code: def binary_cross_entropy(
      pred,
      label,
      weight=None,
      reduction="mean",
      avg_factor=None,
      class_weight=None,
      ignore_index=-100,
      avg_non_ignore=False,
  ):
      """Calculate the binary CrossEntropy loss.
  
      Args:
          pred (torch.Tensor): The prediction with shape (N, 1) or (N, ).
              When the shape of pred is (N, 1), label will be expanded to
              one-hot format, and when the shape of pred is (N, ), label
              will not be expanded to one-hot format.
          label (torch.Tensor): The learning label of the prediction,
              with shape (N, ).
          weight (torch.Tensor, optional): Sample-wise loss weight.
          reduction (str, optional): The method used to reduce the loss.
              Options are "none", "mean" and "sum".
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
          class_weight (list[float], optional): The weight for each class.
          ignore_index (int | None): The label index to be ignored.
              If None, it will be set to default value. Default: -100.
          avg_non_ignore (bool): The flag decides to whether the loss is
              only averaged over non-ignored targets. Default: False.
  
      Returns:
          torch.Tensor: The calculated loss.
      """
      # The default value of ignore_index is the same as F.cross_entropy
      ignore_index = -100 if ignore_index is None else ignore_index
  
      if pred.dim() != label.dim():
          label, weight, valid_mask = _expand_onehot_labels(
              label, weight, pred.size(-1), ignore_index
          )
      else:
          # should mask out the ignored elements
          valid_mask = ((label >= 0) & (label != ignore_index)).float()
          if weight is not None:
              # The inplace writing method will have a mismatched broadcast
              # shape error if the weight and valid_mask dimensions
              # are inconsistent such as (B,N,1) and (B,N,C).
              weight = weight * valid_mask
          else:
              weight = valid_mask
  
      # average loss over non-ignored elements
      if (
          (avg_factor is None)
          and avg_non_ignore
          and reduction == "mean"
      ):
          avg_factor = valid_mask.sum().item()
  
      # weighted element-wise losses
      weight = weight.float()
      loss = F.binary_cross_entropy_with_logits(
          pred, label.float(), pos_weight=class_weight, reduction="none"
      )
      # do the reduction for the weighted loss
      loss = weight_reduce_loss(
          loss, weight, reduction=reduction, avg_factor=avg_factor
      )
  
      return loss
  
imports: import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from mmdet.registry import MODELS
  from .accuracy import accuracy
  from .utils import weight_reduce_loss
label_desc: Use image node_id 3 for calling a global function with example usage: binary_cross_entropy(pred, label, weight, reduction, avg_factor, class_weight, ignore_index, avg_non_ignore) and returns: loss