      if W.hidden_weights == NULL:
          memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float))
      else:
          # Compute hidden-to-output
          cblas.sgemm()(False, True, n.states, n.classes, n.hiddens,
              1.0, <const float *>A.hiddens, n.hiddens,
              <const float *>W.hidden_weights, n.hiddens,
              0.0, A.scores, n.classes)
          # Add bias
          for i in range(n.states):
 
</s> remove             cblas.saxpy()(O, one, <const float*>feature, 1, &output[b*O], 1)
 </s> add             saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1) </s> remove     thinc>=8.1.0.dev0,<8.2.0
 </s> add     thinc>=8.1.0.dev2,<8.2.0 </s> remove     thinc>=8.1.0.dev0,<8.2.0
 </s> add     thinc>=8.1.0.dev2,<8.2.0 </s> remove thinc>=8.1.0.dev0,<8.2.0
 </s> add thinc>=8.1.0.dev2,<8.2.0
        sgemm(cblas)(False, True, n.states, n.classes, n.hiddens,
      if W.hidden_weights == NULL:
          memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float))
      else:
          # Compute hidden-to-output
        sgemm(cblas)(False, True, n.states, n.classes, n.hiddens,
              1.0, <const float *>A.hiddens, n.hiddens,
              <const float *>W.hidden_weights, n.hiddens,
              0.0, A.scores, n.classes)
          # Add bias
          for i in range(n.states):
keep keep keep keep replace keep keep keep keep keep
 Update for CBlas changes in Thinc 8.1.0.dev2 (#10970) 
spacy/ml/parser_model.pyx