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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 supply_and_demand( lcm, choosers, alternatives, alt_segmenter, price_col, base_multiplier=None, clip_change_low=0.75, clip_change_high=1.25, iterations=5, multiplier_func=None):
 """ Adjust real estate prices to compensate for supply and demand effects. Parameters lcm : LocationChoiceModel Used to calculate the probability of agents choosing among alternatives. Must be fully configured and fitted. choosers : pandas.DataFrame alternatives : pandas.DataFrame alt_segmenter : str, array, or pandas.Series Will be used to segment alternatives and probabilities to do comparisons of supply and demand by submarket. If a string, it is expected to be the name of a column in `alternatives`. If a Series it should have the same index as `alternatives`. price_col : str The name of the column in `alternatives` that corresponds to price. This column is what is adjusted by this model. base_multiplier : pandas.Series, optional A series describing a starting multiplier for submarket prices. Index should be submarket IDs. clip_change_low : float, optional The minimum amount by which to multiply prices each iteration. clip_change_high : float, optional The maximum amount by which to multiply prices each iteration. iterations : int, optional Number of times to update prices based on supply/demand comparisons. multiplier_func : function (returns Series, boolean) A function which takes separate demand and supply Series and returns a tuple where the first item is a Series with the ratio of new price to old price (all indexes should be the same) - by default the ratio of demand to supply is the ratio of the new price to the old price. The second return value is a boolean which when True tells this module to stop looping (that convergence has been satisfied) Returns ------- new_prices : pandas.Series Equivalent of the `price_col` in `alternatives`. submarkets_ratios : pandas.Series Price adjustment ratio for each submarket. If `base_multiplier` is given this will be a cummulative multiplier including the `base_multiplier` and the multipliers calculated for this year. """

    logger.debug('start: calculating supply and demand price adjustment')
    # copy alternatives so we don't modify the user's original
    alternatives = alternatives.copy()

    # if alt_segmenter is a string, get the actual column for segmenting demand
    if isinstance(alt_segmenter, str):
        alt_segmenter = alternatives[alt_segmenter]
    elif isinstance(alt_segmenter, np.array):
        alt_segmenter = pd.Series(alt_segmenter, index=alternatives.index)

    choosers, alternatives = lcm.apply_predict_filters(choosers, alternatives)
    alt_segmenter = alt_segmenter.loc[alternatives.index]

    # check base ratio and apply it to prices if given
    if base_multiplier is not None:
        bm = base_multiplier.loc[alt_segmenter]
        bm.index = alt_segmenter.index
        alternatives[price_col] = alternatives[price_col] * bm
        base_multiplier = base_multiplier.copy()

    for _ in range(iterations):
        alts_muliplier, submarkets_multiplier, finished = _calculate_adjustment(
            lcm, choosers, alternatives, alt_segmenter,
            clip_change_low, clip_change_high, multiplier_func=multiplier_func)
        alternatives[price_col] = alternatives[price_col] * alts_muliplier

        # might need to initialize this for holding cumulative multiplier
        if base_multiplier is None:
            base_multiplier = pd.Series(
                np.ones(len(submarkets_multiplier)),
                index=submarkets_multiplier.index)

        base_multiplier *= submarkets_multiplier

        if finished:
            break

    logger.debug('finish: calculating supply and demand price adjustment')
    return alternatives[price_col], base_multiplier