class_docstr: CrossEntropyLoss.
  
  Args:
      use_sigmoid (bool, optional): Whether the prediction uses sigmoid
          of softmax. Defaults to False.
      use_mask (bool, optional): Whether to use mask cross entropy loss.
          Defaults to False.
      reduction (str, optional): . Defaults to 'mean'.
          Options are "none", "mean" and "sum".
      class_weight (list[float] | str, optional): Weight of each class. If in
          str format, read them from a file. Defaults to None.
      loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
      loss_name (str, optional): Name of the loss item. If you want this loss
          item to be included into the backward graph, `loss_` must be the
          prefix of the name. Defaults to 'loss_ce'.
      avg_non_ignore (bool): The flag decides to whether the loss is
          only averaged over non-ignored targets. Default: False.
          `New in version 0.23.0.`
docstr: Loss Name.
  
  This function must be implemented and will return the name of this
  loss function. This name will be used to combine different loss items
  by simple sum operation. In addition, if you want this loss item to be
  included into the backward graph, `loss_` must be the prefix of the
  name.
  
  Returns:
      str: The name of this loss item.
docstr_tok: ["Loss","Name",".","This","function","must","be","implemented","and","will","return","the","name","of","this","loss","function",".","This","name","will","be","used","to","combine","different","loss","items","by","simple","sum","operation",".","In","addition",",","if","you","want","this","loss","item","to","be","included","into","the","backward","graph",",","`","loss_","`","must","be","the","prefix","of","the","name",".","Returns",":","str",":","The","name","of","this","loss","item","."]
code: def loss_name(self):
      """Loss Name.
  
      This function must be implemented and will return the name of this
      loss function. This name will be used to combine different loss items
      by simple sum operation. In addition, if you want this loss item to be
      included into the backward graph, `loss_` must be the prefix of the
      name.
  
      Returns:
          str: The name of this loss item.
      """
      return self._loss_name
  
file_path: dinov2/dinov2/eval/segmentation_m2f/models/losses/cross_entropy_loss.py
imports: import warnings
  import torch
  import torch.nn
  import torch.nn.functional
  from mmseg.models.builder import LOSSES
  from mmseg.models.losses.utils import get_class_weight, weight_reduce_loss
label_desc: Use image node_id 4 for calling the CrossEntropyLoss obj's underlying member method code with example usage: obj.loss_name() and returns: self