def normal(nmr_distributions, nmr_samples, mean=0, std=1, ctype='float', seed=None):
    """Draw random samples from the Gaussian distribution.

    Args:
        nmr_distributions (int): the number of unique continuous_distributions to create
        nmr_samples (int): The number of samples to draw
        mean (float or ndarray): The mean of the distribution
        std (float or ndarray): The standard deviation or the distribution
        ctype (str): the C type of the output samples
        seed (float): the seed for the RNG

    Returns:
        ndarray: A two dimensional numpy array as (nmr_distributions, nmr_samples).
    """
    if is_scalar(mean):
        mean = np.ones((nmr_distributions, 1)) * mean
    if is_scalar(std):
        std = np.ones((nmr_distributions, 1)) * std

    kernel_data = {'mean': Array(mean, as_scalar=True),
                   'std': Array(std, as_scalar=True)}

    kernel = SimpleCLFunction.from_string('''
        void compute(double mean, double std, global uint* rng_state, global ''' + ctype + '''* samples){
            rand123_data rand123_rng_data = rand123_initialize_data((uint[]){
                rng_state[0], rng_state[1], rng_state[2], rng_state[3], 
                rng_state[4], rng_state[5], 0});
            void* rng_data = (void*)&rand123_rng_data;

            for(uint i = 0; i < ''' + str(nmr_samples) + '''; i++){
                double4 randomnr = randn4(rng_data);
                samples[i] = (''' + ctype + ''')(mean + randomnr.x * std);
            }
        }
    ''', dependencies=[Rand123()])

    return _generate_samples(kernel, nmr_distributions, nmr_samples, ctype, kernel_data, seed=seed)