def METHOD_NAME(
    preds: torch.Tensor,
    target: torch.Tensor,
    iou_threshold: Optional[float] = None,
    replacement_val: float = 0,
    aggregate: bool = True,
) -> torch.Tensor:
    r"""Compute Complete Intersection over Union (`CIOU`_) between two sets of boxes.
    Both sets of boxes are expected to be in (x1, y1, x2, y2) format with 0 <= x1 < x2 and 0 <= y1 < y2.
    Args:
        preds:
            The input tensor containing the predicted bounding boxes.
        target:
            The tensor containing the ground truth.
        iou_threshold:
            Optional IoU thresholds for evaluation. If set to `None` the threshold is ignored.
        replacement_val:
            Value to replace values under the threshold with.
        aggregate:
            Return the average value instead of the full matrix of values
    Example::
        By default iou is aggregated across all box pairs e.g. mean along the diagonal of the IoU matrix:
        >>> import torch
        >>> from torchmetrics.functional.detection import complete_intersection_over_union
        >>> preds = torch.tensor(
        ...     [
        ...         [296.55, 93.96, 314.97, 152.79],
        ...         [328.94, 97.05, 342.49, 122.98],
        ...         [356.62, 95.47, 372.33, 147.55],
        ...     ]
        ... )
        >>> target = torch.tensor(
        ...     [
        ...         [300.00, 100.00, 315.00, 150.00],
        ...         [330.00, 100.00, 350.00, 125.00],
        ...         [350.00, 100.00, 375.00, 150.00],
        ...     ]
        ... )
        >>> complete_intersection_over_union(preds, target)
        tensor(0.5790)
    Example::
        By setting `aggregate=False` the IoU score per prediction and target boxes is returned:
        >>> import torch
        >>> from torchmetrics.functional.detection import complete_intersection_over_union
        >>> preds = torch.tensor(
        ...     [
        ...         [296.55, 93.96, 314.97, 152.79],
        ...         [328.94, 97.05, 342.49, 122.98],
        ...         [356.62, 95.47, 372.33, 147.55],
        ...     ]
        ... )
        >>> target = torch.tensor(
        ...     [
        ...         [300.00, 100.00, 315.00, 150.00],
        ...         [330.00, 100.00, 350.00, 125.00],
        ...         [350.00, 100.00, 375.00, 150.00],
        ...     ]
        ... )
        >>> complete_intersection_over_union(preds, target, aggregate=False)
        tensor([[ 0.6883, -0.2072, -0.3352],
                [-0.2217,  0.4881, -0.1913],
                [-0.3971, -0.1543,  0.5606]])
    """
    if not _TORCHVISION_GREATER_EQUAL_0_13:
        raise ModuleNotFoundError(
            f"`{METHOD_NAME.__name__}` requires that `torchvision` version 0.13.0 or newer"
            " is installed."
            " Please install with `pip install torchvision>=0.13` or `pip install torchmetrics[detection]`."
        )
    iou = _ciou_update(preds, target, iou_threshold, replacement_val)
    return _ciou_compute(iou, aggregate)