You trained a model for sentiment analysis in TensorFlow Keras, saved it in SavedModel format, and deployed it with Vertex AI Predictions as a custom container. You selected a random sentence from the test set, and used a REST API call to send a prediction request. The service returned the error: “ Could not find matching concrete function to call loaded from the SavedModel . Got : Tensor ( "inputs:0" , shape =( None ,), dtype = string ). Expected : TensorSpec ( shape =( None , None ), dtype = tf . int64 , name = 'inputs' )”. You want to update the model’s code and fix the error while following Google-recommended best practices. What should you do?