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".
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docstr: Load meta information to visualizer.
docstr_tok: ["Load","meta","information","to","visualizer","."]
code: def _load_metainfo_to_visualizer(
      self, weights: Optional[str], cfg: ConfigType
  ) -> None:
      """Load meta information to visualizer."""
      if hasattr(self, "visualizer"):
          if weights is not None:
              w = _load_checkpoint(weights, map_location="cpu")
              if w and "meta" in w and "dataset_meta" in w["meta"]:
                  self.visualizer.dataset_meta = w["meta"][
                      "dataset_meta"
                  ]
                  return
          if "test_dataloader" in cfg:
              dataset_cfg = copy.deepcopy(cfg.test_dataloader.dataset)
              dataset_cfg["lazy_init"] = True
              dataset_cfg["metainfo"] = None
              dataset = DATASETS.build(dataset_cfg)
              self.visualizer.dataset_meta = dataset.metainfo
          else:
              raise ValueError(
                  "KIEVisualizer requires meta information from weights or "
                  "test dataset, but none of them is provided."
              )
  
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 2 for calling the KIEInferencer obj's underlying member method code with example usage: obj._load_metainfo_to_visualizer(weights, cfg) without return types