keras/src/layers/preprocessing/text_vectorization.py/TextVectorization/get_config
class TextVectorization:    def get_config(self):
        config = {
            "max_tokens": self._lookup_layer.max_tokens,
            "standardize": self._standardize,
            "split": self._split,
            "ngrams": self._ngrams_arg,
            "output_mode": self._output_mode,
            "output_sequence_length": self._output_sequence_length,
            "pad_to_max_tokens": self._lookup_layer.pad_to_max_tokens,
            "sparse": self._lookup_layer.sparse,
            "ragged": self._ragged,
            "vocabulary": listify_tensors(self._lookup_layer.input_vocabulary),
            "idf_weights": listify_tensors(
                self._lookup_layer.input_idf_weights
            ),
            "encoding": self._encoding,
            "vocabulary_size": self.vocabulary_size(),
        }
        base_config = super().get_config()
        return {**base_config, **config}