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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 unique_value_groups(ar, sort=True):
 """Group an array by its unique values. Parameters ar : array-like Input array. This will be flattened if it is not already 1-D. sort : boolean, optional Whether or not to sort unique values. Returns ------- values : np.ndarray Sorted, unique values as returned by `np.unique`. indices : list of lists of int Each element provides the integer indices in `ar` with values given by the corresponding value in `unique_values`. """

    inverse, values = pd.factorize(ar, sort=sort)
    groups = [[] for _ in range(len(values))]
    for n, g in enumerate(inverse):
        if g >= 0:
            # pandas uses -1 to mark NaN, but doesn't include them in values
            groups[g].append(n)
    return values, groups