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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 _read_or_calc_samples( sampler, modelidx=0, n_samples=100, last_step=False, e_range=None, e_npoints=100, threads=None, ):
 """Get samples from blob or compute them from chain and sampler.modelfn """


    if not e_range:
        # return the results saved in blobs
        modelx, model = _process_blob(sampler, modelidx, last_step=last_step)
    else:
        # prepare bogus data for calculation
        e_range = validate_array(
            "e_range", u.Quantity(e_range), physical_type="energy"
        )
        e_unit = e_range.unit
        energy = (
            np.logspace(
                np.log10(e_range[0].value),
                np.log10(e_range[1].value),
                e_npoints,
            )
            * e_unit
        )
        data = {
            "energy": energy,
            "flux": np.zeros(energy.shape) * sampler.data["flux"].unit,
        }
        # init pool and select parameters
        chain = sampler.chain[-1] if last_step else sampler.flatchain
        pars = chain[np.random.randint(len(chain), size=n_samples)]
        blobs = []

        p = Pool(threads)
        modelouts = p.map(partial(sampler.modelfn, data=data), pars)
        p.close()
        p.terminate()

        for modelout in modelouts:
            if isinstance(modelout, np.ndarray):
                blobs.append([modelout])
            else:
                blobs.append(modelout)

        modelx, model = _process_blob(
            blobs, modelidx=modelidx, energy=data["energy"]
        )

    return modelx, model