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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def evaluate_transformer(model, input_data, reference_output, verbose=False):
 """ Evaluate a transformer specification for testing. Parameters spec: [str | MLModel] File from where to load the Model from (OR) a loaded version of MLModel. input_data: list[dict] Test data on which to evaluate the models. reference_output: list[dict] Expected results for the model. verbose: bool Verbosity levels of the predictions. Examples -------- .. sourcecode:: python See Also -------- evaluate_regressor, evaluate_classifier """

    model = _get_model(model)
    if verbose:
        print(model)
        print("")
        print("Other Framework\t\tPredicted")

    num_errors = 0
    for index, row in enumerate(input_data):
        assert isinstance(row, dict)
        sanitized_row = _sanitize_value(row)
        ref_data = _sanitize_value(reference_output[index])
        if verbose:
            print("Input:\n\t", str(row))
            print("Correct output:\n\t", str(ref_data))

        predicted = _sanitize_value(model.predict(sanitized_row))

        assert isinstance(ref_data, dict)
        assert isinstance(predicted, dict)

        predicted_trimmed = dict( (k, predicted[k]) for k in ref_data.keys())

        if verbose:
            print("Predicted:\n\t", str(predicted_trimmed))

        if not _element_equal(predicted_trimmed, ref_data):
            num_errors += 1

    ret = {
        "num_samples": len(input_data),
        "num_errors": num_errors
    }

    if verbose:
        print("results: %s" % ret)
    return ret