code: def forward(self, x, sampled_values, label):
          
          sampled_candidates, _, _ = sampled_values
          # (batch_size,)
          label = label.reshape(shape=(-1,))
          # (num_sampled+batch_size,)
          ids = nd.concat(sampled_candidates, label, dim=0)
          # lookup weights and biases
          weight = self.weight.row_sparse_data(ids)
          bias = self.bias.data(ids.context)
          # (num_sampled+batch_size, dim)
          w_all = nd.Embedding(data=ids, weight=weight, **self._kwargs)
          # (num_sampled+batch_size,)
          b_all = nd.take(bias, indices=ids)
          out, new_targets = self._dense(x, sampled_values, label, w_all, b_all)
          return out, new_targets
docs: Forward computation.