'See base class.'

def create(self, fixed_embeddings, linked_embeddings, context_tensor_arrays, attention_tensor, during_training, stride=None):

    input_tensor = get_input_tensor(fixed_embeddings, linked_embeddings)
    assert (len(context_tensor_arrays) == 2)
    length = context_tensor_arrays[0].size()
    x2i = self._component.get_variable('x2i')
    h2i = self._component.get_variable('h2i')
    c2i = self._component.get_variable('c2i')
    bi = self._component.get_variable('bi')
    x2o = self._component.get_variable('x2o')
    h2o = self._component.get_variable('h2o')
    c2o = self._component.get_variable('c2o')
    bo = self._component.get_variable('bo')
    x2c = self._component.get_variable('x2c')
    h2c = self._component.get_variable('h2c')
    bc = self._component.get_variable('bc')
    i_h_tm1 = context_tensor_arrays[0].read((length - 1))
    i_c_tm1 = context_tensor_arrays[1].read((length - 1))
    if (during_training and (self._input_dropout_rate < 1)):
        input_tensor = tf.nn.dropout(input_tensor, self._input_dropout_rate)
    i_ait = (((tf.matmul(input_tensor, x2i) + tf.matmul(i_h_tm1, h2i)) + tf.matmul(i_c_tm1, c2i)) + bi)
    i_it = tf.sigmoid(i_ait)
    i_ft = (tf.ones([1, 1]) - i_it)
    i_awt = ((tf.matmul(input_tensor, x2c) + tf.matmul(i_h_tm1, h2c)) + bc)
    i_wt = tf.tanh(i_awt)
    ct = tf.add(tf.multiply(i_it, i_wt), tf.multiply(i_ft, i_c_tm1), name='lstm_c')
    i_aot = (((tf.matmul(input_tensor, x2o) + tf.matmul(ct, c2o)) + tf.matmul(i_h_tm1, h2o)) + bo)
    i_ot = tf.sigmoid(i_aot)
    ph_t = tf.tanh(ct)
    ht = tf.multiply(i_ot, ph_t, name='lstm_h')
    if (during_training and (self._recurrent_dropout_rate < 1)):
        ht = tf.nn.dropout(ht, self._recurrent_dropout_rate, name='lstm_h_dropout')
    h = tf.identity(ht, name='layer_0')
    logits = tf.nn.xw_plus_b(ht, tf.get_variable('weights_softmax'), tf.get_variable('bias_softmax'))
    if self._component.spec.attention_component:
        logits += self.attention(ht, attention_tensor)
    logits = tf.identity(logits, name='logits')
    tensors = [ht, ct, h, logits]
    return tensors
