              unfinished.clear()
          free(token_ids)
          free(is_valid)
          free(vectors)
          free(scores)
  
      def beam_parse(self, docs, int beam_width=3, float beam_density=0.001,
              float drop=0.):
 
</s> remove     def beam_parse(self, docs, int beam_width=3, float beam_density=0.001):
 </s> add     def beam_parse(self, docs, int beam_width=3, float beam_density=0.001,
            float drop=0.): </s> remove         cdef float feature
        while not state.is_final():
            state.set_context_tokens(token_ids, nr_feat)
            memset(vectors, 0, nr_hidden * nr_piece * sizeof(float))
            memset(scores, 0, nr_class * sizeof(float))
            sum_state_features(vectors,
                feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
            for i in range(nr_hidden * nr_piece):
                vectors[i] += bias[i]
            V = vectors
            W = hW
            for i in range(nr_hidden):
                if nr_piece == 1:
                    feature = V[0] if V[0] >= 0. else 0.
                elif nr_piece == 2:
                    feature = V[0] if V[0] >= V[1] else V[1]
                else:
                    feature = Vec.max(V, nr_piece)
                for j in range(nr_class):
                    scores[j] += feature * W[j]
                W += nr_class
                V += nr_piece
            for i in range(nr_class):
                scores[i] += hb[i]
            self.moves.set_valid(is_valid, state)
            guess = arg_max_if_valid(scores, is_valid, nr_class)
            action = self.moves.c[guess]
            action.do(state, action.label)
            state.push_hist(guess)
 </s> add         cdef int nr_todo = nr_task
        cdef int i, j
        cdef vector[StateC*] unfinished
        while nr_todo >= 1:
            memset(vectors, 0, nr_todo * nr_hidden * sizeof(float))
            memset(scores, 0, nr_todo * nr_class * sizeof(float))
            for i in range(nr_todo):
                state = states[i]
                state.set_context_tokens(token_ids, nr_feat)
                memset(unmaxed, 0, nr_hidden * nr_piece * sizeof(float))
                sum_state_features(unmaxed,
                    feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
                VecVec.add_i(unmaxed,
                    bias, 1., nr_hidden*nr_piece)
                state_vector = &vectors[i*nr_hidden]
                for j in range(nr_hidden):
                    index = j * nr_piece
                    which = Vec.arg_max(&unmaxed[index], nr_piece)
                    state_vector[j] = unmaxed[index + which]
            # Compute hidden-to-output
            openblas.simple_gemm(scores, nr_todo, nr_class,
                vectors, nr_todo, nr_hidden, hW, nr_hidden, nr_class, 0, 0)
            # Add bias
            for i in range(nr_todo):
                VecVec.add_i(&scores[i*nr_class],
                    hb, 1., nr_class)
            # Validate actions, argmax, take action.
            for i in range(nr_todo):
                state = states[i]
                self.moves.set_valid(is_valid, state)
                guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
                action = self.moves.c[guess]
                action.do(state, action.label)
                state.push_hist(guess)
                if not state.is_final():
                    unfinished.push_back(state)
            for i in range(unfinished.size()):
                states[i] = unfinished[i]
            nr_todo = unfinished.size()
            unfinished.clear() </s> remove         vectors = <float*>calloc(nr_hidden * nr_piece, sizeof(float))
        scores = <float*>calloc(nr_class, sizeof(float))
 </s> add         vectors = <float*>calloc(nr_hidden * nr_task, sizeof(float))
        unmaxed = <float*>calloc(nr_hidden * nr_piece, sizeof(float))
        scores = <float*>calloc(nr_class*nr_task, sizeof(float)) </s> remove             docs, cuda_stream, 0.0)
 </s> add             docs, cuda_stream, drop) </s> remove     def _make_golds(cls, docs, paragraph_tuples):
 </s> add     def _make_golds(cls, docs, paragraph_tuples, make_projective):
        free(unmaxed)
              unfinished.clear()
          free(token_ids)
          free(is_valid)
          free(vectors)
        free(unmaxed)
          free(scores)
  
      def beam_parse(self, docs, int beam_width=3, float beam_density=0.001,
              float drop=0.):
keep keep keep add keep keep keep keep
 Revert "Merge branch 'develop' of https://github.com/explosion/spaCy into develop"

This reverts commit c9ba3d3c2dc7067cf8bd55f878cec45a8c6d73d4, reversing
changes made to 92c26a35d425d4e8ca1b805ea776ea10f5ded3df. 
spacy/syntax/nn_parser.pyx