code: def mask_cross_entropy(
      pred,
      target,
      label,
      reduction="mean",
      avg_factor=None,
      class_weight=None,
      ignore_index=None,
  ):
      """Calculate the CrossEntropy loss for masks.
  
      Args:
          pred (torch.Tensor): The prediction with shape (N, C), C is the number
              of classes.
          target (torch.Tensor): The learning label of the prediction.
          label (torch.Tensor): ``label`` indicates the class label of the mask'
              corresponding object. This will be used to select the mask in the
              of the class which the object belongs to when the mask prediction
              if not class-agnostic.
          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 (None): Placeholder, to be consistent with other loss.
              Default: None.
  
      Returns:
          torch.Tensor: The calculated loss
      """
      assert (
          ignore_index is None
      ), "BCE loss does not support ignore_index"
      # TODO: handle these two reserved arguments
      assert reduction == "mean" and avg_factor is None
      num_rois = pred.size()[0]
      inds = torch.arange(
          0, num_rois, dtype=torch.long, device=pred.device
      )
      pred_slice = pred[inds, label].squeeze(1)
      return F.binary_cross_entropy_with_logits(
          pred_slice, target, weight=class_weight, reduction="mean"
      )[None]
  
file_path: lavis/lavis/common/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from ..builder import LOSSES
  from .utils import get_class_weight, weight_reduce_loss
label_desc: Use image node_id 4 for calling a global function with example usage: mask_cross_entropy(pred, target, label, reduction, avg_factor, class_weight, ignore_index) and returns: F