class_docstr: CrossEntropyLossCost.
  
  Args:
      use_sigmoid (bool): Whether the prediction uses sigmoid
              of softmax. Defaults to True.
      weight (Union[float, int]): Cost weight. Defaults to 1.
class_docstr_tok: ["CrossEntropyLossCost",".","Args",":","use_sigmoid","(","bool",")",":","Whether","the","prediction","uses","sigmoid","of","softmax",".","Defaults","to","True",".","weight","(","Union","[","float",",","int","]",")",":","Cost","weight",".","Defaults","to","1","."]
code: def __init__(
      self, use_sigmoid: bool = True, weight: Union[float, int] = 1.0
  ) -> None:
      super().__init__(weight=weight)
      self.use_sigmoid = use_sigmoid
  
imports: from abc import abstractmethod
  from typing import Optional, Union
  import torch
  import torch.nn.functional
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import TASK_UTILS
  from mmdet.structures.bbox import bbox_overlaps, bbox_xyxy_to_cxcywh
label_desc: Use image node_id 1 to create a new CrossEntropyLossCost object from inherited base classes: BaseMatchCost with example: obj = CrossEntropyLossCost(use_sigmoid, weight)