code: def predict(inputs_list, problem, request_fn):
    
    assert isinstance(inputs_list, list)
    fname = "inputs" if problem.has_inputs else "targets"
    input_encoder = problem.feature_info[fname].encoder
    input_ids_list = [
        _encode(inputs, input_encoder, add_eos=problem.has_inputs)
        for inputs in inputs_list
    ]
    examples = [_make_example(input_ids, problem, fname)
                for input_ids in input_ids_list]
    predictions = request_fn(examples)
    output_decoder = problem.feature_info["targets"].encoder
    outputs = [
        (_decode(prediction["outputs"], output_decoder),
         prediction["scores"])
        for prediction in predictions
    ]
    return outputs
docs: Encodes inputs, makes request to deployed TF model, and decodes outputs.