code: def complete_box_iou_loss(
      boxes1: torch.Tensor,
      boxes2: torch.Tensor,
      reduction: str = "none",
      eps: float = 1e-7,
  ) -> torch.Tensor:
      """
      Gradient-friendly IoU loss with an additional penalty that is non-zero when the
      boxes do not overlap. This loss function considers important geometrical
      factors such as overlap area, normalized central point distance and aspect ratio.
      This loss is symmetric, so the boxes1 and boxes2 arguments are interchangeable.
  
      Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
      ``0 <= x1 < x2`` and ``0 <= y1 < y2``, and The two boxes should have the
      same dimensions.
  
      Args:
          boxes1 : (Tensor[N, 4] or Tensor[4]) first set of boxes
          boxes2 : (Tensor[N, 4] or Tensor[4]) second set of boxes
          reduction : (string, optional) Specifies the reduction to apply to the output:
              ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: No reduction will be
              applied to the output. ``'mean'``: The output will be averaged.
              ``'sum'``: The output will be summed. Default: ``'none'``
          eps : (float): small number to prevent division by zero. Default: 1e-7
  
      Returns:
          Tensor: Loss tensor with the reduction option applied.
  
      Reference:
          Zhaohui Zheng et al.: Complete Intersection over Union Loss:
          https://arxiv.org/abs/1911.08287
  
      """
  
      # Original Implementation from https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/losses.py
  
      if not torch.jit.is_scripting() and not torch.jit.is_tracing():
          _log_api_usage_once(complete_box_iou_loss)
  
      boxes1 = _upcast_non_float(boxes1)
      boxes2 = _upcast_non_float(boxes2)
  
      diou_loss, iou = _diou_iou_loss(boxes1, boxes2)
  
      x1, y1, x2, y2 = boxes1.unbind(dim=-1)
      x1g, y1g, x2g, y2g = boxes2.unbind(dim=-1)
  
      # width and height of boxes
      w_pred = x2 - x1
      h_pred = y2 - y1
      w_gt = x2g - x1g
      h_gt = y2g - y1g
      v = (4 / (torch.pi**2)) * torch.pow(
          (torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2
      )
      with torch.no_grad():
          alpha = v / (1 - iou + v + eps)
  
      loss = diou_loss + alpha * v
  
      # Check reduction option and return loss accordingly
      if reduction == "none":
          pass
      elif reduction == "mean":
          loss = loss.mean() if loss.numel() > 0 else 0.0 * loss.sum()
      elif reduction == "sum":
          loss = loss.sum()
      else:
          raise ValueError(
              f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'"
          )
      return loss
  
imports: import torch
  from ..utils import _log_api_usage_once
  from ._utils import _upcast_non_float
  from .diou_loss import _diou_iou_loss
label_desc: Use image node_id 1 for calling a global function with example usage: complete_box_iou_loss(boxes1, boxes2, reduction, eps) and returns: loss