<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def hist(hists, stacked=True, reverse=False, xpadding=0, ypadding=.1, yerror_in_padding=True, logy=None, snap=True, axes=None, **kwargs):
 """ Make a matplotlib hist plot from a ROOT histogram, stack or list of histograms. Parameters hists : Hist, list of Hist, HistStack The histogram(s) to be plotted stacked : bool, optional (default=True) If True then stack the histograms with the first histogram on the bottom, otherwise overlay them with the first histogram in the background. reverse : bool, optional (default=False) If True then reverse the order of the stack or overlay. xpadding : float or 2-tuple of floats, optional (default=0) Padding to add on the left and right sides of the plot as a fraction of the axes width after the padding has been added. Specify unique left and right padding with a 2-tuple. ypadding : float or 2-tuple of floats, optional (default=.1) Padding to add on the top and bottom of the plot as a fraction of the axes height after the padding has been added. Specify unique top and bottom padding with a 2-tuple. yerror_in_padding : bool, optional (default=True) If True then make the padding inclusive of the y errors otherwise only pad around the y values. logy : bool, optional (default=None) Apply special treatment of a log-scale y-axis to display the histogram correctly. If None (the default) then automatically determine if the y-axis is log-scale. snap : bool, optional (default=True) If True (the default) then the origin is an implicit lower bound of the histogram unless the histogram has both positive and negative bins. axes : matplotlib Axes instance, optional (default=None) The axes to plot on. If None then use the global current axes. kwargs : additional keyword arguments, optional All additional keyword arguments are passed to matplotlib's fill_between for the filled regions and matplotlib's step function for the edges. Returns ------- The return value from matplotlib's hist function, or list of such return values if a stack or list of histograms was plotted. """

    if axes is None:
        axes = plt.gca()
    if logy is None:
        logy = axes.get_yscale() == 'log'
    curr_xlim = axes.get_xlim()
    curr_ylim = axes.get_ylim()
    was_empty = not axes.has_data()
    returns = []
    if isinstance(hists, _Hist):
        # This is a single plottable object.
        returns = _hist(hists, axes=axes, logy=logy, **kwargs)
        _set_bounds(hists, axes=axes,
                    was_empty=was_empty,
                    prev_xlim=curr_xlim,
                    prev_ylim=curr_ylim,
                    xpadding=xpadding, ypadding=ypadding,
                    yerror_in_padding=yerror_in_padding,
                    snap=snap,
                    logy=logy)
    elif stacked:
        # draw the top histogram first so its edges don't cover the histograms
        # beneath it in the stack
        if not reverse:
            hists = list(hists)[::-1]
        for i, h in enumerate(hists):
            kwargs_local = kwargs.copy()
            if i == len(hists) - 1:
                low = h.Clone()
                low.Reset()
            else:
                low = sum(hists[i + 1:])
            high = h + low
            high.alpha = getattr(h, 'alpha', None)
            proxy = _hist(high, bottom=low, axes=axes, logy=logy, **kwargs)
            returns.append(proxy)
        if not reverse:
            returns = returns[::-1]
        _set_bounds(sum(hists), axes=axes,
                    was_empty=was_empty,
                    prev_xlim=curr_xlim,
                    prev_ylim=curr_ylim,
                    xpadding=xpadding, ypadding=ypadding,
                    yerror_in_padding=yerror_in_padding,
                    snap=snap,
                    logy=logy)
    else:
        for h in _maybe_reversed(hists, reverse):
            returns.append(_hist(h, axes=axes, logy=logy, **kwargs))
        if reverse:
            returns = returns[::-1]
        _set_bounds(hists[max(range(len(hists)), key=lambda idx: hists[idx].max())],
                    axes=axes,
                    was_empty=was_empty,
                    prev_xlim=curr_xlim,
                    prev_ylim=curr_ylim,
                    xpadding=xpadding, ypadding=ypadding,
                    yerror_in_padding=yerror_in_padding,
                    snap=snap,
                    logy=logy)
    return returns