sitives &
    negatives. It will try to return as many positives as possible without exceeding `positive_fraction * num_samples`,
    and then try to fill the remaining slots with negatives.

    Args:
        labels (Tensor): (N, ) label vector with values:
            * -1: ignore
            * bg_label: background ("negative") class
            * otherwise: one or more foreground ("positive") classes
        num_samples (int): The total number of labels with value >= 0 to return.
            Values that are not sampled will be filled with -1 (ignore).
        positive_fraction (float): The 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.
    rg