def resample_uv_tensors_to_bbox(
    u: torch.Tensor,
    v: torch.Tensor,
    labels: torch.Tensor,
    box_xywh_abs: IntTupleBox,
) -> torch.Tensor:
    """
    Resamples U and V coordinate estimates for the given bounding box

    Args:
        u (tensor [1, C, H, W] of float): U coordinates
        v (tensor [1, C, H, W] of float): V coordinates
        labels (tensor [H, W] of long): labels obtained by resampling segmentation
            outputs for the given bounding box
        box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
    Return:
       Resampled U and V coordinates - a tensor [2, H, W] of float
    """
    x, y, w, h = box_xywh_abs
    w = max(int(w), 1)
    h = max(int(h), 1)
    u_bbox = F.interpolate(u, (h, w), mode="bilinear", align_corners=False)
    v_bbox = F.interpolate(v, (h, w), mode="bilinear", align_corners=False)
    uv = torch.zeros([2, h, w], dtype=torch.float32, device=u.device)
    for part_id in range(1, u_bbox.size(1)):
        uv[0][labels == part_id] = u_bbox[0, part_id][labels == part_id]
        uv[1][labels == part_id] = v_bbox[0, part_id][labels == part_id]
    return uv

    Resamples U and V coordinate estimates for the given bounding box

    Args:
        u (tensor [1, C, H, W] of float): U coordinates
        v (tensor [1, C, H, W] of float): V coordinates
        labels (tensor [H, W] of long): labels obtained by resampling segmentation
            outputs for the given bounding box
        box_xywh_abs (tuple of 4 int): bounding box that corresponds to predictor outputs
    Return:
       Resampled U and V coordinates - a tensor [2, H, W] of float
    
EVA-02/det/projects/DensePose/densepose/converters/chart_output_to_chart_result.py