class_docstr: Pack the inputs data for key information extraction.
  
  The type of outputs is `dict`:
  
  - inputs: image converted to tensor, whose shape is (C, H, W).
  - data_samples: Two components of ``TextDetDataSample`` will be updated:
  
    - gt_instances (InstanceData): Depending on annotations, a subset of the
      following keys will be updated:
  
      - bboxes (torch.Tensor((N, 4), dtype=torch.float32)): The groundtruth
        of bounding boxes in the form of [x1, y1, x2, y2]. Renamed from
        'gt_bboxes'.
      - labels (torch.LongTensor(N)): The labels of instances.
        Renamed from 'gt_bboxes_labels'.
      - edge_labels (torch.LongTensor(N, N)): The edge labels.
        Renamed from 'gt_edges_labels'.
      - texts (list[str]): The groundtruth texts. Renamed from 'gt_texts'.
  
    - metainfo (dict): 'metainfo' is always populated. The contents of the
      'metainfo' depends on ``meta_keys``. By default it includes:
  
      - "img_path": Path to the image file.
      - "img_shape": Shape of the image input to the network as a tuple
        (h, w). Note that the image may be zero-padded afterward on the
        bottom/right if the batch tensor is larger than this shape.
      - "scale_factor": A tuple indicating the ratio of width and height
        of the preprocessed image to the original one.
      - "ori_shape": Shape of the preprocessed image as a tuple
        (h, w).
  
  Args:
      meta_keys (Sequence[str], optional): Meta keys to be converted to
          the metainfo of ``TextDetSample``. Defaults to ``('img_path',
          'ori_shape', 'img_shape', 'scale_factor', 'flip',
          'flip_direction')``.
code: def __repr__(self) -> str:
      repr_str = self.__class__.__name__
      repr_str += f"(meta_keys={self.meta_keys})"
      return repr_str
  
imports: import numpy
  import torch
  from mmcv.transforms import to_tensor
  from mmcv.transforms.base import BaseTransform
  from mmengine.structures import InstanceData, LabelData
  from mmocr.registry import TRANSFORMS
  from mmocr.structures import KIEDataSample, TextDetDataSample, TextRecogDataSample
label_desc: Use image node_id 3 for calling the PackKIEInputs obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str