def METHOD_NAME(self, unit_std):
    speed = 8.0
    normalizer = ScalarAdaptiveNormalizer(speed=speed, unit_std=unit_std)
    for i in range(self._window_size):
        normalized_tensor = normalizer.normalize(self._tensors[i])
    weights = []
    acc_r = 1.0
    for i in range(self._window_size):
        r = speed / (speed + self._window_size - 1 - i)
        weights.append(r * acc_r)
        acc_r *= 1 - r
    weights = torch.as_tensor(weights[::-1], dtype=torch.float32)
    ones = torch.ones((self._batch_size, ), dtype=torch.float32)
    weights = torch.ger(weights, ones)
    weights /= torch.sum(weights)  # reduce adaptive bias
    self._verify_normalization(
        weights,
        normalized_tensor,
        normalizer._variance_epsilon,
        use_var=not unit_std)