def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor:
    h = self.entity_embeddings(hr_batch[:, 0]).view((- 1), self.embedding_dim)
    r = self.relation_embeddings(hr_batch[:, 1]).view((- 1), self.embedding_dim)
    t = self.entity_embeddings.weight.transpose(1, 0)
    self.regularize_if_necessary(h, r, t)
    x_s = torch.cat([h, r], dim=(- 1))
    x_s = self.input_dropout(x_s)
    x_t = self.mlp(x_s)
    x = (x_t @ t)
    return x
def score_t(self, hr_batch: torch.LongTensor) -> torch.FloatTensor:
    h = self.entity_embeddings(hr_batch[:, 0]).view((- 1), self.embedding_dim)
    r = self.relation_embeddings(hr_batch[:, 1]).view((- 1), self.embedding_dim)
    t = self.entity_embeddings.weight.transpose(1, 0)
    self.regularize_if_necessary(h, r, t)
    x_s = torch.cat([h, r], dim=(- 1))
    x_s = self.input_dropout(x_s)
    x_t = self.mlp(x_s)
    x = (x_t @ t)
    return x