def encode_text(self, inputs: List[str], normalize: bool = True) -> List[ndarray]:
        """
        Encode text inputs into embeddings.

        Args:
            inputs (List): A list of text inputs to be encoded.
            normalize (bool): Whether to normalize the embeddings. Defaults to True.

        Returns:
            List[ndarray]: A list of encoded text embeddings.
        """
        with torch.no_grad():
            outputs = self._textual_model.forward(inputs, self.tokenizer)

        if normalize:
            _shape_before = outputs.shape
            outputs /= self.normalize(outputs)
            assert outputs.shape == _shape_before

        return self._convert_output(outputs)

        Encode text inputs into embeddings.

        Args:
            inputs (List): A list of text inputs to be encoded.
            normalize (bool): Whether to normalize the embeddings. Defaults to True.

        Returns:
            List[ndarray]: A list of encoded text embeddings.
        
src/marqo/inference/native_inference/embedding_models/multilingual_clip_model.py