Unnesting Nested Subscripts and Attributes

[alexnick83]

This PR adds preprocessing functionality to the frontend that un-nests nested subscripts and attributes. Example:

Original code:

def test_1(A: npt.NDArray[np.int32],
           i0: npt.NDArray[np.int32],
           i1: npt.NDArray[np.int32],
           i2: npt.NDArray[np.int32],
           i3: npt.NDArray[np.int32],
           i4: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]:
    
    B = np.zeros_like(A)

    for i in range(A.shape[0]):
        for j in range(A.shape[1]):
            for k in range(A.shape[2]):
                for l in range(A.shape[3]):
                    B[i, :max(i2[j], i3[j]) - min(i2[j], i3[j]), k, l] = A[i0[i1[i]], min(i2[j], i3[j]) : max(i2[j], i3[j]), k, i4[l]]

    return (A + B).sum(axis=0)

Un-nested code:

def test_1(A: npt.NDArray[np.int32], i0: npt.NDArray[np.int32], i1: npt.NDArray[np.int32], i2: npt.NDArray[np.int32], i3: npt.NDArray[np.int32], i4: npt.NDArray[np.int32]) -> npt.NDArray[np.int32]:
    B = np.zeros_like(A)
    for i in range(A.shape[0]):
        for j in range(A.shape[1]):
            for k in range(A.shape[2]):
                for l in range(A.shape[3]):
                    __var_0 = max(i2[j], i3[j]) - min(i2[j], i3[j])
                    __var_1 = i1[i]
                    __var_2 = i0[__var_1]
                    __var_3 = min(i2[j], i3[j])
                    __var_4 = max(i2[j], i3[j])
                    __var_5 = i4[l]
                    B[i, :__var_0, k, l] = A[__var_2, __var_3:__var_4, k, __var_5]
    __var_6 = A + B
    return __var_6.sum(axis=0)