class_docstr: Implementation of `Masked-attention Mask
  Transformer for Universal Image Segmentation
  <https://arxiv.org/pdf/2112.01527>`_.
class_docstr_tok: ["Implementation","of","`","Masked-attention","Mask","Transformer","for","Universal","Image","Segmentation","<","https",":","\/\/arxiv.org\/pdf\/2112.01527",">","`","_","."]
code: def __init__(
      self,
      backbone: ConfigType,
      neck: OptConfigType = None,
      panoptic_head: OptConfigType = None,
      panoptic_fusion_head: OptConfigType = None,
      train_cfg: OptConfigType = None,
      test_cfg: OptConfigType = None,
      data_preprocessor: OptConfigType = None,
      init_cfg: OptMultiConfig = None,
  ):
      super().__init__(
          backbone=backbone,
          neck=neck,
          panoptic_head=panoptic_head,
          panoptic_fusion_head=panoptic_fusion_head,
          train_cfg=train_cfg,
          test_cfg=test_cfg,
          data_preprocessor=data_preprocessor,
          init_cfg=init_cfg,
      )
  
imports: from mmdet.registry import MODELS
  from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
  from .maskformer import MaskFormer
label_desc: Use image node_id 1 to create a new Mask2Former object from inherited base classes: MaskFormer with example: obj = Mask2Former(backbone, neck, panoptic_head, panoptic_fusion_head, train_cfg, test_cfg, data_preprocessor, init_cfg)