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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 make_spectrum_plot(model_plot, data_plot, desc, xmin_clamp=0.01, min_valid_x=None, max_valid_x=None):
 """Make a plot of a spectral model and data. *model_plot* A model plot object returned by Sherpa from a call like `ui.get_model_plot()` or `ui.get_bkg_model_plot()`. *data_plot* A data plot object returned by Sherpa from a call like `ui.get_source_plot()` or `ui.get_bkg_plot()`. *desc* Text describing the origin of the data; will be shown in the plot legend (with "Model" and "Data" appended). *xmin_clamp* The smallest "x" (energy axis) value that will be plotted; default is 0.01. This is needed to allow the plot to be shown on a logarithmic scale if the energy axes of the model go all the way to 0. *min_valid_x* Either None, or the smallest "x" (energy axis) value in which the model and data are valid; this could correspond to a range specified in the "notice" command during analysis. If specified, a gray band will be added to the plot showing the invalidated regions. *max_valid_x* Like *min_valid_x* but for the largest "x" (energy axis) value in which the model and data are valid. Returns: A tuple ``(plot, xlow, xhigh)``, where *plot* an OmegaPlot RectPlot instance, *xlow* is the left edge of the plot bounds, and *xhigh* is the right edge of the plot bounds. """

    import omega as om

    model_x = np.concatenate((model_plot.xlo, [model_plot.xhi[-1]]))
    model_x[0] = max(model_x[0], xmin_clamp)
    model_y = np.concatenate((model_plot.y, [0.]))

    # Sigh, sometimes Sherpa gives us bad values.
    is_bad = ~np.isfinite(model_y)
    if is_bad.sum():
        from .cli import warn
        warn('bad Sherpa model Y value(s) at: %r', np.where(is_bad)[0])
        model_y[is_bad] = 0

    data_left_edges = data_plot.x - 0.5 * data_plot.xerr
    data_left_edges[0] = max(data_left_edges[0], xmin_clamp)
    data_hist_x = np.concatenate((data_left_edges, [data_plot.x[-1] + 0.5 * data_plot.xerr[-1]]))
    data_hist_y = np.concatenate((data_plot.y, [0.]))

    log_bounds_pad_factor = 0.9
    xlow = model_x[0] * log_bounds_pad_factor
    xhigh = model_x[-1] / log_bounds_pad_factor

    p = om.RectPlot()

    if min_valid_x is not None:
        p.add(om.rect.XBand(1e-3 * xlow, min_valid_x, keyText=None), zheight=-1, dsn=1)
    if max_valid_x is not None:
        p.add(om.rect.XBand(max_valid_x, xhigh * 1e3, keyText=None), zheight=-1, dsn=1)

    csp = om.rect.ContinuousSteppedPainter(keyText=desc + ' Model')
    csp.setFloats(model_x, model_y)
    p.add(csp)

    csp = om.rect.ContinuousSteppedPainter(keyText=None)
    csp.setFloats(data_hist_x, data_hist_y)
    p.add(csp)
    p.addXYErr(data_plot.x, data_plot.y, data_plot.yerr, desc + ' Data', lines=0, dsn=1)

    p.setLabels(data_plot.xlabel, data_plot.ylabel)
    p.setLinLogAxes(True, False)
    p.setBounds (xlow, xhigh)
    return p, xlow, xhigh