7c4fd12ab946ad49397ee9e42ae208e6876462e0,src/gluonnlp/data/candidate_sampler.py,UnigramCandidateSampler,hybrid_forward,#UnigramCandidateSampler#Any#Any#Any#Any#,104

Before Change

            are sampled based on the weights specified on creation of the
            UnigramCandidateSampler.
        
        flat_shape = functools.reduce(operator.mul, self._shape)
        idx = F.random.uniform(low=0, high=self.N, shape=flat_shape,
                               dtype="float64").floor()
        prob = F.gather_nd(prob, idx.reshape((1, -1)))
        alias = F.gather_nd(alias, idx.reshape((1, -1)))

After Change

            are sampled based on the weights specified on creation of the
            UnigramCandidateSampler.
        
        candidates_flat = candidates_like.reshape((-1, )).astype("float64")
        idx = F.random.uniform_like(candidates_flat, low=0, high=self.N).floor()
        prob = F.gather_nd(prob, idx.reshape((1, -1)))
        alias = F.gather_nd(alias, idx.reshape((1, -1)))
        where = F.random.uniform_like(candidates_flat) < prob

Instances