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","."]
docstr: Compute match cost.
  
  Args:
      pred_instances (:obj:`InstanceData`): Predicted instances which
          must contain ``scores`` or ``masks``.
      gt_instances (:obj:`InstanceData`): Ground truth which must contain
          ``labels`` or ``masks``.
      img_meta (Optional[dict]): Image information. Defaults to None.
  
  Returns:
      Tensor: Match Cost matrix of shape (num_preds, num_gts).
code: def __call__(
      self,
      pred_instances: InstanceData,
      gt_instances: InstanceData,
      img_meta: Optional[dict] = None,
      **kwargs,
  ) -> Tensor:
      """Compute match cost.
  
      Args:
          pred_instances (:obj:`InstanceData`): Predicted instances which
              must contain ``scores`` or ``masks``.
          gt_instances (:obj:`InstanceData`): Ground truth which must contain
              ``labels`` or ``masks``.
          img_meta (Optional[dict]): Image information. Defaults to None.
  
      Returns:
          Tensor: Match Cost matrix of shape (num_preds, num_gts).
      """
      pred_masks = pred_instances.masks
      gt_masks = gt_instances.masks
      if self.use_sigmoid:
          cls_cost = self._binary_cross_entropy(pred_masks, gt_masks)
      else:
          raise NotImplementedError
  
      return cls_cost * self.weight
  
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 3 for calling the CrossEntropyLossCost obj's underlying member method code with example usage: obj.__call__(pred_instances, gt_instances, img_meta) and returns: unknown