def METHOD_NAME(self, level: Tensor) -> Tensor:
    F = self.F
    # we consider level to be an independent axis and so expand it
    # to shape (num_levels, 1, 1, ...)
    for _ in range(self.all_dim):
        level = level.expand_dims(axis=-1)
    quantiles = F.broadcast_mul(self.value, level.ones_like())
    level = F.broadcast_mul(quantiles.ones_like(), level)
    minus_inf = -quantiles.ones_like() / 0.0
    quantiles = F.where(
        F.broadcast_logical_or(level != 0, F.contrib.isnan(quantiles)),
        quantiles,
        minus_inf,
    )
    nans = level.zeros_like() / 0.0
    quantiles = F.where(level != level, nans, quantiles)
    return quantiles