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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 get_dummy_dynamic_run(nsamples, **kwargs):
 """Generate dummy data for a dynamic nested sampling run. Loglikelihood values of points are generated from a uniform distribution in (0, 1), sorted, scaled by logl_range and shifted by logl_start (if it is not -np.inf). Theta values of each point are each generated from a uniform distribution in (0, 1). Parameters nsamples: int Number of samples in thread. nthread_init: int Number of threads in the inital run (starting at logl=-np.inf). nthread_dyn: int Number of threads in the inital run (starting at randomly chosen points in the initial run). ndim: int, optional Number of dimensions. seed: int, optional If not False, the seed is set with np.random.seed(seed). logl_start: float, optional logl at which thread starts. logl_range: float, optional Scale factor applied to logl values. """

    seed = kwargs.pop('seed', False)
    ndim = kwargs.pop('ndim', 2)
    nthread_init = kwargs.pop('nthread_init', 2)
    nthread_dyn = kwargs.pop('nthread_dyn', 3)
    logl_range = kwargs.pop('logl_range', 1)
    if kwargs:
        raise TypeError('Unexpected **kwargs: {0}'.format(kwargs))
    init = get_dummy_run(nthread_init, nsamples, ndim=ndim, seed=seed,
                         logl_start=-np.inf, logl_range=logl_range)
    dyn_starts = list(np.random.choice(
        init['logl'], nthread_dyn, replace=True))
    threads = nestcheck.ns_run_utils.get_run_threads(init)
    # Seed must be False here so it is not set again for each thread
    threads += [get_dummy_thread(
        nsamples, ndim=ndim, seed=False, logl_start=start,
        logl_range=logl_range) for start in dyn_starts]
    # make sure the threads have unique labels and combine them
    for i, _ in enumerate(threads):
        threads[i]['thread_labels'] = np.full(nsamples, i)
    run = nestcheck.ns_run_utils.combine_threads(threads)
    # To make sure the thread labelling is same way it would when
    # processing a dead points file, tranform into dead points
    samples = nestcheck.write_polychord_output.run_dead_birth_array(run)
    return nestcheck.data_processing.process_samples_array(samples)