def METHOD_NAME(self, values: torch.Tensor, count: int):
    """
    Produce a sample of indices to select data based on confidences
    Args:
        values (torch.Tensor): a tensor of length k that contains confidences
            k: number of points labeled with part_id
        count (int): number of samples to produce, should be positive and <= k
    Return:
        list(int): indices of values (along axis 1) selected as a sample
    """
    k = values.shape[1]
    if k == count:
        index_sample = list(range(k))
    else:
        # take the best count * search_count_multiplier pixels,
        # sample from them uniformly
        # (here best = smallest variance)
        _, sorted_confidence_indices = torch.sort(values[0])
        if self.search_count_multiplier is not None:
            search_count = min(int(count * self.search_count_multiplier), k)
        elif self.search_proportion is not None:
            search_count = min(max(int(k * self.search_proportion), count), k)
        else:
            search_count = min(count, k)
        sample_from_top = random.sample(range(search_count), count)
        index_sample = sorted_confidence_indices[-search_count:][sample_from_top]
    return index_sample