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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 from_files(cls, filepaths, specie, step_skip=10, ncores=None, initial_disp=None, initial_structure=None, **kwargs):
 """ Convenient constructor that takes in a list of vasprun.xml paths to perform diffusion analysis. Args: filepaths ([str]):
 List of paths to vasprun.xml files of runs. ( must be ordered in sequence of MD simulation). For example, you may have done sequential VASP runs and they are in run1, run2, run3, etc. You should then pass in specie (Element/Specie):
 Specie to calculate diffusivity for as a String. E.g., "Li". step_skip (int):
 Sampling frequency of the displacements ( time_step is multiplied by this number to get the real time between measurements) ncores (int):
 Numbers of cores to use for multiprocessing. Can speed up vasprun parsing considerably. Defaults to None, which means serial. It should be noted that if you want to use multiprocessing, the number of ionic steps in all vasprun .xml files should be a multiple of the ionic_step_skip. Otherwise, inconsistent results may arise. Serial mode has no such restrictions. initial_disp (np.ndarray):
 Sometimes, you need to iteratively compute estimates of the diffusivity. This supplies an initial displacement that will be added on to the initial displacements. Note that this makes sense only when smoothed=False. initial_structure (Structure):
 Like initial_disp, this is used for iterative computations of estimates of the diffusivity. You typically need to supply both variables. This stipulates the initial structure from which the current set of displacements are computed. \\*\\*kwargs: kwargs supported by the :class:`DiffusionAnalyzer`_. Examples include smoothed, min_obs, avg_nsteps. """

        if ncores is not None and len(filepaths) > 1:
            import multiprocessing
            p = multiprocessing.Pool(ncores)
            vaspruns = p.imap(_get_vasprun,
                              [(fp, step_skip) for fp in filepaths])
            analyzer = cls.from_vaspruns(
                vaspruns, specie=specie, initial_disp=initial_disp,
                initial_structure=initial_structure, **kwargs)
            p.close()
            p.join()
            return analyzer
        else:
            def vr(filepaths):
                offset = 0
                for p in filepaths:
                    v = Vasprun(p, ionic_step_offset=offset,
                                ionic_step_skip=step_skip)
                    yield v
                    # Recompute offset.
                    offset = (-(v.nionic_steps - offset)) % step_skip

            return cls.from_vaspruns(
                vr(filepaths), specie=specie, initial_disp=initial_disp,
                initial_structure=initial_structure, **kwargs)