class_docstr: Implementation of paper `Extended-IoU Loss: A Systematic
  IoU-Related Method: Beyond Simplified Regression for Better
  Localization <https://ieeexplore.ieee.org/abstract/document/9429909>`_
  
  Code is modified from https://github.com//ShiqiYu/libfacedetection.train.
  
  Args:
      eps (float): Epsilon to avoid log(0).
      reduction (str): Options are "none", "mean" and "sum".
      loss_weight (float): Weight of loss.
      smooth_point (float): hyperparameter, default is 0.1.
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code: def __init__(
      self,
      eps: float = 1e-6,
      reduction: str = "mean",
      loss_weight: float = 1.0,
      smooth_point: float = 0.1,
  ) -> None:
      super().__init__()
      self.eps = eps
      self.reduction = reduction
      self.loss_weight = loss_weight
      self.smooth_point = smooth_point
  
imports: import math
  import warnings
  from typing import Optional
  import torch
  import torch.nn
  from torch import Tensor
  from mmdet.registry import MODELS
  from mmdet.structures.bbox import bbox_overlaps
  from .utils import weighted_loss
label_desc: Use image node_id 1 to create a new EIoULoss object from inherited base classes: nn with example: obj = EIoULoss(eps, reduction, loss_weight, smooth_point)