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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 cluster_uniform_time(data=None, k=None, stride=1, metric='euclidean', n_jobs=None, chunksize=None, skip=0, **kwargs):
 r"""Uniform time clustering If given data, performs a clustering that selects data points uniformly in time and then assigns the data using a Voronoi discretization. Returns a :class:`UniformTimeClustering <pyemma.coordinates.clustering.UniformTimeClustering>` object that can be used to extract the discretized data sequences, or to assign other data points to the same partition. If data is not given, an empty :class:`UniformTimeClustering <pyemma.coordinates.clustering.UniformTimeClustering>` will be created that still needs to be parametrized, e.g. in a :func:`pipeline`. Parameters data : ndarray (T, d) or list of ndarray (T_i, d) or a reader created by source function input data, if available in memory k : int the number of cluster centers. When not specified (None), min(sqrt(N), 5000) is chosen as default value, where N denotes the number of data points stride : int, optional, default = 1 If set to 1, all input data will be used for estimation. Note that this could cause this calculation to be very slow for large data sets. Since molecular dynamics data is usually correlated at short timescales, it is often sufficient to estimate transformations at a longer stride. Note that the stride option in the get_output() function of the returned object is independent, so you can parametrize at a long stride, and still map all frames through the transformer. metric : str metric to use during clustering ('euclidean', 'minRMSD') n_jobs : int or None, default None Number of threads to use during assignment of the data. If None, all available CPUs will be used. chunksize: int, default=None Number of data frames to process at once. Choose a higher value here, to optimize thread usage and gain processing speed. If None is passed, use the default value of the underlying reader/data source. Choose zero to disable chunking at all. skip : int, default=0 skip the first initial n frames per trajectory. Returns ------- uniformTime : a :class:`UniformTimeClustering <pyemma.coordinates.clustering.UniformTimeClustering>` clustering object Object for uniform time clustering. It holds discrete trajectories and cluster center information. .. autoclass:: pyemma.coordinates.clustering.uniform_time.UniformTimeClustering :members: :undoc-members: .. rubric:: Methods .. autoautosummary:: pyemma.coordinates.clustering.uniform_time.UniformTimeClustering :methods: .. rubric:: Attributes .. autoautosummary:: pyemma.coordinates.clustering.uniform_time.UniformTimeClustering :attributes: """

    from pyemma.coordinates.clustering.uniform_time import UniformTimeClustering
    res = UniformTimeClustering(k, metric=metric, n_jobs=n_jobs, skip=skip, stride=stride)
    from pyemma.util.reflection import get_default_args
    cs = _check_old_chunksize_arg(chunksize, get_default_args(cluster_uniform_time)['chunksize'], **kwargs)
    if data is not None:
        res.estimate(data, chunksize=cs)
    else:
        res.chunksize = cs
    return res