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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_blob( sampler, blobidx=0, label=None, last_step=False, figure=None, **kwargs ):
 """ Plot a metadata blob as a fit to spectral data or value distribution Additional ``kwargs`` are passed to `plot_fit`. Parameters sampler : `emcee.EnsembleSampler` Sampler with a stored chain. blobidx : int, optional Metadata blob index to plot. label : str, optional Label for the value distribution. Labels for the fit plot can be passed as ``xlabel`` and ``ylabel`` and will be passed to `plot_fit`. Returns ------- figure : `matplotlib.pyplot.Figure` `matplotlib` figure instance containing the plot. """


    modelx, model = _process_blob(sampler, blobidx, last_step)
    if label is None:
        label = "Model output {0}".format(blobidx)

    if modelx is None:
        # Blob is scalar, plot distribution
        f = plot_distribution(model, label, figure=figure)
    else:
        f = plot_fit(
            sampler,
            modelidx=blobidx,
            last_step=last_step,
            label=label,
            figure=figure,
            **kwargs
        )

    return f