def hybrid_forward(self, F, x, sampled_values, label, weight, bias):
sampled_candidates, _, _ = sampled_values<EOL>label = F.reshape(label, shape=(-<NUM_LIT:1>,))<EOL>ids = F.concat(sampled_candidates, label, dim=<NUM_LIT:0>)<EOL>w_all = F.Embedding(data=ids, weight=weight,<EOL>input_dim=self._num_classes, output_dim=self._in_unit,<EOL>sparse_grad=self._sparse_grad)<EOL>b_all = F.take(bias, indices=ids)<EOL>return self._dense(x, sampled_values, label, w_all, b_all)<EOL>
Forward computation.