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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 evaluate_inverse( distribution, u_data, cache=None, parameters=None ):
 """ Evaluate inverse Rosenblatt transformation. Args: distribution (Dist):
 Distribution to evaluate. u_data (numpy.ndarray):
 Locations for where evaluate inverse transformation distribution at. parameters (:py:data:typing.Any):
 Collection of parameters to override the default ones in the distribution. cache (:py:data:typing.Any):
 A collection of previous calculations in case the same distribution turns up on more than one occasion. Returns: The cumulative distribution values of ``distribution`` at location ``u_data`` using parameters ``parameters``. """

    if cache is None:
        cache = {}
    out = numpy.zeros(u_data.shape)

    # Distribution self know how to handle inverse Rosenblatt.
    if hasattr(distribution, "_ppf"):
        parameters = load_parameters(
            distribution, "_ppf", parameters=parameters, cache=cache)
        out[:] = distribution._ppf(u_data.copy(), **parameters)

    # Approximate inverse Rosenblatt based on cumulative distribution function.
    else:
        from .. import approximation
        parameters = load_parameters(
            distribution, "_cdf", parameters=parameters, cache=cache)
        out[:] = approximation.approximate_inverse(
            distribution, u_data.copy(), cache=cache.copy(), parameters=parameters)

    # Store cache.
    cache[distribution] = out

    return out