66584dea87782aed5509e4269a9f015002e1f5c1,pytext/torchscript/module.py,PyTextEmbeddingModuleWithDense,forward,#PyTextEmbeddingModuleWithDense#,1232

Before Change

        )
        // call model
        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)

        sentence_embedding = self._forward(inputs, dense_tensor)

After Change

        dense_feat: List[List[float]],
    ) -> torch.Tensor:

        input_len = len(texts)
        max_batch = self.get_max_batch_len()
        if max_batch < 0:
            max_batch = input_len

Instances