zero_state(batchSize, tf.float32)

        # This won't work with my data, need to find what goes in...
        with tf.device("/cpu:0"):
            embedding = tf.get_variable("embedding", [vocab_size, size])
            inputs = tf.nn.embedding_lookup(embedding, self._input_data)

        if(isTraining and DORate < 1):
            inputs = tf.nn.dropout(inputs, DORate)

EDIT: Specifically, how to I finish the __init__ function so that it is compatible with my data?