class_docstr: A data structure interface of MMOCR. They are used as interfaces between
  different components.
  
  The attributes in ``KIEDataSample`` are divided into two parts:
  
      - ``gt_instances``(InstanceData): Ground truth of instance annotations.
      - ``pred_instances``(InstanceData): Instances of model predictions.
  
  Examples:
       >>> import torch
       >>> import numpy as np
       >>> from mmengine.structures import InstanceData
       >>> from mmocr.data import KIEDataSample
       >>> # gt_instances
       >>> data_sample = KIEDataSample()
       >>> img_meta = dict(img_shape=(800, 1196, 3),
       ...                 pad_shape=(800, 1216, 3))
       >>> gt_instances = InstanceData(metainfo=img_meta)
       >>> gt_instances.bboxes = torch.rand((5, 4))
       >>> gt_instances.labels = torch.rand((5,))
       >>> data_sample.gt_instances = gt_instances
       >>> assert 'img_shape' in data_sample.gt_instances.metainfo_keys()
       >>> len(data_sample.gt_instances)
       5
       >>> print(data_sample)
       <KIEDataSample(
           META INFORMATION
           DATA FIELDS
           gt_instances: <InstanceData(
                   META INFORMATION
                   pad_shape: (800, 1216, 3)
                   img_shape: (800, 1196, 3)
                   DATA FIELDS
                   labels: tensor([0.8533, 0.1550, 0.5433, 0.7294, 0.5098])
                   bboxes:
                   tensor([[9.7725e-01, 5.8417e-01, 1.7269e-01, 6.5694e-01],
                           [1.7894e-01, 5.1780e-01, 7.0590e-01, 4.8589e-01],
                           [7.0392e-01, 6.6770e-01, 1.7520e-01, 1.4267e-01],
                           [2.2411e-01, 5.1962e-01, 9.6953e-01, 6.6994e-01],
                           [4.1338e-01, 2.1165e-01, 2.7239e-04, 6.8477e-01]])
               ) at 0x7f21fb1b9190>
       ) at 0x7f21fb1b9880>
       >>> # pred_instances
       >>> pred_instances = InstanceData(metainfo=img_meta)
       >>> pred_instances.bboxes = torch.rand((5, 4))
       >>> pred_instances.scores = torch.rand((5,))
       >>> data_sample = KIEDataSample(pred_instances=pred_instances)
       >>> assert 'pred_instances' in data_sample
       >>> data_sample = KIEDataSample()
       >>> gt_instances_data = dict(
       ...                        bboxes=torch.rand(2, 4),
       ...                        labels=torch.rand(2))
       >>> gt_instances = InstanceData(**gt_instances_data)
       >>> data_sample.gt_instances = gt_instances
       >>> assert 'gt_instances' in data_sample
docstr: pred_instances deleter.
code: def pred_instances(self):
      """pred_instances deleter."""
      del self._pred_instances
  
file_path: mmocr/mmocr/structures/kie_data_sample.py
imports: from mmengine.structures import BaseDataElement, InstanceData
label_desc: Use image node_id 6 for calling the KIEDataSample obj's underlying member method code with example usage: obj.pred_instances() without return types