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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 generate_noise(shape: List[int], porosity=None, octaves: int = 3, frequency: int = 32, mode: str = 'simplex'):
 r""" Generate a field of spatially correlated random noise using the Perlin noise algorithm, or the updated Simplex noise algorithm. Parameters shape : array_like The size of the image to generate in [Nx, Ny, Nz] where N is the number of voxels. porosity : float If specified, this will threshold the image to the specified value prior to returning. If no value is given (the default), then the scalar noise field is returned. octaves : int Controls the *texture* of the noise, with higher octaves giving more complex features over larger length scales. frequency : array_like Controls the relative sizes of the features, with higher frequencies giving larger features. A scalar value will apply the same frequency in all directions, given an isotropic field; a vector value will apply the specified values along each axis to create anisotropy. mode : string Which noise algorithm to use, either ``'simplex'`` (default) or ``'perlin'``. Returns ------- image : ND-array If porosity is given, then a boolean array with ``True`` values denoting the pore space is returned. If not, then normally distributed and spatially correlated randomly noise is returned. Notes ----- This method depends the a package called 'noise' which must be compiled. It is included in the Anaconda distribution, or a platform specific binary can be downloaded. See Also -------- porespy.tools.norm_to_uniform """

    try:
        import noise
    except ModuleNotFoundError:
        raise Exception("The noise package must be installed")
    shape = sp.array(shape)
    if sp.size(shape) == 1:
        Lx, Ly, Lz = sp.full((3, ), int(shape))
    elif len(shape) == 2:
        Lx, Ly = shape
        Lz = 1
    elif len(shape) == 3:
        Lx, Ly, Lz = shape
    if mode == 'simplex':
        f = noise.snoise3
    else:
        f = noise.pnoise3
    frequency = sp.atleast_1d(frequency)
    if frequency.size == 1:
        freq = sp.full(shape=[3, ], fill_value=frequency[0])
    elif frequency.size == 2:
        freq = sp.concatenate((frequency, [1]))
    else:
        freq = sp.array(frequency)
    im = sp.zeros(shape=[Lx, Ly, Lz], dtype=float)
    for x in range(Lx):
        for y in range(Ly):
            for z in range(Lz):
                im[x, y, z] = f(x=x/freq[0], y=y/freq[1], z=z/freq[2],
                                octaves=octaves)
    im = im.squeeze()
    if porosity:
        im = norm_to_uniform(im, scale=[0, 1])
        im = im < porosity
    return im