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: Extract elements necessary to represent a prediction into a
  dictionary. It's better to contain only basic data elements such as
  strings and numbers in order to guarantee it's json-serializable.
  
  Args:
      data_sample (TextRecogDataSample): The data sample to be converted.
  
  Returns:
      dict: The output dictionary.
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code: def pred2dict(self, data_sample: KIEDataSample) -> Dict:
      """Extract elements necessary to represent a prediction into a
      dictionary. It's better to contain only basic data elements such as
      strings and numbers in order to guarantee it's json-serializable.
  
      Args:
          data_sample (TextRecogDataSample): The data sample to be converted.
  
      Returns:
          dict: The output dictionary.
      """
      result = {}
      pred = data_sample.pred_instances
      result["scores"] = pred.scores.cpu().numpy().tolist()
      result["edge_scores"] = pred.edge_scores.cpu().numpy().tolist()
      result["edge_labels"] = pred.edge_labels.cpu().numpy().tolist()
      result["labels"] = pred.labels.cpu().numpy().tolist()
      return result
  
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 7 for calling the KIEInferencer obj's underlying member method code with example usage: obj.pred2dict(data_sample) and returns: result