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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_classifier_with_probabilities(model, data, probabilities='probabilities', verbose = False):
 """ Evaluate a classifier specification for testing. Parameters filename: [str | Model] File from where to load the model from (OR) a loaded version of the MLModel. data: [str | Dataframe] Test data on which to evaluate the models (dataframe, or path to a csv file). probabilities: str Column to interpret as the probabilities column verbose: bool Verbosity levels of the predictions. """


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

    max_probability_error, num_key_mismatch = 0, 0

    for _,row in data.iterrows():
        predicted_values = model.predict(dict(row))[_to_unicode(probabilities)]
        other_values = row[probabilities]

        if set(predicted_values.keys()) != set(other_values.keys()):
            if verbose:
                print("Different classes: ", str(predicted_values.keys()), str(other_values.keys()))
            num_key_mismatch += 1
            continue

        for cur_class, cur_predicted_class_values in predicted_values.items():
            delta = cur_predicted_class_values - other_values[cur_class]
            if verbose:
                print(delta, cur_predicted_class_values, other_values[cur_class])

            max_probability_error = max(abs(delta), max_probability_error)

        if verbose:
            print("")

    ret = {
        "num_samples": len(data),
        "max_probability_error": max_probability_error,
        "num_key_mismatch": num_key_mismatch
    }

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

    return ret