Forward computation.

def forward(self, x, sampled_values, label):
        """Forward computation."""
        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