def METHOD_NAME(self, x: Tensor) -> Tensor:
    F = self.F
    # Normalize observations in case of mean_scaling
    sum_x = F.sum(x, axis=-1).expand_dims(axis=-1)
    x = F.broadcast_div(x, sum_x)
    alpha = self.alpha
    sum_alpha = F.sum(alpha, axis=-1)
    log_beta = F.sum(F.gammaln(alpha), axis=-1) - F.gammaln(sum_alpha)
    l_x = F.sum((alpha - 1) * F.log(x), axis=-1)
    ll = l_x - log_beta
    return ll