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: Extra repr.
docstr_tok: ["Extra","repr","."]
code: def extra_repr(self):
      """Extra repr."""
      s = f"avg_non_ignore={self.avg_non_ignore}"
      return s
  
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 2 for calling the CrossEntropyLoss obj's underlying member method code with example usage: obj.extra_repr() and returns: s