class_docstr: Text recogntion packer. It is used to pack the parsed annotation info
  to:
  
  .. code-block:: python
  
      {
          "metainfo":
              {
                  "dataset_type": "TextRecogDataset",
                  "task_name": "textrecog",
              },
          "data_list":
              [
                  {
                      "img_path": "textrecog_imgs/train/test_img.jpg",
                      "instances":
                          [
                              {
                                  "text": "GRAND"
                              }
                          ]
                  }
              ]
      }
docstr: Add meta information to the sample.
  
  Args:
      sample (List): A list of samples of the dataset.
  
  Returns:
      Dict: A dict contains the meta information and samples.
docstr_tok: ["Add","meta","information","to","the","sample",".","Args",":","sample","(","List",")",":","A","list","of","samples","of","the","dataset",".","Returns",":","Dict",":","A","dict","contains","the","meta","information","and","samples","."]
code: def add_meta(self, sample: List) -> Dict:
      """Add meta information to the sample.
  
      Args:
          sample (List): A list of samples of the dataset.
  
      Returns:
          Dict: A dict contains the meta information and samples.
      """
      meta = {
          "metainfo": {
              "dataset_type": "TextRecogDataset",
              "task_name": "textrecog",
          },
          "data_list": sample,
      }
      return meta
  
file_path: mmocr/mmocr/datasets/preparers/packers/textrecog_packer.py
imports: import os.path
  from typing import Dict, List, Tuple
  import mmcv
  from mmengine import mkdir_or_exist
  from mmocr.registry import DATA_PACKERS
  from mmocr.utils import bbox2poly, crop_img, poly2bbox, warp_img
  from .base import BasePacker
label_desc: Use image node_id 2 for calling the TextRecogPacker obj's underlying member method code with example usage: obj.add_meta(sample) and returns: meta