def get_uncertain_point_coords_with_randomness(mask_pred, labels, num_points,
                                               oversample_ratio,
                                               importance_sample_ratio):
    """Get ``num_points`` most uncertain points with random points during
    train.

    Sample points in [0, 1] x [0, 1] coordinate space based on their
    uncertainty. The uncertainties are calculated for each point using
    'get_uncertainty()' function that takes point's logit prediction as
    input.

    Args:
        mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
            mask_height, mask_width) for class-specific or class-agnostic
            prediction.
        labels (list): The ground truth class for each instance.
        num_points (int): The number of points to sample.
        oversample_ratio (int): Oversampling parameter.
        importance_sample_ratio (float): Ratio of points that are sampled
            via importnace sampling.

    Returns:
        point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
            that contains the coordinates sampled points.
    """
    assert oversample_ratio >= 1
    assert 0 <= importance_sample_ratio <= 1
    batch_size = mask_pred.shape[0]
    num_sampled = int(num_points * oversample_ratio)
    point_coords = torch.rand(
        batch_size, num_sampled, 2, device=mask_pred.device)
    point_logits = point_sample(mask_pred, point_coords)
    # It is crucial to calculate uncertainty based on the sampled
    # prediction value for the points. Calculating uncertainties of the
    # coarse predictions first and sampling them for points leads to
    # incorrect results.  To illustrate this: assume uncertainty func(
    # logits)=-abs(logits), a sampled point between two coarse
    # predictions with -1 and 1 logits has 0 logits, and therefore 0
    # uncertainty value. However, if we calculate uncertainties for the
    # coarse predictions first, both will have -1 uncertainty,
    # and sampled point will get -1 uncertainty.
    point_uncertainties = get_uncertainty(point_logits, labels)
    num_uncertain_points = int(importance_sample_ratio * num_points)
    num_random_points = num_points - num_uncertain_points
    idx = torch.topk(
        point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
    shift = num_sampled * torch.arange(
        batch_size, dtype=torch.long, device=mask_pred.device)
    idx += shift[:, None]
    point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
        batch_size, num_uncertain_points, 2)
    if num_random_points > 0:
        rand_roi_coords = torch.rand(
            batch_size, num_random_points, 2, device=mask_pred.device)
        point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
    return point_coords
Get ``num_points`` most uncertain points with random points during
    train.

    Sample points in [0, 1] x [0, 1] coordinate space based on their
    uncertainty. The uncertainties are calculated for each point using
    'get_uncertainty()' function that takes point's logit prediction as
    input.

    Args:
        mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
            mask_height, mask_width) for class-specific or class-agnostic
            prediction.
        labels (list): The ground truth class for each instance.
        num_points (int): The number of points to sample.
        oversample_ratio (int): Oversampling parameter.
        importance_sample_ratio (float): Ratio of points that are sampled
            via importnace sampling.

    Returns:
        point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
            that contains the coordinates sampled points.
    
EVA-01/seg/mmseg_custom/models/utils/point_sample.py