'Annotates a batch of documents using network scores.
Args:
state: MasterState from the \'AdvanceMaster\' op that advances the
underlying master to this component.
network_states: dictionary of component NetworkState objects
during_training: whether the graph is being constructed during training
Returns:
Handle to the state once inference is complete for this Component.
Raises:
RuntimeError: if fixed features are configured'

def build_greedy_inference(self, state, network_states, during_training=False):

    logging.info('Building    component:    %s', self.spec.name)
    if self.spec.fixed_feature:
        raise RuntimeError('Fixed    features    are    not    compatible    with    bulk    annotation.    Use    the    "bulk-features"    component    instead.')
    linked_embeddings = [fetch_linked_embedding(self, network_states, spec) for spec in self.spec.linked_feature]
    if during_training:
        stride = (state.current_batch_size * self.training_beam_size)
    else:
        stride = (state.current_batch_size * self.inference_beam_size)
    with tf.variable_scope(self.name, reuse=True):
        network_tensors = self.network.create([], linked_embeddings, None, None, during_training, stride)
    update_network_states(self, network_tensors, network_states, stride)
    logits = self.network.get_logits(network_tensors)
    return dragnn_ops.bulk_advance_from_prediction(state.handle, logits, component=self.name)
