def _batch_predict_intents(self, combined_sequence_sentence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor) -> Dict[(Text, tf.Tensor)]:
    if (self.all_labels_embed is None):
        raise ValueError('The model was not prepared for prediction. Call `prepare_for_predict` first.')
    sentence_vector = self._last_token(text_transformed, combined_sequence_sentence_feature_lengths)
    sentence_vector_embed = self._tf_layers[f'embed.{TEXT}'](sentence_vector)
    (_, scores) = self._tf_layers[f'loss.{LABEL}'].get_similarities_and_confidences_from_embeddings(sentence_vector_embed[:, tf.newaxis, :], self.all_labels_embed[tf.newaxis, :, :])
    return {'i_scores': scores}
def _batch_predict_intents(self, combined_sequence_sentence_feature_lengths: tf.Tensor, text_transformed: tf.Tensor) -> Dict[(Text, tf.Tensor)]:
    if (self.all_labels_embed is None):
        raise ValueError('The model was not prepared for prediction. Call `prepare_for_predict` first.')
    sentence_vector = self._last_token(text_transformed, combined_sequence_sentence_feature_lengths)
    sentence_vector_embed = self._tf_layers[f'embed.{TEXT}'](sentence_vector)
    (_, scores) = self._tf_layers[f'loss.{LABEL}'].get_similarities_and_confidences_from_embeddings(sentence_vector_embed[:, tf.newaxis, :], self.all_labels_embed[tf.newaxis, :, :])
    return {'i_scores': scores}