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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 plot_incremental_transactions( model, transactions, datetime_col, customer_id_col, t, t_cal, datetime_format=None, freq="D", set_index_date=False, title="Tracking Daily Transactions", xlabel="day", ylabel="Transactions", ax=None, **kwargs ):
 """ Plot a figure of the predicted and actual cumulative transactions of users. Parameters model: lifetimes model A fitted lifetimes model transactions: pandas DataFrame DataFrame containing the transactions history of the customer_id datetime_col: str The column in transactions that denotes the datetime the purchase was made. customer_id_col: str The column in transactions that denotes the customer_id t: float The number of time units since the begining of data for which we want to calculate cumulative transactions t_cal: float A marker used to indicate where the vertical line for plotting should be. datetime_format: str, optional A string that represents the timestamp format. Useful if Pandas can't understand the provided format. freq: str, optional Full list here: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#dateoffset-objects set_index_date: bool, optional When True set date as Pandas DataFrame index, default False - number of time units title: str, optional Figure title xlabel: str, optional Figure xlabel ylabel: str, optional Figure ylabel ax: matplotlib.AxesSubplot, optional Using user axes kwargs Passed into the pandas.DataFrame.plot command. Returns ------- axes: matplotlib.AxesSubplot """

    from matplotlib import pyplot as plt

    if ax is None:
        ax = plt.subplot(111)

    df_cum_transactions = expected_cumulative_transactions(
        model,
        transactions,
        datetime_col,
        customer_id_col,
        t,
        datetime_format=datetime_format,
        freq=freq,
        set_index_date=set_index_date,
    )

    # get incremental from cumulative transactions
    df_cum_transactions = df_cum_transactions.apply(lambda x: x - x.shift(1))
    ax = df_cum_transactions.plot(ax=ax, title=title, **kwargs)

    if set_index_date:
        x_vline = df_cum_transactions.index[int(t_cal)]
        xlabel = "date"
    else:
        x_vline = t_cal
    ax.axvline(x=x_vline, color="r", linestyle="--")
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    return ax