number of subsampled labels with values > 0
            is `min(num_positives, int(positive_fraction * num_samples))`. The number of negatives sampled is
            `min(num_negatives, num_samples - num_positives_sampled)`. In order words, if there are not enough
            positives, the sample is filled with negatives. If there are also not enough negatives, then as many
            elements are sampled as is possible.
        bg_label (int): label index of background ("negative") class.

    Returns:
        pos_idx, neg_idx (Tensor):
            1D vector of indices. The total length of both is `num_samples` or fewer.
    rf