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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 create_uniform(low=0., high=1., mu_err=0., sigma_err=1., seed=None, **kwargs):
 """Generate a data with magnitudes that follows a uniform distribution; the error instead are gaussian. Parameters low : float, optional Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0. high : float, optional Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. mu_err : float (default=0) Mean of the gaussian distribution of magnitudes sigma_err : float (default=1) Standar deviation of the gaussian distribution of magnitude errorrs seed : {None, int, array_like}, optional Random seed used to initialize the pseudo-random number generator. Can be any integer between 0 and 2**32 - 1 inclusive, an array (or other sequence) of such integers, or None (the default). If seed is None, then RandomState will try to read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise. kwargs : optional extra arguments for create_random. Returns ------- data A Data object with a random lightcurves. Examples -------- .. code-block:: pycon Data(id=None, ds_name='feets-synthetic', bands=('B', 'V')) 1.39748799, 1.2171404 ]) """

    random = np.random.RandomState(seed)
    return create_random(
        magf=random.uniform, magf_params={"low": low, "high": high},
        errf=random.normal, errf_params={"loc": mu_err, "scale": sigma_err},
        **kwargs)