class_docstr: MaskFormer fusion head which postprocesses results for panoptic
  segmentation, instance segmentation and semantic segmentation.
class_docstr_tok: ["MaskFormer","fusion","head","which","postprocesses","results","for","panoptic","segmentation",",","instance","segmentation","and","semantic","segmentation","."]
docstr: MaskFormerFusionHead has no training loss.
docstr_tok: ["MaskFormerFusionHead","has","no","training","loss","."]
code: def loss(self, **kwargs):
      """MaskFormerFusionHead has no training loss."""
      return dict()
  
file_path: mmdetection/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from mmengine.structures import InstanceData, PixelData
  from torch import Tensor
  from mmdet.evaluation.functional import INSTANCE_OFFSET
  from mmdet.registry import MODELS
  from mmdet.structures import SampleList
  from mmdet.structures.mask import mask2bbox
  from mmdet.utils import OptConfigType, OptMultiConfig
  from .base_panoptic_fusion_head import BasePanopticFusionHead
label_desc: Use image node_id 2 for calling the MaskFormerFusionHead obj's underlying member method code with example usage: obj.loss() and returns: dict