code: def generalized_intersection_over_union(
      preds: torch.Tensor,
      target: torch.Tensor,
      iou_threshold: Optional[float] = None,
      replacement_val: float = 0,
      aggregate: bool = True,
  ) -> torch.Tensor:
      r"""Compute Generalized Intersection over Union (`GIOU`_) 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 giou is aggregated across all box pairs e.g. mean along the diagonal of the gIoU matrix:
  
          >>> import torch
          >>> from torchmetrics.functional.detection import generalized_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],
          ...     ]
          ... )
          >>> generalized_intersection_over_union(preds, target)
          tensor(0.5638)
  
      Example::
          By setting `aggregate=False` the full IoU matrix is returned:
  
          >>> import torch
          >>> from torchmetrics.functional.detection import generalized_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],
          ...     ]
          ... )
          >>> generalized_intersection_over_union(preds, target, aggregate=False)
          tensor([[ 0.6895, -0.4964, -0.4944],
                  [-0.5105,  0.4673, -0.3434],
                  [-0.6024, -0.4021,  0.5345]])
  
      """
      if not _TORCHVISION_GREATER_EQUAL_0_8:
          raise ModuleNotFoundError(
              f"`{generalized_intersection_over_union.__name__}` requires that `torchvision` version 0.8.0 or newer"
              " is installed."
              " Please install with `pip install torchvision>=0.8` or `pip install torchmetrics[detection]`."
          )
      iou = _giou_update(preds, target, iou_threshold, replacement_val)
      return _giou_compute(iou, aggregate)
  
imports: from typing import Optional
  import torch
  from torchmetrics.utilities.imports import _TORCHVISION_GREATER_EQUAL_0_8
label_desc: Use image node_id 3 for calling a global function with example usage: generalized_intersection_over_union(preds, target, iou_threshold, replacement_val, aggregate) and returns: _giou_compute