code: def hybrid_forward(self, F, x, sampled_values, label, weight, bias):
          
          sampled_candidates, _, _ = sampled_values
          # (batch_size,)
          label = F.reshape(label, shape=(-1,))
          # (num_sampled+batch_size,)
          ids = F.concat(sampled_candidates, label, dim=0)
          # lookup weights and biases
          # (num_sampled+batch_size, dim)
          w_all = F.Embedding(data=ids, weight=weight,
                              input_dim=self._num_classes, output_dim=self._in_unit,
                              sparse_grad=self._sparse_grad)
          # (num_sampled+batch_size, 1)
          b_all = F.take(bias, indices=ids)
          return self._dense(x, sampled_values, label, w_all, b_all)
docs: Forward computation.