'Extracts features and advances a batch using the oracle path.
Args:
state: MasterState from the \'AdvanceMaster\' op that advances the
underlying master to this component.
network_states: dictionary of component NetworkState objects
Returns:
state handle: final state after advancing
cost: regularization cost, possibly associated with embedding matrices
correct: since no gold path is available, 0.
total: since no gold path is available, 0.'

def build_greedy_training(self, state, network_states):

    logging.info('Building    component:    %s', self.spec.name)
    stride = (state.current_batch_size * self.training_beam_size)
    with tf.variable_scope(self.name, reuse=True):
        (state.handle, fixed_embeddings) = fetch_differentiable_fixed_embeddings(self, state, stride)
    linked_embeddings = [fetch_linked_embedding(self, network_states, spec) for spec in self.spec.linked_feature]
    with tf.variable_scope(self.name, reuse=True):
        tensors = self.network.create(fixed_embeddings, linked_embeddings, None, None, True, stride=stride)
    update_network_states(self, tensors, network_states, stride)
    cost = self.add_regularizer(tf.constant(0.0))
    (correct, total) = (tf.constant(0), tf.constant(0))
    return (state.handle, cost, correct, total)
