22cb76a3479ceba89addb1f467c2dd573cb29026,pytext/torchscript/module.py,ScriptPyTextModuleWithDense,forward,#ScriptPyTextModuleWithDense#Any#Any#Any#Any#Any#,100

Before Change

            languages=squeeze_1d(languages),
        )
        input_tensors = self.tensorizer(inputs)
        dense_feat = self.normalizer.normalize(dense_feat)

        dense_tensor = torch.tensor(dense_feat, dtype=torch.float)
        if self.tensorizer.device != "":
            dense_tensor = dense_tensor.to(self.tensorizer.device)
        logits = self.model(input_tensors, dense_tensor)
        return self.output_layer(logits)

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,
        )


class ScriptPyTextEmbeddingModuleWithDenseIndex(ScriptPyTextEmbeddingModuleWithDense):
    def __init__(

Instances