def gaussian_bags_of_words(Y, vocab=vocab1k, sigma=1, bag_size=[25, 50], **kwargs):
    """
    Generate Gaussian bags of words based on label assignments

    Args:
        Y: np.array of true labels
        sigma: (float) the standard deviation of the Gaussian distributions
        bag_size: (list) the min and max length of bags of words

    Returns:
        X: (Tensor) a tensor of indices representing tokens
        D: (list) a list of sentences (strings)

    The sentences are conditionally independent, given a label.
    Note that technically we use a half-normal distribution here because we
        take the absolute value of the normal distribution.

    Example:
        TBD

    """

    def make_distribution(sigma, num_words):
        p = abs(np.random.normal(0, sigma, num_words))
        return p / sum(p)

    num_words = len(vocab)
    word_dists = {y: make_distribution(sigma, num_words) for y in set(Y)}
    bag_sizes = np.random.choice(range(min(bag_size), max(bag_size)), len(Y))

    X = []
    items = []
    for i, (y, length) in enumerate(zip(Y, bag_sizes)):
        x = torch.from_numpy(np.random.choice(num_words, length, p=word_dists[y]))
        X.append(x)
        items.append(" ".join(vocab[j] for j in x))

    return X, items
Generate Gaussian bags of words based on label assignments

    Args:
        Y: np.array of true labels
        sigma: (float) the standard deviation of the Gaussian distributions
        bag_size: (list) the min and max length of bags of words

    Returns:
        X: (Tensor) a tensor of indices representing tokens
        D: (list) a list of sentences (strings)

    The sentences are conditionally independent, given a label.
    Note that technically we use a half-normal distribution here because we
        take the absolute value of the normal distribution.

    Example:
        TBD