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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 rand(S, A, is_sparse=False, mask=None):
 """Generate a random Markov Decision Process. Parameters S : int Number of states (> 1) A : int Number of actions (> 1) is_sparse : bool, optional False to have matrices in dense format, True to have sparse matrices. Default: False. mask : array, optional Array with 0 and 1 (0 indicates a place for a zero probability), shape can be ``(S, S)`` or ``(A, S, S)``. Default: random. Returns ------- out : tuple ``out[0]`` contains the transition probability matrix P and ``out[1]`` contains the reward matrix R. If ``is_sparse=False`` then P is a numpy array with a shape of ``(A, S, S)`` and R is a numpy array with a shape of ``(S, A)``. If ``is_sparse=True`` then P and R are tuples of length ``A``, where each ``P[a]`` is a scipy sparse CSR format matrix of shape ``(S, S)`` and each ``R[a]`` is a scipy sparse csr format matrix of shape ``(S, 1)``. Examples -------- array([[[ 0.21977283, 0.14889403, 0.30343592, 0.32789723], [ 1. , 0. , 0. , 0. ], [ 0. , 0.43718772, 0.54480359, 0.01800869], [ 0.39766289, 0.39997167, 0.12547318, 0.07689227]], <BLANKLINE> [[ 1. , 0. , 0. , 0. ], [ 0.32261337, 0.15483812, 0.32271303, 0.19983549], [ 0.33816885, 0.2766999 , 0.12960299, 0.25552826], [ 0.41299411, 0. , 0.58369957, 0.00330633]], <BLANKLINE> [[ 0.32343037, 0.15178596, 0.28733094, 0.23745272], [ 0.36348538, 0.24483321, 0.16114188, 0.23053953], [ 1. , 0. , 0. , 0. ], [ 0. , 0. , 1. , 0. ]]]) array([[[-0.23311696, 0.58345008, 0.05778984, 0.13608912], [-0.07704128, 0. , -0. , 0. ], [ 0. , 0.22419145, 0.23386799, 0.88749616], [-0.3691433 , -0.27257846, 0.14039354, -0.12279697]], <BLANKLINE> [[-0.77924972, 0. , -0. , -0. ], [ 0.47852716, -0.92162442, -0.43438607, -0.75960688], [-0.81211898, 0.15189299, 0.8585924 , -0.3628621 ], [ 0.35563307, -0. , 0.47038804, 0.92437709]], <BLANKLINE> [[-0.4051261 , 0.62759564, -0.20698852, 0.76220639], [-0.9616136 , -0.39685037, 0.32034707, -0.41984479], [-0.13716313, 0. , -0. , -0. ], [ 0. , -0. , 0.55810204, 0. ]]]) (5, 5) with 3296 stored elements in Compressed Sparse Row format> with 3296 stored elements in Compressed Sparse Row format> True """

    # making sure the states and actions are more than one
    assert S > 1, "The number of states S must be greater than 1."
    assert A > 1, "The number of actions A must be greater than 1."
    # if the user hasn't specified a mask, then we will make a random one now
    if mask is not None:
        # the mask needs to be SxS or AxSxS
        try:
            assert mask.shape in ((S, S), (A, S, S)), (
                "'mask' must have dimensions S×S or A×S×S."
            )
        except AttributeError:
            raise TypeError("'mask' must be a numpy array or matrix.")
    # generate the transition and reward matrices based on S, A and mask
    if is_sparse:
        P, R = _randSparse(S, A, mask)
    else:
        P, R = _randDense(S, A, mask)
    return(P, R)