class_docstr: Key Information Extraction Inferencer.
  
  Args:
      model (str, optional): Path to the config file or the model name
          defined in metafile. For example, it could be
          "sdmgr_unet16_60e_wildreceipt" or
          "configs/kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py".
          If model is not specified, user must provide the
          `weights` saved by MMEngine which contains the config string.
          Defaults to None.
      weights (str, optional): Path to the checkpoint. If it is not specified
          and model is a model name of metafile, the weights will be loaded
          from metafile. Defaults to None.
      device (str, optional): Device to run inference. If None, the available
          device will be automatically used. Defaults to None.
      scope (str, optional): The scope of the model. Defaults to "mmocr".
class_docstr_tok: ["Key","Information","Extraction","Inferencer",".","Args",":","model","(","str",",","optional",")",":","Path","to","the","config","file","or","the","model","name","defined","in","metafile",".","For","example",",","it","could","be","``","sdmgr_unet16_60e_wildreceipt","''","or","``","configs\/kie\/sdmgr\/sdmgr_unet16_60e_wildreceipt.py","''",".","If","model","is","not","specified",",","user","must","provide","the","`","weights","`","saved","by","MMEngine","which","contains","the","config","string",".","Defaults","to","None",".","weights","(","str",",","optional",")",":","Path","to","the","checkpoint",".","If","it","is","not","specified","and","model","is","a","model","name","of","metafile",",","the","weights","will","be","loaded","from","metafile",".","Defaults","to","None",".","device","(","str",",","optional",")",":","Device","to","run","inference",".","If","None",",","the","available","device","will","be","automatically","used",".","Defaults","to","None",".","scope","(","str",",","optional",")",":","The","scope","of","the","model",".","Defaults","to","``","mmocr","''","."]
code: def __init__(
      self,
      model: Union[ModelType, str, None] = None,
      weights: Optional[str] = None,
      device: Optional[str] = None,
      scope: Optional[str] = "mmocr",
  ) -> None:
      super().__init__(
          model=model, weights=weights, device=device, scope=scope
      )
      self._load_metainfo_to_visualizer(weights, self.cfg)
      self.collate_fn = self.kie_collate
  
imports: import copy
  import os.path
  from typing import Any, Dict, List, Optional, Sequence, Union
  import mmcv
  import mmengine
  import numpy
  from mmengine.dataset import Compose, pseudo_collate
  from mmengine.runner.checkpoint import _load_checkpoint
  from mmocr.registry import DATASETS
  from mmocr.structures import KIEDataSample
  from mmocr.utils import ConfigType
  from .base_mmocr_inferencer import BaseMMOCRInferencer, ModelType, PredType
label_desc: Use image node_id 1 to create a new KIEInferencer object from inherited base classes: BaseMMOCRInferencer with example: obj = KIEInferencer(model, weights, device, scope)