22cb76a3479ceba89addb1f467c2dd573cb29026,pytext/torchscript/module.py,ScriptPyTextEmbeddingModuleWithDense,forward,#ScriptPyTextEmbeddingModuleWithDense#Any#Any#Any#Any#Any#,464

Before Change

            languages=squeeze_1d(languages),
        )
        // call model
        dense_feat = self.normalizer.normalize(dense_feat)
        dense_tensor = torch.tensor(dense_feat, dtype=torch.float)

        sentence_embedding = self._forward(inputs, dense_tensor)
        if self.concat_dense:
            return torch.cat([sentence_embedding, dense_tensor], 1)
        else:
            return sentence_embedding


class ScriptPyTextEmbeddingModuleWithDenseIndex(ScriptPyTextEmbeddingModuleWithDense):
    def __init__(

After Change

        languages: Optional[List[str]] = None,
        dense_feat: Optional[List[List[float]]] = None,
    ):
        return self.forward_impl(
            texts,
            multi_texts,
            tokens,
            languages,
            dense_feat,
        )

    @torch.jit.script_method
    def make_prediction(
        self,

Instances